[Expert recommendations for magnetic resonance imaging of muscle disorders]

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.

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.

Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation

BACKGROUND

Automated segmentation of spinal magnetic resonance imaging (MRI) plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures is challenging.

METHODS

This retrospective study, approved by the ethical committee, involved translating T1-weighted and T2-weighted images into computed tomography (CT) images in a total of 263 pairs of CT/MR series. Landmark-based registration was performed to align image pairs. We compared two-dimensional (2D) paired - Pix2Pix, denoising diffusion implicit models (DDIM) image mode, DDIM noise mode - and unpaired (SynDiff, contrastive unpaired translation) image-to-image translation using "peak signal-to-noise ratio" as quality measure. A publicly available segmentation network segmented the synthesized CT datasets, and Dice similarity coefficients (DSC) were evaluated on in-house test sets and the "MRSpineSeg Challenge" volumes. The 2D findings were extended to three-dimensional (3D) Pix2Pix and DDIM.

RESULTS

2D paired methods and SynDiff exhibited similar translation performance and DCS on paired data. DDIM image mode achieved the highest image quality. SynDiff, Pix2Pix, and DDIM image mode demonstrated similar DSC (0.77). For craniocaudal axis rotations, at least two landmarks per vertebra were required for registration. The 3D translation outperformed the 2D approach, resulting in improved DSC (0.80) and anatomically accurate segmentations with higher spatial resolution than that of the original MRI series.

CONCLUSIONS

Two landmarks per vertebra registration enabled paired image-to-image translation from MRI to CT and outperformed all unpaired approaches. The 3D techniques provided anatomically correct segmentations, avoiding underprediction of small structures like the spinous process.

RELEVANCE STATEMENT

This study addresses the unresolved issue of translating spinal MRI to CT, making CT-based tools usable for MRI data. It generates whole spine segmentation, previously unavailable in MRI, a prerequisite for biomechanical modeling and feature extraction for clinical applications.

KEY POINTS

• Unpaired image translation lacks in converting spine MRI to CT effectively. • Paired translation needs registration with two landmarks per vertebra at least. • Paired image-to-image enables segmentation transfer to other domains. • 3D translation enables super resolution from MRI to CT. • 3D translation prevents underprediction of small structures.

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.

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis

BACKGROUND

Contrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare.

METHODS

A three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level.

RESULTS

On a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen's kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05).

CONCLUSIONS

AI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients.

CRITICAL RELEVANCE STATEMENT

Al-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions.

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.

Implementation of GAN-Based, Synthetic T2-Weighted Fat Saturated Images in the Routine Radiological Workflow Improves Spinal Pathology Detection

(1) Background and Purpose: In magnetic resonance imaging (MRI) of the spine, T2-weighted (T2-w) fat-saturated (fs) images improve the diagnostic assessment of pathologies. However, in the daily clinical setting, additional T2-w fs images are frequently missing due to time constraints or motion artifacts. Generative adversarial networks (GANs) can generate synthetic T2-w fs images in a clinically feasible time. Therefore, by simulating the radiological workflow with a heterogenous dataset, this study's purpose was to evaluate the diagnostic value of additional synthetic, GAN-based T2-w fs images in the clinical routine. (2) Methods: 174 patients with MRI of the spine were retrospectively identified. A GAN was trained to synthesize T2-w fs images from T1-w, and non-fs T2-w images of 73 patients scanned in our institution. Subsequently, the GAN was used to create synthetic T2-w fs images for the previously unseen 101 patients from multiple institutions. In this test dataset, the additional diagnostic value of synthetic T2-w fs images was assessed in six pathologies by two neuroradiologists. Pathologies were first graded on T1-w and non-fs T2-w images only, then synthetic T2-w fs images were added, and pathologies were graded again. Evaluation of the additional diagnostic value of the synthetic protocol was performed by calculation of Cohen's ĸ and accuracy in comparison to a ground truth (GT) grading based on real T2-w fs images, pre- or follow-up scans, other imaging modalities, and clinical information. (3) Results: The addition of the synthetic T2-w fs to the imaging protocol led to a more precise grading of abnormalities than when grading was based on T1-w and non-fs T2-w images only (mean ĸ GT versus synthetic protocol = 0.65; mean ĸ GT versus T1/T2 = 0.56; p = 0.043). (4) Conclusions: The implementation of synthetic T2-w fs images in the radiological workflow significantly improves the overall assessment of spine pathologies. Thereby, high-quality, synthetic T2-w fs images can be virtually generated by a GAN from heterogeneous, multicenter T1-w and non-fs T2-w contrasts in a clinically feasible time, which underlines the reproducibility and generalizability of our approach.

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.

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 (T_2w ) 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 T_2w 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 T_2w 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 T_2w showed a decreased T_2w with increasing PDFF. Mean T_2w and mean T_2w ^{without fatty voxels} (PDFF < 10%) show good agreement, whereas standard deviation (σ) T_2w and σ T_2w ^{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 T_2w value distribution on the exclusion of fatty voxels is present. Because of the two opposite effects that influence T_2w in a voxel, namely, (i) a pathophysiologically increased water mobility leading to T_2w elevation, and (ii) a dependency of T_2w on the PDFF leading to decreased T_2w , the T_2w 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 T_2w mean values can wrongly suggest healthy muscle tissue. A deeper analysis of the underlying value distribution is necessary. Therefore, a quantitative analysis of T_2w histograms is a potential alternative.

Postoperative Bildgebung der Wirbelsäule

Die Bildgebung der postoperativen Wirbelsäule hat im Wesentlichen zwei Aufgaben: Sie dient der Kontrolle des operativen Erfolgs und der Identifikation von Komplikationen. Dafür stehen die konventionelle Röntgenaufnahme, Computertomographie (CT), Myelographie und Magnetresonanztomographie (MRT) zur Verfügung. Unter Berücksichtigung der präoperativen Situation, der durchgeführten Operation und der postoperativen Beschwerdekonstellation ist es Aufgabe der Radiologinnen und Radiologen, die passende Modalität für eine suffiziente Diagnostik zu wählen. Insbesondere der Zustand nach Implantation von Fremdmaterial bedeutet eine technische Herausforderung im Rahmen der Bildakquisition. In der Befundung sehen sich die Radiologinnen und Radiologen mit der Aufgabe konfrontiert, zwischen natürlichen, zu erwartenden postoperativen Veränderungen und relevanten Komplikationen zu differenzieren. Ein reger Austausch mit Patientinnen und Patienten und zuweisenden Klinikerinnen und Klinikern ist dabei unerlässlich. Insbesondere klinische Hinweise auf einen Infekt, neue oder deutliche progrediente neurologische Ausfallserscheinungen und das Konus-Kauda-Syndrom erfordern eine zeitnahe Diagnosestellung, um eine rasche Therapieeinleitung zu gewährleisten.

Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment

Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.

Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study

Generative adversarial networks (GANs) can synthesize high-contrast MRI from lower-contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well as visualization of model confidence further augment their utility, provided that the GAN generalizes reliably across different scanners. We here investigate the generalizability of a refined GAN for synthesizing high-contrast double inversion recovery (DIR) images and propose the use of uncertainty maps to further enhance its clinical utility and trustworthiness. A GAN was trained to synthesize DIR from input fluid-attenuated inversion recovery (FLAIR) and T1w of 50 MS patients (training data). In another 50 patients (test data), two blinded readers (R1 and R2) independently quantified lesions in synthetic DIR (synthDIR), acquired DIR (trueDIR) and FLAIR. Of the 50 test patients, 20 were acquired on the same scanner as training data (internal data), while 30 were scanned at different scanners with heterogeneous field strengths and protocols (external data). Lesion-to-Background ratios (LBR) for MS-lesions vs. normal appearing white matter, as well as image quality parameters were calculated. Uncertainty maps were generated to visualize model confidence. Significantly more MS-specific lesions were found in synthDIR compared to FLAIR (R1: 26.7 ± 2.6 vs. 22.5 ± 2.2 p < 0.0001; R2: 22.8 ± 2.2 vs. 19.9 ± 2.0, p = 0.0005). While trueDIR remained superior to synthDIR in R1 [28.6 ± 2.9 vs. 26.7 ± 2.6 (p = 0.0021)], both sequences showed comparable lesion conspicuity in R2 [23.3 ± 2.4 vs. 22.8 ± 2.2 (p = 0.98)]. Importantly, improvements in lesion counts were similar in internal and external data. Measurements of LBR confirmed that lesion-focused GAN training significantly improved lesion conspicuity. The use of uncertainty maps furthermore helped discriminate between MS lesions and artifacts. In conclusion, this multicentric study confirms the external validity of a lesion-focused Deep-Learning tool aimed at MS imaging. When implemented, uncertainty maps are promising to increase the trustworthiness of synthetic MRI.

Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography

Background: Most artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a learned norm. Here, we investigate whether diagnostic accuracy and reporting times can be improved by an anomaly detection tool for head computed tomography (CT), tailored to provide patient-level triage and voxel-based highlighting of pathologies. Methods: Four neuroradiologists with 1–10 years of experience each investigated a set of 80 routinely acquired head CTs containing 40 normal scans and 40 scans with common pathologies. In a random order, scans were investigated with and without AI-predictions. A 4-week wash-out period between runs was included to prevent a reminiscence effect. Performance metrics for identifying pathologies, reporting times, and subjectively assessed diagnostic confidence were determined for both runs. Results: AI-support significantly increased the share of correctly classified scans (normal/pathological) from 309/320 scans to 317/320 scans (p = 0.0045), with a corresponding sensitivity, specificity, negative- and positive- predictive value of 100%, 98.1%, 98.2% and 100%, respectively. Further, reporting was significantly accelerated with AI-support, as evidenced by the 15.7% reduction in reporting times (65.1 ± 8.9 s vs. 54.9 ± 7.1 s; p < 0.0001). Diagnostic confidence was similar in both runs. Conclusion: Our study shows that AI-based triage of CTs can improve the diagnostic accuracy and accelerate reporting for experienced and inexperienced radiologists alike. Through ad hoc identification of normal CTs, anomaly detection promises to guide clinicians towards scans requiring urgent attention.

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.

Regional variation of thigh muscle fat infiltration in patients with neuromuscular diseases compared to healthy controls

Background

Chemical shift encoding-based water-fat magnetic resonance imaging (CSE-MRI) measures a quantitative biomarker: the proton density fat fraction (PDFF). The aim was to assess regional and proximo-distal PDFF variations at the thigh in patients with myotonic dystrophy type 2 (DM2), limb-girdle muscular dystrophy type 2A (LGMD2A), and late-onset Pompe disease (LOPD) as compared to healthy controls.

Methods

Seven patients (n=2 DM2, n=2 LGMD2A, n=3 LOPD) and 20 controls were recruited. A 3D-spoiled gradient echo sequence was used to scan the thigh musculature. Muscles were manually segmented to generate mean muscle PDFF.

Results

In all three disease entities, there was an increase in muscle fat replacement compared to healthy controls. However, within each disease group, there were patients with a shorter time since symptom onset that only showed mild PDFF elevation (range, 10% to 20%) compared to controls (P≤0.05), whereas patients with a longer period since symptom onset showed a more severe grade of fat replacement with a range of 50% to 70% (P<0.01). Increased PDFF of around 5% was observed for vastus medialis, semimembranosus and gracilis muscles in advanced compared to early DM2. LGMD2A_1 showed an early disease stage with predominantly mild PDFF elevations over all muscles and levels (10.9%±7.1%) compared to controls. The quadriceps, gracilis and biceps femoris muscles showed the highest difference between LGMD2A_1 with 5 years since symptom onset (average PDFF 11.1%±6.9%) compared to LGMD2A_2 with 32 years since symptom onset (average PDFF 66.3%±6.3%). For LOPD patients, overall PDFF elevations were observed in all major hip flexors and extensors (range, 25.8% to 30.8%) compared to controls (range, 1.7% to 2.3%, P<0.05). Proximal-to-distal PDFF highly varied within and between diseases and within controls. The intra-reader reliability was high (reproducibility coefficient ≤2.19%).

Conclusions

By quantitatively measuring muscle fat infiltration at the thigh, we identified candidate muscles for disease monitoring due to their gradual PDFF elevation with longer disease duration. Regional variation between proximal, central, and distal muscle PDFF was high and is important to consider when performing longitudinal MRI follow-ups in the clinical setting or in longitudinal studies.

Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength

Texture analysis (TA) has shown promise as a surrogate marker for tissue structure, based on conventional and quantitative MRI sequences. Chemical-shift-encoding-based MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of paraspinal muscles has been associated with various medical conditions including lumbar back pain (LBP) and neuromuscular diseases (NMD). Its application has been shown to improve the prediction of paraspinal muscle strength beyond muscle volume. Since mean PDFF values do not fully reflect muscle tissue structure, the purpose of our study was to investigate PDFF-based TA of paraspinal muscles as a predictor of muscle strength, as compared to mean PDFF. We performed 3T-MRI of the lumbar spine in 26 healthy subjects (age = 30 ± 6 years; 15 females) using a six-echo 3D spoiled gradient echo sequence for chemical-shift-encoding-based water–fat separation. Erector spinae (ES) and psoas (PS) muscles were segmented bilaterally from level L2–L5 to extract mean PDFF and texture features. Muscle flexion and extension strength was measured with an isokinetic dynamometer. Out of the eleven texture features extracted for each muscle, Kurtosis(global) of ES showed the highest significant correlation (r = 0.59, p = 0.001) with extension strength and Variance(global) of PS showed the highest significant correlation (r = 0.63, p = 0.001) with flexion strength. Using multivariate linear regression models, Kurtosis(global) of ES and BMI were identified as significant predictors of extension strength (R²_adj = 0.42; p < 0.001), and Variance(global) and Skewness(global) of PS were identified as significant predictors of flexion strength (R²_adj = 0.59; p = 0.001), while mean PDFF was not identified as a significant predictor. TA of CSE-MRI-based PDFF maps improves the prediction of paraspinal muscle strength beyond mean PDFF, potentially reflecting the ability to quantify the pattern of muscular fat infiltration. In the future, this may help to improve the pathophysiological understanding, diagnosis, monitoring and treatment evaluation of diseases with paraspinal muscle involvement, e.g., NMD and LBP.

Association of quadriceps muscle, gluteal muscle, and femoral bone marrow composition using chemical shift encoding-based water-fat MRI: a preliminary study in healthy young volunteers

Background

We investigated the composition of the gluteal (gluteus maximus, medius, and minimus) and quadriceps (rectus femoris, vastus lateralis, medialis, and intermedius) muscle groups and its associations with femoral bone marrow using chemical shift encoding-based water-fat magnetic resonance imaging (CSE-MRI) to improve our understanding of muscle-bone interaction.

Methods

Thirty healthy volunteers (15 males, aged 30.5 ± 4.9 years [mean ± standard deviation]; 15 females, aged 29.9 ± 7.1 years) were recruited. A six-echo three-dimensional spoiled gradient-echo sequence was used for 3-T CSE-MRI at the thigh and hip region. The proton density fat fraction (PDFF) of the gluteal and quadriceps muscle groups as well as of the femoral head, neck, and greater trochanter bone marrow were extracted and averaged over both sides.

Results

PDFF values of all analysed bone marrow compartments were significantly higher in men than in women (p ≤ 0.047). PDFF values of the analysed muscles showed no significant difference between men and women (p ≥ 0.707). After adjusting for age and body mass index, moderate significant correlations of PDFF values were observed between the gluteal and quadriceps muscle groups (r = 0.670) and between femoral subregions (from r = 0.613 to r = 0.655). Regarding muscle-bone interactions, only the PDFF of the quadriceps muscle and greater trochanter bone marrow showed a significant correlation (r = 0.375).

Conclusions

The composition of the muscle and bone marrow compartments at the thigh and hip region in young, healthy subjects seems to be quite distinct, without evidence for a strong muscle-bone interaction.

Association of thigh and paraspinal muscle composition in young adults using chemical shift encoding-based water-fat MRI

Background

Paraspinal and thigh muscles comprise the major muscle groups of the body. We investigated the composition of the psoas, erector spinae, quadriceps femoris and hamstring muscle groups and their association to each other using chemical shift encoding-based water-fat magnetic resonance imaging (MRI) in adult volunteers. Our aim was to elucidate fat distribution patterns within these muscle groups.

Methods

Thirty volunteers [15 males, age: 30.5±4.9 years, body mass index (BMI): 27.6±2.8 kg/m² and 15 females, age: 29.9±7.0 years, BMI: 25.8±1.4 kg/m²] were recruited for this study. A six-echo 3D spoiled gradient echo sequence was used for chemical shift encoding-based water-fat separation at the lumbar spine and bilateral thigh. Proton density fat fraction (PDFF), cross-sectional area (CSA) and contractile mass index (CMI) of the psoas, erector spinae, quadriceps femoris and hamstring muscle groups were determined bilaterally and averaged over both sides.

Results

CSA and CMI values calculated for the erector spinae, psoas, quadriceps and hamstring muscle groups showed significant differences between men and women (P<0.05). With regard to PDFF measurement only the erector spinae showed significant differences between men and women (9.5%±2.4% vs. 11.7%±2.8%, P=0.015). The CMI of the psoas muscle as well as the erector spinae muscle showed significant correlations with the quadriceps muscle (r=0.691, P<0.0001 and r=0.761, P<0.0001) and the hamstring group (r=0.588, P=0.001 and r=0.603, P<0.0001).

Conclusions

CMI values of the erector spinae and psoas muscles were associated with those of the quadriceps femoris and hamstring musculature. These findings suggest a concordant spatial fat accumulation within the analyzed muscles in young adults and warrants further investigations in ageing and diseased muscle.

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 (T_2w ) 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 T_2w 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 T_2w 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 T_2w 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 T_2w 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 T_2w 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 T_2w mapping in the thigh muscles of NMD patients.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1727-1736.

Correction to: Associations of thigh muscle fat infiltration with isometric strength measurements based on chemical shift encoding-based water-fat magnetic resonance imaging

After publication of this article [1], it is noticed it contained some errors in the Methods section.

Associations of thigh muscle fat infiltration with isometric strength measurements based on chemical shift encoding-based water-fat magnetic resonance imaging

BACKGROUND

Assessment of the thigh muscle fat composition using magnetic resonance imaging (MRI) can provide surrogate markers in subjects suffering from various musculoskeletal disorders including knee osteoarthritis or neuromuscular diseases. However, little is known about the relationship with muscle strength. Therefore, we investigated the associations of thigh muscle fat with isometric strength measurements.

METHODS

Twenty healthy subjects (10 females; median age 27 years, range 22-41 years) underwent chemical shift encoding-based water-fat MRI, followed by bilateral extraction of the proton density fat fraction (PDFF) and calculation of relative cross-sectional area (relCSA) of quadriceps and ischiocrural muscles. Relative maximum voluntary isometric contraction (relMVIC) in knee extension and flexion was measured with a rotational dynamometer. Correlations between PDFF, relCSA, and relMVIC were evaluated, and multivariate regression was applied to identify significant predictors of muscle strength.

RESULTS

Significant correlations between the PDFF and relMVIC were observed for quadriceps and ischiocrural muscles bilaterally (p = 0.001 to 0.049). PDFF, but not relCSA, was a statistically significant (p = 0.001 to 0.049) predictor of relMVIC in multivariate regression models, except for left-sided relMVIC in extension. In this case, PDFF (p = 0.005) and relCSA (p = 0.015) of quadriceps muscles significantly contributed to the statistical model with R²_adj = 0.548.

CONCLUSION

Chemical shift encoding-based water-fat MRI could detect changes in muscle composition by quantifying muscular fat that correlates well with both extensor and flexor relMVIC of the thigh. Our results help to initiate early, individualised treatments to maintain or improve muscle function in subjects who do not or not yet show pathological fatty muscle infiltration.

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.

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 (T_2w ) 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 T_2w 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 T_2w values with increasing PDFF with R²  = 0.45 (p < 10^-3 ) (linear fit) and with R²  = 0.51 (exponential fit). The observed dependence of T_2w on PDFF should be considered when using T_2w as a marker in NMD imaging and when performing single-voxel MRS for T_2w in regions enclosing edematous, nonedematous and fatty infiltrated muscle tissue.

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.

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 (R²_adj = 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 (R²_adj = 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.

Associations Between Lumbar Vertebral Bone Marrow and Paraspinal Muscle Fat Compositions-An Investigation by Chemical Shift Encoding-Based Water-Fat MRI

Purpose: Advanced magnetic resonance imaging (MRI) methods enable non-invasive quantification of body fat situated in different compartments. At the level of the lumbar spine, the paraspinal musculature is the compartment spatially and functionally closely related to the vertebral column, and both vertebral bone marrow fat (BMF) and paraspinal musculature fat contents have independently shown to be altered in various metabolic and degenerative diseases. However, despite their close relationships, potential correlations between fat compositions of these compartments remain largely unclear. Materials and Methods: Thirty-nine female subjects (38.5% premenopausal women, 29.9 ± 7.1 years; 61.5% postmenopausal women, 63.2 ± 6.3 years) underwent MRI at 3T of the lumbar spine using axially- and sagittally-prescribed gradient echo sequences for chemical shift encoding-based water-fat separation. The erector spinae muscles and vertebral bodies of L1-L5 were segmented to determine the proton density fat fraction (PDFF) of the paraspinal and vertebral bone marrow compartments. Correlations were calculated between the PDFF of the paraspinal muscle and bone marrow compartments. Results: The average PDFF of the paraspinal muscle and bone marrow compartments were significantly lower in premenopausal women when compared to postmenopausal women (11.6 ± 2.9% vs. 24.6 ± 7.1% & 28.8 ± 8.3% vs. 47.2 ± 8.5%; p < 0.001 for both comparisons). In premenopausal women, no significant correlation was found between the PDFF of the erector spinae muscles and the PDFF of the bone marrow of lumbar vertebral bodies (p = 0.907). In contrast, a significant correlation was shown in postmenopausal women (r = 0.457, p = 0.025). Significance was preserved after inclusion of age and body mass index (BMI) as control variables (r = 0.472, p = 0.027). Conclusion: This study revealed significant correlations between the PDFF of paraspinal and vertebral bone marrow compartments in postmenopausal women. The PDFF of the paraspinal and vertebral bone marrow compartments and their correlations might potentially serve as biomarkers; however, future studies including more subjects are required to evaluate distinct clinical value and reliability. Future studies should also follow up our findings in patients suffering from metabolic and degenerative diseases to clarify how these correlations change in the course of such diseases.

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.

B1-insensitive T2 mapping of healthy thigh muscles using a T2-prepared 3D TSE sequence

Purpose

To propose a T2-prepared 3D turbo spin echo (T2prep 3D TSE) sequence for B1-insensitive skeletal muscle T2 mapping and compare its performance with 2D and 3D multi-echo spin echo (MESE) for T2 mapping in thigh muscles of healthy subjects.

Methods

The performance of 2D MESE, 3D MESE and the proposed T2prep 3D TSE in the presence of transmit B1 and B0 inhomogeneities was first simulated. The thigh muscles of ten young and healthy subjects were then scanned on a 3 T system and T2 mapping was performed using the three sequences. Transmit B1-maps and proton density fat fraction (PDFF) maps were also acquired. The subjects were scanned three times to assess reproducibility. T2 values were compared among sequences and their sensitivity to B1 inhomogeneities was compared to simulation results. Correlations were also determined between T2 values, PDFF and B1.

Results

The left rectus femoris muscle showed the largest B1 deviations from the nominal value (from 54.2% to 92.9%). Significant negative correlations between T2 values and B1 values were found in the left rectus femoris muscle for 3D MESE (r = -0.72, p<0.001) and 2D MESE (r = -0.71, p<0.001), but not for T2prep 3D TSE (r = -0.32, p = 0.09). Reproducibility of T2 expressed by root mean square coefficients of variation (RMSCVs) were equal to 3.5% in T2prep 3D TSE, 2.6% in 3D MESE and 2.4% in 2D MESE. Significant differences between T2 values of 3D sequences (T2prep 3D TSE and 3D MESE) and 2D MESE were found in all muscles with the highest values for 2D MESE (p<0.05). No significant correlations were found between PDFF and T2 values.

Conclusion

A strong influence of an inhomogeneous B1 field on the T2 values of 3D MESE and 2D MESE was shown, whereas the proposed T2prep 3D TSE gives B1-insensitive and reproducible thigh muscle T2 mapping.