Fat quantification in MRI-defined lumbar muscles

Abstract

Abstract-Some studies suggest fat infiltration in the lumbar muscles (LM) is associated with lower back pain (LBP) in adults. Usually fat in MRI-defined lumbar muscles is qualitatively evaluated by visual grading via a 3 point scale, whereas a quantitative continuous (0 -100%) approach may provide a greater insight. In this paper, we propose a method to precisely quantify the fat content / infiltration in a user-defined region of the lumbar muscles, which may aid better diagnosis. The key steps are segmenting the region of interest (ROI) from the lumbar muscles, identifying the fatty regions in the segmented region based on the selected threshold and softness levels, computing the parameters (such as total and region-wise fat content percentage, total-cross sectional area (TCSA), functional cross-sectional area (FCSA)) and exporting the computations and associated patient information from the MRI, into a database. A standalone application using MATLAB R2010a was developed to perform the required computations along with an intuitive GUI.

Repeatability of Dixon magnetic resonance imaging and magnetic resonance spectroscopy for quantitative muscle fat assessments in the thigh

“…9,10 It is interesting that in elderly subjects, muscle mass declines at a slower rate than strength, emphasizing a strong role of muscle fat infiltration and potentially of the amount of perimuscular fat. A number of diagnostic techniques mostly based on magnetic resonance imaging (MRI) have been introduced [11][12][13][14] for the assessment of fat. In the present study, multi-point T 2 *-corrected Dixon MRI and multi-echo T 2 -corrected magnetic resonance spectroscopy (MRS) were used to measure proton density fat fraction (PDFF) and proton density water fraction (PDWF) while minimizing MR-specific effects.…”

An interactive segmentation tool for quantifying fat in lumbar muscles using axial lumbar-spine MRI

“…Our previous work [27] is extended in the following way: automatically detecting the center of spinal column to quantify fat in the fragments of lumbar muscles with reference to the center of spinal column, computing a global image threshold using Otsu's method [28] and using it as initial reference for identifying fatty regions in the region of interest and the use of livewire interactive segmentation [21] for defining the region of interest. To automatically detect the spinal column two methods are proposed: 1) Using the spinal cord as reference, 2) Automatic region detection using HOG features and an SVM classifier.…”