Effects of perfusion on DTI and DKI estimates in the skeletal muscle

Simultaneous multi-slice technique for reducing acquisition times in diffusion tensor imaging of the knee: a feasibility study

OBJECTIVES

To explore the feasibility of simultaneous multi-slice (SMS) technique for reducing acquisition times in readout-segmented echo planar imaging (RESOLVE) for diffusion tensor imaging (DTI) of the knee.

MATERIALS AND METHODS

A total of 30 healthy volunteers and 23 patients with knee acute injury (12 cases with anterior ligament (ACL) tears and 16 cases with patellar cartilage (PC) injury) were enrolled in this prospective study. Three DTI protocols were used: conventional RESOLVE-DTI with 12 directions (protocol 1), SMS-RESOLVE-DTI with 12 directions (protocol 2) and 20 directions (protocol 3). DTI parameters of gastrocnemius, ACL and posterior cruciate ligament (PCL), and PC from three protocols were quantitatively assessed.

RESULTS

For volunteers, protocol 2 significantly reduced acquisition time by 38.6% and 34.2% compared to protocols 1 and 3 while maintaining similar high-quality images and similar diffusive parameters, except for the fractional anisotropy (FA) and axial diffusivity (AD) of the PC between protocols 2 and 1 (P < 0.05). For injured ACL and PC, protocols 1 and 2 showed similar accurate diffusive parameters (except for AD, P = 0.025) and similar diagnostic efficacy, which demonstrated significantly lower FA and higher radial diffusivity (RD) in protocols 1 and 2 compared to volunteers (P < 0.05).

CONCLUSIONS

The 12-direction SMS-RESOLVE-DTI demonstrated a favorable balance between acquisition time and image quality, making it a promising alternative to conventional DTI for evaluating ligament and cartilage injuries.

ADVANCES IN KNOWLEDGE

The SMS technique greatly reduces acquisition time while maintaining image quality, which signified the possibility of DTI's clinical application.

From Voxels to Physiology: A Review of Diffusion Magnetic Resonance Imaging Applications in Skeletal Muscle

Skeletal muscle has a classic structure function relationship; both skeletal muscle microstructure and architecture are directly related to force generating capacity. Biopsy, the gold standard for evaluating muscle microstructure, is highly invasive, destructive to muscle, and provides only a small amount of information about the entire volume of a muscle. Similarly, muscle fiber lengths and pennation angles, key features of muscle architecture predictive of muscle function, are traditionally studied via cadaveric dissection. Noninvasive techniques such as diffusion magnetic resonance imaging (dMRI) offer quantitative approaches to study skeletal muscle microstructure and architecture. Despite its prevalence in applications for musculoskeletal research, clinical adoption is hindered by a lack of understanding regarding its sensitivity to clinically important biomarkers such as muscle fiber cross-sectional area. This review aims to elucidate how dMRI has been utilized to study skeletal muscle, covering fundamentals of muscle physiology, dMRI acquisition techniques, dMRI modeling, and applications where dMRI has been leveraged to noninvasively study skeletal muscle changes in response to disease, aging, injury, and human performance. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Accelerated IVIM-corrected DTI in acute hamstring injury: towards a clinically feasible acquisition time

BACKGROUND

Intravoxel incoherent motion (IVIM)-corrected diffusion tensor imaging (DTI) potentially enhances return-to-play (RTP) prediction after hamstring injuries. However, the long scan times hamper clinical implementation. We assessed accelerated IVIM-corrected DTI approaches in acute hamstring injuries and explore the sensitivity of the perfusion fraction (f) to acute muscle damage.

METHODS

Athletes with acute hamstring injury received DTI scans of both thighs < 7 days after injury and at RTP. For a subset, DTI scans were repeated with multiband (MB) acceleration. Data from standard and MB-accelerated scans were fitted with standard and accelerated IVIM-corrected DTI approach using high b-values only. Segmentations of the injury and contralateral healthy muscles were contoured. The fitting methods as well as the standard and MB-accelerated scan were compared using linear regression analysis. For sensitivity to injury, Δ(injured minus healthy) DTI parameters between the methods and the differences between injured and healthy muscles were compared (Wilcoxon signed-rank test).

RESULTS

The baseline dataset consisted of 109 athletes (16 with MB acceleration); 64 of them received an RTP scan (8 with MB acceleration). Linear regression of the standard and high-b DTI fitting showed excellent agreement. With both fitting methods, standard and MB-accelerated scans were comparable. Δ(injured minus healthy) was similar between standard and accelerated methods. For all methods, all IVIM-DTI parameters except f were significantly different between injured and healthy muscles.

CONCLUSIONS

High-b DTI fitting with MB acceleration reduced the scan time from 11:08 to 3:40 min:s while maintaining sensitivity to hamstring injuries; f was not different between healthy and injured muscles.

RELEVANCE STATEMENT

The accelerated IVIM-corrected DTI protocol, using fewer b-values and MB acceleration, reduced the scan time to under 4 min without affecting the sensitivity of the quantitative outcome parameters to hamstring injuries. This allows for routine clinical monitoring of hamstring injuries, which could directly benefit injury treatment and monitoring.

KEY POINTS

• Combining high-b DTI-fitting and multiband-acceleration dramatically reduced by two thirds the scan time. • The accelerated IVIM-corrected DTI approaches maintained the sensitivity to hamstring injuries. • The IVIM-derived perfusion fraction was not sensitive to hamstring injuries.

Clinical applications of skeletal muscle diffusion tensor imaging

Diffusion tensor imaging (DTI) may allow the determination of new threshold values, based on water anisotropy, to differentiate between healthy muscle and various pathological processes. Additionally, it may quantify treatment monitoring or training effects. Most current studies have evaluated the potential of DTI of skeletal muscle to assess sports-related injuries or therapy, and training monitoring. Another critical area of application of this technique is the characterization and monitoring of primary and secondary myopathies. In this manuscript, we review the application of DTI in the evaluation of skeletal muscle in these and other novel clinical scenarios, with emphasis on the use of quantitative imaging-derived biomarkers. Finally, the main limitations of the introduction of DTI in the clinical setting and potential areas of future use are discussed.

Diffusion tensor imaging and quantitative T2 mapping to monitor muscle recovery following hamstring injury

MRI examinations are accurate for diagnosing sports-related acute hamstring injuries. However, sensitive imaging methods for assessing recovery of these injuries are lacking. Diffusion tensor imaging (DTI) and quantitative T2 (qT2) mapping have both shown promise for assessing recovery of muscle micro trauma and exercise effects. The purpose of this study was to explore the potential of DTI and qT2 mapping for monitoring the muscle recovery processes after acute hamstring injury. In this prospective study, athletes with an acute hamstring injury underwent a 3-T MRI examination of the injured and contralateral hamstrings including DTI and qT2 measurements at three time points: (1) within 1 week after sustaining the injury, (2) 2 weeks after time point 1, and (3) return to play (RTP). A linear mixed model was used for time-effect analysis and paired t-tests for the detection of differences between injured and uninjured muscles. Forty-one athletes (age 27.8 ± 7 years; two females and 39 males) were included. Mean RTP time was 50 (range 12-169) days. A significant time effect was found for mean diffusivity, radial diffusivity, and the second and third eigenvalues (p ≤ 0.001) in the injured muscles. Fractional anisotropy (p = 0.40), first eigenvalue (p = 0.02), and qT2 (p = 0.61) showed no significant time effect. All DTI indices, except for fractional anisotropy, were significantly elevated compared with control muscles right after the injury (p < 0.001). Values normalized during the recovery period, with no significant differences between control and injured muscles at RTP (p values ranged from 0.08 to 0.51). Mean qT2 relaxation times in injured muscles were not significantly elevated compared with control muscles at any time point (p > 0.04). In conclusion, DTI can be used to monitor recovery after an acute hamstring injury. Future work should explore the potential of DTI indices to predict RTP and recovery times in athletes after an acute strain injury.

Model-based reconstructions for intravoxel incoherent motion and diffusion tensor imaging parameter map estimations

Intravoxel incoherent motion (IVIM) imaging and diffusion tensor imaging (DTI) facilitate noninvasive quantification of tissue perfusion and diffusion. Both are promising biomarkers in various diseases and a combined acquisition is therefore desirable. This comes with challenges, including noisy parameter maps and long scan times, especially for the perfusion fraction f and pseudo-diffusion coefficient D*. A model-based reconstruction has the potential to overcome these challenges. As a first step, our goal was to develop a model-based reconstruction framework for IVIM and combined IVIM-DTI parameter estimation. The IVIM and IVIM-DTI models were implemented in the PyQMRI model-based reconstruction framework and validated with simulations and in vivo data. Commonly used voxel-wise nonlinear least-squares fitting was used as the reference. Simulations with the IVIM and IVIM-DTI models were performed with 100 noise realizations to assess accuracy and precision. Diffusion-weighted data were acquired for IVIM reconstruction in the liver (n = 5), as well as for IVIM-DTI in the kidneys (n = 5) and lower-leg muscles (n = 6) of healthy volunteers. The median and interquartile range (IQR) values of the IVIM and IVIM-DTI parameters were compared to assess bias and precision. With model-based reconstruction, the parameter maps exhibited less noise, which was most pronounced in the f and D* maps, both in the simulations and in vivo. The bias values in the simulations were comparable between model-based reconstruction and the reference method. The IQR was lower with model-based reconstruction compared with the reference for all parameters. In conclusion, model-based reconstruction is feasible for IVIM and IVIM-DTI and improves the precision of the parameter estimates, particularly for f and D* maps.

Quantitative muscle MRI displays clinically relevant myostructural abnormalities in long-term ICU-survivors: a case-control study

BACKGROUND

Long-term data on ICU-survivors reveal persisting sequalae and a reduced quality-of-life even after years. Major complaints are neuromuscular dysfunction due to Intensive care unit acquired weakness (ICUAW). Quantitative MRI (qMRI) protocols can quantify muscle alterations in contrast to standard qualitative MRI-protocols.

METHODS

Using qMRI, the aim of this study was to analyse persisting myostructural abnormalities in former ICU patients compared to controls and relate them to clinical assessments. The study was conducted as a cohort/case-control study. Nine former ICU-patients and matched controls were recruited (7 males; 54.8y ± 16.9; controls: 54.3y ± 11.1). MRI scans were performed on a 3T-MRI including a mDTI, T2 mapping and a mDixonquant sequence. Water T2 times, fat-fraction and mean values of the eigenvalue (λ₁), mean diffusivity (MD), radial diffusivity (RD) and fractional anisotropy (FA) were obtained for six thigh and seven calf muscles bilaterally. Clinical assessment included strength testing, electrophysiologic studies and a questionnaire on quality-of-life (QoL). Study groups were compared using a multivariate general linear model. qMRI parameters were correlated to clinical assessments and QoL questionnaire using Pearson´s correlation.

RESULTS

qMRI parameters were significantly higher in the patients for fat-fraction (p < 0.001), water T2 time (p < 0.001), FA (p = 0.047), MD (p < 0.001) and RD (p < 0.001). Thighs and calves showed a different pattern with significantly higher water T2 times only in the calves. Correlation analysis showed a significant negative correlation of muscle strength (MRC sum score) with FA and T2-time. The results were related to impairment seen in QoL-questionnaires, clinical testing and electrophysiologic studies.

CONCLUSION

qMRI parameters show chronic next to active muscle degeneration in ICU survivors even years after ICU therapy with ongoing clinical relevance. Therefore, qMRI opens new doors to characterize and monitor muscle changes of patients with ICUAW. Further, better understanding on the underlying mechanisms of the persisting complaints could contribute the development of personalized rehabilitation programs.

Advances in The Application and Research of Magnetic Resonance Diffusion Kurtosis Imaging in The Musculoskeletal System

Magnetic resonance diffusion kurtosis imaging (DKI) is an emerging magnetic resonance imaging (MRI) technique that can reflect microstructural changes in tissue through non-Gaussian diffusion of water molecules. Compared to traditional diffusion weighted imaging (DWI), the DKI model has shown greater sensitivity for diagnosis of musculoskeletal diseases and can help formulate more reasonable treatment plans. Moreover, DKI is an important auxiliary examination for evaluation of the motor function of the musculoskeletal system. This article briefly introduces the basic principles of DKI and reviews the application and research of DKI in the evaluation of disorders of the musculoskeletal system (including bone tumors, soft tissue tumors, spinal lesions, chronic musculoskeletal diseases, musculoskeletal trauma, and developmental disorders) as well as the normal musculoskeletal tissues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.

Mean kurtosis-Curve (MK-Curve) correction improves the test-retest reproducibility of diffusion kurtosis imaging at 3 T

Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)-Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3-T scanner and included a T1-weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK-Curve DKI fit was performed, including intraclass correlation (ICC) and Bland-Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor- and diffusion tensor-derived MK-Curve-corrected metrics (ICC values: 0.77-0.98 and 0.87-0.98, respectively), with the exception of two DKI-derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non-MK-Curve-corrected kurtosis tensor-derived metrics ranged from 0.01 to 0.52 and diffusion tensor-derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK-Curve-corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test-retest setting.

Advanced intraoperative MRI in pediatric brain tumor surgery

Introduction: In the pediatric brain tumor surgery setting, intraoperative MRI (ioMRI) provides "real-time" imaging, allowing for evaluation of the extent of resection and detection of complications. The use of advanced MRI sequences could potentially provide additional physiological information that may aid in the preservation of healthy brain regions. This review aims to determine the added value of advanced imaging in ioMRI for pediatric brain tumor surgery compared to conventional imaging. Methods: Our systematic literature search identified relevant articles on PubMed using keywords associated with pediatrics, ioMRI, and brain tumors. The literature search was extended using the snowball technique to gather more information on advanced MRI techniques, their technical background, their use in adult ioMRI, and their use in routine pediatric brain tumor care. Results: The available literature was sparse and demonstrated that advanced sequences were used to reconstruct fibers to prevent damage to important structures, provide information on relative cerebral blood flow or abnormal metabolites, or to indicate the onset of hemorrhage or ischemic infarcts. The explorative literature search revealed developments within each advanced MRI field, such as multi-shell diffusion MRI, arterial spin labeling, and amide-proton transfer-weighted imaging, that have been studied in adult ioMRI but have not yet been applied in pediatrics. These techniques could have the potential to provide more accurate fiber tractography, information on intraoperative cerebral perfusion, and to match gadolinium-based T1w images without using a contrast agent. Conclusion: The potential added value of advanced MRI in the intraoperative setting for pediatric brain tumors is to prevent damage to important structures, to provide additional physiological or metabolic information, or to indicate the onset of postoperative changes. Current developments within various advanced ioMRI sequences are promising with regard to providing in-depth tissue information.

Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH)

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.

Multi-parametric quantitative magnetic resonance imaging of the upper arm muscles of patients with spinal muscular atrophy

Quantitative magnetic resonance imaging (qMRI) is frequently used to map the disease state and disease progression in the lower extremity muscles of patients with spinal muscular atrophy (SMA). This is in stark contrast to the almost complete lack of data on the upper extremity muscles, which are essential for carrying out daily activities. The aim of this study was therefore to assess the disease state in the upper arm muscles of patients with SMA in comparison with healthy controls by quantitative assessment of fat fraction, diffusion indices, and water T2 relaxation times, and to relate these measures to muscle force. We evaluated 13 patients with SMA and 15 healthy controls with a 3-T MRI protocol consisting of DIXON, diffusion tensor imaging, and T2 sequences. qMRI measures were compared between groups and related to muscle force measured with quantitative myometry. Fat fraction was significantly increased in all upper arm muscles of the patients with SMA compared with healthy controls and correlated negatively with muscle force. Additionally, fat fraction was heterogeneously distributed within the triceps brachii (TB) and brachialis muscle, but not in the biceps brachii muscle. Diffusion indices and water T2 relaxation times were similar between patients with SMA and healthy controls, but we did find a slightly reduced mean diffusivity (MD), λ1, and λ3 in the TB of patients with SMA. Furthermore, MD was positively correlated with muscle force in the TB of patients with SMA. The variation in fat fraction further substantiates the selective vulnerability of muscles. The reduced diffusion tensor imaging indices, along with the positive correlation of MD with muscle force, point to myofiber atrophy. Our results show the feasibility of qMRI to map the disease state in the upper arm muscles of patients with SMA. Longitudinal data in a larger cohort are needed to further explore qMRI to map disease progression and to capture the possible effects of therapeutic interventions.

A diffusion tensor-based method facilitating volumetric assessment of fiber orientations in skeletal muscle

Background

The purpose of this study was to develop a DTI-based method to quantitatively assess fiber angles and changes therein in leg muscles in order to facilitate longitudinal studies on muscle fiber architectural adaptations in healthy subjects.

Methods

The upper legs of five volunteers were scanned twice on the same day. The right lower legs of five volunteers were scanned twice with the ankle in three positions, i.e. -15° dorsiflexion, 0° neutral position, and 30° plantarflexion. The MRI protocols consisted of a noise scan, a 3-point mDixon scan and a DTI scan. Fiber-angle color maps were generated for four muscles in the upper legs and two muscles in the lower leg. Voxel-wise fiber angles (θ) were calculated from the angle between the principal eigenvector of the diffusion tensor and a reference line defined between the origo and insertion points of each muscle. Bland-Altman analysis, intraclass correlation coefficient (ICC), coefficient of variation (CV%), minimal detectable change (MDC), standard error (SE) and Friedman test were used for assessing the feasibility of this method and in order to have an indication of the repeatability and the sensitivity.

Results

Bland-Altman analysis showed good repeatability (CV%<10 and 0.7≤ICC≤0.9) with exception of the Tibialis Anterior (TA) muscle in dorsiflexion position(CV%: 12.2) and the Semitendinosus (ST) muscle (left leg) (CV%: 11.4). The best repeatability metrics were found for the SOL muscle in neutral position (CV%: 2.6). Changes in average θ in TA and SOL with ankle positions were observed in accordance with expected agonist and antagonist functions of both muscles. For example, for the anterior left compartment the change in fiber angle Δθ with respect to the neutral position Δθ = -1.6° ± 0.8° and 2.2° ± 2.8° (p = 0.008), for dorsiflexion and plantarflexion, respectively.

Conclusion

Our method facilitates fast inspection and quantification of muscle fiber angles in the lower and upper leg muscles in rest and detection of changes in lower-leg muscle fiber angles with varying ankle angles.

Diffusion kurtosis imaging of white matter in bipolar disorder

White matter pathology likely contributes to the pathogenesis of bipolar disorder (BD). Most studies of white matter in BD have used diffusion tensor imaging (DTI), but the advent of more advanced multi-shell diffusion MRI imaging offers the possibility to investigate other aspects of white matter microstructure. Diffusion kurtosis imaging (DKI) extends the DTI model and provides additional measures related to diffusion restriction. Here, we investigated white matter in BD by applying whole-brain voxel-based analysis (VBA) and a network-based connectivity approach using constrained spherical deconvolution tractography to assess differences in DKI and DTI metrics between BD (n = 25) and controls (n = 24). The VBA showed lower mean kurtosis in the corona radiata and posterior association fibers in BD. Regional differences in connectivity were indicated by lower mean kurtosis and kurtosis anisotropy in streamlines traversing the temporal and occipital lobes, and lower mean axial kurtosis in the right cerebellar, thalamo-subcortical pathways in BD. Significant differences were not seen in DTI metrics following FDR-correction. The DKI findings indicate altered connectivity across cortical, subcortical and cerebellar areas in BD. DKI is sensitive to different microstructural properties and is a useful complementary technique to DTI to more fully investigate white matter in BD.

Residual quadrupolar couplings observed in 7 Tesla deuterium MR spectra of skeletal muscle

Purpose

Deuterium metabolic imaging could potentially be used to investigate metabolism in skeletal muscle noninvasively. However, skeletal muscle is a tissue with a high degree of spatial organization. In this study, we investigated the effect of incomplete motional averaging on the naturally abundant deuterated water signal in 7 Tesla deuterium spectra of the lower leg muscles and the dependence on the angle between the muscle fibers and the main magnetic field B₀, as determined by DTI.

Methods

Natural abundance deuterium MRSI measurements of the right lower leg muscles were performed at 7 Tesla. Three subjects were scanned in a supine position, with the right leg parallel with the B₀ field. One subject was scanned twice; during the second scan, the subject was laying on his right side and the right knee was bent such that the angle between the right lower leg and B₀ was approximately 45°. DTI was performed in the same subjects in the same positions at 3 Tesla to determine muscle fiber angles.

Results

We observed splittings in the natural abundance deuterated water signal. The size of the splittings varied between different muscles in the lower leg but were mostly similar among subjects for each muscle. The splittings depended on the orientation of the muscle fibers with respect to the main magnetic field B₀.

Conclusion

Partial molecular alignment in skeletal muscle leads to residual deuteron quadrupolar couplings in deuterated water, the size of which depends on the angle between the muscle fibers and B₀.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Diffusion Tensor Imaging of the Lateral Pterygoid Muscle in Patients with Temporomandibular Joint Disorders and Healthy Volunteers

Objective

This study aimed to explore the feasibility of functional evaluation of the lateral pterygoid muscle (LPM) using diffusion tensor imaging (DTI) in patients with temporomandibular joint disorders (TMDs).

Materials and Methods

A total of 119 patients with TMD (23 male and 96 female; mean age ± standard deviation, 41 ± 15 years; 58 bilateral and 61 unilateral involvements for a total of 177 joints) and 20 healthy volunteers (9 male and 11 female; 40 ± 13 years; 40 joints) were included in this prospective study. Based on DTI of the jaw in the resting state, the diffusion parameters, apparent diffusion coefficient (ADC), fractional anisotropy (FA), λ1, λ2, and λ3 of the superior and inferior heads of the LPM (SHLPM and IHLPM) were measured. Patients with TMD with normal disc position (ND), anterior disc displacement with reduction (ADWR), and anterior disc displacement without reduction (ADWOR) were compared.

Results

Patients with TMD overall, and ADWR and ADWOR subgroups had significantly higher ADC, λ1, λ2, and λ3 in both the SHLPM and IHLPM than those in volunteers (p < 0.05 for all), whereas the ND subgroup only had significantly higher ADC and λ1 (p < 0.001). Meanwhile, significant differences in FA in the SHLPM and IHLPM were found between volunteers and ADWOR (p = 0.014 and p = 0.037, respectively). Among the three TMD subgroups, except for λ3 and FA in the ADWR subgroup, ADWR and ADWOR subgroups had significantly higher ADC, λ1, λ2, and λ3 and lower FA than those in the ND group (p < 0.050). There was no significant difference in diffusion variables between ADWR and ADWOR. In ADWOR, the osteoarthritis group had significantly higher λ3 and lower FA values in the IHLPM than those in the non-osteoarthritis group.

Conclusion

DTI successfully detected functional changes in the LPM in patients with TMD. The unsynchronized diffusivity changes in the LPM in different subgroups of TMD signified the possibility of using diffusion parameters as indicators to identify the severity of LPM hyperfunction at various stages of TMD.

Intravoxel Incoherent Motion Magnetic Resonance Imaging in Skeletal Muscle: Review and Future Directions

Throughout the body, muscle structure and function can be interrogated using a variety of noninvasive magnetic resonance imaging (MRI) methods. Recently, intravoxel incoherent motion (IVIM) MRI has gained momentum as a method to evaluate components of blood flow and tissue diffusion simultaneously. Much of the prior research has focused on highly vascularized organs, including the brain, kidney, and liver. Unique aspects of skeletal muscle, including the relatively low perfusion at rest and large dynamic range of perfusion between resting and maximal hyperemic states, may influence the acquisition, postprocessing, and interpretation of IVIM data. Here, we introduce several of those unique features of skeletal muscle; review existing studies of IVIM in skeletal muscle at rest, in response to exercise, and in disease states; and consider possible confounds that should be addressed for muscle-specific evaluations. Most studies used segmented nonlinear least squares fitting with a b-value threshold of 200 sec/mm² to obtain IVIM parameters of perfusion fraction (f), pseudo-diffusion coefficient (D*), and diffusion coefficient (D). In healthy individuals, across all muscles, the average ± standard deviation of D was 1.46 ± 0.30 × 10^-3  mm² /sec, D* was 29.7 ± 38.1 × 10^-3  mm² /sec, and f was 11.1 ± 6.7%. Comparisons of reported IVIM parameters in muscles of the back, thigh, and leg of healthy individuals showed no significant difference between anatomic locations. Throughout the body, exercise elicited a positive change of all IVIM parameters. Future directions including advanced postprocessing models and potential sequence modifications are discussed. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging

PURPOSE

To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades.

MATERIALS AND METHODS

Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, 1500, 2000, and 2500 s/mm2. Apparent Diffusion Coefficient (ADC), Mean Kurtosis (MK), and Mean Diffusivity (MD) maps were generated. The average values were compared among the molecular glioma subgroups of IDH-mutant and IDH-wildtype astrocytoma, and 1p/19q-codeleted oligodendroglioma. One-way ANOVA with post-hoc Games-Howell correction compared average ADC, MD, and MK values between molecular glioma groups. A Receiver Operating Characteristic (ROC) analysis determined the area under the curve (AUC).

RESULTS

Two b-value-dependent ADC-based evaluations presented statistically significant differences between the three molecular glioma sub-groups (p < 0.001, respectively).

CONCLUSIONS

High-b-value ADC from preoperative DWI may be used to stratify integrated molecular glioma subgroups and save time compared to diffusion kurtosis imaging. Higher b-values of up to 2500 s/mm2 may present an important step towards increasing diagnostic accuracy compared to standard DWI protocol.

Quantitative Muscle-MRI Correlates with Histopathology in Skeletal Muscle Biopsies

BACKGROUND

Skeletal muscle biopsy is one of the gold standards in the diagnostic workup of muscle disorders. By histopathologic analysis, characteristic features like inflammatory cellular infiltrations, fat and collagen replacement of muscle tissue or structural defects of the myofibers can be detected. In the past years, novel quantitative MRI (qMRI) techniques have been developed to quantify tissue parameters, thus providing a non-invasive diagnostic tool in several myopathies.

OBJECTIVE

This proof-of-principle study was performed to validate the qMRI-techniques to skeletal muscle biopsy results.

METHODS

Ten patients who underwent skeletal muscle biopsy for diagnostic purposes were examined by qMRI. Fat fraction, water T2-time and diffusion parameters were measured in the muscle from which the biopsy was taken. The proportion of fat tissue, the severity of degenerative and inflammatory parameters and the amount of type 1- and type 2- muscle fibers were determined in all biopsy samples. The qMRI-data were then correlated to the histopathological findings.

RESULTS

The amount of fat tissue in skeletal muscle biopsy correlated significantly with the fat fraction derived from the Dixon sequence. The water T2-time, a parameter for tissue edema, correlated with the amount of vacuolar changes of myofibers and endomysial macrophages in the histopathologic analysis. No significant correlations were found for diffusion parameters.

CONCLUSION

In this proof-of-principle study, qMRI techniques were related to characteristic histopathologic features in neuromuscular disorders. The study provides the basis for further development of qMRI methods in the follow-up of patients with neuromuscular disorders, especially in the context of emerging treatment strategies.

Application of diffusion kurtosis tensor MR imaging in characterization of renal cell carcinomas with different pathological types and grades

Background

To probe the feasibility and reproducibility of diffusion kurtosis tensor imaging (DKTI) in renal cell carcinoma (RCC) and to apply DKTI in distinguishing the subtypes of RCC and the grades of clear cell RCC (CCRCC).

Methods

Thirty-eight patients with pathologically confirmed RCCs [CCRCC for 30 tumors, papillary RCC (PRCC) for 5 tumors and chromophobic RCC (CRCC) for 3 tumors] were involved in the study. Diffusion kurtosis tensor MR imaging were performed with 3 b-values (0, 500, 1000s/mm²) and 30 diffusion directions. The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr) values and mean diffusity (MD) for RCC and contralateral normal parenchyma were acquired. The inter-observer agreements of all DKTI metrics of contralateral renal cortex and medulla were evaluated using Bland-Altman plots. Statistical comparisons with DKTI metrics of 3 RCC subtypes and between low-grade (Furman grade I ~ II, 22 cases) and high-grade (Furman grade III ~ IV, 8 cases) CCRCC were performed with ANOVA test and Student t test separately. Receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic efficacy of DKTI metrics for predicting nuclear grades of CCRCC. Correlations between DKTI metrics and nuclear grades were also evaluated with Spearman correlation analysis.

Results

Inter-observer measurements for each metric showed great reproducibility with excellent ICCs ranging from 0.81 to 0.87. There were significant differences between the DKTI metrics of RCCs and contralateral renal parenchyma, also among the subtypes of RCC. MK and Ka values of CRCC were significantly higher than those of CCRCC and PRCC. Statistical difference of the MK, Ka, Kr and MD values were also obtained between CCRCC with high- and low-grades. MK values were more effective for distinguishing between low- and high- grade CCRCC (area under the ROC curve: 0.949). A threshold value of 0.851 permitted distinction with high sensitivity (90.9%) and specificity (87.5%).

Conclusion

Our preliminary results suggest a possible role of DKTI in differentiating CRCC from CCRCC and PRCC. MK, the principle DKTI metric might be a surrogate biomarker to predict nuclear grades of CCRCC.

Trial registration

ChiCTC, ChiCTR-DOD-17010833, Registered 10 March, 2017, retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=17559.

Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle

Diffusion tensor imaging (DTI) is becoming a relevant diagnostic tool to understand muscle disease and map muscle recovery processes following physical activity or after injury. Segmenting all the individual leg muscles, necessary for quantification, is still a time-consuming manual process. The purpose of this study was to evaluate the impact of a supervised semi-automatic segmentation pipeline on the quantification of DTI indices in individual upper leg muscles. Longitudinally acquired MRI datasets (baseline, post-marathon and follow-up) of the upper legs of 11 subjects were used in this study. MR datasets consisted of a DTI and Dixon acquisition. Semi-automatic segmentations for the upper leg muscles were performed using a transversal propagation approach developed by Ogier et al on the out-of-phase Dixon images at baseline. These segmentations were longitudinally propagated for the post-marathon and follow-up time points. Manual segmentations were performed on the water image of the Dixon for each of the time points. Dice similarity coefficients (DSCs) were calculated to compare the manual and semi-automatic segmentations. Bland-Altman and regression analyses were performed, to evaluate the impact of the two segmentation methods on mean diffusivity (MD), fractional anisotropy (FA) and the third eigenvalue (λ₃ ). The average DSC for all analyzed muscles over all time points was 0.92 ± 0.01, ranging between 0.48 and 0.99. Bland-Altman analysis showed that the 95% limits of agreement for MD, FA and λ₃ ranged between 0.5% and 3.0% for the transversal propagation and between 0.7% and 3.0% for the longitudinal propagations. Similarly, regression analysis showed good correlation for MD, FA and λ₃ (r = 0.99, p < 60; 0.0001). In conclusion, the supervised semi-automatic segmentation framework successfully quantified DTI indices in the upper-leg muscles compared with manual segmentation while only requiring manual input of 30% of the slices, resulting in a threefold reduction in segmentation time.

Associations between apparent diffusion coefficient values and histopathological tissue alterations in myopathies

OBJECTIVES

Diffusion-weighted imaging (DWI) can reflect histopathologic changes in muscle disorders. The present study sought to elucidate possible associations between histopathology derived from muscle biopsies and DWI in myositis and other myopathies.

METHODS

Nineteen patients (10 women, 52.6%) with a mean age 51.43 ± 19 years were included in this retrospective study. Apparent diffusion coefficients (ADC) were evaluated with a histogram approach of the biopsied muscle. The histopathology analysis included the scoring systems proposed by Tateyama et al., Fanin et al., Allenbach et al. and immunhistochemical stainings for MHC, CD68, CD8, and CD4.

RESULTS

There was a tendency that skewness was lowered with increasing Tateyama score, but it did not reach statistical significance (p = .14). No statistical differences for the other scores were identified. There was a tendency that kurtosis was higher in MHC negative stained patient compared to positive patients, but statistically significance was not reached (p = .07). ADC histogram parameters did not correlate with CD68 and CD8 positive stained cells. There was a trend for skewness to correlate with the amount of CD4-positive cells (r = .57, p = .07).

CONCLUSION

The present study could not identify statistical significant associations between DWI and histopathology in muscle diseases based upon a small patient sample. Presumably, the investigated histopathology scores are more specific for certain disease aspects, whereas ADC values reflect the whole cellularity of the investigated muscle, which might cause the negative results.

MRI of Skeletal Muscles in Participants with Type 2 Diabetes with or without Diabetic Polyneuropathy

BackgroundDiabetic polyneuropathy (DPN) is associated with loss of muscle strength. MRI including diffusion-tensor imaging (DTI) may enable detection of muscle abnormalities related to type 2 diabetes mellitus (DM2) and DPN.PurposeTo assess skeletal muscle abnormalities in participants with DM2 with or without DPN by using MRI.Materials and MethodsThis prospective cross-sectional study included participants with DM2 and DPN (DPN positive), participants with DM2 without DPN (DPN negative), and healthy control (HC) participants enrolled between August 2017 and June 2018. Muscle strength at the knee and ankle was determined with isokinetic dynamometry. MRI of the lower extremities included the Dixon sequence, multicomponent T2 mapping, and DTI calculated fat fractions (FFs), T2 relaxation of muscle (T2_water), fractional anisotropy (FA), and diffusivity (mean, axial, and radial). One-way analysis of variance and Tukey honestly significant difference were applied for comparison between groups, and multivariate regression models were used for association between MRI parameters, nerve conduction, strength, and body mass index (BMI).ResultsTwenty participants with DPN (mean age, 65 years ± 9 [standard deviation]; 70% men; mean BMI, 34 kg/m² ± 5), 20 participants without DPN (mean age, 64 years ± 9; 55% men; mean BMI, 30 kg/m² ± 6), and 20 HC participants (mean age, 61 years ± 10; 55% men; mean BMI, 27 kg/m² ± 5) were enrolled in this study. Muscle strength adjusted for age, sex, and BMI was lower in participants with DPN than in DPN-negative and HC participants in the upper and lower leg (plantar flexors [PF], 62% vs 78% vs 89%; P < .001; knee extensors [KE], 73% vs 95% vs 93%; P < .001). FF was higher in leg muscle groups of participants with DPN than in DPN-negative and HC participants (PF, 20% vs 10% vs 8%; P < .001; KE, 13% vs 8% vs 6%; P < .001). T2_water was prolonged in leg muscle groups of participants with DPN when compared with HC participants (PF, 33 msec vs 31 msec; P < .001; KE, 32 msec vs 31 msec; P = .002) and in the lower leg when compared with participants without DPN (PF, 33 msec vs 32 msec; P = .03). In multivariate regression models, strength was associated with FA (b = -0.0004), T2_water (b = -0.03 msec), and FF (b = -0.1%) at thigh level (P < .001). Furthermore, FA (b = -0.007), T2_water (b = -0.53 msec), and FF (b = -4.0%) were associated with nerve conduction at calf level (P < .001).ConclusionMRI of leg muscle groups revealed fat accumulation, differences in water composition, and structural changes in participants with type 2 diabetes mellitus and neuropathy. Abnormalities were most pronounced in the plantar flexors.© RSNA, 2020Online supplemental material is available for this article.See also the editorial by Sneag and Tan in this issue.

Quantitative MRI of skeletal muscle in a cross-sectional cohort of patients with spinal muscular atrophy types 2 and 3

The aim of this study was to document upper leg involvement in spinal muscular atrophy (SMA) with quantitative MRI (qMRI) in a cross-sectional cohort of patients of varying type, disease severity and age. Thirty-one patients with SMA types 2 and 3 (aged 29.6 [7.6-73.9] years) and 20 healthy controls (aged 37.9 [17.7-71.6] years) were evaluated in a 3 T MRI with a protocol consisting of DIXON, T2 mapping and diffusion tensor imaging (DTI). qMRI measures were compared with clinical scores of motor function (Hammersmith Functional Motor Scale Expanded [HFMSE]) and muscle strength. Patients exhibited an increased fat fraction and fractional anisotropy (FA), and decreased mean diffusivity (MD) and T2 compared with controls (all P < .001). DTI parameters FA and MD manifest stronger effects than can be accounted for the effect of fatty replacement. Fat fraction, FA and MD show moderate correlation with muscle strength and motor function: FA is negatively associated with HFMSE and Medical Research Council sum score (τ = -0.56 and -0.59; both P < .001) whereas for fat fraction values are τ = -0.50 and -0.58, respectively (both P < .001). This study shows that DTI parameters correlate with muscle strength and motor function. DTI findings indirectly indicate cell atrophy and act as a measure independently of fat fraction. Combined these data suggest the potential of muscle DTI in monitoring disease progression and to study SMA pathogenesis in muscle.

Diffusion kurtosis imaging in head and neck cancer: A correlation study with dynamic contrast enhanced MRI

PURPOSE

To investigate the biophysical meaning of Diffusion Kurtosis Imaging (DKI) parameters via correlations with the perfusion parameters obtained from a long Dynamic Contrast Enhanced MRI scan, in head and neck (HN) cancer.

METHODS

Twenty two patients with newly diagnosed HN tumor were included in the present retrospective study. Some patients had multiple lesions, therefore a total of 26 lesions were analyzed. DKI was acquired using 5b values at 0, 500, 1000,1500 and 2000 s/mm². DCE-MRI was obtained with 130 dynamic volumes, with a temporal resolution of 5 s, to achieve a long scan time (>10 min). The apparent diffusion coefficient D_app and apparent diffusional kurtosis K_app were calculated voxel-by-voxel, removing the point at b value = 0 to eliminate possible perfusion effects on the parameter estimations. The transfer constants K^trans and K_ep, v_e, and the histogram-based entropy (En) and interquartile range (IQR) of each DCE-MRI parameter were quantified. Correlations between all variables were investigated by the Spearman's Rho correlation test.

RESULTS

Moderate relationships emerged between D_app and K_ep (Rho =  - 0.510, p = 0.009), and between D_app and v_e (Rho = 0.418, p = 0.038). En(K_ep) was significantly related to K_app (Rho = 0.407, p = 0.043), while IQR(K_ep) showed an inverse association with D_app (Rho = -0.422, p = 0.035).

CONCLUSIONS

A weak to intermediate correlation was found between DKI parameters and both K_ep and v_e. The kurtosis was associated to the intratumoral heterogeneity and complexity of the capillary permeability, expressed by En(K_ep).

Quantitative MRI Reveals Microstructural Changes in the Upper Leg Muscles After Running a Marathon

Background

The majority of sports‐related injuries involve skeletal muscle. Unlike acute trauma, which is often caused by a single traumatic event leading to acute symptoms, exercise‐induced microtrauma may remain subclinical and difficult to detect. Therefore, novel methods to detect and localize subclinical exercise‐induced muscle microtrauma are desirable.

Purpose

To assess acute and delayed microstructural changes in upper leg muscles with multiparametric quantitative MRI after running a marathon.

Study Type

Longitudinal; 1‐week prior, 24–48 hours postmarathon and 2‐week follow‐up

Population

Eleven men participants (age: 47–68 years).

Field Strength/Sequence

Spin‐echo echo planar imaging (SE‐EPI) with diffusion weighting, multispin echo, Dixon, and fat‐suppressed turbo spin‐echo (TSE) sequences at 3T. MR datasets and creatine kinase (CK) concentrations were obtained at three timepoints.

Assessment

Diffusion parameters, perfusion fractions, and quantitative (q)T₂ values were determined for hamstring and quadriceps muscles, TSE images were scored for acute injury. The vastus medialis and biceps femoris long head muscles were divided and analyzed in five segments to assess local damage.

Statistical Tests

Differences between timepoints in MR parameters were assessed with a multilevel linear mixed model and in CK concentrations with a Friedman test. Mean diffusivity (MD) and qT₂ for whole muscle and muscle segments were compared using a multivariate analysis of covariance (MANCOVA).

Results

CK concentrations were elevated (1194 U/L [166–3906], P < 0.001) at 24–48 hours postmarathon and returned to premarathon values (323 U/L [56–2216]) at 2‐week follow‐up. Most of the MRI diffusion indices in muscles without acute injury changed at 24–48 hours postmarathon and returned to premarathon values at follow‐up (MD, RD, and λ3; P < 0.006). qT₂ values (P = 0.003) and perfusion fractions (P = 0.003) were higher at baseline compared to follow‐up. Local assessments of MD and qT₂ revealed more pronounced changes than whole muscle assessment (2–3‐fold; P < 0.01).

Data Conclusion

Marathon running‐induced microtrauma was detected with MRI in individual whole upper leg muscles and even more pronounced on local segments.

Level of Evidence

2

Technical Efficacy Stage

3 J. Magn. Reson. Imaging 2020;52:407–417.

Exploration of New Contrasts, Targets, and MR Imaging and Spectroscopy Techniques for Neuromuscular Disease - A Workshop Report of Working Group 3 of the Biomedicine and Molecular Biosciences COST Action BM1304 MYO-MRI

Neuromuscular diseases are characterized by progressive muscle degeneration and muscle weakness resulting in functional disabilities. While each of these diseases is individually rare, they are common as a group, and a large majority lacks effective treatment with fully market approved drugs. Magnetic resonance imaging and spectroscopy techniques (MRI and MRS) are showing increasing promise as an outcome measure in clinical trials for these diseases. In 2013, the European Union funded the COST (co-operation in science and technology) action BM1304 called MYO-MRI (www.myo-mri.eu), with the overall aim to advance novel MRI and MRS techniques for both diagnosis and quantitative monitoring of neuromuscular diseases through sharing of expertise and data, joint development of protocols, opportunities for young researchers and creation of an online atlas of muscle MRI and MRS. In this report, the topics that were discussed in the framework of working group 3, which had the objective to: Explore new contrasts, new targets and new imaging techniques for NMD are described. The report is written by the scientists who attended the meetings and presented their data. An overview is given on the different contrasts that MRI can generate and their application, clinical needs and desired readouts, and emerging methods.

The repeatability of bilateral diffusion tensor imaging (DTI) in the upper leg muscles of healthy adults

Objectives

Assessment of the repeatability of diffusion parameter estimations in the upper leg muscles of healthy adults over the time course of 2 weeks, from a simultaneous bilateral upper leg DTI measurement.

Methods

SE-EPI DTI datasets were acquired at 3 T in the upper legs of 15 active adults at a time interval of 2 weeks. ROIs were manually drawn for four quadriceps and three hamstring muscles of both legs. The following DTI parameters were analyzed: 1st, 2nd, and 3rd eigenvalue (λ₁, λ₂, and λ₃), mean diffusivity (MD), and fractional anisotropy (FA). DTI parameters per muscle were calculated with and without intravoxel incoherent motion (IVIM) correction together with SNR levels per muscle. Bland-Altman plots and within-subject coefficient of variation (wsCV) were calculated. Left-right differences between muscles were assessed.

Results

The Bland-Altman analysis showed good repeatability of all DTI parameters except FA for both the IVIM-corrected and standard data. wsCV values show that MD has the highest repeatability (4.5% IVIM; 5.6% standard), followed by λ₂ (4.9% IVIM; 5.5% standard), λ₁ (5.3% IVIM; 7.5% standard), and λ₃ (5.7% IVIM; 5.7% standard). wsCV values of FA were 15.2% for the IVIM-corrected data and 13.9% for the standard analysis. The SNR (41.8 ± 16.0 right leg, 41.7 ± 17.1 left leg) and wsCV values were similar for the left and right leg and no left-right bias was detected.

Conclusions

Repeatability was good for standard DTI data and slightly better for IVIM-corrected DTI data. Our protocol is suitable for DTI of the upper legs with overall good SNR.

Key Points

• The presented DTI protocol is repeatable and therefore suitable for bilateral DT imaging of the upper legs.

• Additional B1⁺calibrations improve SNR and repeatability.

• Correcting for perfusion effects improves repeatability.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06403-5) contains supplementary material, which is available to authorized users.

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.

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

Multiparametric quantitative MRI assessment of thigh muscles in limb-girdle muscular dystrophy 2A and 2B

INTRODUCTION

The aim of this study was to apply quantitative MRI (qMRI) to assess structural modifications in thigh muscles of subjects with limb girdle muscular dystrophy (LGMD) 2A and 2B with long disease duration.

METHODS

Eleven LGMD2A, 9 LGMD2B patients and 11 healthy controls underwent a multi-parametric 3T MRI examination of the thigh. The protocol included structural T1-weighted images, DIXON sequences for fat fraction calculation, T2 values quantification and diffusion MRI. Region of interest analysis was performed on 4 different compartments (anterior compartment, posterior compartment, gracilis, sartorius).

RESULTS

Patients showed high levels of fat infiltration as measured by DIXON sequences. Sartorius and anterior compartment were more infiltrated in LGMD2B than LGMD2A patients. T2 values were mildly reduced in both disorders. Correlations between clinical scores and qMRI were found.

CONCLUSIONS

qMRI measures may help to quantify muscular degeneration, but careful interpretation is needed when fat infiltration is massive. Muscle Nerve 58: 550-558, 2018.

Effect of Perfusion on Diffusion Kurtosis Imaging Estimates for In Vivo Assessment of Integrated 2016 WHO Glioma Grades : A Cross-Sectional Observational Study

PURPOSE

To assess the role of perfusion-related signal decay on diffusion kurtosis imaging (DKI) estimates for in vivo stratification of glioma according to the integrated approach of the 2016 World Health Organization classification of tumors of the central nervous system (2016 CNS WHO).

METHODS

In this study 77 patients with histopathologically confirmed glioma were retrospectively assessed between January 2013 and February 2017 in a prospective trial. Mean kurtosis (MK) and mean diffusivity (MD) metrics from DKI were assessed by two physicians blinded to the study from a volume of interest around the entire solid tumor. Wilcoxon's signed-rank test compared perfusion-biased and perfusion-corrected MK (MK_pb and MK_pc) and MD (MD_pb, MD_pc) values. One-way ANOVA was used to compare MK_pb&pc and MD_pb&pc values between 2016 WHO glioma grades. Spearman's correlation coefficient was used to correlate them with 2016 WHO glioma grades. Receiver operating characteristic (ROC) analysis was performed on MK_pb&pc and MD_pb&pc for the significant results.

RESULTS

The MK_pc values were significantly higher than MK_pb values (p < 0.001), whereas MD_pc values were significantly lower than MD_pb values (p < 0.001). For stratifying gliomas, MK_pb values (ROC AUC range, 0.818-0.979) showed a higher diagnostic performance than MK_pc values (ROC AUC range, 0.773-0.975), whereas MD_pb values (ROC AUC range, 0.744-0.928) showed less diagnostic performance than MD_pc values (ROC AUC range, 0.753-0.934). The diagnostic accuracy of MK_pb was 80.0%.

CONCLUSION

The MK and MD estimates of DKI are influenced by microcapillary blood perfusion; however, taking the effect of perfusion on DKI metrics into account does not substantially impact their overall diagnostic performance in classifying glioma according to the 2016 CNS WHO.

Diffusional kurtosis imaging (DKI) incorporation into an intravoxel incoherent motion (IVIM) MR model to measure cerebral hypoperfusion induced by hyperventilation challenge in healthy subjects

OBJECTIVES

The objectives were to investigate the diffusional kurtosis imaging (DKI) incorporation into the intravoxel incoherent motion (IVIM) model for measurements of cerebral hypoperfusion in healthy subjects.

MATERIALS AND METHODS

Eight healthy subjects underwent a hyperventilation challenge with a 4-min diffusion weighted imaging protocol, using 8 b values chosen with the Cramer-Rao Lower Bound optimization approach. Four regions of interest in gray matter (GM) were analyzed with the DKI-IVIM model and the bi-exponential IVIM model, for normoventilation and hyperventilation conditions.

RESULTS

A significant reduction in the perfusion fraction (f) and in the product fD* of the perfusion fraction with the pseudodiffusion coefficient (D*) was found with the DKI-IVIM model, during the hyperventilation challenge. In the cerebellum GM, the percentage changes were f: -43.7 ± 40.1, p = 0.011 and fD*: -50.6 ± 32.1, p = 0.011; in thalamus GM, f: -47.7 ± 34.7, p = 0.012 and fD*: -47.2 ± 48.7, p = 0.040. In comparison, using the bi-exponential IVIM model, only a significant decrease in the parameter fD* was observed for the same regions of interest. In frontal-GM and posterior-GM, the reduction in f and fD* did not reach statistical significance, either with DKI-IVIM or the bi-exponential IVIM model.

CONCLUSION

When compared to the bi-exponential IVIM model, the DKI-IVIM model displays a higher sensitivity to detect changes in perfusion induced by the hyperventilation condition.