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The April 6 movement has chosen Amr Ali as its new head, succeeding founder and long-time leader Ahmed Maher.
In the group's first elections since its launch in 2008, they chose Amr Ali, co-founder and member of the organisational committee, with almost 56 percent of votes.
The results were announced in a press conference Monday, a week after the elections took place.
Maher, who has been the leader of the group for almost six years, did not run in the elections, choosing instead to take on an advisory role. He is expected to join a council of elders, to be elected soon.
The April 6 Youth Movement was founded in 2008 to support protesting workers in the Nile Delta's Mahalla. It soon developed into one of the leading oppositional movements during the Mubarak era.
However, the group has recently lost much of its popularity due to the emergence of alternative oppositional movements and a spotlight on the group's allies and funding sources.
Ali said he intends to make changes to the movement in the coming period, in order to regain its popularity on the street.
"However, the group's basics and principals will remain untouched," he told Al-Ahram's Arabic news website in his first interview after the elections.
The group's new leader said the movement will not join parliamentary elections, because it does not aspire to formal political power. However, it will support various "revolutionary figures" running in the elections.
Ali acknowledged the group had experienced internal conflict, but maintained, "confusion was not limited to April 6; the street was deeply polarised in the recent transitional phase, which directly impacted revolutionary and political forces."


text:
\hypertarget{classmotorcar_1_1Pointer}{\section{motorcar\-:\-:Pointer Class Reference}
\label{classmotorcar_1_1Pointer}\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}}
}

{\ttfamily \#include $<$pointer.\-h$>$}

\subsection*{Public Member Functions}
\begin{DoxyCompactItemize}
\item
\hyperlink{classmotorcar_1_1Pointer_abfbd5397af7d39289d7f3d7eb2db734d}{Pointer} (\hyperlink{classmotorcar_1_1Seat}{Seat} $\ast$seat)
\item
glm\-::vec2 \hyperlink{classmotorcar_1_1Pointer_a19f25225f16b8f4f2289e008bc75d957}{local\-Positon} () const
\item
void \hyperlink{classmotorcar_1_1Pointer_abe379b3238e6fe31b959b8affdc00ea2}{set\-Local\-Positon} (const glm\-::vec2 \&\hyperlink{classmotorcar_1_1Pointer_a19f25225f16b8f4f2289e008bc75d957}{local\-Positon})
\item
\hyperlink{classmotorcar_1_1WaylandSurface}{Wayland\-Surface} $\ast$ \hyperlink{classmotorcar_1_1Pointer_a6b93402a87e64c673fef44d0102be63b}{focus} () const
\item
void \hyperlink{classmotorcar_1_1Pointer_afea4df13d7805e699c523c7aaaf30c4c}{set\-Focus} (\hyperlink{classmotorcar_1_1WaylandSurface}{Wayland\-Surface} $\ast$\hyperlink{classmotorcar_1_1Pointer_a6b93402a87e64c673fef44d0102be63b}{focus})
\item
\hyperlink{classmotorcar_1_1WaylandSurfaceNode}{Wayland\-Surface\-Node} $\ast$ \hyperlink{classmotorcar_1_1Pointer_a97586b3be93be794761ca2b97864fc9e}{cursor\-Node} () const
\item
void \hyperlink{classmotorcar_1_1Pointer_a0bb8c2cfc6523155cc4b3f37487937fa}{set\-Cursor\-Node} (\hyperlink{classmotorcar_1_1WaylandSurfaceNode}{Wayland\-Surface\-Node} $\ast$\hyperlink{classmotorcar_1_1Pointer_a97586b3be93be794761ca2b97864fc9e}{cursor\-Node})
\item
glm\-::ivec2 \hyperlink{classmotorcar_1_1Pointer_ae847516f0a34b7018bf0cdbd26a7d831}{cursor\-Hotspot} () const
\item
void \hyperlink{classmotorcar_1_1Pointer_ae8f489d37a9520e41d8f5162cff3992e}{set\-Cursor\-Hotspot} (const glm\-::ivec2 \&\hyperlink{classmotorcar_1_1Pointer_ae847516f0a34b7018bf0cdbd26a7d831}{cursor\-Hotspot})
\end{DoxyCompactItemize}

\subsection{Constructor \& Destructor Documentation}
\hypertarget{classmotorcar_1_1Pointer_abfbd5397af7d39289d7f3d7eb2db734d}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!Pointer@{Pointer}}
\index{Pointer@{Pointer}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{Pointer}]{\setlength{\rightskip}{0pt plus 5cm}Pointer\-::\-Pointer (
\begin{DoxyParamCaption}
\item[{{\bf Seat} $\ast$}]{seat}
\end{DoxyParamCaption}
)}}\label{classmotorcar_1_1Pointer_abfbd5397af7d39289d7f3d7eb2db734d}

\subsection{Member Function Documentation}
\hypertarget{classmotorcar_1_1Pointer_ae847516f0a34b7018bf0cdbd26a7d831}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!cursor\-Hotspot@{cursor\-Hotspot}}
\index{cursor\-Hotspot@{cursor\-Hotspot}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{cursor\-Hotspot}]{\setlength{\rightskip}{0pt plus 5cm}glm\-::ivec2 Pointer\-::cursor\-Hotspot (
\begin{DoxyParamCaption}
{}
\end{DoxyParamCaption}
) const}}\label{classmotorcar_1_1Pointer_ae847516f0a34b7018bf0cdbd26a7d831}
\hypertarget{classmotorcar_1_1Pointer_a97586b3be93be794761ca2b97864fc9e}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!cursor\-Node@{cursor\-Node}}
\index{cursor\-Node@{cursor\-Node}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{cursor\-Node}]{\setlength{\rightskip}{0pt plus 5cm}{\bf Wayland\-Surface\-Node} $\ast$ Pointer\-::cursor\-Node (
\begin{DoxyParamCaption}
{}
\end{DoxyParamCaption}
) const}}\label{classmotorcar_1_1Pointer_a97586b3be93be794761ca2b97864fc9e}
\hypertarget{classmotorcar_1_1Pointer_a6b93402a87e64c673fef44d0102be63b}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!focus@{focus}}
\index{focus@{focus}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{focus}]{\setlength{\rightskip}{0pt plus 5cm}{\bf Wayland\-Surface} $\ast$ Pointer\-::focus (
\begin{DoxyParamCaption}
{}
\end{DoxyParamCaption}
) const}}\label{classmotorcar_1_1Pointer_a6b93402a87e64c673fef44d0102be63b}
\hypertarget{classmotorcar_1_1Pointer_a19f25225f16b8f4f2289e008bc75d957}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!local\-Positon@{local\-Positon}}
\index{local\-Positon@{local\-Positon}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{local\-Positon}]{\setlength{\rightskip}{0pt plus 5cm}glm\-::vec2 Pointer\-::local\-Positon (
\begin{DoxyParamCaption}
{}
\end{DoxyParamCaption}
) const}}\label{classmotorcar_1_1Pointer_a19f25225f16b8f4f2289e008bc75d957}
\hypertarget{classmotorcar_1_1Pointer_ae8f489d37a9520e41d8f5162cff3992e}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!set\-Cursor\-Hotspot@{set\-Cursor\-Hotspot}}
\index{set\-Cursor\-Hotspot@{set\-Cursor\-Hotspot}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{set\-Cursor\-Hotspot}]{\setlength{\rightskip}{0pt plus 5cm}void Pointer\-::set\-Cursor\-Hotspot (
\begin{DoxyParamCaption}
\item[{const glm\-::ivec2 \&}]{cursor\-Hotspot}
\end{DoxyParamCaption}
)}}\label{classmotorcar_1_1Pointer_ae8f489d37a9520e41d8f5162cff3992e}
\hypertarget{classmotorcar_1_1Pointer_a0bb8c2cfc6523155cc4b3f37487937fa}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!set\-Cursor\-Node@{set\-Cursor\-Node}}
\index{set\-Cursor\-Node@{set\-Cursor\-Node}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{set\-Cursor\-Node}]{\setlength{\rightskip}{0pt plus 5cm}void Pointer\-::set\-Cursor\-Node (
\begin{DoxyParamCaption}
\item[{{\bf Wayland\-Surface\-Node} $\ast$}]{cursor\-Node}
\end{DoxyParamCaption}
)}}\label{classmotorcar_1_1Pointer_a0bb8c2cfc6523155cc4b3f37487937fa}
\hypertarget{classmotorcar_1_1Pointer_afea4df13d7805e699c523c7aaaf30c4c}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!set\-Focus@{set\-Focus}}
\index{set\-Focus@{set\-Focus}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{set\-Focus}]{\setlength{\rightskip}{0pt plus 5cm}void Pointer\-::set\-Focus (
\begin{DoxyParamCaption}
\item[{{\bf Wayland\-Surface} $\ast$}]{focus}
\end{DoxyParamCaption}
)}}\label{classmotorcar_1_1Pointer_afea4df13d7805e699c523c7aaaf30c4c}
\hypertarget{classmotorcar_1_1Pointer_abe379b3238e6fe31b959b8affdc00ea2}{\index{motorcar\-::\-Pointer@{motorcar\-::\-Pointer}!set\-Local\-Positon@{set\-Local\-Positon}}
\index{set\-Local\-Positon@{set\-Local\-Positon}!motorcar::Pointer@{motorcar\-::\-Pointer}}
\subsubsection[{set\-Local\-Positon}]{\setlength{\rightskip}{0pt plus 5cm}void Pointer\-::set\-Local\-Positon (
\begin{DoxyParamCaption}
\item[{const glm\-::vec2 \&}]{local\-Positon}
\end{DoxyParamCaption}
)}}\label{classmotorcar_1_1Pointer_abe379b3238e6fe31b959b8affdc00ea2}

The documentation for this class was generated from the following files\-:\begin{DoxyCompactItemize}
\item
/home/dave/thesis/motorcar/src/compositor/wayland/input/\hyperlink{pointer_8h}{pointer.\-h}\item
/home/dave/thesis/motorcar/src/compositor/wayland/input/\hyperlink{pointer_8cpp}{pointer.\-cpp}\end{DoxyCompactItemize}

redpajama_set_name: RedPajamaGithub


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There is betting excitement aplenty, notably in the valuable Sprint.


text:
The Biggest Lessons From Virginia, New Jersey Elections? Personality Matters
ByJohn Giokaris
So the center-right Chris Christie wins by a mile in a blue state while the far right Ken Cuccinelli loses a close one in a purple state. I know elections go way beyond ideology, but for all the pure ideological dorks and extremist pundits out there who refuse to acknowledge such, I'd be very curious to hear how they justify Tuesday night's results in their minds.
The truth is there was way more at play in both elections than the ideologues care to learn. But for the rest of us, the main lesson we should take from both results is that the cult of personality as well as public perception play a much larger factor in determining who wins elections than anything else.
Let's start with Virginia, since it was the closer one. Voter turnout in non-presidential election years is always disappointingly low. The fact is there just aren't that many high information voters out there and the low information voters only show up to vote for the president because they think he's king. Case in point, in the last gubernatorial election four years ago (arguably a state where you'd expect a higher than average share of high information voters since the northern half's entire economy is centered around the nation's capitol), voter turnout was just 35.6% with about 2 million votes. Tuesday night there were about 2.2 million voters who showed up at the ballot box, so the turnout wasn't much higher than in 2009.
So of the less than 40% of voters who turned out and are arguably mostly high information, Democrat Terry McAuliffe received about 245,000 more votes than his Democratic predecessor four years ago, while Republican Ken Cuccinelli received around 155,000 votes less than Governor Bob McDonnell (as of this writing). While there will be endless speculation among the über-nerd pundits about how Libertarian candidate Robert Sarvis' presence in the race made a difference, if any, to assume every single voter who showed up to vote for Sarvis would've also showed up to vote for Cuccinelli and only Cuccinelli absent Sarvis is too speculative for me to believe (at least without any evidence).
There were bigger factors at play here. First, and perhaps most importantly, there is personality to consider. Cuccinelli was successfully portrayed as a right-wing extremist (something that you'd think would help him according to the Rush Limbaughs and Sean Hannitys of America) with his positions on gay marriage, abortion, and social issues which were used relentlessly against him in ads throughout the growing Northern Virginia suburbs.
Second, Cuccinelli was running against the legacy of Gov. McDonnell's administration (fairly or unfairly), which was plagued by allegations of corruption and a growing ethics scandal.
Third, McAuliffe benefited from over $35 million in tremendous campaign fundraising efforts by superstar juggernauts like Bill and Hillary Clinton as well as President Barack Obama himself – nearly double Cuccinelli's total.
Fourth, as I've been illustrating for the last year, Democrats have been outhustling the GOP in all swing states when it comes to the ground game of growing their pie. For the last eight years, Democratic forces have been combing through blue districts in all the swing states and registering more voters in their column (then getting them to the polls as quickly and conveniently as possible starting on day one of early voting). They are aggressively marketing their brand into every corner of America that's within their reach while Republicans are foolishly still waiting for voters to come to them as well as cluelessly relying on 20th Century models to get out the vote such as landline phone banking and chain mail messaging.
Finally, McAuliffe was able to successfully portray himself as a mainstream centrist Democrat who would focus on bipartisan governing – which brings me to Christie.
Turnout for the New Jersey gubernatorial election was understandably lower than it was four years ago, as most people knew it was a given Christie would win re-election. But there is something to be said that of the 15 states that have the biggest margin of registered Democrats, only two of those states currently have a Republican governor: Christie and Michigan's Rick Snyder (the Michigan GOP swept Lansing in 2010 in the wake of that state's economic collapse). For all the flak Christie gets from the far right for… whatever they hate him for, if anyone bothered to check his record they would learn he's a actually a pretty conservative leader. He's lowered taxes, cut spending, balanced the budget, created more charter schools, vetoed radical gun control bills, and even vetoed a bill that would have legalized same sex marriage in his state.
Yet like McAuliffe, he's been able to successfully portray himself as a mainstream centrist Republican focusing on bipartisan governing through "a pox on both houses" rhetoric and carefully orchestrated photo ops of him shaking hands with and high-fiving Democrats, such as President Obama. While the far right will tear him a new one in their radio booths and online chat rooms for doing so, he just managed to win re-election as a Republican in a D+12 state with 60% of the vote.
So what are the lessons to take from all this? Well, for one, personality and perception matter. In non-presidential election years, turnout will hover anywhere from 35% to 40%. The bulk of that turnout is comprised of mostly high information voters who show up to vote in off election years, not just presidential ones. And it turns out most high information voters are gravitated toward personalities who present themselves as bipartisan leaders willing to work with the other side of the aisle. In these cases, Christie has a Democrat majority legislature he has to get along with while McAuliffe will similarly have to deal with a Republican majority legislature.
While the ideologues refuse to listen to any reasoning or rationale that doesn't reinforce their preconceived notions about what "Americans really want," any objective observer will understand how both elections ended up producing the results they did.

redpajama_set_name: RedPajamaCommonCrawl


What Facebook Ads Work Best?
When it comes to Facebook marketing, one-size-fits-all approach does not work. The only way to know which Facebook ads are going to work best for your business depends on your goals, your audience, and your unique business offerings. For the best results, the Facebook ad formats that are going to work best for your business will depend on the objectives of your campaign and what your target audience expects from your brand.
At Australian Internet Advertising, we've created and launched highly customised Facebook ad campaigns for businesses in many different industries and niches. Please check out our case studies and client testimonials to see what we can do for your business.
Fortunately for marketers, Facebook ads are incredibly versatile, and almost any business can generate results with a well-planned and executed Facebook ads campaign. But how can you know what types of Facebook ads will meet your goals and expectations? In today's post, we're going to cover the different types of Facebook ads, and how they can advance your business goals.
What kind of results do businesses see with Facebook ads?
Facebook is a social media platform that is an excellent tool for companies who want to increase their brand awareness, generate more leads, and increase their sales numbers. Facebook doesn't limit marketers to just pay-per-click ads but gives them a way to create and launch a holistic marketing plan with advertisements working as a complementary marketing strategy. Around 3 million businesses use Facebook ads, and that many people wouldn't be investing in the platform if it didn't work.
The average cost-per-click for Facebook ads is $1.72.
The average revenue generated per user with Facebook ads is $2.62.
Some of the biggest reasons why businesses use Facebook for displaying ads is because there are so many people on the platform. There are an estimated 1.39 billion active monthly users on Facebook, and the social media giant's user base keeps growing. Most importantly, Facebook is one of the only advertising platforms that has a demographically diverse range of users. People of all ages use the platform, and usage is split about evenly between both men and women.
On average, a Facebook user spends 40 minutes per day scrolling through their feed, catching up with friends and family, and liking and sharing content. For businesses who want to showcase their unique offerings in front of more people, Facebook ads are an excellent way to do that.
What are the most important components about Facebook ads that marketers need to know?
Before you can get started with Facebook ads, it's important to know the different sections of an advertisement so you can better understand how they work.
A Facebook ad gives you three options for text or ad copy fields. You have a headline, the main text, and a newsfeed link description. The length of your ad copy, and the tone and word choices you use are all extremely important. Generally, though, less is more. The more concise, relevant, and entertaining your ad copy, the better results the ad will generate. Long, dense, or complicated language can frustrate or bore viewers, and they won't interact with your ad.
Also, Facebook ads also include a space to add an image or a video, and sizes will vary based on your specific ad format. Polished, highly relevant and striking ads that are aligned with your brand positioning are a must. Short and informative how-to videos tend to perform the best. Let's break down the different types of Facebook ads based on a company's specific goals.
You want to generate traffic and leads to a website landing page.
Some of the most common types of Facebook ads are ads designed to drive traffic to a dedicated landing page somewhere other than on the Facebook platform. Your goal may be to entice viewers to buy a product on your site or get them to sign up for your company newsletter. If this is your goal, several different types of Facebook ad formats can help you obtain it.
Link click ads are one of the most popular choices for driving traffic to a landing page. These types of ads can be placed on the user's desktop newsfeed, their mobile newsfeed, on the right column, the audience network, or on Instagram. You can deliver the ad to multiple placements. You'll reach more people on Facebook if you're not limited to a single ad placement location.
Not only are link click ads useful for generating traffic, but they'll also help you increase page likes and follows. If you use a link click ad, make sure you check the comments to engage with your target audience and build trust. What are the specifications governing a link click ad?
An image size of 1200 x 628 pixels.
25 characters to a headline.
Ad copy text within 90 characters or less.
A link description of no more than 30 characters.
Do you have a Facebook post that generated a ton of likes, comments, and traffic to your site? Facebook gives you the option to "boost" the post, meaning it will turn it into an ad to display to members of your target audience. The boosted post will look exactly like your original Facebook post, except it will include the word "sponsored" at the top.
Unlimited characters for ad copy.
A headline of 25 characters.
A link description of up to 30 characters.
Facebook video ads can display on both desktop and mobile newsfeeds, the audience network, and Instagram. Video ads are similar to link click ads, with the only difference being that in place of the image on a link click ad, there is a video instead. Videos can be as long as 120 minutes, but the best performing videos are only fifteen to thirty seconds long. It's all about grabbing the viewer's attention with a memorable and compelling ad.
Ad copy of up to 90 characters.
Videos aspect ratios 16:9 to 9:16 are supported.
The file size is up to 4 GB maximum.
Videos can play on a continuous loop.
Your goal is to increase your leads and sales with Facebook ads.
eCommerce stores and companies with several offerings they want to promote can benefit from Facebook advertising. Certain ad formats allow you to display several products in a single ad.
If you want to increase newsletter signups, Facebook also offers ad formats that allow the user to sign up for offerings directly on the ad, which reduces the time it usually takes to acquire new leads. Also, allowing users to interact without leaving the platform increases load times and gives the user a more enjoyable experience when they interact with your brand.
Link description of 30 characters.
Image sizes of either 1080 x 1080 or 600 x 600 pixels.
Do your users overwhelming use mobile devices? Facebook canvas ads only display on mobile devices. If members of your target audience are heavy mobile users, a canvas ad might be the ideal Facebook ad format for your unique goals.
With canvas ads, your viewers can swipe through images in a carousel format. They can also tilt the advertisement in different directions, and zoom in and out on your pictures. Not only are canvas-style ads interactive, but they're also super fast. Canvas ads load up to ten times faster than other mobile ads, which makes for a much more memorable user experience.
Do you have a lead magnet and want to generate new sign-ups? Facebook lead ads are designed for lead generation, where users can download your free offering without leaving the Facebook platform. Downloads and load times are quick when users can stay in one spot to obtain their magnet, and the increased user experience will generate more leads and sign ups for your business. Facebook lead ads can display on desktop and mobile newsfeeds, the audience network, and Instagram.
Images of 1200 x 628 pixels.
Ad copy text of 90 characters.
Context card in paragraph format with no character limit, except an 80 character limit for bullet-points.
When someone signs up and downloads your product through the lead ad, their email address will be stored within your Facebook ads account so you can add it to your subscriber list. There is software available that can automate the process of moving email addresses from Facebook to your CRM.
Your goal is to generate likes and engagement for your page.
Promoting your posts is a good idea if you want to increase your Facebook page likes and engagement. Facebook's organic reach isn't what it used to be. Nowadays, your Facebook posts will only display to about 2 – 3% of your followers unless you advertise. Two particular ad formats are ideal for increasing likes and engagement.
Page post video ads give you the ability to target visitors based on how much of your video they watched. This type of ad is ideal for creating new, custom audiences for future re-targeting efforts. With a page post photo ad, you can display a stunning photo to generate likes and comments. Page post text ads allow you to use a short, concise line of text to generate comments and engagement. However, this type of ad won't typically perform as well as a page post ad with an image or video on display.
Getting a high volume of page likes increases your social proof and can help generate buzz and interest in your brand. Page like ads can display on the right column, and desktop and mobile newsfeeds. These ads display a highly visible CTA button asking your viewers to "like" your page. If you use a page like ad, remember quality over quantity. You want the right people liking your page. Make sure to choose an image that will most resonate with your ideal customer.
Choosing the right Facebook ad format for your business really boils down to what your goals are for the ad, and what formats are going to most resonate and compel your target audience. Your ads need a strong foundation from which to launch to give your business the maximum ROI. Fortunately for marketers, Facebook offers a broad range of different ad types and formats that can help you reach a variety of goals. For the best results, it's a good idea to test a couple of different ads to see which one drives the actions you want from viewers. Testing can also help you get good results for a lower cost.
At Australian Internet Advertising, our digital marketing experts have years of experience working with and testing Facebook ads for maximum ROI. We know how to optimise any ad format with striking designs and compelling ad copy that will generate the results you want. Our representatives are standing by to answer any questions you might have about Facebook advertising. Please contact us today to get started with an effective Facebook advertising campaign that's customised for your business goals.


In at least one embodiment, a method for diagnosing vascular disease is provided, the method comprising the steps of obtaining a vessel image showing a vasculature of a vessel, identifying at least two measurements from the vasculature of the vessel, the measurements relating to at least two parameters, calculating a relationship between the at least two parameters from the at least two measurements to generate one or more vasculature data points, and comparing the one or more vasculature data points to data relative to a model vasculature to determine the extent of vascular disease. In an another embodiment, a method for diagnosing vascular disease in a patient's vascular tree is provided, the method comprising the steps of generating a model vascular tree from a minimum energy hypothesis calculation, and comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease.
The present application is related to and claims the benefit of U.S. Provisional Patent Application Serial No. 60/881,833, entitled "APPLICATIONS OF SCALING LAWS OF TREE STRUCTURES," filed January 23, 2007.
The disclosure of the present application relates generally to diagnosis of vascular disease, in particular relating to using morphological features of the coronary artery tree to diagnose coronary artery disease.
Diffuse coronary artery disease (DCAD), a common form of atherosclerosis, is difficult to diagnose because the arterial lumen cross-sectional area is diffusely reduced along the length of the vessels. Typically, for patients with even mild segmental stenosis, the lumen cross- sectional area is diffusely reduced by 30 to 50%. The failure of improved coronary flow reserve after angioplasty may mainly be due to the coexistence of diffuse narrowing and focal stenosis. Whereas angiography has been regarded as the "gold standard" in the assessment of focal stenosis of coronary arteries, its viability to diagnose DCAD remains questionable. The rationale of conventional angiography in the assessment of coronary artery disease is to calculate the percent lumen diameter reduction by comparison of the target segment with the adjacent 'normal' reference segment. In the presence of DCAD, however, an entire vessel may be diffusely narrowed so that no true reference (normal) segment exists. Therefore, in the presence of DCAD, standard angiography significantly underestimates the severity of the disease.
To overcome the difficulty of using angiography in the diagnosis of DCAD, intravascular ultrasound (IVUS) has been the subject of extensive studies. IVUS has the advantage of directly imaging the cross-sectional area along the length of the vessel using a small catheter. The disadvantage of IVUS, however, is that its extensive interrogation of diseased segments may pose a risk for plaque rupture.
The disclosure of the present application addresses the need in multiple applications by the application of derived equations that can be used to diagnose disease as well as aid in the efficient fabrication of micro-fluidic channel devices. In at least one embodiment of a method for diagnosing vascular disease according to the present disclosure, the method comprises the steps of obtaining a vessel image showing a vasculature of a vessel, identifying at least two measurements from the vasculature of the vessel, the measurements relating to at least two parameters, calculating a relationship between the at least two parameters from the at least two measurements to generate one or more vasculature data points, and comparing the one or more vasculature data points to data relative to a model vasculature to determine the extent of vascular disease. In another embodiment, the vessel image is an image selected from the group consisting of an angiograph, a CT image, and an MRI. In yet another embodiment, the at least two parameters comprise parameters relating to volume and length from the vasculature of the vessel. In at least one embodiment of a method for diagnosing vascular disease according to the present disclosure, the step of calculating a relationship between the at least two parameters is performed using a volume-length relation. In another embodiment, the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation. In yet another embodiment, the at least two parameters comprise parameters relating to diameter and length from the vasculature of the vessel. In an additional embodiment, the step of calculating a relationship between the at least two parameters is performed using a diameter-length relation.
In at least one embodiment of a method for diagnosing vascular disease according to the present disclosure, the at least two parameters comprise parameters relating to flow rate and diameter from the vasculature of the vessel. In another embodiment, the step of calculating a relationship between the at least two parameters is performed using a flow rate-diameter relation. In yet another embodiment, the at least two parameters comprise parameters relating to resistance, length, and volume from the vasculature of the vessel. In an additional embodiment, the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation. In at least one embodiment of a method for diagnosing vascular disease according to the present disclosure, the at least two parameters comprise parameters relating to flow rate and length from the vasculature of the vessel. In another embodiment, the step of calculating a relationship between the at least two parameters is performed using a flow rate-length relation. In yet another embodiment, the step of comparing the one or more vasculature data points to data relative to a model vasculature is performed by graphically comparing said data points to data relative to a model vasculature to determine the extent of vascular disease by identifying graphical differences between said data points to data relative to a model vasculature. In an additional embodiment, the step of comparing the one or more vasculature data points to data relative to a model vasculature is performed by comparing said data points to data relative to a model vasculature in table form to determine the extent of vascular disease by identifying numerically calculated differences between said data points to data relative to a model vasculature.
In at least one embodiment of a method for diagnosing vascular disease according to the present disclosure, the method comprises the steps of generating a model vascular tree from a minimum energy hypothesis calculation, and comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease. In another embodiment, the step of generating a model vascular tree from a minimum energy hypothesis calculation further comprises the step of calculating a relationship between at least two parameters from at least two measurements to generate one or more model vasculature data points within the model vascular tree. In yet another embodiment, the at least two parameters comprise parameters relating to volume and length. In an additional embodiment, the step of calculating a relationship between the at least two parameters is performed using a volume-length relation. In a further embodiment, the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
In at least one embodiment of a method for diagnosing vascular disease according to the present disclosure, the at least two parameters comprise parameters relating to diameter and length. In another embodiment, the step of calculating a relationship between the at least two parameters is performed using a diameter-length relation. In yet another embodiment, the at least two parameters comprise parameters relating to flow rate and diameter. In an additional embodiment, the step of calculating a relationship between the at least two parameters is performed using a flow rate-diameter relation.
In at least one embodiment of a method for diagnosing vascular disease according to the present disclosure, the at least two parameters comprise parameters relating to resistance, length, and volume. In another embodiment, the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation. In yet another embodiment, the at least two parameters comprise parameters relating to flow rate and length. In an additional embodiment, the step of calculating a relationship between the at least two parameters is performed using a flow rate-length relation. In at least one embodiment of a method for diagnosing vascular disease according to the present disclosure, the step of comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease is performed by graphically comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease by identifying graphical differences between the patient's vascular tree with the model vascular tree. In another embodiment, the step of comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease is performed by comparing the patient's vascular tree with the model vascular tree in table form to determine the extent of vascular disease by identifying numerically calculated differences between the patient's vascular tree with the model vascular tree. In at least one embodiment of a system for diagnosing vascular disease according to the present disclosure, the system comprises a processor, a storage medium operably connected to the processor, the storage medium capable of receiving and storing data relative of measurements from a vasculature of a vessel, wherein the processor is operable to obtain a vessel image showing a vasculature of a vessel, identify at least two measurements from the vasculature of the vessel, the measurements relating to at least two parameters, calculate a relationship between the at least two parameters from the at least two measurements to generate one or more vasculature data points, and compare the one or more vasculature data points to data relative to a model vasculature to determine the extent of vascular disease. In another embodiment, the vessel image is an image selected from the group consisting of an angiograph, a CT image, and an MRI. In yet another embodiment, the at least two parameters comprise parameters relating to volume and length from the vasculature of the vessel. In an additional embodiment, the calculation of a relationship between the at least two parameters is performed using a volume-length relation.
In at least one embodiment of a system for diagnosing vascular disease according to the present disclosure, the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation. In another embodiment, the at least two parameters comprise parameters relating to diameter and length from the vasculature of the vessel. In yet another embodiment, the calculation of a relationship between the at least two parameters is performed using a diameter-length relation.
In at least one embodiment of a system for diagnosing vascular disease according to the present disclosure, the at least two parameters comprise parameters relating to flow rate and diameter from the vasculature of the vessel. In another embodiment, the calculation of a relationship between the at least two parameters is performed using a flow rate-diameter relation. In yet another embodiment, the at least two parameters comprise parameters relating to resistance, length, and volume from the vasculature of the vessel. In an additional embodiment, the calculation of a relationship between the at least two parameters is performed using a resistance-length and volume relation.
In at least one embodiment of a system for diagnosing vascular disease according to the present disclosure, the at least two parameters comprise parameters relating to flow rate and length from the vasculature of the vessel. In another embodiment, the calculation of a relationship between the at least two parameters is performed using a flow rate-length relation. In yet another embodiment, the comparison of the one or more vasculature data points to data relative to a model vasculature is performed by graphically comparing said data points to data relative to a model vasculature to determine the extent of vascular disease by identifying graphical differences between said data points to data relative to a model vasculature. In an additional embodiment, the comparison of the one or more vasculature data points to data relative to a model vasculature is performed by comparing said data points to data relative to a model vasculature in table form to determine the extent of vascular disease by identifying numerically calculated differences between said data points to data relative to a model vasculature. In at least one embodiment of a system for diagnosing vascular disease according to the present disclosure, the system further comprises a program stored upon the storage medium, said program operable by the processor upon data relative of measurements from a vasculature of a vessel. In another embodiment, the system comprises a user system and a server system, and wherein the user system and the server system are operably connected to one another.
In at least one embodiment of a system for diagnosing vascular disease in a patient's vascular tree according to the present disclosure, the system comprises a processor and a storage medium operably connected to the processor, the storage medium capable of receiving and storing data relative of measurements from a vasculature of a vessel, wherein the processor is operable to, generate a model vascular tree from a minimum energy hypothesis calculation, and compare the patient's vascular tree with the model vascular tree to determine the extent of vascular disease. In another embodiment, the generation of a model vascular tree from a minimum energy hypothesis calculation is performed by calculating a relationship between at least two parameters from at least two measurements to generate one or more model vasculature data points within the model vascular tree. In at least one embodiment of a system for diagnosing vascular disease in a patient's vascular tree according to the present disclosure, the at least two parameters comprise parameters relating to volume and length. In another embodiment, the calculation of a relationship between the at least two parameters is performed using a volume-length relation. In yet another embodiment, the calculation of a relationship between the at least two parameters is performed using a resistance-length and volume relation.
In at least one embodiment of a system for diagnosing vascular disease in a patient's vascular tree according to the present disclosure, the at least two parameters comprise parameters relating to diameter and length. In another embodiment, the calculation of a relationship between the at least two parameters is performed using a diameter-length relation. In yet another embodiment, the at least two parameters comprise parameters relating to flow rate and diameter. In an additional embodiment, the calculation of a relationship between the at least two parameters is performed using a flow rate-diameter relation.
In at least one embodiment of a system for diagnosing vascular disease in a patient's vascular tree according to the present disclosure, the at least two parameters comprise parameters relating to resistance, length, and volume. In another embodiment, the calculation of a relationship between the at least two parameters is performed using a resistance-length and volume relation. In yet another embodiment, the at least two parameters comprise parameters relating to flow rate and length. In an additional embodiment, the calculation of a relationship between the at least two parameters is performed using a flow rate-length relation.
In at least one embodiment of a system for diagnosing vascular disease in a patient's vascular tree according to the present disclosure, the comparison of the patient's vascular tree with the model vascular tree to determine the extent of vascular disease is performed by graphically comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease by identifying graphical differences between the patient's vascular tree with the model vascular tree. In another embodiment, the comparison of the patient's vascular tree with the model vascular tree to determine the extent of vascular disease is performed by comparing the patient's vascular tree with the model vascular tree in table form to determine the extent of vascular disease by identifying numerically calculated differences between the patient's vascular tree with the model vascular tree. In yet another embodiment, the system further comprises a program stored upon the storage medium, said program operable by the processor upon data relative of measurements from a vasculature of a vessel. In an additional embodiment, the system comprises a user system and a server system, and wherein the user system and the server system are operably connected to one another.
In at least one embodiment of a program having a plurality of program steps to be executed on a computer having a processor and a storage medium to analyze data relative of measurements from a vasculature of a vessel according to the present disclosure, the program is operable to obtain a vessel image showing a vasculature of a vessel, identify at least two measurements from the vasculature of the vessel, the measurements relating to at least two parameters, calculate a relationship between the at least two parameters from the at least two measurements to generate one or more vasculature data points, and compare the one or more vasculature data points to data relative to a model vasculature to determine the extent of vascular disease.
In at least one embodiment of a program having a plurality of program steps to be executed on a computer having a processor and a storage medium to analyze data relative of measurements from a vasculature of a vessel according to the present disclosure, the program is operable to generate a model vascular tree from a minimum energy hypothesis calculation, and compare the patient's vascular tree with the model vascular tree to determine the extent of vascular disease. In at least one embodiment of a method to design an energy efficient microfluidic channel device according to the present disclosure, the method comprises the steps of identifying at least two parameters relating to a desired microfluidic channel device, calculating a relationship between the at least two parameters to generate at least two measurements, and utilizing the at least two measurements to design the microfluidic channel device. In another embodiment, the at least two parameters comprise parameters relating to volume and length of a desired microfluidic channel device. In yet another embodiment, the step of calculating a relationship between the at least two parameters is performed using a volume-length relation. In an additional embodiment, the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
In at least one embodiment of a method to design an energy efficient microfluidic channel device according to the present disclosure, the at least two parameters comprise parameters relating to diameter and length. In another embodiment, the step of calculating a relationship between the at least two parameters is performed using a diameter-length relation. In yet another embodiment, the at least two parameters comprise parameters relating to flow rate and diameter. In an additional embodiment, the step of calculating a relationship between the at least two parameters is performed using a flow rate-diameter relation.
In at least one embodiment of a method to design an energy efficient microfluidic channel device according to the present disclosure, the at least two parameters comprise parameters relating to resistance, length, and volume. In another embodiment, the step of calculating a relationship between the at least two parameters is performed using a resistance- length and volume relation. In yet another embodiment, the at least two parameters comprise parameters relating to flow rate and length. In an additional embodiment, the step of calculating a relationship between the at least two parameters is performed using a flow rate-length relation. In at least one embodiment of a method to design an energy efficient microfluidic channel device according to the present disclosure, the method further comprises the step of utilizing the at least two measurements to fabricate the microfluidic channel device.
The disclosure of the present application applies concepts from biomimetics and microfluidics to analyze vascular tree structure, thus improving the efficacy and accuracy of diagnostics involving vascular diseases such as DCAD. Scaling laws are developed in the form of equations that use the relationships between arterial volume, cross-sectional area, blood flow and the distal arterial length to quantify moderate levels of diffuse coronary artery disease. For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the embodiments illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the present disclosure is thereby intended. Biomimetics (also known as bionics, biognosis, biomimicry, or bionical creativity engineering) is defined as the application of methods and systems found in nature to the study and design of engineering systems and modern technology. The mimic of technology from nature is based on the premise that evolutionary pressure forces natural systems to become highly optimized and efficient. Some examples include (1) the development of dirt- and water- repellent paint from the observation that the surface of the lotus flower plant is practically unsticky, (2) hulls of boats imitating the thick skin of dolphins, and (3) sonar, radar, and medical ultrasound imaging imitating the echolocation of bats.
Microfluidics is the study of the behavior, control and manipulation of microliter and nanoliter volumes of fluids. It is a multidisciplinary field comprising physics, chemistry, engineering and biotechnology, with practical applications to the design of systems in which such small volumes of fluids may be used. Microfluidics is used in the development of DNA chips, micro-propulsion, micro-thermal technologies, and lab-on-a-chip technology.
Regarding the minimum energy hypothesis, the architecture (or manifolds) of the transport network is essential for transport of material in microfiuid channels for various chips. The issue is how to design new devices, and more particularly, how to fabricate microfluidic channels that provide a minimum cost of operation. Nature has developed optimal channels (or transport systems) that utilize minimum energy for transport of fluids. The utility of nature's design of transport systems in engineering applications is an important area of biomimetics.
Biological trees (for example, vascular trees) are either used to conduct fluids such as blood, air, bile or urine. Energy expenditure is required for the conduction of fluid through a tree structure because of frictional losses. The frictional losses are reduced when the vessel branches have larger diameters. This comes with a cost, however, for the metabolic construction and maintenance of the larger volume of the structure. The question is what physical or physiological factors dictate the design of vascular trees. The answer is that the design of vascular trees obeys the "minimum energy hypothesis", i.e., the cost of construction and operation of the vascular system appears to be optimized.
In Equation #3, Q represents flow rate of a stem, O,max represents the flow rate of the most proximal stem, V represents vessel diameter, Fmax represents the diameter of the most proximal stem, and £! represents the crown flow resistance, which is equal to the ratio of metabolic to viscous power dissipation. Regarding the aforementioned Equations, a vessel segment is referred to as a "stem," and the entire tree distal to the stem is referred as a "crown." The aforementioned parameters relate to the crown flow resistance and is equal to the ratio of maximum metabolic-to-viscous power dissipation.
In Equation #5, Q represents flow rate of a stem, Qmw. represents the flow rate of the most proximal stem, L represents vessel length, and Zmax represents the cumulative vessel length of the entire crown.
In at least one embodiment of the disclosure of the present application, the application of one or more of the aforementioned Equations to acquired vessel data may be useful diagnose and/or aid in the diagnosis of disease.
By way of example, the application of one or more of the aforementioned Equations are useful to diagnose DCAD. For such a diagnosis, the applications of Equations #l-#3 may provide the "signatures" of normal vascular trees and impart a rationale for diagnosis of disease processes. The self-similar nature of these laws implies that the analysis can be carried out on a partial tree as obtained from an angiogram, a computed tomography (CT) scan, or an magnetic resonance imaging (MRI). Hence, the application of these Equations to the obtained images may serve for diagnosis of vascular disease that affect the lumen dimension, volume, length (vascularity) or perfusion (flow rate). Additionally, the fabrication of the microfluidic channels can be governed by Equations #l-#5 to yield a system that requires minimum energy of construction and operation. Hence, energy requirements will be at a minimum to transport the required microfluidics.
In one exemplary embodiment, the application of the volume-length relation (Equation #1) to actual obtained images is considered as shown in Fig. 1. First, images (angiograms in this example) of swine coronary arties were obtained. The application of Equation #1 on various volumes and lengths from the angiograms resulted in the individual data points shown within Fig. 1 (on a logarithmic scale). The line depicted within Fig. 1 represents the mean of the data points (the best fit) among the identified data points. In Fig. 2, the mean of the data (solid line) is compared to an animal with diffuse disease at three different vessel sizes: proximal (1), middle (2), and distal (3). The reductions in volume shown on Fig. 2 correspond to approximately 40% stenosis, which is typically undetectable with current methodologies. At each diffuse stenosis, the length remains constant but the diameter (cross-sectional, and hence, volume) changes. The length is unlikely to change unless the flow becomes limiting (more than approximately 80% stenosis) and the vascular system experiences vessel loss (rarefication) and remodeling. It is clear that a 40% stenosis deviates significantly from the y-axis (as determined by statistical tests) from the normal vasculature, and as such, 40% stenosis can be diagnosed by the system and method of the disclosure of the present application. It can be appreciated that the disclosure of the present application can predict inefficiencies as low as about 10%, compared to well-trained clinicians who can only predict inefficiencies at about 60% at best.
This exemplary statistical test compares the deviation of disease to normality relative to the variation within normality. The location of the deviation along the x-axis corresponds to the size of the vessel. The vessel dimensions range as proximal > mid > distal. Hence, by utilizing the system and method of the disclosure of the present application, the diagnosis of the extent of disease and the dimension of the vessel branch is now possible. Similar embodiments with other scaling relations as described herein can be applied similarly to model and actual vascular data.
The techniques disclosed herein have tremendous application in a large number of technologies. For example, a software program or hardware device may be developed to diagnose the percentage of inefficiency (hence, occlusion) in a circulatory vessel or system. Regarding the computer-assisted determination of such diagnoses, an exemplary system of the disclosure of the present application is provided. Referring now to Fig. 3, there is shown a diagrammatic view of an embodiment of diagnostic system 300 of the present disclosure. In the embodiment shown in Fig. 3, diagnostic system 300 comprises user system 302. In this exemplary embodiment, user system 302 comprises processor 304 and one or more storage media 306. Processor 304 operates upon data obtained by or contained within user system 302. Storage medium 306 may contain database 308, whereby database 308 is capable of storing and retrieving data. Storage media 306 may contain a program (including, but not limited to, database 308), the program operable by processor 304 to perform a series of steps regarding data relative of vessel measurements as described in further detail herein.
Any number of storage media 306 may be used with diagnostic system 300 of the present disclosure, including, but not limited to, one or more of random access memory, read only memory, EPROMs, hard disk drives, floppy disk drives, optical disk drives, cartridge media, and smart cards, for example. As related to user system 302, storage media 306 may operate by storing data relative of vessel measurements for access by a user and/or for storing computer instructions. Processor 304 may also operate upon data stored within database 308.
Regardless of the embodiment of diagnostic system 300 referenced herein and/or contemplated to be within the scope of the present disclosure, each user system 302 may be of various configurations well known in the art. By way of example, user system 302, as shown in Fig. 3, comprises keyboard 310, monitor 312, and printer 314. Processor 304 may further operate to manage input and output from keyboard 310, monitor 312, and printer 314. Keyboard 310 is an exemplary input device, operating as a means for a user to input information to user system 302. Monitor 312 operates as a visual display means to display the data relative of vessel measurements and related information to a user using a user system 302. Printer 314 operates as a means to display data relative of vessel measurements and related information. Other input and output devices, such as a keypad, a computer mouse, a fingerprint reader, a pointing device, a microphone, and one or more loudspeakers are contemplated to be within the scope of the present disclosure. It can be appreciated that processor 304, keyboard 310, monitor 312, printer 314 and other input and output devices referenced herein may be components of one or more user systems 302 of the present disclosure. It can be appreciated that diagnostic system 300 may further comprise one or more server systems 316 in bidirectional communication with user system 302, either by direct communication (shown by the single line connection on Fig. 3), or through a network 318 (shown by the double line connections on Fig. 3) by one of several configurations known in the art. Such server systems 316 may comprise one or more of the features of a user system 302 as described herein, including, but not limited to, processor 304, storage media 306, database 308, keyboard 310, monitor 312, and printer 314, as shown in the embodiment of diagnostic system 300 shown in Fig. 3. Such server systems 316 may allow bidirectional communication with one or more user systems 302 to allow user system 302 to access data relative of vessel measurements and related information from the server systems 316. It can be appreciated that a user system 302 and/or a server system 316 referenced herein may be generally referred to as a "computer."
The disclosure of the present application also relates to the design and fabrication of micro-fluidic chambers for use in research and development, thereby designing a chamber that maximizes flow conditions while minimizing the amount of material needed to construct the chamber. Many other uses are also possible and within the scope of the disclosure of the present application.
The foregoing disclosure of the exemplary embodiments of the present application has been presented for purposes of illustration and description and can be further modified within the scope and spirit of this disclosure. It is not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. This application is therefore intended to cover any variations, uses, or adaptations of a device, system and method of the present application using its general principles. Further, this application is intended to cover such departures from the present disclosure as may come within known or customary practice in the art to which this system of the present application pertains. Many variations and modifications of the embodiments described herein will be apparent to one of ordinary skill in the art in light of the above disclosure. The scope of the present disclosure is to be defined only by the claims appended hereto, and by their equivalents.
Further, in describing representative embodiments of the present disclosure, the specification may have presented the method and/or process of the present disclosure as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. As one of ordinary skill in the art would appreciate, other sequences of steps may be possible. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. In addition, the claims directed to the method and/or process of the present disclosure should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the present disclosure.
I . A method for diagnosing vascular disease, the method comprising the steps of: obtaining a vessel image showing a vasculature of a vessel; identifying at least two measurements from the vasculature of the vessel, the measurements relating to at least two parameters; calculating a relationship between the at least two parameters from the at least two measurements to generate one or more vasculature data points; and comparing the one or more vasculature data points to data relative to a model vasculature to determine the extent of vascular disease.
2. The method of claim 1, wherein the vessel image is an image selected from the group consisting of an angiograph, a computed tomography scan, and magnetic resonance imaging.
3. The method of claim 1, wherein the at least two parameters comprise parameters relating to volume and length from the vasculature of the vessel.
4. The method of claim 3, wherein the step of calculating a relationship between the at least two parameters is performed using a volume-length relation.
5. The method of claim 3, wherein the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
6. The method of claim 1, wherein the at least two parameters comprise parameters relating to diameter and length from the vasculature of the vessel.
7. The method of claim 6, wherein the step of calculating a relationship between the at least two parameters is performed using a diameter-length relation.
8. The method of claim 1, wherein the at least two parameters comprise parameters relating to flow rate and diameter from the vasculature of the vessel.
9. The method of claim 8, wherein the step of calculating a relationship between the at least two parameters is performed using a flow rate-diameter relation.
10. The method of claim 1 , wherein the at least two parameters comprise parameters relating to resistance, length, and volume from the vasculature of the vessel.
I I . The method of claim 10, wherein the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
12. The method of claim 1, wherein the at least two parameters comprise parameters relating to flow rate and length from the vasculature of the vessel.
13. The method of claim 12, wherein the step of calculating a relationship between the at least two parameters is performed using a flow rate-length relation.
14. The method of claim 1, wherein the step of comparing the one or more vasculature data points to data relative to a model vasculature is performed by graphically comparing said data points to data relative to a model vasculature to determine the extent of vascular disease by identifying graphical differences between said data points to data relative to a model vasculature.
15. The method of claim 1, wherein the step of comparing the one or more vasculature data points to data relative to a model vasculature is performed by comparing said data points to data relative to a model vasculature in table form to determine the extent of vascular disease by identifying numerically calculated differences between said data points to data relative to a model vasculature.
16. A method for diagnosing vascular disease in a patient's vascular tree, the method comprising the steps of: generating a model vascular tree from a minimum energy hypothesis calculation; and comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease.
17. The method of claim 16, wherein the step of generating a model vascular tree from a minimum energy hypothesis calculation further comprises the step of calculating a relationship between at least two parameters from at least two measurements to generate one or more model vasculature data points within the model vascular tree.
18. The method of claim 17, wherein the at least two parameters comprise parameters relating to volume and length.
19. The method of claim 18, wherein the step of calculating a relationship between the at least two parameters is performed using a volume-length relation.
20. The method of claim 18, wherein the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
21. The method of claim 17, wherein the at least two parameters comprise parameters relating to diameter and length.
22. The method of claim 21, wherein the step of calculating a relationship between the at least two parameters is performed using a diameter-length relation.
23. The method of claim 17, wherein the at least two parameters comprise parameters relating to flow rate and diameter.
24. The method of claim 23, wherein the step of calculating a relationship between the at least two parameters is performed using a flow rate-diameter relation.
25. The method of claim 17, wherein the at least two parameters comprise parameters relating to resistance, length, and volume.
26. The method of claim 25, wherein the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
27. The method of claim 17, wherein the at least two parameters comprise parameters relating to flow rate and length.
28. The method of claim 27, wherein the step of calculating a relationship between the at least two parameters is performed using a flow rate-length relation.
29. The method of claim 16, wherein the step of comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease is performed by graphically comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease by identifying graphical differences between the patient's vascular tree with the model vascular tree.
30. The method of claim 16, wherein the step of comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease is performed by comparing the patient's vascular tree with the model vascular tree in table form to determine the extent of vascular disease by identifying numerically calculated differences between the patient's vascular tree with the model vascular tree.
31. A system for diagnosing vascular disease, the system comprising: a processor; a storage medium operably connected to the processor, the storage medium capable of receiving and storing data relative of measurements from a vasculature of a vessel; wherein the processor is operable to: obtain a vessel image showing a vasculature of a vessel; identify at least two measurements from the vasculature of the vessel, the measurements relating to at least two parameters; calculate a relationship between the at least two parameters from the at least two measurements to generate one or more vasculature data points; and compare the one or more vasculature data points to data relative to a model vasculature to determine the extent of vascular disease.
32. The system of claim 31, wherein the vessel image is an image selected from the group consisting of an angiograph, a computed tomography scan, and magnetic resonance imaging.
33. The system of claim 31, wherein the at least two parameters comprise parameters relating to volume and length from the vasculature of the vessel.
34. The system of claim 33, wherein the calculation of a relationship between the at least two parameters is performed using a volume-length relation.
35. The system of claim 33, wherein the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
36. The system of claim 31, wherein the at least two parameters comprise parameters relating to diameter and length from the vasculature of the vessel.
37. The system of claim 36, wherein the calculation of a relationship between the at least two parameters is performed using a diameter-length relation.
38. The system of claim 31, wherein the at least two parameters comprise parameters relating to flow rate and diameter from the vasculature of the vessel.
39. The system of claim 38, wherein the calculation of a relationship between the at least two parameters is performed using a flow rate-diameter relation.
40. The system of claim 31 , wherein the at least two parameters comprise parameters relating to resistance, length, and volume from the vasculature of the vessel.
41. The system of claim 40, wherein the calculation of a relationship between the at least two parameters is performed using a resistance-length and volume relation.
42. The system of claim 31, wherein the at least two parameters comprise parameters relating to flow rate and length from the vasculature of the vessel.
43. The system of claim 42, wherein the calculation of a relationship between the at least two parameters is performed using a flow rate-length relation.
44. The system of claim 31, wherein the comparison of the one or more vasculature data points to data relative to a model vasculature is performed by graphically comparing said data points to data relative to a model vasculature to determine the extent of vascular disease by identifying graphical differences between said data points to data relative to a model vasculature.
45. The system of claim 31, wherein the comparison of the one or more vasculature data points to data relative to a model vasculature is performed by comparing said data points to data relative to a model vasculature in table form to determine the extent of vascular disease by identifying numerically calculated differences between said data points to data relative to a model vasculature.
46. The system of claim 31, further comprising a program stored upon the storage medium, said program operable by the processor upon data relative of measurements from a vasculature of a vessel.
47. The system of claim 31 , wherein the system comprises a user system and a server system, and wherein the user system and the server system are operably connected to one another.
48. A system for diagnosing vascular disease in a patient's vascular tree, the system comprising: a processor; and a storage medium operably connected to the processor, the storage medium capable of receiving and storing data relative of measurements from a vasculature of a vessel; wherein the processor is operable to: generate a model vascular tree from a minimum energy hypothesis calculation; and compare the patient's vascular tree with the model vascular tree to determine the extent of vascular disease.
49. The system of claim 48, wherein the generation of a model vascular tree from a minimum energy hypothesis calculation is performed by calculating a relationship between at least two parameters from at least two measurements to generate one or more model vasculature data points within the model vascular tree.
50. The system of claim 49, wherein the at least two parameters comprise parameters relating to volume and length.
51. The system of claim 50, wherein the calculation of a relationship between the at least two parameters is performed using a volume-length relation.
52. The system of claim 50, wherein the calculation of a relationship between the at least two parameters is performed using a resistance-length and volume relation.
53. The system of claim 49, wherein the at least two parameters comprise parameters relating to diameter and length.
54. The system of claim 53, wherein the calculation of a relationship between the at least two parameters is performed using a diameter-length relation.
55. The system of claim 49, wherein the at least two parameters comprise parameters relating to flow rate and diameter.
56. The system of claim 55, wherein the calculation of a relationship between the at least two parameters is performed using a flow rate-diameter relation.
57. The system of claim 49, wherein the at least two parameters comprise parameters relating to resistance, length, and volume.
58. The system of claim 57, wherein the calculation of a relationship between the at least two parameters is performed using a resistance-length and volume relation.
59. The system of claim 49, wherein the at least two parameters comprise parameters relating to flow rate and length.
60. The system of claim 59, wherein the calculation of a relationship between the at least two parameters is performed using a flow rate-length relation.
61. The system of claim 48, wherein the comparison of the patient's vascular tree with the model vascular tree to determine the extent of vascular disease is performed by graphically comparing the patient's vascular tree with the model vascular tree to determine the extent of vascular disease by identifying graphical differences between the patient's vascular tree with the model vascular tree.
62. The system of claim 48, wherein the comparison of the patient's vascular tree with the model vascular tree to determine the extent of vascular disease is performed by comparing the patient's vascular tree with the model vascular tree in table form to determine the extent of vascular disease by identifying numerically calculated differences between the patient's vascular tree with the model vascular tree.
63. The system of claim 48, further comprising a program stored upon the storage medium, said program operable by the processor upon data relative of measurements from a vasculature of a vessel.
64. The system of claim 48, wherein the system comprises a user system and a server system, and wherein the user system and the server system are operably connected to one another.
65. A program having a plurality of program steps to be executed on a computer having a processor and a storage medium to analyze data relative of measurements from a vasculature of a vessel, the program operable to: obtain a vessel image showing a vasculature of a vessel; identify at least two measurements from the vasculature of the vessel, the measurements relating to at least two parameters; calculate a relationship between the at least two parameters from the at least two measurements to generate one or more vasculature data points; and compare the one or more vasculature data points to data relative to a model vasculature to determine the extent of vascular disease.
66. A program having a plurality of program steps to be executed on a computer having a processor and a storage medium to analyze data relative of measurements from a vasculature of a vessel, the program operable to: generate a model vascular tree from a minimum energy hypothesis calculation; and compare the patient's vascular tree with the model vascular tree to determine the extent of vascular disease.
67. A method to design an energy efficient microfluidic channel device, comprising the steps of: identifying at least two parameters relating to a desired microfluidic channel device; calculating a relationship between the at least two parameters to generate at least two measurements; and utilizing the at least two measurements to design the microfluidic channel device.
68. The method of claim 67, wherein the at least two parameters comprise parameters relating to volume and length of a desired microfluidic channel device.
69. The method of claim 68, wherein the step of calculating a relationship between the at least two parameters is performed using a volume-length relation.
70. The method of claim 68, wherein the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
71. The method of claim 67, wherein the at least two parameters comprise parameters relating to diameter and length.
72. The method of claim 71, wherein the step of calculating a relationship between the at least two parameters is performed using a diameter-length relation.
73. The method of claim 67, wherein the at least two parameters comprise parameters relating to flow rate and diameter.
74. The method of claim 73, wherein the step of calculating a relationship between the at least two parameters is performed using a flow rate-diameter relation.
75. The method of claim 67, wherein the at least two parameters comprise parameters relating to resistance, length, and volume.
76. The method of claim 75, wherein the step of calculating a relationship between the at least two parameters is performed using a resistance-length and volume relation.
77. The method of claim 67, wherein the at least two parameters comprise parameters relating to flow rate and length.
78. The method of claim 77, wherein the step of calculating a relationship between the at least two parameters is performed using a flow rate-length relation.
79. The method of claim 67, further comprising the step of utilizing the at least two measurements to fabricate the microfluidic channel device.
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