MRI quantifies neuromuscular disease progression

Forbes, Sean C.; Willcocks, Rebecca J.; Rooney, William D.; Walter, Glenn A.; Vandenborne, Krista

doi:10.1016/s1474-4422(15)00320-8

Cited by 21 publications

(17 citation statements)

References 12 publications

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“…In addition to previous studies revealing the fat‐fraction as a promising biomarker, we suggest that muscle volume as detected by semi‐automatic segmentation may serve as a useful biomarker in neuromuscular disease, as it is a reliable and time‐efficient method and correlates well with clinical markers of disease severity. Another valuable topic for future studies could be the influence of activity levels on muscle volume in neuromuscular diseases.…”

Section: Discussionsupporting

confidence: 57%

“…Compared with other studies, our findings in the double track test allow the conclusion that disease‐related changes are not only validly detectable by the more time‐consuming examination of single MRI slices, but also by semi‐automatic segmentation and quantification of muscle volumes.…”

Section: Discussionmentioning

confidence: 49%

“…This method of in vivo detection using a noninvasive imaging marker might expedite reliable treatment response measurements and diagnostic options in neuromuscular disorders. To date, many studies on muscle MRI were done using single or a few consecutive slices . However, due to the highly variable degree to which muscle changes in neuromuscular disorders can occur, single‐slice measurements could over‐ or underestimate the extent of muscle changes along the leg or even the whole body.…”

Section: Introductionmentioning

confidence: 99%

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Semi‐automated volumetry of MRI serves as a biomarker in neuromuscular patients

et al. 2020

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Background: Muscle MRI is of increasing importance for neuromuscular patients to detect changes in muscle volume, fat-infiltration, and edema. We developed a method for semi-automated segmentation of muscle MRI datasets.Methods: An active contour-evolution algorithm implemented within the ITK-SNAP software was used to segment T1-weighted MRI, and to quantify muscle volumes of neuromuscular patients (n = 65).Results: Semi-automated compared with manual segmentation was shown to be accurate and time-efficient. Muscle volumes and ratios of thigh/lower leg volume were lower in myopathy patients than in controls (P < .0001; P < .05). We found a decrease of lower leg muscle volume in neuropathy patients compared with controls (P < .01), which correlated with clinical parameters. In myopathy patients, muscle volume showed a positive correlation with muscle strength (r left = 0.79, p left < .0001).Muscle volumes were independent of body mass index and age.Conclusions: Our method allows for exact and time-efficient quantification of muscle volumes with possible use as a biomarker in neuromuscular patients. K E Y W O R D S biomarker, MR imaging, muscle dystrophy, neuromuscular disorders, segmentation, thresholding

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Section: Discussionsupporting

confidence: 57%

Section: Discussionmentioning

confidence: 49%

Section: Introductionmentioning

confidence: 99%

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Semi‐automated volumetry of MRI serves as a biomarker in neuromuscular patients

et al. 2020

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“…The response of skeletal muscle deep tissue injury is known to follow a well-known pathway with multiple overlapping pathological processes. 29 Several studies have concluded that a single MRI contrast cannot capture all of the pathological processes involved in skeletal muscle damage, 22,25,28,[41][42][43][44][45][46][47][48][49][50][51][52][53] which therefore warrants a multi-parametric approach using several complementary MRI readouts to characterize different aspects of injury and recovery. Furthermore, thus far there is limited knowledge on the spatio-temporal changes in the biomechanical properties of the muscle tissue following deformation-induced muscle damage.…”

Section: Resultsmentioning

confidence: 99%

Magnetic resonance elastography of skeletal muscle deep tissue injury

et al. 2019

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The current state‐of‐the‐art diagnosis method for deep tissue injury in muscle, a subcategory of pressure ulcers, is palpation. It is recognized that deep tissue injury is frequently preceded by altered biomechanical properties. A quantitative understanding of the changes in biomechanical properties preceding and during deep tissue injury development is therefore highly desired. In this paper we quantified the spatial–temporal changes in mechanical properties upon damage development and recovery in a rat model of deep tissue injury. Deep tissue injury was induced in nine rats by two hours of sustained deformation of the tibialis anterior muscle. Magnetic resonance elastography (MRE), T 2 ‐weighted, and T 2 ‐mapping measurements were performed before, directly after indentation, and at several timepoints during a 14‐day follow‐up. The results revealed a local hotspot of elevated shear modulus (from 3.30 ± 0.14 kPa before to 4.22 ± 0.90 kPa after) near the center of deformation at Day 0, whereas the T 2 was elevated in a larger area. During recovery there was a clear difference in the time course of the shear modulus and T 2 . Whereas T 2 showed a gradual normalization towards baseline, the shear modulus dropped below baseline from Day 3 up to Day 10 (from 3.29 ± 0.07 kPa before to 2.68 ± 0.23 kPa at Day 10, P < 0.001), followed by a normalization at Day 14. In conclusion, we found an initial increase in shear modulus directly after two hours of damage‐inducing deformation, which was followed by decreased shear modulus from Day 3 up to Day 10, and subsequent normalization. The lower shear modulus originates from the moderate to severe degeneration of the muscle. MRE stiffness values were affected in a smaller area as compared with T 2 . Since T 2 elevation is related to edema, distributing along the muscle fibers proximally and distally from the injury, we suggest that MRE is more specific than T 2 for localization of the actual damaged area.

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“…The muscle fat fraction was serially assessed and showed correlation with functional measures [79]. Using diffusion tractography, fractional anisotropy was shown to correlate with axon pathology in ALS [80,81].…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%