Detection of Parkinson's disease by MRI: Spin‐lattice distribution imaging

Abstract

We have developed an advanced MRI technique for detecting Parkinson's Disease (PD) which depends on an image constructed as a ratio of images from two inversion recovery sequences (one generating a white matter suppressed image, the other a gray matter suppressed image). This technique was designed to be exceptionally sensitive to the spin-lattice relaxation time T(1). It was refined with the introduction of segmentation analysis and given the acronym SIRRIM (Segmented Inversion Recovery Ratio Imaging). Our objectives are, first, to reinvestigate the sensitivity of MRI with new subjects and second, to investigate whether a new form of analysis, using the gray level distribution of signal in the image, may prove more sensitive than SIRRIM. For each subject, a ratio image was constructed (WMS/GMS) and the substantia nigra segmented out to be displayed as an isolated structure. From the segmented image a measure of disease severity, the Radiological Index (RI), was calculated for each subject. Since the pixel value in the ratio image is a strong function of the local T(1) relaxation time, the distribution of pixel values gives the distribution of spin-lattice relaxation times. A refinement in the analysis is introduced, the Spin-Lattice Distribution Index (SI), which is an automated measure of MRI signal in the Substantia Nigra pars compacta (SN(C)). Both RI and SI were calculated for each of 24 subjects, 12 patients and 12 controls. The SI may further improve the separation of patient and control groups, and may therefore be more sensitive than the RI. Unlike the RI it is completely automatic and circumvents two of the limitations of the RI. The work is consistent with the proposition that MRI, when properly configured, is a highly sensitive marker for PD.