Integrated image registration for cardiac magnetic resonance perfusion data

#In appearance-based methods for object detection and/or recognition, segmentation of images indicative of the objects of interest may be complicated by dynamic motion during the acquisition of a time-wise sequence of images. An exemplary application is segmentation of cardiac perfusion image data. Ischemic heart disease, the obstruction of blood flow to the heart, typically results from excess fat or plaque deposits, which may narrow the veins that supply oxygenated blood to the heart. The reduced blood supply to the heart is typically manifested as reduced blood perfusion to the myocardium (“MC”) heart muscle. Clinically, the myocardial perfusion measurements are routinely performed with single-photon emission computed tomography (“SPECT”) images, and/or with positron emission tomography (“PET”) images. Drawbacks and limitations of these existing techniques include the low spatial resolution, attenuation artifacts of SPECT and limited availability of PET.

#Myocardial perfusion analysis using magnetic resonance (“MR”) images holds great promise, and also permits quantitative analysis of blood flow. In MR perfusion analysis, typically about 60 to 100 short axis 2-dimensional (“2D”) MR images of the heart are acquired after injecting contrast agent into the blood. Unfortunately, as the heart is beating, the contrast in the acquired MR images is typically rapidly changing. The contrast agent passes through the right ventricle (“RV”) to the left ventricle (“LV”), and then perfuses into the myocardium.

#To perform the perfusion analysis, it is necessary to segment the myocardium in all of the MR images acquired in a perfusion scan. Segmenting the myocardium in all of the MR images is currently performed manually, and requires significant labor from skilled physicians. This is a tedious and labor-intensive job, given that there are typically 60 to 100 images in each scan. The problem is compounded by the fact that the contrast in the images is typically rapidly changing. When the contrast agent is in the LV, the blood pool brightens up and makes it easy to segment the inner wall of the myocardium, the endocardium. However, when there is no contrast agent in the LV, it is very difficult to segment the endocardium.

#The present disclosure teaches a system and method of Integrated Image Registration For Cardiac Magnetic Resonance Perfusion Data for appearance-based object detection in accordance with the following exemplary figures, in which:

#FIG. 1 shows a block diagram for an integrated image registration system according to an illustrative embodiment of the present disclosure;

#FIG. 2 shows two comparative image sequences for synthetic data, the second sequence according to an illustrative embodiment of the present disclosure;

#The present disclosure provides an integrated image registration algorithm for segmenting the heart muscle or myocardium (“MC”). A sequence of magnetic resonance (“MR”) images of the heart is acquired after injection of a contrast agent. An analysis of the perfusion of the contrast agent into the myocardium requires segmentation of the MC in each of the images acquired. This segmentation task is especially difficult due to the rapidly changing contrast in the images. Accordingly, the present disclosure presents an information registration framework that integrates two channels of information, the pixel intensities and the local gradient information, to reliably and accurately segment the myocardium.

#FIG. 1 shows a block diagram of a system 100 for integrated image registration using cardiac magnetic resonance perfusion data according to an illustrative embodiment of the present disclosure. The system 100 includes at least one processor or central processing unit (“CPU”) 102 in signal communication with a system bus 104. A read only memory (“ROM”) 106, a random access memory (“RAM”) 108, a display adapter 110, an I/O adapter 112, a user interface adapter 114, a communications adapter 128, and an imaging adapter 130 are also in signal communication with the system bus 104. A display unit 116 is in signal communication with the system bus 104 via the display adapter 110. A disk storage unit 118, such as, for example, a magnetic or optical disk storage unit is in signal communication with the system bus 104 via the I/O adapter 112. A mouse 120, a keyboard 122, and an eye tracking device 124 are in signal communication with the system bus 104 via the user interface adapter 114. A magnetic resonance imaging device 132 is in signal communication with the system bus 104 via the imaging adapter 130. An edge estimation unit 170 and an image registration unit 180 are also included in the system 100 and in signal communication with the CPU 102 and the system bus 104. While the estimation unit 170 and the registration unit 180 are illustrated as coupled to the at least one processor or CPU 102, these components are preferably embodied in computer program code stored in at least one of the memories 106, 108 and 118, wherein the computer program code is executed by the CPU 102. As will be recognized by those of ordinary skill in the pertinent art based on the teachings herein, alternate embodiments are possible, such as, for example, embodying some or all of the computer program code in registers located on the processor chip 102. Given the teachings of the disclosure provided herein, those of ordinary skill in the pertinent art will contemplate various alternate configurations and implementations of the estimation unit 170 and the registration unit 180, as well as the other elements of the system 100, while practicing within the scope and spirit of the present disclosure.

#In FIG. 2, the reference numeral 200 generally indicates two sequences of image frames of synthetic data where, in addition to rapidly changing pixel intensities, there is a sub-pixel shift to the right. Thus, the first row of images, frames 210 through 218, respectively, show the results obtained using a mutual information (“MI”) based template-matching where it is assumed that there are no sub-pixel shifts. The registration is done using only pixel intensities, and the hand drawn contours are utilized to specify only the region of interest (“ROI”). Under this assumption, the sub-pixel drifts accumulate quickly, leading to noticeable shifts towards the end of the sequence, such as shown in frame 218. In the second row of images, frames 220 through 228, respectively, the results are obtained using MI based template-matching where it is again assumed that there are no sub-pixel shifts. Here, however, the registration is done using edge information, in addition to the pixel-intensities, while the hand drawn contours are again utilized to specify only the ROI. The results of frames 220 through 228, using registration with edge information, are significantly more accurate.