[Expert recommendations for magnetic resonance imaging of muscle disorders]. / Expertenempfehlung zur Magnetresonanztomographie bei Muskelerkrankungen.

BACKGROUND: Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking. OBJECTIVES: The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders. METHODS: This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and a focused literature search. RESULTS: The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of a suitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for a structured radiological assessment are presented. CONCLUSIONS: The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides a possible basis for the standardisation of the measurement protocols at all clinical centres in Germany.

On the water-fat in-phase assumption for quantitative susceptibility mapping.

PURPOSE: To (a) define multi-peak fat model-based effective in-phase echo times for quantitative susceptibility mapping (QSM) in water-fat regions, (b) analyze the relationship between fat fraction, field map quantification bias and susceptibility bias, and (c) evaluate the susceptibility mapping performance of the proposed effective in-phase echoes in comparison to single-peak in-phase echoes and water-fat separation for regions where both water and fat are present. METHODS: Effective multipeak in-phase echo times for a bone marrow and a liver fat spectral model were derived from a single voxel simulation. A Monte Carlo simulation was performed to assess the field map estimation error as a function of fat fraction for the different in-phase echoes. Additionally, a phantom scan and in vivo scans in the liver, spine, and breast were performed and evaluated with respect to quantification accuracy. RESULTS: The use of single-peak in-phase echoes can introduce a worst-case susceptibility bias of 0.43 $$ 0.43 $$  ppm. The use of effective multipeak in-phase echoes shows a similar quantitative performance in the numerical simulation, the phantom and in all in vivo anatomies when compared to water-fat separation-based QSM. CONCLUSION: QSM based on the proposed effective multipeak in-phase echoes can alleviate the quantification bias present in QSM based on single-peak in-phase echoes. When compared to water-fat separation-based QSM the proposed effective in-phase echo times achieve a similar quantitative performance while drastically reducing the computational expense for field map estimation.

Compositional and Functional MRI of Skeletal Muscle: A Review.

Due to its exceptional sensitivity to soft tissues, MRI has been extensively utilized to assess anatomical muscle parameters such as muscle volume and cross-sectional area. Quantitative Magnetic Resonance Imaging (qMRI) adds to the capabilities of MRI, by providing information on muscle composition such as fat content, water content, microstructure, hypertrophy, atrophy, as well as muscle architecture. In addition to compositional changes, qMRI can also be used to assess function for example by measuring muscle quality or through characterization of muscle deformation during passive lengthening/shortening and active contractions. The overall aim of this review is to provide an updated overview of qMRI techniques that can quantitatively evaluate muscle structure and composition, provide insights into the underlying biological basis of the qMRI signal, and illustrate how qMRI biomarkers of muscle health relate to function in healthy and diseased/injured muscles. While some applications still require systematic clinical validation, qMRI is now established as a comprehensive technique, that can be used to characterize a wide variety of structural and compositional changes in healthy and diseased skeletal muscle. Taken together, multiparametric muscle MRI holds great potential in the diagnosis and monitoring of muscle conditions in research and clinical applications. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset.

OBJECTIVES: T2-weighted (w) fat sat (fs) sequences, which are important in spine MRI, require a significant amount of scan time. Generative adversarial networks (GANs) can generate synthetic T2-w fs images. We evaluated the potential of synthetic T2-w fs images by comparing them to their true counterpart regarding image and fat saturation quality, and diagnostic agreement in a heterogenous, multicenter dataset. METHODS: A GAN was used to synthesize T2-w fs from T1- and non-fs T2-w. The training dataset comprised scans of 73 patients from two scanners, and the test dataset, scans of 101 patients from 38 multicenter scanners. Apparent signal- and contrast-to-noise ratios (aSNR/aCNR) were measured in true and synthetic T2-w fs. Two neuroradiologists graded image (5-point scale) and fat saturation quality (3-point scale). To evaluate whether the T2-w fs images are indistinguishable, a Turing test was performed by eleven neuroradiologists. Six pathologies were graded on the synthetic protocol (with synthetic T2-w fs) and the original protocol (with true T2-w fs) by the two neuroradiologists. RESULTS: aSNR and aCNR were not significantly different between the synthetic and true T2-w fs images. Subjective image quality was graded higher for synthetic T2-w fs (p = 0.023). In the Turing test, synthetic and true T2-w fs could not be distinguished from each other. The intermethod agreement between synthetic and original protocol ranged from substantial to almost perfect agreement for the evaluated pathologies. DISCUSSION: The synthetic T2-w fs might replace a physical T2-w fs. Our approach validated on a challenging, multicenter dataset is highly generalizable and allows for shorter scan protocols. KEY POINTS: • Generative adversarial networks can be used to generate synthetic T2-weighted fat sat images from T1- and non-fat sat T2-weighted images of the spine. • The synthetic T2-weighted fat sat images might replace a physically acquired T2-weighted fat sat showing a better image quality and excellent diagnostic agreement with the true T2-weighted fat images. • The present approach validated on a challenging, multicenter dataset is highly generalizable and allows for significantly shorter scan protocols.

Comparing myelin-sensitive magnetic resonance imaging measures and resulting g-ratios in healthy and multiple sclerosis brains.

The myelin concentration and the degree of myelination of nerve fibers can provide valuable information on the integrity of human brain tissue. Magnetic resonance imaging (MRI) of myelin-sensitive parameters can help to non-invasively evaluate demyelinating diseases such as multiple sclerosis (MS). Several different myelin-sensitive MRI methods have been proposed to determine measures of the degree of myelination, in particular the g-ratio. However, variability in underlying physical principles and different biological models influence measured myelin concentrations, and consequently g-ratio values. We therefore investigated similarities and differences between five different myelin-sensitive MRI measures and their effects on g-ratio mapping in the brains of both MS patients and healthy volunteers. We compared two different estimates of the myelin water fraction (MWF) as well as the inhomogeneous magnetization transfer ratio (ihMTR), magnetization transfer saturation (MTsat), and macromolecular tissue volume (MTV) in 13 patients with MS and 14 healthy controls. In combination with diffusion-weighted imaging, we derived g-ratio parameter maps for each of the five different myelin measures. The g-ratio values calculated from different myelin measures varied strongly, especially in MS lesions. While, compared to normal-appearing white matter, MTsat and one estimate of the MWF resulted in higher g-ratio values within lesions, ihMTR, MTV, and the second MWF estimate resulted in lower lesion g-ratio values. As myelin-sensitive measures provide rough estimates of myelin content rather than absolute myelin concentrations, resulting g-ratio values strongly depend on the utilized myelin measure and model used for g-ratio mapping. When comparing g-ratio values, it is, thus, important to utilize the same MRI methods and models or to consider methodological differences. Particular caution is necessary in pathological tissue such as MS lesions.

Beyond mean value analysis - a voxel-based analysis of the quantitative MR biomarker water T2 in the presence of fatty infiltration in skeletal muscle tissue of patients with neuromuscular diseases.

The main pathologies in the muscles of patients with neuromuscular diseases (NMD) are fatty infiltration and edema. Recently, quantitative magnetic resonance (MR) imaging for determination of the MR biomarkers proton density fat fraction (PDFF) and water T2 (T2w ) has been advanced. Biophysical effects or pathology can have different effects on MR biomarkers. Thus, for heterogeneously affected muscles, the routinely performed mean or median value analyses of MR biomarkers are questionable. Our work presents a voxel-based histogram analysis of PDFF and T2w images to point out potential quantification errors. In 12 patients with NMD, chemical-shift encoding-based water-fat imaging for PDFF and T2 mapping with spectral adiabatic inversion recovery (SPAIR) for T2w determination was performed. Segmentation of nine thigh muscles was performed bilaterally (n = 216). PDFF and T2 maps were coregistered. A voxel-based comparison of PDFF and T2w showed a decreased T2w with increasing PDFF. Mean T2w and mean T2w without fatty voxels (PDFF < 10%) show good agreement, whereas standard deviation (σ) T2w and σ T2w without fatty voxels show increasing difference with increasing values of σ. Thereby two subgroups can be observed, referring to muscles in which the exclusion of fatty voxels has a negligible influence versus muscles in which a strong dependency of the T2w value distribution on the exclusion of fatty voxels is present. Because of the two opposite effects that influence T2w in a voxel, namely, (i) a pathophysiologically increased water mobility leading to T2w elevation, and (ii) a dependency of T2w on the PDFF leading to decreased T2w , the T2w distribution within a muscle might be heterogenous and the routine mean or median analysis can lead to a misinterpretation of the muscle health. It was concluded that muscle T2w mean values can wrongly suggest healthy muscle tissue. A deeper analysis of the underlying value distribution is necessary. Therefore, a quantitative analysis of T2w histograms is a potential alternative.

Risk and consequences of dehydration following colorectal cancer resection with diverting ileostomy. A systematic review and meta-analysis.

AIM: This systematic review aims to assess dehydration prevalence and dehydration-related morbidity from diverting ileostomy compared to resections without ileostomy formation in adults undergoing colorectal resection for cancer. METHOD: MEDLINE, Embase, CENTRAL and ClinicalTrials.gov were searched for studies of any design that reported dehydration, renal function and dehydration-related morbidity in adult colorectal cancer patients with diverting ileostomy (last search 12 August 2020). Bias was assessed using the Cochrane Collaboration's tool for assessing risk of bias in randomized trials and the Risk of Bias in Non-randomized Studies of Interventions tool. RESULTS: Of 1927 screened papers, 22 studies were included (21 cohort studies and one randomized trial) with a total of 19 485 patients (12 209 with ileostomy). The prevalence of dehydration was 9.00% (95% CI 5.31-13.45, P < 0.001). The relative risk of dehydration following diverting ileostomy was 3.37 (95% CI 2.30-4.95, P < 0.001). Three studies assessing long-term trends in renal function demonstrated progressive renal impairment persisting beyond the initial insult. Consequences identified included unplanned readmission, delay or non-commencement of adjuvant chemotherapy, and development of chronic kidney disease. DISCUSSION: Significant dehydration is common following diverting ileostomy; it is linked to acute kidney injury and has a long-term impact on renal function. This study suggests that ileostomy confers significant morbidity particularly related to dehydration and renal impairment.

Water T2 Mapping in Fatty Infiltrated Thigh Muscles of Patients With Neuromuscular Diseases Using a T2 -Prepared 3D Turbo Spin Echo With SPAIR.

BACKGROUND: Muscle water T2 (T2w ) has been proposed as a biomarker to monitor disease activity and therapy effectiveness in patients with neuromuscular diseases (NMD). Multi-echo spin-echo (MESE) is known to be affected by fatty infiltration. A T2 -prepared 3D turbo spin echo (TSE) is an alternative method for T2 mapping, but has been only applied in healthy muscles. PURPOSE: To examine the performance of T2 -prepared 3D TSE in combination with spectral adiabatic inversion recovery (SPAIR) in measuring T2w in fatty infiltrated muscles based on simulations and in vivo measurements in thigh muscles of patients with NMD. STUDY TYPE: Prospective. SUBJECTS: One healthy volunteer, 34 NMD patients. FIELD STRENGTH/SEQUENCE: T2 -prepared stimulated echo acquisition mode (STEAM) magnetic resonance spectroscopy (MRS), SPAIR STEAM MRS, and SPAIR T2 -prepared STEAM MRS were performed in the subcutaneous fat of a healthy volunteer's thigh. T2 mapping based on SPAIR 2D MESE and SPAIR T2 -prepared 3D TSE was performed in the NMD patients' thigh region. Multi-TE STEAM MRS was performed for measuring a reference T2w at different thigh locations. ASSESSMENT: The behavior of the fat spectrum in the SPAIR T2 -prepared 3D TSE was simulated using Bloch simulations. The in vivo T2 results of the imaging methods were compared to the in vivo T2w MRS results. STATISTICAL TESTS: Pearson correlation coefficient with slope and intercept, relative error. RESULTS: The simulated T2 for the SPAIR T2 -prepared 3D TSE sequence remained constant within a relative error of not more than 4% up to a fat fraction of 80%. In vivo T2 values of SPAIR T2 -prepared 3D TSE were in good agreement with the T2w values of STEAM MRS (R = 0.86; slope = 1.12; intercept = -1.41 ms). In vivo T2 values of SPAIR 2D MESE showed large deviations from the T2w values of STEAM MRS (R = 0.14; slope = 0.32; intercept = 38.83 ms). DATA CONCLUSION: The proposed SPAIR T2 -prepared 3D TSE shows reduced sensitivity to fatty infiltration for T2w mapping in the thigh muscles of NMD patients. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1727-1736.

Decreased water T2 in fatty infiltrated skeletal muscles of patients with neuromuscular diseases.

Quantitative imaging techniques are emerging in the field of magnetic resonance imaging of neuromuscular diseases (NMD). T2 of water (T2w ) is considered an important imaging marker to assess acute and chronic alterations of the muscle fibers, being generally interpreted as an indicator for "disease activity" in the muscle tissue. To validate the accuracy and robustness of quantitative imaging methods, 1 H magnetic resonance spectroscopy (MRS) can be used as a gold standard. The purpose of the present work was to investigate T2w of remaining muscle tissue in regions of higher proton density fat fraction (PDFF) in 40 patients with defined NMD using multi-TE single-voxel 1 H MRS. Patients underwent MR measurements on a 3 T system to perform a multi-TE single-voxel stimulated echo acquisition method (STEAM) MRS (TE = 11/15/20/25(/35) ms) in regions of healthy, edematous and fatty thigh muscle tissue. Muscle regions for MRS were selected based on T2 -weighted water and fat images of a two-echo 2D Dixon TSE. MRS results were confined to regions with qualitatively defined remaining muscle tissue without edema and high fat content, based on visual grading of the imaging data. The results showed decreased T2w values with increasing PDFF with R2  = 0.45 (p < 10-3 ) (linear fit) and with R2  = 0.51 (exponential fit). The observed dependence of T2w on PDFF should be considered when using T2w as a marker in NMD imaging and when performing single-voxel MRS for T2w in regions enclosing edematous, nonedematous and fatty infiltrated muscle tissue.

Multi-center evaluation of stability and reproducibility of quantitative MRI measures in healthy calf muscles.

The purpose of this study was to evaluate temporal stability, multi-center reproducibility and the influence of covariates on a multimodal MR protocol for quantitative muscle imaging and to facilitate its use as a standardized protocol for evaluation of pathology in skeletal muscle. Quantitative T2, quantitative diffusion and four-point Dixon acquisitions of the calf muscles of both legs were repeated within one hour. Sixty-five healthy volunteers (31 females) were included in one of eight 3-T MR systems. Five traveling subjects were examined in six MR scanners. Average values over all slices of water-T2 relaxation time, proton density fat fraction (PDFF) and diffusion metrics were determined for seven muscles. Temporal stability was tested with repeated measured ANOVA and two-way random intraclass correlation coefficient (ICC). Multi-center reproducibility of traveling volunteers was assessed by a two-way mixed ICC. The factors age, body mass index, gender and muscle were tested for covariance. ICCs of temporal stability were between 0.963 and 0.999 for all parameters. Water-T2 relaxation decreased significantly (P < 10-3 ) within one hour by ~ 1 ms. Multi-center reproducibility showed ICCs within 0.879-0.917 with the lowest ICC for mean diffusivity. Different muscles showed the highest covariance, explaining 20-40% of variance for observed parameters. Standardized acquisition and processing of quantitative muscle MRI data resulted in high comparability among centers. The imaging protocol exhibited high temporal stability over one hour except for water T2 relaxation times. These results show that data pooling is feasible and enables assembling data from patients with neuromuscular diseases, paving the way towards larger studies of rare muscle disorders.

Paraspinal Muscle DTI Metrics Predict Muscle Strength.

BACKGROUND: The paraspinal muscles play an important role in the onset and progression of lower back pain. It would be of clinical interest to identify imaging biomarkers of the paraspinal musculature that are related to muscle function and strength. Diffusion tensor imaging (DTI) enables the microstructural examination of muscle tissue and its pathological changes. PURPOSE: To investigate associations of DTI parameters of the lumbar paraspinal muscles with isometric strength measurements in healthy volunteers. STUDY TYPE: Prospective. SUBJECTS: Twenty-one healthy subjects (12 male, 9 female; age = 30.1 ± 5.6 years; body mass index [BMI] = 27.5 ± 2.6 kg/m2 ) were recruited. FIELD STRENGTH/SEQUENCE: 3 T/single-shot echo planar imaging (ss-EPI) DTI in 24 directions; six-echo 3D spoiled gradient echo sequence for chemical shift encoding-based water-fat separation. ASSESSMENT: Paraspinal muscles at the lumbar spine were examined. Erector spinae muscles were segmented bilaterally; cross-sectional area (CSA), proton density fat fraction (PDFF), and DTI parameters were calculated. Muscle flexion and extension maximum isometric torque values [Nm] at the back were measured with an isokinetic dynamometer and the ratio of extension to flexion strength (E/F) calculated. STATISTICAL TESTS: Pearson correlation coefficients; multivariate regression models. RESULTS: Significant positive correlations were found between the ratio of extension to flexion (E/F) strength and mean diffusivity (MD) (P = 0.019), RD (P = 0.02) and the eigenvalues (λ1: P = 0.026, λ2: P = 0.033, λ3: P = 0.014). In multivariate regression models λ3 of the erector spinae muscle λ3 and gender remained statistically significant predictors of E/F (R2adj = 0.42, P = 0.003). DATA CONCLUSION: DTI allowed the identification of muscle microstructure differences related to back muscle function that were not reflected by CSA and PDFF. DTI may potentially track subtle changes of back muscle tissue composition. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:816-823.

Association of paraspinal muscle water-fat MRI-based measurements with isometric strength measurements.

OBJECTIVES: Chemical shift encoding-based water-fat MRI derived proton density fat fraction (PDFF) of the paraspinal muscles has been emerging as a surrogate marker in subjects with sarcopenia, lower back pain, injuries and neuromuscular disorders. The present study investigates the performance of paraspinal muscle PDFF and cross-sectional area (CSA) in predicting isometric muscle strength. METHODS: Twenty-six healthy subjects (57.7% women; age: 30 ± 6 years) underwent 3T axial MRI of the lumbar spine using a six-echo 3D spoiled gradient echo sequence for chemical shift encoding-based water-fat separation. Erector spinae and psoas muscles were segmented bilaterally from L2 level to L5 level to determine CSA and PDFF. Muscle flexion and extension maximum isometric torque values [Nm] at the back were measured with an isokinetic dynamometer. RESULTS: Significant correlations between CSA and muscle strength measurements were observed for erector spinae muscle CSA (r = 0.40; p = 0.044) and psoas muscle CSA (r = 0.61; p = 0.001) with relative flexion strength. Erector spinae muscle PDFF correlated significantly with relative muscle strength (extension: r = -0.51; p = 0.008; flexion: r = -0.54; p = 0.005). Erector spinae muscle PDFF, but not CSA, remained a statistically significant (p < 0.05) predictor of relative extensor strength in multivariate regression models (R2adj = 0.34; p = 0.002). CONCLUSIONS: PDFF measurements improved the prediction of paraspinal muscle strength beyond CSA. Therefore, chemical shift encoding-based water-fat MRI may be used to detect subtle changes in the paraspinal muscle composition. KEY POINTS: • We investigated the association of paraspinal muscle fat fraction based on chemical shift encoding-based water-fat MRI with isometric strength measurements in healthy subjects. • Erector spinae muscle PDFF correlated significantly with relative muscle strength. • PDFF measurements improved prediction of paraspinal muscle strength beyond CSA.

Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine.

BACKGROUND: Magnetic resonance imaging (MRI) is the modality of choice for diagnosing and monitoring muscular tissue pathologies and bone marrow alterations in the context of lower back pain, neuromuscular diseases and osteoporosis. Chemical shift encoding-based water-fat MRI allows for reliable determination of proton density fat fraction (PDFF) of the muscle and bone marrow. Prior to quantitative data extraction, segmentation of the examined structures is needed. Performed manually, the segmentation process is time consuming and therefore limiting the clinical applicability. Thus, the development of automated segmentation algorithms is an ongoing research focus. CONSTRUCTION AND CONTENT: This database provides ground truth data which may help to develop and test automatic lumbar muscle and vertebra segmentation algorithms. Lumbar muscle groups and vertebral bodies (L1 to L5) were manually segmented in chemical shift encoding-based water-fat MRI and made publically available in the database MyoSegmenTUM. The database consists of water, fat and PDFF images with corresponding segmentation masks for lumbar muscle groups (right/left erector spinae and psoas muscles, respectively) and lumbar vertebral bodies 1-5 of 54 healthy Caucasian subjects. The database is freely accessible online at https://osf.io/3j54b/?view_only=f5089274d4a449cda2fef1d2df0ecc56 . CONCLUSION: A development and testing of segmentation algorithms based on this database may allow the use of quantitative MRI in clinical routine.

T2-Weighted Dixon Turbo Spin Echo for Accelerated Simultaneous Grading of Whole-Body Skeletal Muscle Fat Infiltration and Edema in Patients With Neuromuscular Diseases.

OBJECTIVE: The assessment of fatty infiltration and edema in the musculature of patients with neuromuscular diseases (NMDs) typically requires the separate performance of T1-weighted and fat-suppressed T2-weighted sequences. T2-weighted Dixon turbo spin echo (TSE) enables the generation of T2-weighted fat- and water-separated images, which can be used to assess both pathologies simultaneously. The present study examines the diagnostic performance of T2-weighted Dixon TSE compared with the standard sequences in 10 patients with NMDs and 10 healthy subjects. METHODS: Whole-body magnetic resonance imaging was performed including T1-weighted Dixon fast field echo, T2-weighted short-tau inversion recovery, and T2-weighted Dixon TSE. Fatty infiltration and intramuscular edema were rated by 2 radiologists using visual semiquantitative rating scales. To assess intermethod and interrater agreement, weighted Cohen's κ coefficients were calculated. RESULTS: The ratings of fatty infiltration showed high intermethod and high interrater agreement (T1-weighted Dixon fast field echo vs T2-weighted Dixon TSE fat image). The evaluation of edematous changes showed high intermethod and good interrater agreement (T2-weighted short-tau inversion recovery vs T2-weighted Dixon TSE water image). CONCLUSIONS: T2-weighted Dixon TSE imaging is an alternative for accelerated simultaneous grading of whole-body skeletal muscle fat infiltration and edema in patients with NMDs.

T2 mapping with magnetization-prepared 3D TSE based on a modified BIR-4 T2 preparation.

The purpose of this work was to investigate the performance of the modified BIR-4 T2 preparation for T2 mapping and propose a method to remove T2 quantification errors in the presence of large B1 and B0 offsets. The theoretical investigation of the magnetization evolution during the T2 preparation in the presence of B1 and B0 offsets showed deviations from a mono-exponential T2 decay (two parameter fit). A three parameter fit was used to improve T2 accuracy. Furthermore, a two parameter fit with an additional saturation preparation scan was proposed to improve T2 accuracy and precision. These three fitting methods were compared based on simulations, phantom measurements and an in vivo healthy volunteer study of the neck musculature using a 3D TSE readout. The results based upon the pure two parameter fit overestimated T2 in regions with high B0 offsets (up to 40% in phantoms). The three parameter fit T2 values were robust to B0 offsets but with higher standard deviation (up to 40% in simulations). The two parameter fit with the saturation preparation yielded high robustness towards B0 offsets with a noise performance comparable to that of the two parameter fit. In the volunteer study the T2 values obtained by the pure two parameter fit showed a dependence on the field inhomogeneities, whereas the T2 values from the proposed fitting approach were shown to be insensitive to B0 offsets. The proposed method enabled accurate and precise T2 mapping in the presence of large B1 and B0 offsets.

Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis.

BACKGROUND: Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice. OBJECTIVE: We performed a systematic review and meta-analysis on the use of QSM in MS. METHODS: Our review was prospectively registered on PROSPERO (CRD42022309563). We searched five databases for studies published between inception and 30th April 2023. We identified 83 English peer-reviewed studies that applied QSM images on MS cohorts. Fifty-five included studies had at least one of the following outcome measures: deep grey matter QSM values in MS, either compared to healthy controls (HC) (k = 13) or correlated with the score on the Expanded Disability Status Scale (EDSS) (k = 7), QSM lesion characteristics (k = 22) and their clinical correlates (k = 17), longitudinal correlates (k = 11), histological correlates (k = 7), or correlates with other imaging techniques (k = 12). Two meta-analyses on deep grey matter (DGM) susceptibility data were performed, while the remaining findings could only be analyzed descriptively. RESULTS: After outlier removal, meta-analyses demonstrated a significant increase in the basal ganglia susceptibility (QSM values) in MS compared to HC, caudate (k = 9, standardized mean difference (SDM) = 0.54, 95 % CI = 0.39-0.70, I2 = 46 %), putamen (k = 9, SDM = 0.38, 95 % CI = 0.19-0.57, I2 = 59 %), and globus pallidus (k = 9, SDM = 0.48, 95 % CI = 0.28-0.67, I2 = 60 %), whereas thalamic QSM values exhibited a significant reduction (k = 12, SDM = -0.39, 95 % CI = -0.66--0.12, I2 = 84 %); these susceptibility differences in MS were independent of age. Further, putamen QSM values positively correlated with EDSS (k = 4, r = 0.36, 95 % CI = 0.16-0.53, I2 = 0 %). Regarding rim lesions, four out of seven studies, representing 73 % of all patients, reported rim lesions to be associated with more severe disability. Moreover, lesion development from initial detection to the inactive stage is paralleled by increasing, plateauing (after about two years), and gradually decreasing QSM values, respectively. Only one longitudinal study provided clinical outcome measures and found no association. Histological data suggest iron content to be the primary source of QSM values in DGM and at the edges of rim lesions; further, when also considering data from myelin water imaging, the decrease of myelin is likely to drive the increase of QSM values within WM lesions. CONCLUSIONS: We could provide meta-analytic evidence for DGM susceptibility changes in MS compared to HC; basal ganglia susceptibility is increased and, in the putamen, associated with disability, while thalamic susceptibility is decreased. Beyond these findings, further investigations are necessary to establish the role of QSM in MS for research or even clinical routine.

LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation.

Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D-UNets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1w and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI additionally includes a lesion location annotation tool, labeling lesion location according to the 2017 McDonald criteria (periventricular, infratentorial, juxtacortical, subcortical). We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10mm3 and 100mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.

LST-AI: A deep learning ensemble for accurate MS lesion segmentation.

Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm3 and 100 mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.

[Expert recommendations for magnetic resonance imaging of muscle disorders]. / Expertenempfehlung zur Magnetresonanztomographie bei Muskelerkrankungen.

BACKGROUND: Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking. OBJECTIVES: The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders. METHODS: This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and a focused literature search. RESULTS: The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of a suitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for a structured radiological assessment are presented. CONCLUSIONS: The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides a possible basis for the standardisation of the measurement protocols at all clinical centres in Germany.

Longitudinal Assessment of Multiple Sclerosis Lesion Load With Synthetic Magnetic Resonance Imaging-A Multicenter Validation Study.

INTRODUCTION: Double inversion recovery (DIR) has been validated as a sensitive magnetic resonance imaging (MRI) contrast in multiple sclerosis (MS). Deep learning techniques can use basic input data to generate synthetic DIR (synthDIR) images that are on par with their acquired counterparts. As assessment of longitudinal MRI data is paramount in MS diagnostics, our study's purpose is to evaluate the utility of synthDIR longitudinal subtraction imaging for detection of disease progression in a multicenter data set of MS patients. METHODS: We implemented a previously established generative adversarial network to synthesize DIR from input T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences for 214 MRI data sets from 74 patients and 5 different centers. One hundred and forty longitudinal subtraction maps of consecutive scans (follow-up scan-preceding scan) were generated for both acquired FLAIR and synthDIR. Two readers, blinded to the image origin, independently quantified newly formed lesions on the FLAIR and synthDIR subtraction maps, grouped into specific locations as outlined in the McDonald criteria. RESULTS: Both readers detected significantly more newly formed MS-specific lesions in the longitudinal subtractions of synthDIR compared with acquired FLAIR (R1: 3.27 ± 0.60 vs 2.50 ± 0.69 [ P = 0.0016]; R2: 3.31 ± 0.81 vs 2.53 ± 0.72 [ P < 0.0001]). Relative gains in detectability were most pronounced in juxtacortical lesions (36% relative gain in lesion counts-pooled for both readers). In 5% of the scans, synthDIR subtraction maps helped to identify a disease progression missed on FLAIR subtraction maps. CONCLUSIONS: Generative adversarial networks can generate high-contrast DIR images that may improve the longitudinal follow-up assessment in MS patients compared with standard sequences. By detecting more newly formed MS lesions and increasing the rates of detected disease activity, our methodology promises to improve clinical decision-making.