Magnetic resonance imaging uses the nuclear magnetic resonance (NMR) phenomenon to produce images. When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins, and after the excitation signal B1 is terminated, this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. Each measurement is referred to in the art as a “view” and the number of views determines the quality of the image. The resulting set of received NMR signals, or views, or k-space samples, are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
Projection reconstruction methods have been known since the inception of magnetic resonance imaging. Rather than sampling k-space in a rectilinear, or Cartesian, scan pattern as is done in Fourier imaging, projection reconstruction methods sample k-space with a series of views that sample radial lines extending outward from the center of k-space. If an insufficient number of views are acquired, streak artifacts are produced in the reconstructed image.
In addition to the aforementioned image acquisition protocol, with sensors rotating around the object for multi-angle views subjected to reconstruction, a new protocol was introduced to acquire multi-slices of images through confocal microscopy, for example the U.S. application Ser. No. 11/169,890, entitled “Bio-Expression System and The Method of The Same.” Such a methodology is appropriate for the acquisition of high resolution bioimages at the cellular level and for the reconstruction of a 3D cellular network, such as in the brain, so as to study the physiology or pathology of the organ. It is therefore a plausible task to reconstruct the whole neural network in the brain by systematic collections of different neurons in the whole brain and a rational reassignment of their proper position in a standard brain model system with coordinate reference. In other words, a 3D image database for neurons in the whole brain is feasible if all 3D neuronal images have been arranged with algorithms of anatomical significance. This is especially true when the technique mentioned in the U.S. Pat. No. 6,472,216 B1, filed on Oct. 29, 2002, entitled “Aqueous Tissue Clearing Solution” is employed in conjunction. However, there are tens of millions (in Drosophila) or even billions (in human) of neural images that need to be processed for the circuitry construction. A repertoire of algorithms for coherent 3D image data preprocessing is critical for the consistency of neural images categorized in the database.
Nerve tissue in human beings and other creatures, such as insects, includes neurons with elongated axonal portions arranged to form neural fibers or fiber bundles along which electrochemical signals are transmitted. In the brain, for example, functional areas defined by very high neural densities are typically linked by structurally complex neural networks of axonal fiber bundles. Tracts of neural fibers are therefore significantly relevant to functions associated with brain regions
Diagnosis of neural diseases, planning for brain surgery, and other neurologically related clinical activities as well as research activities on brain functioning can benefit from detailed anatomical information such as tracking of the axonal fibers and fiber bundles.
Therefore, the present invention provides a novel image preprocessing system to establish a 3D brain image database for research or possible clinical applications.