ObjectiveTo examine the diaphragm and chest wall dynamics with cine breathing magnetic resonance imaging (MRI) in ambulatory boys with Duchenne muscular dystrophy (DMD) without respiratory symptoms and controls.MethodsIn 11 DMD boys and 15 controls, cine MRI of maximal breathing was recorded for 10 sec. The lung segmentations were done by an automated pipeline based on a Holistically‐Nested Network model (HNN method). Lung areas, diaphragm, and chest wall motion were measured throughout the breathing cycle.ResultsThe HNN method reliably identified the contours of the lung and the diaphragm in every frame of each dataset (~180 frames) within seconds. The lung areas at maximal inspiration and expiration were reduced in DMD patients relative to controls (P = 0.02 and <0.01, respectively). The change in the lung area between inspiration and expiration correlated with percent predicted forced vital capacity (FVC) in patients (r s = 0.75, P = 0.03) and was not significantly different between groups. The diaphragm position, length, contractility, and motion were not significantly different between groups. Chest wall motion was reduced in patients compared to controls (P < 0.01).InterpretationCine breathing MRI allows independent and reliable assessment of the diaphragm and chest wall dynamics during the breathing cycle in DMD patients and controls. The MRI data indicate that ambulatory DMD patients breathe at lower lung volumes than controls when their FVC is in the normal range. The diaphragm moves normally, whereas chest wall motion is reduced in these boys with DMD.

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ObjectivesTo characterize muscle involvement and evaluate disease severity in patients with GNE myopathy using skeletal muscle MRI and proton magnetic resonance spectroscopy (1H-MRS).MethodsSkeletal muscle imaging of the lower extremities was performed in 31 patients with genetically confirmed GNE myopathy, including T1-weighted and STIR images, T1 and T2 mapping and 1H-MRS. Measures evaluated included longitudinal relaxation time (T1), transverse relaxation time (T2), and 1H-MRS fat fraction (FF). Thigh muscle volume was correlated with relevant measures of strength, function, and patient-reported outcomes.ResultsThe cohort was representative of a wide range of disease progression. Contractile thigh muscle volume ranged from 5.51% to 62.95%, and correlated with thigh strength (r = 0.91), the 6-minute walk test (r = 0.82), the adult myopathy assessment tool (r = 0.83), the activities-specific balance confidence scale (r = 0.65), and the inclusion body myositis functional rating scale (r = 0.62). Four stages of muscle involvement were distinguished by qualitative (T1W and STIR images) and quantitative methods: stage I) Unaffected muscle (T1 = 1,033 ± 74.2 ms, T2 = 40.0 ± 1.9 ms, FF = 7.4 ± 3.5%); Stage II) STIR hyperintense muscle with minimal or no fat infiltration (T1 = 1,305 ± 147 ms, T2 = 50.2 ± 3.5 ms, FF = 27.6 ± 12.7%); Stage III) Fat infiltration and STIR hyperintensity (T1 = 1,209 ± 348 ms, T2 = 73.3 ± 12.6 ms, FF = 57.5 ± 10.6%); and Stage IV) Complete fat replacement (T1 = 318 ± 39.9 ms, T2 = 114 ± 21.2 ms, FF = 85.6 ± 4.2%). 1H-MRS showed a significant decrease in intramyocellular lipid and trimethylamines (TMA) between stage I and II, suggesting altered muscle metabolism at early stages.ConclusionMRI biomarkers can monitor muscle involvement and determine disease severity non-invasively in patients with GNE myopathy.ClinicalTrials.gov IdentifierNCT01417533.

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Duchenne muscular dystrophy (DMD) is a childhood-onset neuromuscular disease that results in the degeneration of muscle, starting in the extremities, before progressing to more vital areas, such as the lungs. Respiratory failure and pneumonia due to respiratory muscle weakness lead to hospitalization and early mortality. However, tracking the disease in this region can be difficult, as current methods are based on breathing tests and are incapable of distinguishing between muscle involvements. Cine MRI scans give insight into respiratory muscle movements, but the images suffer due to low spatial resolution and poor signal-to-noise ratio. Thus, a robust lung segmentation method is required for accurate analysis of the lung and respiratory muscle movement. We deployed a deep learning approach that utilizes sequence-specific prior information to assist the segmentation of lung in cine MRI. More specifically, we adopt a holistically nested network to conduct image-to-image holistic training and prediction. One frame of the cine MRI is used in the training and applied to the remainder of the sequence ([Formula: see text] frames). We applied this method to cine MRIs of the lung in the axial, sagittal, and coronal planes. Characteristic lung motion patterns during the breathing cycle were then derived from the segmentations and used for diagnosis. Our data set consisted of 31 young boys, age [Formula: see text] years, 15 of whom suffered from DMD. The remaining 16 subjects were age-matched healthy volunteers. For validation, slices from inspiratory and expiratory cycles were manually segmented and compared with results obtained from our method. The Dice similarity coefficient for the deep learning-based method was [Formula: see text] for the sagittal view, [Formula: see text] for the axial view, and [Formula: see text] for the coronal view. The holistic neural network approach was compared with an approach using Demon's registration and showed superior performance. These results suggest that the deep learning-based method reliably and accurately segments the lung across the breathing cycle.

on Hippel-Lindau (VHL) syndrome is a familial neoplasia syndrome that occurs in approximately one of 36 000 live births (1). The clinical manifestations of VHL include central nervous system hemangioblastomas, retinal hemangiomas, endolymphatic sac tumors, pheochromocytomas, pancreatic tumors and cysts, including pancreatic neuroendocrine tumors (PNETs), and renal cysts and renal cell carcinomas (1). The condition is caused by a germline pathogenic variation in the VHL tumor suppressor gene and is inherited in an autosomal dominant fashion (2).Most of the recommended screening guidelines for pancreas tumors involve nonionizing radiation-emitting imaging modalities (sonography and MRI) instead of CT. If repeated during a lifetime, CT can be associated with substantial radiation exposure (Table 1) (3-8). Both CT (9) and MRI (10) have high sensitivity for the depiction of PNETs, and one report (11) suggested superior sensitivity for intravenous contrast agent-enhanced CT over MRI for the depiction of small PNETs. Hence, some organizations recommend the use of CT in asymptomatic adults (3,12). The frequency of imaging examinations varies in the different guidelines, with some recommending annual screening (1,(4)(5)(6)(7)12,13) and others, biennial evaluations (3,14) depending on whether a pancreatic and/or kidney tumor was already detected in the patient. The medical surveillance of VHL-related PNETs is adapted according to clinical characteristics of the patient and imaging features. MRI is most often used for surveillance of VHL-associated renal cell carcinoma. However, CT is certainly acceptable and may be recommended when needed (3,5,7).The surveillance protocols for patients with VHL are mainly based on several analyses performed in the National Institutes of Health prospective studies and primarily involve repeated CT scans.

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