Magnetic Resonance in Medicine

Purpose: To quantify the contributions of signal-to-noise ratio (SNR) and velocity-to-encoding ratio (v/VENC) to velocity uncertainty in phase-contrast (PC) MRI and to develop a framework for in vivo voxel-wise uncertainty estimation. Methods: Through-plane 2D PC-MRI of the ascending aorta was acquired using multiple velocity encodings (150, 200, 300 cm/s) and flip angles (0, 5, 15, 20 degrees) to vary v/VENC and SNR. Voxel-wise SNR and velocity uncertainty maps were generated using empirically calibrated phase-noise modeling. Phase-resolved subject-level analyses were performed to quantify the relative contributions of SNR and |v|/VENC to percent velocity uncertainty (%unc). Uncertainty was propagated to flow, stroke volume (SV), and cardiac output (CO). Results: Velocity uncertainty varied substantially across the cardiac cycle and depended on both SNR and |v|/VENC. Across cardiac phases, |v|/VENC accounted for most explained variance in %unc (partial R2=0.666), while SNR provided a smaller but meaningful contribution (partial R2=0.287; full R2=0.909). Near peak systole, SNR contributed more strongly while overall uncertainty remained low. In contrast, diastolic %unc became unstable as velocity approached zero. These effects were most pronounced at low |v|/VENC, where higher VENC settings increased uncertainty despite similar SNR. SV uncertainty ranged from 0.27% to 1.07% across VENCxFA protocols. Conclusion: Velocity uncertainty in PC-MRI depends on both SNR and VENC adequacy in a physiologically phase-dependent manner. Relative uncertainty may become inadequate for precise quantification in low-flow applications, such as diastolic regurgitant jets, despite adequate SNR. Spatiotemporal uncertainty mapping provides a framework for uncertainty-aware PC-MRI acquisition and interpretation.

Purpose: To develop and evaluate a flexible, software-defined radar platform for contactless, vendor-independent motion detection and correction in MRI. Methods: A continuous-wave (CW) Doppler radar was implemented using a software-defined radio and the open-source GNU Radio framework. The system was deployed inside a 1.5T MRI scanner and synchronized with MRI acquisitions. We evaluated the performance in a custom-developed internal motion phantom and in healthy volunteers to track respiration and bulk motion. The radar-derived signal was validated against cine MRI and used to demonstrate both retrospective and prospective motion management techniques in phantom and in healthy volunteers. Results: The radar provided robust motion signals that correlated strongly with image-based ground truth signals in both phantom and volunteer experiments. Signal characteristics were found to be frequency-dependent, enabling optimization for different motion regimes. Retrospective correction of free-breathing abdominal data using the radar signal effectively suppressed respiratory artifacts, achieving image quality comparable to a self-gating approach. Prospective triggering successfully reduced motion artifacts in the phantom study. The system also reliably detected sporadic events such as swallowing during neck imaging. Conclusion: Software-defined radar was demonstrated to be an effective platform for both prospective and retrospective motion correction. Its independence from the MRI system, ultra-wide band capabilities, and body-region versatility enable the adaptation of the technique for a wide range of imaging applications and protocols.

Diffusion MRI simulations based on realistic tissue microstructure provide a means to validate biophysical models and optimize acquisition protocols, but their computational cost restricts most studies to domains far smaller than a clinical voxel. The objective of this study was to develop an automated and scalable framework that converts whole-slide histology into diffusion MRI simulations at clinically relevant spatial scales while remaining feasible on standard workstation hardware. We present an end-to-end pipeline integrating two-dimensional whole-slide cell segmentation, mesh generation, and finite element Bloch-Torrey simulation. To enable simulations at large spatial scales without prohibitive memory growth, we introduce a subdomain tiling strategy in which the tissue domain is partitioned into extended subdomains simulated independently under no-flux boundary conditions. Signals are aggregated only from the central regions of each subdomain to minimize boundary artifacts. For an 800 {micro}m x 800 {micro}m histology-based domain, the aggregated signal differed by 0.07% from the corresponding full-domain finite element simulation while reducing wall-clock time from several days to hours and maintaining bounded memory usage independent of global domain size. When applied to a 2016 {micro}m x 2016 {micro}m heterogeneous region approximating the in-plane dimensions of a clinical voxel, the apparent diffusion coefficient obtained from the full domain differed from values computed in smaller dense and sparse subregions, demonstrating the influence of structural heterogeneity at clinically relevant scales on derived diffusion metrics. The proposed framework establishes an automated and memory-stable approach for generating diffusion MRI simulations directly from routine histology.

Multi-site MRI studies in preclinical neuroimaging are emerging, but unlike in human studies, characterization of inter-scanner variability remains limited. In this study, we assessed intra- and inter-scanner variability between two similarly equipped 7 Tesla MRI scanners using a phantom and ex vivo mouse brain specimens. Diffusion-weighted imaging revealed slight differences in gradient amplitudes between the scanners, while estimated apparent diffusion coefficient (ADC) values showed a coefficient of variation below 1.5% and inter-scanner differences below 2% near the magnet center. Volumetric analysis based on proton density-weighted images showed negligible intra-scanner differences across sessions, while inter-scanner volumetric differences were mostly less than 2% and spatially non-uniform across the brain. Quantitative maps of R1, R2*, and MTsat showed inter-scanner relative differences of less than 5%, 10%, and 20%, respectively, with white matter exhibiting greater variability than gray matter. These findings provide a foundation for future large-scale, multi-scanner preclinical neuroimaging studies.

Most high-field MRI scanners conduct imaging using phased-array coils, in which the signals received by an array of coil elements are combined for downstream processing. Optimally combining these signals requires knowledge of each coil's spatial sensitivity profile, which can be acquired from a volume coil with homogeneous sensitivity across the field-of-view. However, this approach is not often used on high-field MRI scanners, especially on non-clinical systems; therefore, this work uses an algorithm based on the singular-value decomposition (SVD), called SVD-B1, to estimate coil sensitivities directly from the array data itself. Images produced by SVD-B1 are devoid of wormhole artifacts and open-ended fringe lines commonly seen in more conventional reconstructions. Quantitative Susceptibility Maps (QSMs) produced using the algorithm were compared to those produced using other combination algorithms across clinically relevant regions of in-vivo and postmortem human brains. As progressive levels of simulated noise were added to the data, SVD-B1's QSMs were up to 3% (in-vivo) and 13% (postmortem) more consistent (as measured by their Intraclass Correlation Coefficient) than those from other algorithms. Additionally, these QSMs were up to 8.5% (in-vivo) and 36% (postmortem) more accurate than other QSMs with respect to a "single-coil" reference. A parallel imaging extension of SVD-B1, called SVD-B1 GRAPPA, achieved similar results for QSMs generated from progressively more accelerated acquisition data. These results show that SVD-B1 can improve the sensitivity of high-resolution QSM to subtle changes in fine-grained tissue structures (e.g., in neurodegenerative disease) and help reduce scan times in clinical settings where shorter scans are imperative.

Aceruloplasminemia (ACP) is a rare neurodegenerative disorder characterized by extreme cerebral iron overload and a shift towards larger iron aggregates, providing a unique possibility to study how iron aggregation shapes MRI contrast in vivo. We introduce a clinically feasible, multi-parametric quantitative MRI (qMRI) framework that combines quantitative susceptibility mapping (QSM), [Formula], and R2 to disentangle changes in total iron concentration from alterations in iron aggregation and its spatial organization at the cellular scale. Our biophysical model links the microstructure sensitive [Formula] ratio and the slope of the susceptibility-relaxation relationship (iron) to iron aggregation size and distribution. In a 3T qMRI study of three patients with ACP and three matched controls, we observe a marked increase in [Formula] and a pronounced increase of the [Formula]-QSM slope (iron: controls 154.09 {+/-} 52.89 s-1ppm-1; patients 296.68 {+/-} 57.18 s-1ppm-1; p = 0.016), consistent with enhanced iron aggregation and altered spatial organization. Model-based decomposition of transverse relaxation indicates that up to approximately 40% of the observed R2* elevation in ACP is attributable to changes in iron distribution beyond increased iron concentration alone. These findings establish a robust, translational qMRI approach for quantitative in vivo assessment of iron aggregation, revealing microstructural drivers of iron-related neurodegeneration that extend beyond bulk iron load.

PurposeTo develop a data-efficient deep learning framework for rapid reconstruction of highly accelerated 4D arterial spin labeling (ASL) magnetic resonance angiography (MRA) with robust generalization using extremely limited acquired data, addressing the challenges of prolonged acquisition and reconstruction time. MethodsA simulation-driven, few-shot transfer learning approach was adopted by leveraging publicly available 3D time-of-flight (TOF)-MRA data to generate realistic multi-coil complex-valued pseudo-ASL k-space datasets for large-scale pre-training. A 3D unrolled reconstruction network was trained on this simulated data using a histogram-weighted loss and subsequently extended to 4D using lightweight temporal fusion modules. Fine-tuning was performed using only two experimentally acquired 4D ASL-MRA datasets. The method was evaluated on retrospectively and prospectively undersampled Cartesian 4D ASL-MRA data acquired at 3T and compared with compressed sensing (CS) and locally low-rank (LLR) reconstructions. ResultsThe proposed method achieved superior reconstruction quality compared with CS and LLR, with improved vessel depiction, particularly in distal branches, and enhanced temporal fidelity. Quantitative evaluation demonstrated higher vessel-masked peak signal-to-noise ratio and structural similarity index measure, along with increased error entropy, indicating reduced noise and structured artifacts. The initial pre-trained model already outperformed conventional methods, while additional 4D fine-tuning further improved performance. Robust reconstruction was demonstrated in prospectively undersampled data and multi-slab acquisitions, enabling large-coverage, time-resolved angiography within clinically feasible scan times (4-6 min). ConclusionsSimulation-driven pre-training combined with few-shot fine-tuning enables accurate and rapid reconstruction of highly accelerated 4D ASL-MRA in data-limited settings. The proposed framework provides a practical pathway toward clinically feasible, non-contrast dynamic cerebrovascular imaging.

Hypoxia-targeted BOLD MRI is a novel technique, which probes oxygenation physiology in response to a controlled transient hypoxia stimulus. In glioblastoma, the signal response is spatially and temporally heterogeneous. We developed a voxel-wise temporal decomposition framework for hypoxia-targeted BOLD MRI that separates the arrival of responses, transition phases, and steady state during controlled isocapnic hypoxia. Twenty healthy controls underwent 3-T BOLD MRI during a double hypoxic step challenge to establish a normative reference. Three patients with newly diagnosed glioblastoma were included as proof-of-concept cases. For each voxel, we estimated response arrival delay (Delaycorr), delay to plateau, delay to return and an O2-normalized steady-state response (HypoxiaSS). Healthy-control maps were used to construct a voxel-wise normative atlas and, for HypoxiaSS, a global-response-adjusted model for patient deviation mapping. In healthy controls, HypoxiaSS showed lower supratentorial between-subject variabilitythan both whole-stimulus comparators (coefficient of variation: 1.77 versus 2.36 for Hypoxiaavg) and higher voxel-level step-to-step agreement (ICC(2,1): median 0.951 versus 0.792 for Hypoxiaavg). Whole-stimulus averaging exhibited a systematic step-2 signal amplification present in 19 of 20 subjects, which was absent from HypoxiaSS. Asingle global response scalar explained a median 72.5% of voxel-wise between-subject variance in HypoxiaSS. In proof-of-concept patient analyses, G-adjusted HypoxiaSS deviation maps and timing maps identified spatially coherentabnormalities that were partly complementary and extended beyond conventional MRI-defined lesion margins.Temporal decomposition improves the stability and interpretability of hypoxia-targeted BOLD MRI and provides a practical framework for population-referenced physiological mapping and atlas-based deviation mapping in glioblastoma.

Background: Understanding how transcranial direct current stimulation (tDCS) affects brain activity critically benefits from the use of functional magnetic resonance imaging (fMRI) to measure the related BOLD (blood-oxygenation-level-dependent) signal changes. However, the small magnetic fields induced by the stimulation currents can cause artifacts in the fMRI images that can compromise findings from concurrent tDCS-fMRI studies. Objective: To identify how the current-induced magnetic fields affect fMRI data and establish a quantitative framework for evaluating their impact on concurrent tDCS-fMRI measurements. Methods: Magnetic fields induced by currents inside the head and electrode cables were calculated for a standard motor cortex montage. Their effects on echo-planar images (EPI) were simulated based on a framework derived from MR physics first principles and validated using phantom experiments. The framework was applied to artificially induce artifacts related to the tDCS current flow in current-free fMRI time series from 5 participants. These were compared to active runs from the same participants where tDCS intensity was varied in a block design. Results: Currents in the electrode cables were the main contributors to the current flow-related artifacts in the EPI images, which occurred both locally by causing geometric distortions and remotely by affecting the dynamic update of the scanner demodulation frequency. The artificially induced fMRI activations corresponded well to those measured during real tDCS on the single-subject level for intensities of 2 mA and higher. Conclusion: The current-induced magnetic fields can cause intensity changes comparable to typical BOLD responses. Their impact on the statistical results depends on the chosen experimental design (electrode locations, cable paths, imaging parameters, fMRI paradigm). The simulation framework provides a principled approach to evaluate the impact of these artifacts during the design and data analyses of concurrent tDCS-fMRI studies.

Mitochondria are essential for cellular energy production and are particularly critical for brain development and function. Neurons rely predominantly on oxidative phosphorylation for energy production, rendering the brain highly vulnerable to mitochondrial dysfunction. Consequently, impaired mitochondrial function contributes to a broad spectrum of neurological and systemic disorders, making mitochondria attractive therapeutic targets. Despite this importance, there is currently no non-invasive, spatially resolved method to assess mitochondrial function in the intact living brain. Here, we establish a non-invasive functional MRI approach--4D Oxy-wavelet MRI--to probe in vivo mitochondrial electron transport chain (ETC) function in a spatially specific manner across the lifespan, from fetal to adult brains. This method employs a low-rank k-t sub-Nyquist acquisition strategy to achieve simultaneous structural and functional imaging with high spatial (78 m) and temporal ([~]14 ms) resolution, enabling motion-robust imaging in multi-fetal mouse pregnancies. Mitochondrial ETC function is interrogated by measuring oxygen homeostasis responses to brief hypoxic challenges, analyzed using computational time-frequency wavelet profiling. We validate this approach in mouse models of mitochondrial respiratory chain disease and late-onset Alzheimers disease, from in utero fetuses to adults, and demonstrate reproducibility and specificity using pharmacological hyperemia and ETC complex I inhibition. We further show parallel wavelet responses in placenta and fetal brain, enabling multi-organ interrogation of the placenta-brain axis. Finally, we present first-in-human feasibility data, supporting translational potential for non-invasive assessment of mitochondrial function in living brains across the lifespan.

Pediatric patients with epicardial cardiac implantable electronic devices (CIEDs) are frequently excluded from the Magnetic Resonance Imaging (MRI) primarily due to RF heating safety concerns. In this study we evaluate RF heating of two bipolar epicardial leads during MRI at 0.55 T and 1.5 T under different termination conditions. Our findings showed that the mean RF heating was significantly reduced at 0.55 T MRI compared to that at 1.5 T. Similarly, the RF heating at 0.55 T MRI was highest for full system whereas, during MRI at 1.5 T, the RF heating was highest for the capped abandoned lead, showing dependence of RF heating pattern on MRI field strength. While RF heating at both fields surpassed the safety limit, the capped abandoned leads at 1.5 T MRI showed significantly higher RF heating with temperature rise surpassing 50{degrees}C in some of the cases. These results highlight the difference in RF heating of bipolar epicardial leads compared to the previously reported findings for monopolar epicardial lead which showed smallest heating for capped abandoned lead at both field strengths. These findings emphasize the necessity of device-specific evaluations at each field-strength to inform clinical decision-making and expand MRI access for this vulnerable population.

Introduction: Structural neuroimaging relies on T1-weighted (T1w) magnetic resonance imaging (MRI) for brain morphometry, yet at 7 Tesla (7 T) transmit field (B1+) inhomogeneity remains a major source of bias. Although Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) improves the tissue contrast, residual B1+ effects may persist and may be exacerbated in aging or clinical populations, where anatomical and physiological factors further challenge image quality and preprocessing. The impact of B1+ inhomogeneity on automated quality assessment and morphometric statistical inference remains insufficiently understood. Methods: Submillimeter 7 T MP2RAGE brain acquisitions from carriers of a mitochondrial gene mutation (m.3243A>G) and controls were retrieved from previous studies. Image quality before and after B1+ inhomogeneity correction was assessed by multiple automated pipelines. Case-control morphometric studies, including regional volume and mean cortical thickness, were analyzed in both registration based and deep learning based segmentation frameworks. Changes in image quality metrics (IQMs) and morphometric statistical significance were evaluated to determine the impact of B1+ inhomogeneity correction. Results: Overall image quality rating and metrics sensitive to intensity non-uniformity and topological integrity consistently improved after B1+ inhomogeneity correction. However, its impact on morphometric statistical inferences was strongly method-dependent. Some pipelines showed redistribution of significant regions, whereas others predominantly demonstrated increased effects in sensitivity. Across methods, B1+ inhomogeneity correction altered the findings of morphometric analyses, particularly in cortical regions. Conclusion: Residual B1+ inhomogeneity at 7 T substantially influences both image quality control and morphometric evaluations. Current automated quality control approaches can hardly capture these effects reliably. B1+ inhomogeneity correction will not only improve intensity uniformity, but also change sensitivity of morphometric statistical inferences. To establish reliable morphometric biomarkers at UHF strengths, explicit B1+ correction and customized preprocessing are practically necessary and highly recommended.

PurposeTo apply voxel-wise tracer kinetic model selection, characterise the spatial distribution of best-fitting models across the brain, and evaluate whether model selection improves sensitivity for differentiating normal-appearing tissue from pathological tissue compared to the Patlak model. MethodsExtended Tofts, Patlak, and intravascular models were fit to DCE-MRI data from stroke survivors and controls, as well as simulated data. The best-fitting model was chosen for each voxel using the Akaike Information Criterion, and model selection Ktrans (estimates from the best-fitting model for each voxel) compared to Patlak model Ktrans. ResultsIn simulated data, the Extended Tofts model was best-fitting at Ktrans>10-3 min-1, where the Patlak model systematically underestimated Ktrans. Patlak was optimal at Ktrans between 10-4-10-3 min-1, where Extended Tofts estimates had greater variability. The intravascular model was selected for Ktrans[~]10-4 min-1. The Patlak model was chosen in most control voxels. In chronic stroke, the Extended Tofts model was preferred in most cortical and white matter hyperintensity voxels, while the Patlak model was selected in most deep grey matter and normal-appearing white matter voxels. Model selection Ktrans estimates were significantly greater than Patlak estimates in the cortex and white matter hyperintensities, with greater inter-patient variability, likely reflecting biological variability in blood-brain barrier leakage resulting from stroke. ConclusionVoxel-wise model selection may provide more accurate estimates of a wider range of Ktrans values than any single model, revealing greater differences between normal and pathological tissue and offering a more sensitive and physiologically appropriate framework for DCE-MRI analysis of blood-brain barrier dysfunction.

BackgroundDynamic contrast-enhanced (DCE) MRI has the potential to be a useful tool for non-invasively assessing renal haemodynamics and function, however insufficient standardisation and difficulties in post-processing remain barriers to clinical translation. PurposeTo develop expert consensus-based technical recommendations for performing renal DCE-MRI in humans, relating to aspects of patient preparation, MRI hardware and acquisition parameters, and data analysis. Study TypeSystematic consensus process using an approximation to the two-step modified Delphi method. PopulationNot applicable. Field Strength / Sequence1.5 T and 3 T / Renal gradient echo-based 3D DCE-MRI. AssessmentAn international panel of experts were recruited and surveyed following a modified Delphi method to create consensus-based technical recommendations. Key areas for consensus were initially identified through a mixture of online and in-person discussions, and an initial survey round consisting of open- and close-ended questions. Consensus statements were formulated and iteratively refined to create the final recommendations. Statistical TestsConsensus was defined as [≥] 75% agreement in response (excluding abstentions), and clear preference was defined as [60-74]% agreement among the experts. Statements with [≥]40% abstentions were either excluded from subsequent survey rounds or recirculated as a modified statement. Results22 experts initially participated in the Delphi panel, of which 16 responded to the first survey. 15 panellists responded to all subsequent surveys. Out of 46 statements, 37 reached consensus and one showed clear preference. [≥]40% abstention was found in seven statements which were excluded from the final set of recommendations. Data conclusionThese recommendations provide a starting point for MRI centres worldwide wishing to perform renal DCE-MRI, contributing to the harmonisation of DCE-MRI scan protocols and facilitating clinical translation. These recommendations provide a practical minimum technical dataset for renal DCE-MRI acquisition and analysis to improve cross-site comparability and support responsible clinical translation.

The white matter (WM) cerebrovascular response remains poorly understood compared with grey matter (GM), partly due to technical challenges in perfusion quantification. Previous hypercapnia studies using BOLD MRI have reported reduced or negative WM cerebrovascular reactivity, but whether these findings reflect true reductions in cerebral blood flow (CBF) remains unclear. We used multi-delay pseudo-continuous arterial spin labeling (pCASL) to quantify hypercapnia-induced CBF changes ({Delta}CBF) while accounting for regional variability in arterial transit time. Twenty-five healthy young adults underwent MRI during normocapnia and hypercapnia (inhalation of a 4% CO2 gas mixture). Hypercapnia induced robust positive {Delta}CBF in cortical GM (26.7 {+/-} 13.5%), superficial WM (17.2 {+/-} 12.6%), periventricular regions (13.6 {+/-} 10.6%), and subcortical GM (25.7 {+/-} 14.1%) (all p < 0.0001). In contrast, deep WM exhibited a near-zero group-mean CBF response (1.0 {+/-} 8.9%, p = 0.57), with 10 of 25 participants demonstrating negative {Delta}CBF. Negative responses were consistently localized to the corona radiata, centrum semiovale, and optic radiation. Quality-control analyses showed that deep-WM {Delta}CBF estimates are robust at our long post-labeling delays, supporting the reliability of these findings. Across tissue compartments, higher baseline CBF was associated with reduced hypercapnic responsiveness, and deep-WM responses were strongly coupled with cortical GM responses across individuals. These results demonstrate that hypercapnia-induced perfusion responses are highly heterogeneous across tissue depths and provide evidence that negative CBF responses can occur in healthy deep WM. The findings challenge the assumption of uniformly positive cerebrovascular responses during hypercapnia and support a potential role for flow redistribution arising from regional differences in vascular resistance and reserve capacity.

Motivation: Quantitative assessment of neonatal internal capsule (IC) maturation remains largely reliant on qual- itative visual evaluation, limiting objectivity and scalability. Approach: We developed a fully automated 3D deep learning framework for anatomically detailed segmentation of IC subregions and PLIC myelin-related signal from structural T2-weighted MRI, trained on both high-resolution 7T and conventional 3T neonatal datasets. Volumetric and intensity-based metrics were derived, and developmental trajectories were modelled using postmenstrual age (PMA) and postnatal age (PNA), with normative modelling used to quantify individual deviations. Results: The pipeline achieved high segmentation accuracy across field strengths (Dice > 0.95, relative volume difference < 5%). IC metrics showed robust age-related changes, with volumetric measures increasing and intensity- based measures decreasing with PMA. PNA effects indicated prematurity-related modulation at equivalent maturational age. These patterns generalized to 3T, where normative modelling revealed significant deviations in preterm infants, particularly for myelin-related intensity measures. Conclusion: Structural T2-weighted MRI, combined with anatomically informed segmentation, enables quantitative and biologically meaningful assessment of neonatal IC maturation. This provides a scalable framework for studying early white matter development and supports potential clinical translation.

MICAFlow is a fully automated MRI preprocessing pipeline designed to translate advanced neuroimaging workflows from research into routine clinical practice. The pipeline emphasizes speed, robustness, and ease of use, focusing on structural and diffusion MRI. Key innovations include a Label-Augmented Modality-Agnostic Registration (LAMAReg) technique driven by deep learning segmentations for reliable cross-modal alignment, integration of state-of-the-art distortion corrections, and adherence to reproducible standards (Snakemake workflow, BIDSApp specifications). We describe the design of MICAFlow and evaluate its performance across heterogeneous datasets. First, accessibility: MICAFlow processes a multimodal MRI exam in minutes with clinically accessible hardware and without requiring GPU access, making it feasible for same-day clinical use. Second, registration accuracy: LAMAReg achieves cutting-edge multi-modal registration accuracy, yielding accurate alignment of diffusion MRI, FLAIR, and intra-subject T1-weighted images while remaining generally robust to common artifacts. Third, data reliability: Using identifiability, we show MICAFlow maintains consistent performance across diverse datasets, including subjects with pathology, and is closely comparable to contemporary pipelines. In sum, MICAFlows combination of machine learning and efficient workflows produces research-grade data quality with clinical-grade speed. This work demonstrates that advanced MRI preprocessing can be done fast and robustly, helping close the gap between research neuroimaging and broad clinical application of quantitative MRI techniques. The source code for MICAFlow is available here: https://github.com/MICA-MNI/micaflow, and for LAMAReg here: https://github.com/MICA-MNI/LAMAReg.

Purpose: Accurate assessment of the pressure gradient ({Delta}P) across aortic coarctation (CoA) is critical for determining disease severity and the need for intervention. Current non-invasive methods are unreliable, while invasive catheterization remains the clinical gold standard. This study evaluates a novel MRI acquisition strategy, 4D-FlowP, that simultaneously encodes blood velocity and acceleration to enable reliable non-invasive pressure gradient mapping in CoA. Methods: Patient-specific compliant aortic phantoms were created from clinical MRI data of two patients with CoA. Additional geometries were synthetically generated by increasing stenosis severity. Phantoms were studied in an MRI compatible flow loop under physiologically realistic flow and pressure conditions. Pressure gradients were estimated using conventional 4D-Flow MRI, 4D-FlowP, and fluid-structure interaction (FSI) simulations. Results were compared against ground-truth catheter-based measurements across multiple flow rates and stenosis severities. Results: Conventional 4D-Flow consistently underestimated {Delta}P (slope = 0.63, R2=0.75) relative to catheter measurements. In contrast, 4D-FlowP demonstrated substantially improved agreement (slope = 0.95, R2=0.75). FSI simulations showed the highest overall agreement with catheter-derived {Delta}P (slope = 1.14, R2=0.82). Scan times for 4D-FlowP were comparable to 4D-Flow (26 vs. 24 minutes). Conclusion: 4D-FlowP enables a more accurate MRI-based pressure gradient mapping in CoA than conventional 4D-Flow, when compared to ground truth catheter measurements. These findings support further in vivo evaluation of 4D-FlowP as a non-invasive alternative for functional assessment of CoA severity

Background: Cardiovascular magnetic resonance (CMR) quantification of the left ventricular (LV) volumes and ejection fraction (EF) typically involves manual segmentation of many short axis (SAx) and long axis (LAx) slices of the left ventricle. The scan time and the number of breath holds is proportional to the number of slices. We aimed to evaluate a geometric model of the left ventricle that could enable planimetry from a reduced number of slices. We sought to determine whether acceptable accuracy was retained for evaluating the End Diastolic Volume (EDV), End Systolic Volume (ESV), Stroke Volume (SV), and EF to provide a rapid and reliable clinical alternative. Methods: A cohort of 342 patients, median age: 54 (40 - 65) years, with full-stack CMR examinations was used. Nine geometrical combinations were evaluated: 3, 4 or 5 short axis slices and one of three LAx orientations (2-chamber, 3-chamber or 4-chamber) by retrospectively decimating the full-stack acquisition. LV volumes were calculated as a sum of trapezoidal approximations for apical and mid-cavity slices and a generalized prismoidal model at the base. The accuracy of the volume calculations was quantified against the full-stack reference for the EDV, ESV, SV, and EF using concordance correlation coefficient (CCC), two-way repeated measures ANOVA, pairwise tests, and Bayes factor log10(BF10) analysis. Results: The choice of the long axis (LAx) view was the most influential driver of accuracy (g2 = 0.104, for EDV), approximately 50 times more impactful than the number of SAx slices (g2 = 0.002, for EDV). Volumes calculated using the combination of 2-chamber LAx view and 5 SAx slices had the highest concordance with the full stack (CCC>0.90). While the estimated absolute volumes displayed a systematic negative bias, EF and SV remained highly robust due to bias cancellation. For a 2ch + 5 SAx protocol, EF bias was just 0.83% (LoA: -6.18 to 7.84%), with a minimum detectable change (MDC) of 7.01%, compared to 8.7% reported for expert human readers, suggesting strong concordance. Bayesian paired-samples t-tests yielded log10(BF10) = 6.42 in favor of 5 SAx over 3 SAx, constituting decisive evidence on the Jeffreys scale. The bias and limits of agreement (LoA) for stroke volume and ejection fraction were found to be lower than scan-rescan reproducibility in literature. Conclusion: This reduced-slice geometric model allows for reduced number of breath holds compared to a conventional full-stack CMR acquisition and provides an acceptable accuracy with bias less than scan-rescan variability.

Diffusion-weighted magnetic resonance imaging (dMRI)-based tractometry enables the quantification of white matter tissue properties in living humans while preserving anatomical specificity. Although tractometry is highly reproducible when the same scanner and acquisition protocol are used, its generalizability across scanners and protocols remains unclear. To address this gap, we performed a traveling-head experiment involving five subjects to evaluate tractometry across progressively different acquisition conditions, including multiple scanners, different scanner models, and two distinct protocols. Tractometry was performed for 20 major white matter tracts using diffusion tensor imaging metrics, neurite orientation dispersion and density imaging (NODDI) metrics, and a semi-quantitative ratio metric (T1w/b0). Generalizability across dataset pairs was quantified using the intraclass correlation coefficient (ICC). Tractometry showed consistently high ICCs when the scanner and protocol were identical; however, ICCs declined as differences in scanner model and acquisition protocol increased. Fractional anisotropy and orientation dispersion index retained relatively high ICCs across these comparisons, whereas other metrics showed marked declines when scanners or protocols differed. ComBat harmonization partially mitigated these declines, but ICCs did not reach the levels observed for datasets acquired using identical scanners and protocols. Finally, the minimum detectable change (MDC) for tractometry in datasets pooled across scanners and protocols varied by tract; for example, the optic radiation showed a lower MDC than the cingulum hippocampus. These findings highlight both the strengths and limitations of tractometry in multisite studies and highlight the importance of quantifying scanner- and protocol-dependent effects for specific metrics and tracts when interpreting measurements from heterogeneous datasets.