Magnetic Resonance Imaging

Background and PurposeWhite matter lesions are common imaging biomarkers associated with aging and neurodegenerative diseases, yet their underlying pathology remains unclear due to limitations in imaging-based characterization. We aim to develop and validate a comprehensive workflow enabling precise MRI-guided histological sampling of white matter lesions to bridge neuroimaging and neuropathology. MethodsWe establish a workflow integrating agarose-saccharose brain embedding, ultra-high field 7T MRI acquisition, reusable 3D-printed cutting guides, and semi-automated MRI-blockface alignment. Postmortem brains are stabilized in the embedding medium and scanned using optimized MRI protocols. Coronal sectioning is guided by standardized 3D-printed cutting guides, and knife traces are digitally matched to MRI planes. White matter lesions are segmented on MRI and aligned for histopathological sampling. This approach is validated in over 100 postmortem human brains. ResultsThe workflow enables reproducible brain sectioning, minimizes imaging artifacts, and achieves precise spatial alignment between MRI and histology. Consistent, high-resolution MRI data facilitated accurate lesion detection and sampling. The use of standardized cutting guides and alignment protocols reduce variability and improve efficiency. ConclusionsOur cost-effective, scalable workflow reliably links neuroimaging findings with histological analysis, enhancing the understanding of white matter lesion pathology. This framework holds significant potential for advancing translational research in aging and neurodegenerative diseases.

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.

PurposeCerebral blood flow (CBF) is commonly measured by pseudo-continuous arterial spin labeling (PCASL) in human research, but recent advancements in methodology have limited data reuse. The object of this work is to harmonize two distinct PCASL techniques within a cohort with a wide range of CBF values. MethodsParticipants had two PCASL sequences collected within a single session: a single post-label delay sequence with a 2D echo-planar imaging (EPI) readout, "CBF2D,1PLD", and a five post-label delay sequence with gradient and spin echo (GRASE) 3D readout, "CBF3D,5PLD". Linear regression modeling to impute CBF3D,5PLD from CBF2D,1PLD, hemoglobin, and age were assessed within gray matter (GM) and white matter (WM) using leave-one-out cross-validation for prediction errors and confidence intervals. Within-subject coefficient of variation (wsCV) and inter-class correlation coefficient (ICC) were calculated using CBF3D,5PLD imputed vs. measured as pseudo test-retest pairs. ResultsFifty participants, ages 8-45 (median 25) years, had usable CBF3D,5PLD and CBF2D,1PLD, including 17 participants with sickle cell disease (SCD), who were matched by age (p = 0.90) and sex (p = 0.16) to those without SCD. A multiple linear regression model including hemoglobin and age fit GM CBF (R2adj. = 0.82; for WM CBF R2adj. = 0.78). The wsCV for CBF3D,5PLD was 9.1% for GM, 11.3% for WM. ICC was 0.89 for GM and 0.87 for WM. Models without age or hemoglobin fit slightly worse. ConclusionOur study demonstrates feasibility to impute 3D-GRASE multi-PLD CBF from a 2D-EPI single-PLD technique, which promotes data sharing and harmonization.

Peritumoral vasogenic edema of the brain is a major confounding factor for diffusion MRI tractography. Excessive fluids accumulated in edematous white matter decrease anisotropy of water self-diffusion which affects tracking algorithms. We address this hurdle with ODF-Fingerprinting (ODF-FP) -- a dictionary-based fiber reconstruction algorithm that accommodates variability of neural tissue. By adding a regularization term to the ODF-FP matching formula, we boost diffusion anisotropy to improve white matter fiber identification in edematous regions.

BackgroundMyocardial T1 and T2 mapping provide a non-invasive quantitative assessment of cardiac tissue. While established at 1.5-T and 3.0-T MRI, the mapping performance and reference values at 5.0-T MRI remain to be investigated. This study aims to evaluate the feasibility and establish reference values for cardiac T1 and T2 mapping at 5.0-T MRI in healthy subjects. MethodsIn this prospective study, 50 healthy volunteers underwent cardiac MRI at both 3.0-T and 5.0-T MRI systems from February to April 2025. Mapping protocols included MOdified Look Locker Inversion recovery (MOLLI, T1), T2-prepared gradient-echo (T2-prep-GRE, T2), and a simultaneous multi-parametric mapping technique (Multimap, T1 and T2). At 3.0-T MRI, both balanced steady-state free precession (bSSFP) and fast low-angle shot (FLASH) readouts were employed, while only the FLASH readout was used at 5.0-T MRI. Intra-observer, inter-observer, and scan-rescan reproducibility were assessed. Statistical analyses included coefficient of variation (CoV), intraclass correlation coefficient (ICC), and Bland-Altman analysis. ResultsAll techniques at 5.0-T MRI yielded high-quality, artifact-free images. Native T1 values were significantly higher at 5.0 T than at 3.0-T MRI (MOLLI: 1452.9 {+/-} 33.2 ms vs. 1305.9 {+/-} 38.3 ms, P < 0.0001), while T2 values were significantly lower (T2-prep-GRE: 37.53 {+/-} 1.74 ms vs. 43.97 {+/-} 2.95 ms, P < 0.0001). Scan-rescan reproducibility at 5.0 T (ICC: 0.87-0.92; CoV: 2.41%-3.25%) was comparable to 3.0-T MRI. The measurement precision of 5.0 T was higher than 3.0-T MRI with FLASH readout and slightly inferior to bSSFP-based techniques. Multimap achieved efficient, simultaneous T1 and T2 quantification with acceptable reproducibility at 5.0-T MRI. ConclusionMyocardial T1 and T2 mapping at 5.0-T MRI are reliable and reproducible in healthy individuals, offering reference values for normal myocardium at this field strength. The good measurement reproducibility and precision of 5.0-T cardiac mapping support its clinical potential for myocardial tissue characterization.

PurposeNeonatal imaging is particularly challenging because newborns have a high likelihood of head motion, which can degrade image quality and complicate interpretation. Improving MRI brain image quality may help reduce diagnostic uncertainty and facilitate the nuanced assessment of early myelinating structures in the neonatal brain. Although deep learning reconstruction algorithms designed to improve MRI image quality have been evaluated in pediatric imaging, they have not been specifically studied in exclusively neonatal populations. We sought to evaluate image quality improvement through the employment of a deep learning reconstruction algorithm in neonatal brain imaging. Methods3D T1-weighted brain MRIs were obtained in 15 neonates. A deep-learning reconstruction algorithm was applied to the image sets using low, medium, and high levels of denoising. Three radiologists qualitatively rated image quality (signal-to-noise ratio, presence of artifacts, and overall clarity) on a 4-point scale of eight early myelinating structures. Objective apparent signal-to-noise ratio (aSNR) and apparent contrast-to-noise ratio (aCNR), based on signal intensities of white-and gray-matter, was measured across all three denoising levels. ResultsEvaluation by radiologists indicated an overall increase in all image quality categories and increased conspicuity of the early myelinating structures as the level of denoising increased. Objective aSNR and aCNR values also increased progressively with denoising, with significant differences observed for nearly all pairwise comparisons. ConclusionOur findings suggest that the use of the proposed deep learning reconstruction algorithm improves image quality in 3D T1-weighted neonatal brain MRIs at 3T.

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 aim of this study was to evaluate the contributions of age, sex, and MRI vendor to variance in Diffusion Tensor Imaging (DTI) metrics, with a focus on understanding the impact of these factors in large-scale healthy brain datasets. A dataset of 2,700 DTI scans from healthy controls across multiple sites and MRI vendors was analyzed. The DTI scalar metrics fractional anisotropy (FA) and mean diffusivity (MD) were processed and the influence of age, sex, vendor, and brain atlas selection were determined. A statistical analysis was conducted and revealed significant (p<0.05) age-related differences in DTI metrics, with older participants showing reduced FA and increased MD, in line with known microstructural changes. Sex differences were observed, with females exhibiting slightly higher FA and lower MD in certain brain regions. Vendor variability was also noted, with all three MRI vendors showing significant differences in FA with Siemens machines typically exhibiting higher FA values and GE machines lower FA values (i.e. FASiemens > FAPhilips > FAGE). Atlas selection also highlighted some specific ROI behaviour (e.g. tapetum of the corpus callosum) as one of the most significant regions of interest (ROIs) in the JHU-Tracts atlas that demonstrated a large amount of deterioration with age, particularly in females. These findings emphasize the need to account for biological factors such as age and sex, as well as technical factors like ROI selection and MRI vendor, when interpreting DTI data. The results demonstrate the potential of large-scale, multi-vendor datasets to uncover meaningful biological trends, while also addressing the challenges of scanner-specific variability. Although previous work has shown sex and age differences, this is the first large scale DTI analysis that has included age, sex, and MRI vendor as sources of variance in one model.

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.

Coronary artery disease increases risk of cognitive decline and stroke and is associated with white matter alterations. However, the biological basis of these changes remains unclear. Myelin content and iron deposition are crucial measures of white matter health and can be measured with quantitative MRI. This study investigated whether myelin and iron alterations occur in coronary artery disease, and their relationship with cognition. In this cross-sectional study, 46 individuals with coronary artery disease and 40 healthy controls aged > 50 years, with normal cognition underwent 3T MRI and cognitive assessments. Quantitative MRI metrics (susceptibility, magnetization transfer saturation, R2* and R1 relaxation rates) were calculated in the border zones between adjacent arterial territories (watershed regions) and in the areas outside these borders (non-watershed regions). Relative to controls, the coronary artery disease group showed lower myelin and higher iron content, as measured by lower magnetization transfer saturation and R1, and higher susceptibility specifically in watershed regions. Importantly, these microstructural alterations were associated with poorer cognitive performance in the coronary artery disease group with lower magnetization transfer and R1related to poorer global cognition and with higher magnetic susceptibility with poorer verbal memory. These findings suggest that coronary artery disease is associated with demyelination and iron deposition in white matter, most prominently in watershed regions, which are known for their susceptibility to stroke. The association of these microstructural alterations with cognition highlights the role of white matter as a key vulnerable region and a promising focus for future mechanistic and therapeutic studies.

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.

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.