Embodiments of the invention relate generally to image processing techniques and, more particularly, to systems and methods for distributed and coordinated image processing of tomographic images utilizing processors on a medical imaging device and processors on a separate workstation.
Cardiac imaging is a critical function in clinical applications. Characterization of myocardial motion enables better understanding of the physiology of a heart and early detection of cardiovascular diseases. Particularly, cardiologists employ CT angiography (CTA) images to diagnose and characterize the extent of heart disease. Imaging the heart, however, is particularly challenging, as the heart is a moving object that rotates, translates and deforms non-rigidly in a three-dimensional (3D) space. Conventional CT image reconstruction methods generally assume that an object is stationary during data acquisition. In cardiac imaging, application of the conventional reconstruction methods may result in image blurring and other motion artifacts in the reconstructed images due to heart motion. The artifacts can severely affect a diagnosis that uses these reconstructed images, especially if the imaged features are small. For example, plaques formed in coronary arteries are generally indicative of a risk of a potential heart attack, but are difficult to image due to their small size. Non-optimal reconstruction of such small features may result in incorrect diagnosis resulting in serious consequences. Therefore, an ability to produce high-resolution images is critical to clinical diagnosis.
In an effort to mitigate effects of motion artifacts in CT imaging, some current imaging methods employ complex image reconstruction techniques. For example, one approach utilized in present day scanners is the reconstruction of images at multiple phases in an attempt to select a volume reconstructed at the most quiescent phase of the heart. However, the temporal resolution in currently available CT scanners does not suffice for motion free cardiac imaging of all coronary segments at higher heart rates or highly variable heart rates. Certain other techniques relate to model-based estimation requiring reconstructions of several cardiac phases to estimate the motion. Such techniques, however, involve computationally intensive image reconstruction.
While such complex image reconstruction techniques employed in cardiac CT imaging may produce high-resolution images in which motion artifacts are mitigated, such complex image reconstruction techniques also raise a number of computational challenges. That is, it is recognized that if complex image reconstruction employs reconstruction processors associated with the CT scanner, availability of the CT scanner for the next patient may be compromised, as the image reconstruction may impact system performance during a next clinical exam, such as preventing the rapid processing and display of CT images needed during a next clinical exam.
It is also recognized that, with respect to cardiac CT imaging, the clinical workflow of patients able to be imaged by the CT system can be negatively affected if too much responsibility is placed on the technologist performing the scanning operation. In a typical cardiac CT imaging process, once the image data has been acquired, the CT system technologist looks at the data to confirm that good data has been acquired, so that the patient can be released and the next patient can be scanned. However, the technologist may not have any time to focus on identifying any areas within a region-of-interest that may have been missed, such as coronary arteries or portions of the coronary arteries that automatic segmentation algorithms may have missed. Ideally, such identification of deficiencies in the image processing could be performed by a clinician or a clinician's assistant, such that deficiencies in the processing can be rapidly edited and reprocessed and motion estimation and correction may be applied to these regions at the CT system without any further input from the CT system technologist. It is further desirable that review of reconstructed images by a physician be provided for in a timely manner, so as to allow for the directing of additional processing in conjunction with clinical assessment of the images.
Therefore, it would be desirable to develop a system and method for processing imaging data that accommodates the competing desires for an automated workflow that still allows human interaction, minimal data transfer across a network, the use of computational processing in multiple locations within a network, and the ability to direct additional processing in conjunction with clinical assessment of the images.