Magnetic Resonance Imaging

Abdominal quantitative susceptibility mapping (QSM), especially in small animals, is challenging because of respiratory motion and blood flow that, in addition to noise, deteriorate the quality of the input data. Efficient artefact suppression in QSM reconstruction is crucial in these conditions. Single-step QSM algorithms combine background field removal and magnetic field-to-susceptibility inverse problem regularization in a single optimization equation. Here, we propose a single-step QSM algorithm that uses spherical mean value kernels of different radii for background field removal and structure prior (consistency with magnitude image) with L1 norm for regularization. The optimization problem is solved using the split-Bregman method on the graphic processor unit. The method was compared with previously reported singlestep methods: a method using discrete Laplacian instead of spherical mean value kernels, a method using total variational penalty instead of structure prior, and a method using L2 norm for structure prior. With the proposed method relative to the previous ones, a numerical susceptibility phantom was reconstructed more precisely. In living mice, susceptibility maps with more homogeneous liver, higher contrast between liver and blood vessels, and well-preserved structural details were obtained. In patients, susceptibility maps with more homogeneous subcutaneous fat and higher contrast between subcutaneous fat and liver were obtained. These results show the potential of the proposed single-step method for abdominal QSM in small animals and humans.

PurposeInterest is growing in MR-only radiotherapy (RT) planning for prostate cancer (PCa) due to the potential reductions in cost and patient exposure to radiation, and a more streamlined work-flow and patient imaging pathway. However, in MRI, the gold fiducial markers (FMs) used for target localization appear as signal voids, complicating differentiation from other void sources such as calcifications and bleeds. This work investigates using Quantitative Susceptibility Mapping (QSM), an MRI phase post-processing technique, to aid in the differentiation task. It also presents deep learning models that capture nuanced information and automate the segmentation task, facilitating a streamlined approach to MR-only RT. MethodsCT and MRI, including GRE and T1-weighted imaging, were acquired from 26 PCa patients, each with three implanted gold FMs. GRE data were post-processed into QSM, T 2*, and R2* maps using QSMxTs body imaging pipeline. Statistical analyses were conducted to investigate the quantitative differentiation of FMs and calcification in each contrast. 3D U-Nets were developed using fastMONAI to automate the segmentation task using various combinations of MR-derived contrasts, with a model trained on CT used as a baseline. Models were evaluated using precision and recall calculated using a leave-one-out cross-validation scheme. ResultsSignificant differences were observed between FM and calcification regions in CT, QSM and T 2*, though overlap was observed in QSM and T 2*. The baseline CT U-Net achieved an FM-level precision of {approx} 98% and perfect recall. The best-performing QSM-based model achieved precision and recall of 80% and 90%, respectively, while conventional MRI had values below 70% and 80%, respectively. The QSM-based model produced segmentations with good agreement with the ground truth, including a challenging FM that coincided with a bleed. ConclusionThe model performance highlights the value of using QSM over indirect measures in MRI, such as signal voids in magnitude-based contrasts. The results also indicate that a U-Net can capture more information about the presentation of FMs and other sources than would be possible using susceptibility quantification alone, which may be less reliable due to the diverse presentation of sources across a patient population. In our study, QSM was a reliable discriminator of FMs and other sources in the prostate, facilitating an accurate and streamlined approach to MR-only RT.

Repetitive mild traumatic brain injury (mTBI) has long term health effects and may result in the development of neurodegenerative or neuropsychiatric disorders. Histology shows axonal and dendritic beading, synaptic atrophy, vasodilation and gliosis occuring within hours/days post-mTBI. However, current neuroimaging techniques are unable to detect the early effects of repetitive mTBI. Consequently, mTBI brain scans are normal appearing and inconclusive. Hence, neuroimaging markers capable of detecting subtle microstructural and functional alterations are needed. We present results from longitudinal, multiparametric magnetic resonance imaging (MRI) assessment of repetitive mTBI in rats. We employ advanced in-vivo diffusion MRI (dMRI) to probe brain microstructural alterations, perfusion MRI to assess cerebral blood flow (CBF), close to the injury site, and proton MR spectroscopy to assess metabolic alterations in the ipsilateral cerebral cortex. High resolution anatomical scans were also acquired. In agreement with clinical observations, anatomical scans of rats were normal appearing even after repeated mTBI. Throughout, significance is regarded as p<0.05 post false discovery rate correction. dMRI revealed significant microstructural remodelling in ipsilateral hippocampus (reduced radial kurtosis), may be due to axonal/dendritic beading, demyelination, synaptic atrophy and edema. Consistent with prior reports of reduced cell/fiber density in mTBI, we find significantly increased mean diffusivity in ipsilateral corpus callosum. We also find significantly decreased glutathione (GSH) and increased total Choline (tCho) following second and third mTBI (vs baseline), also reported in clinical mTBI cohorts. Reduced GSH suggests oxidative stress and increase in tCho indicate cell damage/repair. CBF did not change significantly, however, high variability in CBF following the second and third mTBI suggest increased variability in CBF likely due to tissue hypoxia and oxidative stress. Oxidative stress may affect capillary blood flow by disturbing pericyte capillary contraction. Around 40% of pericytes retract after mTBI causing pericyte depletion and white matter dysfunction as suggested by dMRI findings. Multiparametric MRI detects meaningful mTBI-induced alterations otherwise undetectable with conventional MRI. Similar strategies may provide useful information to aid diagnosis of human mTBI.

ObjectivesTo determine the impact of segmentation techniques on radiomic features extracted from ultrahigh-field (UHF) MRI of the brain. Materials and MethodsTwenty-one 7T MRI scans of the brain, including a 3D magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) T1-weighted sequence with an isotropic 0.63 mm3 voxel size, were analyzed. Radiomic features (histogram, texture, and shape; total n=101) from six brain regions -cerebral gray and white matter, basal ganglia, ventricles, cerebellum, and brainstem-were extracted from segmentation masks constructed with four different techniques: the iGT (reference standard), based on a custom pipeline that combined automatic segmentation tools and expert reader correction; the deep-learning algorithm Cerebrum-7T; the Freesurfer-v7 software suite; and the Nighres algorithm. Principal components (PCs) were calculated for histogram and texture features. To test the reproducibility of radiomic features, intraclass correlation coefficients (ICC) were used to compare Cerebrum-7, Freesurfer-v7, and Nighres to the iGT, respectively. ResultsFor histogram PCs, median ICCs for Cerebrum-7T, Freesurfer-v7, and Nighres were 0.99, 0.42, and 0.11 for the gray matter; 0.84, 0.25, and 0.43 for the basal ganglia; 0.89, 0.063, and 0.036 for the white matter; 0.84, 0.21, and 0.33 for the ventricles; 0.94, 0.64, and 0.93 for the cerebellum; and 0.78, 0.21, and 0.53 for the brainstem. For texture PCs, median ICCs for Cerebrum-7T, Freesurfer-v7, and Nighres were 0.95, 0.21, and 0.15 for the gray matter; 0.70, 0.36, and 0.023 for the basal ganglia; 0.91, 0.25, and 0.023 for the white matter; 0.80, 0.75, and 0.59 for the ventricles; 0.95, 0.43, and 0.86 for the cerebellum; and 0.72, 0.39, and 0.46 for the brainstem. For shape features, median ICCs for Cerebrum-7T, FreeSurfer-v7, and Nighres were 0.99, 0.91, and 0.36 for the gray matter; 0.89, 0.90, and 0.13 for the basal ganglia; 0.98, 0.91, and 0.027 for the white matter; 0.91, 0.91, and 0.36 for the ventricles; 0.80, 0.68, and 0.47 for the cerebellum; and 0.79, 0.17, and 0.15 for the brainstem. ConclusionsRadiomic features in UHF MRI of the brain show substantial variability depending on the segmentation algorithm. The deep learning algorithm Cerebrum-7T enabled the highest reproducibility. Dedicated software tools for UHF MRI may be needed to achieve more stable results.

The application of multi-echo functional magnetic resonance imaging (fMRI) studies has considerably increased in the last decade due to its superior BOLD sensitivity compared to single-echo fMRI. Various methods have been developed that combine the fMRI time-series derived at different echo times to improve the data quality. Here we evaluated three multi-echo combination schemes, i.e. optimal combination (T2*-weighted), temporal Signal-to-Noise Ratio (tSNR) weighted, and temporal Contrast-to-Noise Ratio (tCNR) weighted combination. For the first time, the effect of these multi-echo combinations on functional resting-state networks was assessed in the temporal and spatial domain, and compared to networks derived from the second echo (35 ms) functional images. Sixteen healthy volunteers were scanned during a 5 minutes resting-state fMRI session. After obtaining the networks, several temporal and spatial metrics were calculated for their time-series and spatial maps. Our results showed that, compared to the second echo network time-series, the Pearson correlation and root mean square error were the most consistent for the optimal combination time-series and the least with those derived from tSNR-weighted combination. The frequency analysis further suggested that the time-series from the tSNR-weighted combination method reduced hardware- and physiological-related artifacts as reflected by the reduced power for the associated frequencies in almost all networks. Moreover, the spatial stability and extent of the networks significantly increased after multi-echo combination, primarily for the optimal combination, followed by the tSNR-weighted combination. The performance of the tCNR-weighted combination lacked robustness and instead varied remarkedly between resting-state networks in both the temporal and spatial domain. The results highlight the benefits of multi-echo sequences on resting-state networks as well as the importance of adjusting the choice of multi-echo combination method to the research question and domain of interest.

PurposeThe purpose of this study was to determine the quantitative variability of diffusion weighted imaging and apparent diffusion coefficient values across a large fleet of MR systems. Using a NIST traceable magnetic resonance imaging diffusion phantom, imaging was reproducible and the measurements were quantitatively compared to known values. MethodsA fleet of 23 clinical MRI scanners was investigated in this study. A NIST/QIBA DWI phantom was imaged with protocols provided with the phantom. The resulting images were analyzed and ADC maps were generated. User-directed region-of-interests on each of the different vials provided ADC measurements among a wide range of known ADC values. ResultsThree diffusion phantoms were used in this study and compared to one another. From the one-way analysis of the variance, the mean and standard deviation of the percent errors from each phantom were not significantly different from one another. The low ADC vials showed larger errors and variation and appear directly related to SNR. Across all the MR systems and data, the coefficient of variation was calculated and Bland-Altman analysis was performed. ADC measurements were similar to one another except for the vials with the lower ADC values, which had a higher coefficient of variation. ConclusionADC values among the three phantoms showed good agreement and were not significantly different from one another. The large percent errors seen primarily at the low ADC values were shown to be a consequence of the SNR dependence and very little bias was observed between magnetic strengths and manufacturers. ADC values between diffusion phantoms were not statistically significant. Future investigations will be performed to study differences in magnetic field strength, vendor, MR system models, gradients, and bore size. More data across different MR platforms would facilitate quantitative measurements for multi-platform and multi-site imaging studies. With the increasing usage of diffusion weighted imaging in the clinic, the characterization of ADC variability for MR systems provides an improved quality control over the MR systems.

The Fluid And White matter Suppression (FLAWS) MRI sequence allows for the acquisition of multiple T1-weighted contrasts in a single sequence acquisition. However, its acquisition time is prohibitive for use in clinical practice when the k-space is linearly downsampled and reconstructed using the Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) technique. This study proposes a FLAWS sequence optimization tailored to allow for the acquisition of FLAWS images with a Cartesian phyllotaxis k-space undersampling and compressed sensing (CS) reconstruction at 3T. The CS FLAWS sequence parameters were determined using a method previously employed to optimize FLAWS imaging at 1.5T and 7T. In-vivo experiments show that the proposed CS FLAWS optimization allows to reduce the FLAWS sequence acquisition time from 8 mins to 6 mins without decreasing the FLAWS image quality. In addition, this study demonstrates for the first time that T1-weighted imaging with low B1 sensitivity and T1 mapping can be performed with the FLAWS sequence at 3T for both GRAPPA and CS reconstructions. The FLAWS T1 mapping was validated using in-silico, in-vitro and in-vivo experiments with comparison against the inversion recovery turbo spin echo and MP2RAGE T1 mappings. These new results suggest that the recent advances in FLAWS imaging allow to combine the MP2RAGE imaging benefits (T1-weigthed imaging with low B1 sensitivity and T1 mapping) and with the previous version of FLAWS imaging benefits (multi T1-weighted contrast imaging) in a single 6 mins sequence acquisition. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

White matter (WM) tract detection is critical in presurgical planning of tumor resection however, standard-of-care imaging techniques including T1-weighted, T2-weighted, and diffusion tensor imaging (DTI) often fail to characterize WM tracts within regions of edema. This failure arises because edema increases the isotropic diffusion component within a voxel, reducing sensitivity to the anisotropic diffusion that reflects WM integrity and directionality. More advanced diffusion modeling techniques such as free water-corrected DTI (FW-DTI), the Standard Model of Imaging (SMI), and Neurite Orientation Dispersion and Density Imaging (NODDI) address this limitation by quantifying the diffusion signals as arising from different tissue microenvironments, allowing for separation of free water, intra-neurite, and extra-neurite contributions. These techniques better preserve directionality metrics even in the presence of edema and may enhance tractography accuracy. In this study, we use multi-shell diffusion MRI data obtained from patients with meningioma brain tumors, specifically because meningiomas typically displace rather than infiltrate the surrounding WM--allowing us to isolate the effects of edema without confounding tumor invasion. We compared fractional anisotropy (FA from DTI), FW-FA (FW-DTI), P{square}(SMI), and orientation dispersion index (ODI from NODDI) in edematous and contralateral healthy WM regions and evaluated tractography performance across models as well. Our results show that NODDI, SMI, and FW-DTI provide improved characterization of WM within edema, yielding comparable diffusion metrics across regions and greater tract coverage compared to DTI. These improvements highlight the potential of advanced diffusion models for preoperative mapping. Future work will extend these methods to gliomas, where infiltrative tumor margins complicate WM detection, and translation into surgical navigation workflows.

Background & AimsThe increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective methods of histopathological assessment. Conventional histological approaches are time-consuming and rely on interpreters experience. Therefore, the results obtained may suffer from high variability and only offer coarse categorisation. In this study, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features for MASH/MASLD assessment. MethodsSegmentation was applied to H&E sections from 45 mice and 44 humans with MASH/MASLD. The method, which we named qHisto (quantitative histology), utilises the nnU-Net framework and quantifies key histological components of the MASH score, including macro- and microvesicular steatosis, fibrosis, inflammation, hepatocellular ballooning and glycogenated nuclei. Additionally, we characterized the tissue using novel features that are inaccessible through manual histology, such as the distribution of fat droplet sizes, aspect ratio of nuclei and heatmaps. ResultsqHisto parameters showed strong positive correlations with conventional histology scores (fat area R=0.91, inflammation density R=0.7, ballooning density R=0.49) and also with quantitative magnetic resonance imaging (fat area vs. hepatic fat fraction R=0.87). Our novel scores showed that deformation of nuclei is driven by large fat droplets rather than the overall amount of fat. ConclusionsA key advantage of our method is spatially resolved, precise histological quantification. These features provide a finely resolved assessment of disease severity than conventional categorical scoring. By automating time-consuming and repetitive readouts, qHisto improves standardisation and reproducibility of MASH/MASLD feature quantification and provides scalable, slide-wide readouts that can support histopathologists and enhance clinical assessment and therapeutic development. Impact and ImplicationsThe proposed method provides an objective, automatic tool for comprehensive, histological liver analysis of MASH/MASLD, which can be extended to other diseases and organs. By offering classic and novel quantitative parameters and scores, our method could support histologists in their daily routines and provide researchers with further insight into steatotic liver diseases.

Susceptibility weighted magnetic resonance imaging (MRI) is sensitive to the local concentration of iron and myelin. Here, we describe a robust image processing pipeline for quantitative susceptibility mapping (QSM) and R2* mapping of fixed post-mortem, whole-brain data. Using this pipeline, we compare the resulting quantitative maps in brains from patients with amyotrophic lateral sclerosis (ALS) and controls, with validation against iron and myelin histology. Twelve post-mortem brains were scanned with a multi-echo gradient echo sequence at 7T, from which susceptibility and R2* maps were generated. Semi-quantitative histological analysis for ferritin (the principal iron storage protein) and myelin proteolipid protein was performed in the primary motor, anterior cingulate and visual cortices. Magnetic susceptibility and R2* values in primary motor cortex were higher in ALS compared to control brains. Magnetic susceptibility and R2* showed positive correlations with both myelin and ferritin estimates from histology. Four out of nine ALS brains exhibited clearly visible hyperintense susceptibility and R2* values in the primary motor cortex. Our results demonstrate the potential for MRI-histology studies in whole, fixed post-mortem brains to investigate the biophysical source of susceptibility weighted MRI signals in neurodegenerative diseases like ALS.

INTRODUCTIONElevated brain iron levels are common in Alzheimers disease (AD). Quantitative Susceptibility Mapping (QSM) is an advanced MRI technique for assessing iron accumulation. The optimized QSM at 7 Tesla (7T) MRI may further improve the sensitivity to detect subtle susceptibility changes in AD. METHODSWe optimized a QSM processing pipeline for 7T MRI by systematically comparing multiple reconstruction algorithms. Evaluation criteria included image quality, artifact suppression, and anatomical clarity. The finalized pipeline was applied to individuals with AD and healthy controls (HCs). RESULTSThe results revealed significantly elevated magnetic susceptibility values in the globus pallidus and dentate nucleus of the AD group compared to HCs. These findings were confirmed through both visual inspection and quantitative analysis of high-resolution QSM maps. DISCUSSIONOur results highlight the importance of optimizing QSM pipelines at 7T for accurate susceptibility quantification. We identified an optimal pipeline suitable for future applications in patients with AD and other neurological conditions.

BackgroundClinical intracranial vessel wall imaging (VWI) requires high spatial resolution leading to long scan times and artifacts. PurposeTo accelerate standard-of-care (SOC) 3D T1-weighted variable-flip-angle turbo-spin-echo (VFA-TSE) sequence with parallel imaging (Generalized Autocalibrating Partially Parallel Acquisitions, GRAPPA) using compressed sensing (CS) or Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA, CAIPI) with either standard or large field-of-view (FOV) configurations to reduce scan time, artifacts and accommodate head sizes. Study TypeProspective study. SubjectsTen healthy volunteers. Field Strength/Sequence3 Telsa, 20-channel head coil, T1-weighted VFA-TSE AssessmentAccelerated sequences were compared to SOC GRAPPA (R=2), including standard FOV CAIPI (SFCAIPI, R=4), CS (SFCS7, R=7), and large FOV CS (LFCS7, R=7; LFCS10, R=10). Four neuroradiologists rated image quality (IQ) and signal-to-noise ratio (SNR) using a 4-point Likert scale. Scores of 3-4 were categorized as clinically interpretable. Lumen and wall diameters were measured. Statistical AnalysisDescriptive statistics are reported. McNemars test compared proportions of IQ- and SNR-based clinically interpretable scans between relevant sequences of interest. Inter- and intra-rater reliabilities were calculated with Fleiss Kappa and weighted Cohens Kappa, respectively. Lumen and wall diameters of the CS- and CAIPI-accelerated sequences were compared to SOC using paired t-tests. ResultsSFCAIPI showed the lowest mean IQ and SNR scores. SFCS7 showed no significant difference in the proportion of IQ-based clinically interpretable scans compared to SFGRAPPA. When testing FOV, LFCS7 (35/40 scans; time of acquisition (TA)=3:45) showed a significantly higher proportion of IQ-based clinically interpretable scans compared to SFCS7 (27/40, p=0.03; TA=6:37). Upon increasing acceleration (R=10), there was no difference in the proportion of IQ-based clinically interpretable scans between LFCS7 and LFCS10 (36/40, p=0.65). Large FOV eliminated aliasing artifacts compared standard FOV (aliasing in 7 of 10 subjects). LFCS10 (TA=4:55) achieved a 50.6% reduction in TA relative to SFGRAPPA (TA=9:57). ConclusionLarge FOV CS VWI sequence with 10x acceleration achieved a 50.6% reduction in scan time while delivering image quality comparable to SOC standard FOV GRAPPA.

PurposeDemonstrating quantitative multi-parametric mapping in the placenta with combined T2*-diffusion MRI at low-field (0.55T). MethodsWe present 57 placental MRI scans performed on a commercially available 0.55T scanner. We acquired the images using a combined T2*-diffusion technique scan that simultaneously acquires multiple diffusion preparations and echo times. We processed the data to produce quantitative T2* and diffusivity maps using a combined T2*-ADC model. We compared the derived quantitative parameters across gestation in healthy controls and a cohort of clinical cases. ResultsQuantitative parameter maps closely resemble those from previous experiments at higher field strength, with similar trends in T2* and ADC against gestational age observed. ConclusionCombined T2*-diffusion placental MRI is reliably achievable at 0.55T. The advantages of lower field strength - such as cost, ease of deployment, increased accessibility and patient comfort due to the wider bore, and increased T2* for larger dynamic ranges - can support the widespread roll out of placental MRI as an adjunct to ultrasound during pregnancy.

Cardiac magnetic resonance imaging (CMR) provides many cardiac functional insights. The reliance of standard cine CMR upon breath holds is not feasible for some patients. Its process of combining multiple heartbeats is unsuited to arrhythmias. Real-time cine methods sidestep these problems but can introduce respiratory displacement of the heart. To aid CMR acquisitions during breathing, we developed post-processing software to diminish the effects of respiratory displacement of the heart. It uses principal component analysis to resolve respiratory motions from cardiac cycles in the dynamic image. The software groups heartbeats from expiration and inspiration to decrease the appearance of respiratory motion. The effects of respiratory motion and such motion correction were evaluated on short-axis views (acquired with compressed sensing) of 11 healthy subjects and 8 cardiac patients. The smallest correlation coefficients between end-systolic frames of the original dynamic scans averaged 0.79. After segregation of cardiac cycles by respiratory phase, the mean correlation coefficients between cardiac cycles were 0.94 {+/-} 0.03 at end-expiration and 0.90 {+/-} 0.08 at end-inspiration. The improvements in correlation coefficients were significant in paired t-tests, i.e., P [&le;] 0.01 for healthy subjects and P [&le;] 0.001 for heart patients at end-expiration. Two expert cardiothoracic radiologists, blinded to the processing, assessed the dynamic images in terms of blood-myocardial contrast, endocardial interface definition, and motion artifacts. Clinical assessment preferred cardiac cycles during end-expiration, which maintained or enhanced scores in 90% of healthy subjects and 83% of the heart patients. Performance remained high in a case of arrhythmia and irregular breathing. Heartbeats collected from end-expiration reliably mitigated respiratory motion when the new software was applied to DICOM files from real-time acquisitions.

In this work, we demonstrate the sodium magnetic resonance imaging (MRI) capabilities of a three-dimensional (3D) dual-echo ultrashort echo time (UTE) sequence with a novel rosette petal trajectory (PETALUTE), in comparison to the 3D density-adapted (DA) radial spokes UTE sequence. We scanned five healthy subjects using a 3D dual-echo PETALUTE acquisition and two comparable implementations of 3D DA-radial spokes acquisitions, one matching the number of k-space projections (Radial - Matched Spokes) and the other matching the total number of samples (Radial - Matched Samples) acquired in k-space. The PETALUTE acquisition enabled equivalent sodium quantification in articular cartilage volumes of interest (168.8 {+/-} 29.9 mM) to those derived from the 3D radial acquisitions (171.62 {+/-} 28.7 mM and 149.8 {+/-} 22.2 mM, respectively). We achieved a 41% shorter scan time of 2:06 for 3D PETALUTE, compared to 3:36 for 3D radial acquisitions. We also evaluated the feasibility of further acceleration of the PETALUTE sequence through retrospective compressed sensing with 2x and 4x acceleration of the first echo and showed structural similarity of 0.89 {+/-} 0.03 and 0.87 {+/-} 0.03 when compared to non-retrospectively accelerated reconstruction. Together, these results demonstrate improved scan time with equivalent performance of the PETALUTE sequence compared to the 3D DA-radial sequence for sodium MRI of articular cartilage.

Brain tissue microstructure is characterized by heterogeneous diffusivity and transversal relaxation rates. Standard diffusion MRI (dMRI) is acquired using a single echo time (TE) and only provides information about heterogeneous diffusivity in the underlying tissue. Combined relaxation diffusion MRI (rdMR) integrates dMRI with multiple TEs to probe the coupling between relaxation rate and diffusivity. This work introduces a method to model rdMRI data signals by characterizing the apparent relaxation rate related to dMRI with different b-values. The proposed approach can extrapolate dMRI signals to ultra-long or ultra-short TEs to increase or reduce signals from intra-neurite water to improve the characterization of neurite microstructure without solving multi-compartment models. The performance of the proposed method was examined using an in vivo dataset acquired from a clinical scanner to estimate neurite sizes.

The pathology in Duchenne muscular dystrophy (DMD) is characterized by degenerating muscle fibers, inflammation, fibro-fatty infiltrate, and edema, and these pathological processes replace normal healthy muscle tissue. The mdx mouse model is one of the most commonly used preclinical models to study DMD. Mounting evidence has emerged illustrating that muscle disease progression varies considerably in mdx mice, with inter-animal differences as well as intra-muscular differences in pathology in individual mdx mice. This variation is important to consider when conducting assessments of drug efficacy and in longitudinal studies. Magnetic resonance imaging (MRI) is a non-invasive method that can be used qualitatively or quantitatively to measure muscle disease progression in the clinic and in preclinical models. Although MR imaging is highly sensitive, image acquisition and analysis can be time intensive. The purpose of this study was to develop a semi-automated muscle segmentation and quantitation pipeline that can quickly and accurately estimate muscle disease severity in mice. Herein, we show that the newly developed segmentation tool accurately divides muscle. We show that measures of skew and interdecile range based on segmentation sufficiently estimate muscle disease severity in healthy wildtype and diseased mdx mice. Moreover, the semi-automated pipeline reduced analysis time by nearly 10-fold. Use of this rapid, non-invasive, semi-automated MR imaging and analysis pipeline has the potential to transform preclinical studies, allowing for pre-screening of dystrophic mice prior to study enrollment to ensure more uniform muscle disease pathology across treatment groups, improving study outcomes.

PurposeTo evaluate the effectiveness of the MR-MinMo head stabilisation device in mitigating motion artifacts during long-duration, high-resolution 7T MRI scans, with and without retrospective motion correction. MethodsThe MR-MinMo was tested on 7 paediatric and 12 adult healthy volunteers using 0.6mm isotropic, 3D Multi-Echo Gradient Echo (ME-GRE) scans. A [~]10-minute ME-GRE scan (linear sampling acceleration factor 2x2) and a [~]20-minute scan (DISORDER sampling acceleration factor 1.4x1.4) were obtained. Both scans were acquired with and without the MR-MinMo in a 2x2 factorial study design. Qualitative and quantitative assessment of image quality of the first echo image was obtained via visual inspection and the normalized gradient squared (NGS) metric respectively. A repeated measures ANOVA was used to determine individual and combined effects of the device and DISORDER images with retrospective motion correction on NGS values, with age as a binary factor (paediatric/adult). T2* maps were generated and the standard deviation of white matter R2*(=1/T2*) values assessed across conditions. ResultsThe MR-MinMo significantly reduced motion artifacts both visually and in terms of NGS scores, particularly in paediatric volunteers. There was a significant interaction between MR-MinMo and DISORDER motion correction, suggesting that MR-MinMo improved retrospective motion correction. T2* maps demonstrated improved visual appearance and reduced WM variance with the MR-MinMo. ConclusionThe MR-MinMo can improve image quality via motion reduction in high-resolution 7T scans By keeping motion within a correctable regime, the device can also improve the performance of retrospective motion correction methods.

Magnetic Resonance Relaxometry is a quantitative MRI-based technique able to estimate tissue relaxation times T1 and T2. This approach allows increasing the MRI diagnostic accuracy mostly in case of brain neoplasia or neurodegenerative disorders in human medicine. However, few reports are available on the application of this technique in the clinical field of veterinary medicine. For this reason, in this work, we developed a relaxometry based approach on experimentally induced brain hemorrhages on rabbits. Specifically, the methodology is based on a hierarchical clustering procedure driven by the T1 relaxometry signals from a set of regions of interest selected on the T2 map. The approach is multivariate since it combines both T1 and T2 information and allows the diagnosis at the subject level by comparing "suspected" pathological regions with healthy homologous ones within the same brain. To validate the proposed technique, the scanned brains underwent histopathological analyses to estimate the performance of the proposed classifier in terms of Receiver Operator Curve analyses. The results showed that, in terms of identification of the lesion and its contours, the proposed approach resulted accurate and outperformed the standard techniques based on T1w and T2w images. Finally, since the proposed protocol in terms of the adopted scanner, sequences, and analysis tools, is suitable for the clinical practice, it can be potentially validated through large-scale multi-center clinical studies.

Breast cancer screening is necessary to reduce mortality due to undetected breast cancer. Current methods have limitations, and as a result many women forego regular screening. Magnetic resonance imaging (MRI) can overcome most of these limitations, but access to conventional MRI is not widely available for routine annual screening. Here, we used an MRI scanner operating at ultra-low field (ULF) to image the left breasts of 11 women (mean age, 35 years {+/-}13 years) in the prone position. Three breast radiologists reviewed the imaging and were able to discern the breast outline and distinguish fibroglandular tissue (FGT) from intramammary adipose tissue. Additionally, the expert readers agreed on their assessment of the breast tissue pattern including fatty, scattered FGT, heterogeneous FGT, and extreme FGT. This preliminary work demonstrates that ULF breast MRI is feasible and may be a potential option for comfortable, widely deployable, and low-cost breast cancer diagnosis and screening.