Magnetic Resonance in Medicine

ObjectivesConventional gadolinium-enhanced cardiac magnetic resonance imaging (MRI) typically evaluates myocardial tissues at a single post-contrast time point. In contrast, dynamic T1 mapping enables the estimation of contrast agent concentrations and subsequent pharmacokinetic modeling. This study compared a normal composite two-compartment model incorporating myocardial vascular components with the conventional Brix model. Materials and MethodsThis retrospective study included 107 participants who underwent dynamic T1 mapping at 2, 5, 9, and 15 min after contrast administration. Exclusion criteria included contraindications to MR imaging, acute coronary syndrome, pregnancy, an estimated glomerular filtration rate < 30 mL/min/1.73 m2, claustrophobia, and known allergy to gadolinium-based contrast medium. Contrast agent concentrations derived from MOLLI-based T1 maps were fitted using the Brix and composite pharmacokinetic models. Model performance was assessed using the residual sum of squares (RSS), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The myocardial blood fraction estimated by the composite model was compared with the extracellular volume (ECV). ResultsThe composite model exhibited significantly lower RSS, AIC, and BIC values than the Brix model (all p < 0.001). Absolute parameter estimation errors were reduced across all time points. The estimated myocardial blood fraction averaged 35.0% and demonstrated a positive correlation with the ECV (r = 0.61, p < 0.001). ConclusionsIn myocardial pharmacokinetic analysis using dynamic T1 maps, the composite model achieved superior fitting performance compared with the Brix model. Explicit incorporation of vascular kinetics improves the longitudinal characterization of contrast behavior and enhances quantitative assessment of myocardial tissue properties.

Diffuse correlation spectroscopy (DCS) is a promising technique for noninvasive measurement of blood flow, especially for cerebral blood flow where other noninvasive techniques have shortcomings. Conventional DCS often requires multiple simultaneous measurements to enhance the signal-to-noise ratio (SNR) especially when probing deep into the brain with large source-detector separations where photons are scarce. However, this limits scalability when using discrete optical detectors. This study demonstrates the application of the 500 x 500 single-photon avalanche diode (SPAD) array, SwissSPAD3, coupled with a custom field-programmable gate array (FPGA) design, which enables significant increases in SNR compared to conventional DCS systems. We validate the fiber-coupled SPAD camera system against a lab-standard CW-DCS system in two-layer liquid phantoms and in human measurements, and demonstrate robust blood-flow tracking at source-detector separations up to 3.25 cm. These results support SPAD-based parallel detection as a scalable route to improved deep-tissue DCS performance in humans.

Accurate quantification of arterial blood T2 can be useful for non-invasive assessment of blood oxygenation and blood-brain barrier (BBB) function. While arterial spin labeling (ASL) combined with multi-echo readouts offers a contrast-agent-free approach to map arterial blood T2, in vivo applications remain challenging due to rapid signal decay and low signal-to-noise ratio (SNR) at longer echo times (TEs), likely leading to overestimation of T2 values. We propose a novel temporal evolution acquisition based ASL (TEA-ASL) sequence incorporating an optimized variable refocusing flip angle (RFA) train to preserve signal across all TEs. Data were acquired on a 5T MRI system combining a pseudo-continuous ASL (pCASL) with the proposed TEA readout with 12 echo times (32-384 ms). The variable RFA scheme significantly improved signal stability across the echo train compared to conventional acquisition with constant RFAs. Accuracy and clinical feasibility of the proposed method was validated by simulations, phantom scans, in-vivo test/retest experiments and in a patient with middle cerebral artery stenosis. The proposed TEA-ASL technique provides robust arterial T2 mapping at ultra-high field, offering a promising tool for probing oxygenation-related hemodynamics and BBB-associated pathophysiology.

Sodium (23Na) magnetic resonance imaging (MRI) provides valuable metabolic information, but it is limited by a low signal-to-noise ratio (SNR) and long acquisition times. To overcome these challenges, we present a Deep Image Prior (DIP)-based framework that combines anatomically guided proton (1H) MRI and metabolically guided 23Na MRI denoising via a fused proton-sodium prior within a directional total variation (dTV) regularization scheme. The DIP-Fusion approach minimizes a variational loss function combining data fidelity, fused dTV regularization, gradient consistency, and bias-field correction to reconstruct sodium images. MRI data were acquired from healthy volunteers and breast cancer patients. Healthy datasets were retrospectively undersampled at multiple factors, and fully sampled scans served as the ground truth. Patient datasets acquired for clinical purposes were reconstructed using the baseline DIP and the proposed DIP-Fusion methods. Sodium images were reconstructed using sum-of-squares (SoS) and adaptive combined (ADC) coil combination methods. We evaluated reconstruction performance using quantitative image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean squared error (MSE), learned perceptual image patch similarity (LPIPS), feature similarity index (FSIM), and Laplacian focus. In healthy volunteers, DIP-Fusion outperformed state-of-the-art reconstruction techniques across all undersampling factors. In patient datasets, DIP-Fusion demonstrated superior performance compared with baseline DIP, achieving improved structural fidelity and sodium-specific signal preservation. These results demonstrate the potential for robust, highquality sodium MRI reconstruction under accelerated acquisition, which could lead to reduced scan times and enhanced clinical feasibility.

Chemical shift-encoded magnetic resonance imaging using high-resolved 3D Dixon techniques enables the non-invasive and radiation-free assessment of whole-body adipose tissue and ectopic fat distribution. Automatic deep learning-based segmentation of metabolically relevant adipose tissue compartments and ectopic fat deposits in parenchymal tissue is the most important image processing step for the quantification of adipose tissue volumes and ectopic fat percentages from whole-body imaging. This work presents a segmentation model dedicated to the segmentation of 19 metabolically relevant adipose tissue compartments and ectopic fat deposits from whole-body Dixon MRI. The trained segmentation model is available upon request. Related post-processing routines to compute volumes and fat percentages are publicly available: https://github.com/tobihaui/WholeBodyATQuantification.

PurposeTo develop and evaluate a novel double bowtie filter integrating a K-edge material layer with a conventional Teflon filter for pediatric spectral computed tomography (CT). The proposed design aims to enhance spectral signal-to-noise ratio (SNR) and spectral separation while maintaining radiation dose levels suitable for pediatric imaging. MethodsA simulation framework was set up and used to model a rapid kVp-switching CT system operating at 70/110 kVp with realistic tube power and geometry constraints. Pediatric phantoms of three sizes (100- 200 mm anterior-posterior width) were used to evaluate performance. Five accessible and safe filter materials-gadolinium (Gd), holmium (Ho), erbium (Er), silver (Ag), and tin (Sn)-were tested in combination with a Teflon bowtie. System performance was quantified using virtual monoenergetic image (VMI) SNR at 40 keV and 70 keV, and the area under the monoenergetic SNR curve (AUMC) as a comprehensive spectral image quality metric. Dose consistency with a traditional Teflon bowtie reference was enforced. ResultsThe Teflon + Gd configuration achieved the highest performance, improving AUMC by 47.5 % on average and up to 56 % for the largest phantom. VMI SNR increased by approximately 49 % at 40 keV and 42 % at 70 keV. ConclusionsThe double-bowtie concept substantially enhances spectral performance. The Teflon + Gd design provides a manufacturable, pediatric-optimized solution adaptable to kVp-switching and other spectral CT architectures, offering improved diagnostic quality at low dose levels.

Background and PurposeThe magnetic resonance imaging (MRI) access for patients with active and passive implants is limited by radiofrequency (RF) safety. The time-averaged root-mean-square RF field (B1+rms) and specific absorption rate (SAR) are being evaluated to monitor and control RF-induced heating near conductive metallic implants, such as deep brain stimulation (DBS) leads, during MRI. However, experimental methods to assess the relationship between RF power, B1+rms, and SAR are lacking for RF coils, metallic implants, and ionic solutions. Materials and MethodsA method is developed to evaluate the variation of RF power, B1+rms, and SAR with RF coils, metallic implants, and ionic solutions using phantoms consisting of water (H2O) and sodium chloride (NaCl) with four ionic concentrations (0, 1, 2, 3 %), four metallic wavelengths (0,{lambda} /2,{lambda} , 2{lambda}), two RF coils (body, head) transmit/receive (Tx/Rx) combinations, and five RF pulse flip angles (30{degrees}, 45{degrees}, 60{degrees}, 75{degrees}, 90{degrees}) in two B0 fields (1.5T and 3T). ResultsThe scanner-reported RF power and SAR varied with RF pulse sequences, RF coils, Tx/Rx, metallic implants, and ionic solutions, whereas B1+rms varied only with RF pulse sequences. The RF power, B1+rms, and SAR relationship depends on RF pulse sequences, RF coils, Tx/Rx, implant wavelengths, and ionic concentrations. SAR (whole-body, head) scaled with RF power by absorption ratios () variable with experimental conditions. ConclusionsB1+rms is insensitive to the presence and absence of conductive metallic implants and ionic solutions, implant wavelengths, ionic concentrations, RF coils, and Tx/Rx combinations. RF power must be monitored because scanner-reported SAR may vary unpredictably with experiments.

PurposeAqueous humor drains fluid from the eye not only via the conventional pathway through the trabecular meshwork and Schlemms canal, but also within the eye is known to occur via pathways through the posterior chamber and optic nerve to the cerebrospinal fluid (CSF) surrounding the optic nerve. The mechanism is poorly understood, and non-invasive method for evaluation in living humans has not been established. We previously showed that eye drops containing O-17-labeled water (H217O) distribute in the anterior chamber but not the vitreous. This study aimed to evaluate the distribution of H217O in the CSF along the optic nerve. MethodsFive ophthalmologically normal participants (20-31 years, all females) were selected from a previous prospective study based on 1H MR images of the eyes that included the optic nerve. They received eye drops of 10 mol% H217O in their right eye. Dynamic image time series was created by normalizing the signal of each 1H-T2WI by the pre-drop average signal. Region-of-interest analyses were performed for signal changes in the anterior chamber, vitreous, and CSF. ResultsIn the quantitative evaluation, the normalized intensity in the anterior chamber and CSF was significantly lower than that in the pre-drop signal (anterior chamber: 0.78 {+/-} 0.07, p < 0.005; CSF: 0.89 {+/-} 0.07, p < 0.05). No distribution was identified in the vitreous. Qualitatively, the distribution of H217O in the anterior chamber was detected in all five participants and in the CSF of four participants (80%). ConclusionH217O eye drops were distributed in the anterior chamber and CSF, but not in the vitreous. These findings suggest that the visualization of aqueous humor outflow, not via the Schlemms canal, may contribute to ocular fluid homeostasis, including the ocular glymphatic system.

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables non-invasive characterization of carotid atherosclerotic plaque. PurposeTo evaluate the performance and reproducibility of a simplified DCE-MRI quantification method for carotid plaque assessment. MethodsT1-weighted black-blood DCE-MRI of the carotid arteries at 3T was performed at baseline and after six months in patients with mild-to-moderate atherosclerotic lesions in a pilot placebo-controlled randomized trial evaluating the effects of low-dose (0.5mg daily) colchicine therapy on carotid plaque volume. DCE-MRI signal intensity was measured in manually drawn regions of interest in the plaque core, remote non-atherosclerotic vessel wall, and skeletal muscle. Peak signal intensities were normalized to skeletal muscle signal in the same slice. ResultsIn patients (n=28, median [interquartile range] age 72 [64-74] years, 36% female, n=13/15 colchicine/placebo), normalized peak signal intensity was higher in the plaque core than in the remote vessel wall at both baseline (3.5 [2.3-4.1] vs 2.1 [1.7-2.5], p<0.001) and follow-up (3.2 [2.5-4.4] vs 2.0 [1.7-2.5], p<0.001). Measurements did not differ between baseline and follow-up for all patients (0.7{+/-}0.7 for plaque core, 0.6{+/-}0.4 for remote vessel wall, p>0.80 for both) nor between colchicine intervention and placebo control (p>0.35 for either region). ConclusionsNormalised peak signal intensity on DCE-MRI was consistently higher in the carotid plaque core than in the remote vessel wall, showed excellent reproducibility in both regions over six months, and was not altered by colchicine treatment. This simplified, muscle-normalised approach may facilitate future studies exploring DCE-MRI measures potentially related to plaque vulnerability.

Magnetic Resonance Spectroscopy Imaging (MRSI) offers spatially-resolved, neurometabolic information, acquired non-invasively at whole-brain scales from human subjects. Analysis of MRSI however, is extremely challenging. The metabolic information is highly convolved, and sparsely distributed across millions of spatial-spectral datapoints, allowing for little direct human interpretation. Conversely, the overall low signal-to-noise with high-intensity artifacts can confound unsupervised machine learning approaches. These technical barriers have left much of the potential of MRSI unrealized. We acquired MRSI data from 4 human subjects with a diagnosis of multiple sclerosis (MS), incorporating experimental design into an informed machine learning approach. MRSI acquisitions were registered to anatomical MRI to label 105k spectra from brain tissue and 162 spectra from white matter hyperintensities (WMHs), an imaging biomarker associated with MS lesions. Spectral labels were then used in contrastive principal component analysis (cPCA) to filter artifacts and background features in the MRSI data from lesion salient features and clustered into statistically significant states based on features that could be interpreted from the original data. Our approach renders MRSI data into testable representations of neurometabolism, enabling the method for fundamental and clinical research. Graphical AbstractAnalysis workflow for neurometabolic profiling of MS lesions. MRSI and anatomical MRI is acquired and processed in parallel for spectral data and anatomical labels. Spectra are then labeled and separated into experimental vs background data for contrastive PCA. Spectra are clustered for similarity, further labeled, and projected onto a brain atlas for a neurometabolic view. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/26346248v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@21a1eorg.highwire.dtl.DTLVardef@e312org.highwire.dtl.DTLVardef@3bce70org.highwire.dtl.DTLVardef@6e56ae_HPS_FORMAT_FIGEXP M_FIG C_FIG

Phosphorus magnetic resonance spectroscopy (31P-MRS) enables non-invasive measurement of brain metabolism, yet its reproducibility in clinical settings remains unclear. We systematically assessed intra- and intersession variability as well as inter-individual differences of key phosphorus metabolites at 3 Tesla in healthy individuals and persons with Parkinsons disease under various experimental condition. Intersession variability, as measured by coefficients of variation (CoV) increased notably for longer scan intervals ([~]1 year), and metabolite ratios from well-resolved spectral signals (i.e., adenosine triphosphate (ATP), phosphocreatine (PCr), intracellular inorganic phosphate Pi) exhibited consistently higher stability compared to ratios calculated from metabolite signals overlapping on the spectrum (e.g., total nicotinamide adenine dinucleotide (tNAD), as well as phosphate monoesters (PMEs) and phosphate diesters (PDEs). Test-retest variability ranged from [~]5-25 CoV%, where PCr, ATP- and ATP-{gamma} were the most stable while glycerophosphocholine (GPC), glycerophosphoethanolamine (GPE), phosphoethanolamine (PE) and tNAD varied considerably. Inter-individual variability was found to be higher than intra-individual variability for all metabolite ratios, ranging from [~]9-33 CoV%. By systematically quantifying intra-individual and inter-individual variability, as well as providing explicit sample-size recommendations, this study facilitates more reliable longitudinal and cross-sectional clinical trials and translational studies of brain metabolism featuring 31P-MRS.

Focused ultrasound can be delivered through the temporal window to modulate heterogeneously located brain areas. Acoustic simulations allow for safety assessments when dynamically targeting brain structures, but the mismatch between simulation and measured focal pressure can vary across the steerable range due to mechanically inaccurate assumptions made about the skull and transducer. Here, we describe efficient methods for simulation-measurement calibration using axisymmetric projections and sparse sampling across a 3D steerable subspace encompassing deep brain targets across 157 subjects. To address the simulation-reality mismatch in skull transmission, we used the measured and predicted pressure values through eight human temporal window fragments to derive an optimized bone attenuation coefficient. Collectively, the calibration framework and optimized temporal window coefficients can be used broadly across studies to improve the accuracy of reporting and dependent safety assessment for personalized neuromodulation treatments.

Background and Purpose: Magnetic resonance imaging (MRI) for radiation therapy treatment planning is currently being used in many anatomical sites to better visualize soft tissue landmarks, a technique known as an MRI simulation. A core component of modern MRI simulation configurations are the use of external laser positioning systems (ELPS) to help set up the patient. Though necessary for accurate and reproducible patient setup, the ELPS, if left on during imaging, may interfere negatively with image quality due to leaking electronic noise, of which MRI is sensitive to. It is currently unknown whether this leakage of electronic noise may further affect quantitative values derived from clinically employed relaxometric, diffusion, and fat fraction sequences. Therefore, in this study, we aim to characterize the impact of MRI simulation lasers on general image quality and quantitative imaging accuracy. Materials and Methods: First, a cine acquisition was used to visualize the real-time changes in image signal-to-noise ratio (SNR) from when the ELPS was deactivated to activated. To validate this effect quantitatively, the SNR was measured using the American College of Radiology (ACR) recommended protocol in a homogeneous phantom with the integrated body, 18-channel UltraFlex small, 18-channel UltraFlex large, 32-channel spine, and 16-channel shoulder coils. Next, a geometric distortion algorithm was tested in two vendor-provided phantoms while using the integrated body coil and the ACR Large Phantom protocol was tested. Finally, a series of quantitative MRI scans were performed using a CaliberMRI Model 137 Mini Hybrid phantom to validate quantitative T1, T2, and ADC while a Calimetrix PDFF-R2* phantom was used for quantitative PDFF and R2*. All scans were performed with both the ELPS both deactivated and activated. Results: Visible electronic noise artifacts were seen when using the integrated body coil when the ELPS was activated on the cine acquisition which led to a four-fold decrease in SNR using the ACR protocol. This SNR drop was not seen when using the remaining tested coils. The automatic fiducial detection algorithm was affected negatively by ELPS activation leading to misidentification when identified perfectly with the ELPS deactivated. Degradation in image intensity uniformity, percent signal ghosting, and low contrast object detectability was seen during ACR Large Phantom testing using the 20-channel Head/Neck coil. Concordance across quantitative MRI values was similar when the ELPS was both deactivated and activated while a consistent increase in standard deviation inside the ADC vials was seen when the ELPS was activated. Discussion: The extra noise induced from the activation of the ELPS during imaging should be avoided due to its potential to unnecessarily increase image noise. This is particularly true when conducting mandatory quality assurance testing for image quality and geometric distortion which utilize the integrated body coil which is most susceptible to ELPS-induced noise. Clear clinical guidelines should be implemented to make this issue known to the MRI technologists, physicists, and other relevant staff using an MRI with a supplementary ELPS for patient alignment.

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.

BackgroundBreast cancer-related lymphedema (BCRL) is a common complication following breast cancer treatment. While lymphoscintigraphy is considered the diagnostic gold standard, it is unsuitable for routine periodic monitoring or assessment of treatment efficacy. Shear wave elastography (SWE) offers a possible alternative, but traditional modes of operation limit its potential. Proposed SolutionsThe Holder-Optimized Elastography (HOE) method is introduced to eliminate pressure issues introduced by manual operation of ultrasound probes by stabilizing them above the cutis. MethodsThe HOE method was used to acquire ARFI images of high-velocity areas (HVAs, with shear wave velocity greater than 7 m/s) in limbs with and without BCRL (as confirmed and characterized by lymphoscintigraphy) in two cohorts of 15 and 125 patients. ResultsThe HOE method enabled ARFI elastography to directly and consistently visualize the effects caused by both obstructed lymphatic vessels and intraluminal lymphatic fluid as HVAs, whereas traditional hand-held methods did not. Inter-limb differences in HVA burden showed moderate diagnostic performance for detecting BCRL and grading obstruction with modest sensitivity. However, there was systematic underestimation of both early and confluent advanced lesions. ConclusionHOE-based HVA imaging has potential for rapid and non-invasive monitoring of lymphedema course and treatment response and may serve as a useful adjunct to existing diagnostic tools for BCRL. However, further technical refinements and quantitative analytic methods will be required to fully exploit the richer SWV information provided by HOE and to enhance the diagnostic utility of HVAs. Summary StatementThe Holder-Optimized Elastography method ("HOE" method) increases the diagnostic capability of ARFI elastography for breast cancer-related lymphedema, allowing for the non-invasive detection of some lymphatic obstructions but not all. Key ResultsThe Holder-Optimized Elastography (HOE) method revealed the effects caused by fluid-filled lymphatic vessels as "High-Velocity Areas" (HVAs), which are difficult to detect by conventional methods. HVA counts for detecting lymphedema (any obstruction vs. no obstruction) showed high specificity (0.86-1.00) but low sensitivity (0.57-0.67). Conversely, HVA counts for staging lymphedema (i.e. total vs. partial obstruction) showed high sensitivity (up to 1.00) but low specificity (0.48-0.66). The inter-limb difference of HVAs counted in whole-limb scans between affected and unaffected limbs (aka, the "Global Mean Difference") provided the most balanced diagnostic performance (sensitivity 0.67-0.79, specificity 0.88-0.89).

An age-related decline in vasodilation mediated by neurovascular nitric oxide (NO) and vasoconstriction driven by the Piezo1 receptor precede the aggregation of soluble brain proteins such as amyloid-{beta} (A{beta}), which then causes further disruption of brain solute homeostasis, ultimately leading to neurodegeneration in Alzheimers disease. Preclinical studies show that restoring these vascular functions increases neurofluidic efflux and improves cognitive outcomes. Here, we tested effects of sublingual NO and/or Piezo1 receptor-targeted mechanotransductive whole-body vibrations (WBVp) in healthy adults (n = 29) on brain fluid dynamics and CNS-to-blood protein efflux using multimodal neuroimaging and blood biomarker analysis. The combined vasomechanic interventions (NO+WBVp) produced a synergistic enhancement of brain fluid transport and markedly increased the efflux of soluble brain-derived proteins (A{beta}40&42, glial fibrillary acidic protein) into the bloodstream. The effects increase with age and the magnitude of NO-induced hypotension. Importantly, the combined intervention was well-tolerated, with no severe adverse physiological responses. Results demonstrate that a simple, non-invasive vasomechanic intervention can transiently promote brain-to-blood protein clearance in humans, highlighting a potentially safe and accessible therapeutic avenue for neurodegenerative conditions characterized by impaired brain solute removal and protein aggregation.

Handheld ultrasound devices have revolutionized point-of-care diagnostics, but their effectiveness remains limited by operator dependency and the need for specialized training. This paper presents an intelligent guidance and diagnostic assistance system for the handheld wireless ultrasound device, enabling automated carotid artery and thyroid examinations through handheld operation. Drawing inspiration from the Actor-Critic framework, we implement a simulation-based reinforcement learning approach for real-time probe navigation toward standard anatomical views. The system integrates YOLOv8n-based detection networks for carotid plaque and thyroid nodule identification, achieving real-time inference at 30 frames per second. Furthermore, we propose a hybrid measurement approach combining UNet segmentation with the Snake algorithm for precise biometric quantification, including carotid intima-media thickness (IMT), lumen diameter, and lesion dimensions. Experimental validation on clinical datasets demonstrates that the proposed system achieves 91.2% accuracy in standard plane acquisition, 87.5% mean average precision (mAP) for plaque detection, and 89.3% mAP for nodule identification. Measurement results show excellent agreement with expert sonographers, with IMT measurements exhibiting a mean absolute difference of 0.08 mm. These findings demonstrate the feasibility of intelligent handheld ultrasound examination, significantly reducing operator dependency while maintaining diagnostic accuracy comparable to experienced clinicians.

Deep-learning based super-resolution has shown promise for enhancing the spatial resolution of brain magnetic resonance images, which may help visualize small anatomical structures more clearly. However, when only limited training data are available, it remains uncertain which model assessment method provides the most reliable estimate of out-of-sample performance. In this study, three widely used assessment strategies (three-way holdout, k-fold cross-validation, and nested cross-validation) were compared for evaluating the performance of such models in small datasets. Across 30 iterations, we randomly selected subsets of 20 T2-weighted images from the 1,113 scans of the Human Connectome Project. Each subset was used to train a model and estimate performance using the three methods. The ground truth error was computed from the remaining images. The assessment error is the difference between the estimated error and the ground truth error. The median assessment errors were 0.11,- 0.13, - 0.32 for three-way holdout, k-fold cross-validation, and nested cross-validation, respectively, with the cross-validation methods showing considerably smaller dispersions. Nested cross-validation selected fewer epochs, indicating more conservative model selection, but required substantially greater computational time, over three times longer than three-way holdout and more than twenty times longer than k-fold cross-validation. Our findings suggest that k-fold cross-validation offers the most favourable balance between accuracy, stability, and computational feasibility in small datasets. Further research is needed to determine how model complexity, dataset size, and the number of cross-validation folds influence assessment accuracy.

Rationale and ObjectivesContrast-enhanced (CE) MRI provides clear corticomedullary contrast for renal compartment delineation but may be contraindicated or undesirable in routine practice. We aimed to enable automated extraction of renal imaging biomarkers from routine non-contrast-enhanced (NCE) T1-weighted MRI by transferring CE-derived compartment labels. Materials and MethodsThis retrospective single-center study (January 2017 to December 2021) included 200 participants with paired arterial-phase CE and NCE T1-weighted MRI. Cortex, medulla, and sinus were manually segmented on CE MRI and rigidly transferred to NCE MRI to provide voxel-level reference labels. A hierarchical 3D Deep Neural Patchworks model was trained on 100 examinations (90 training/10 validation) and evaluated on an independent test set of 100 examinations using the transferred CE masks on NCE as reference. Performance was assessed using Dice similarity of segmentations and biomarker agreement using volumes and surface areas (Pearson/Spearman, MAE, Lins CCC, and Bland-Altman). ResultsWhole-kidney segmentation Dice was 0.950 (left) and 0.953 (right). Total kidney volume showed high agreement with minimal bias (MAE 8.76 mL, 2.5% of mean; CCC 0.983; bias -1.56 mL; 95% limits of agreement -28.81 to 25.69 mL). Cortex volume was modestly overestimated and medulla volume underestimated, shifting predicted compartment fractions toward cortex (74.7% vs. 72,1% in ground truth; medulla 21.5% vs. 24.3%; sinus 3.8% vs. 3.6%. Sinus volume maintained high concordance despite higher Dice dispersion. Surface area was systematically underestimated with low concordance. ConclusionCE-supervised knowledge transfer enables accurate, well-calibrated kidney volumetry from routine NCE MRI and supports contrast-free renal biomarker extraction. Surface area estimation remains challenging. Take-home MessagesO_LICE-supervised label transfer enables accurate, well-calibrated contrast-free kidney volumetry on routine non-contrast T1-weighted MRI. C_LIO_LICompartment volumetry is feasible but shows systematic cortex overestimation and medulla underestimation; surface area remains non-interchangeable due to boundary uncertainty. C_LI

BackgroundMotor threshold (MT) estimation is fundamental to transcranial magnetic stimulation (TMS), guiding individualized stimulation intensity in research and therapy. Conventional methods such as the 5-out-of-10 rule require many stimuli, while adaptive approaches like Parameter Estimation by Sequential Testing (PEST) improve efficiency but can exhibit poor convergence under certain conditions. ObjectiveThis study introduces the Bayesian Uncertainty Dynamic Algorithm for Parameter Estimation by Sequential Testing (BUDAPEST), a Bayesian adaptive method for fast, accurate MT estimation with user-controlled uncertainty. The aims were to validate its accuracy in simulations and human data, promote usability through a MATLAB-based graphical interface, and evaluate experimental utility through resting and active MT comparisons and session-to-session reliability. MethodsBUDAPEST infers MT from binary MEP responses using sequential Bayesian updating and terminates when a user-defined uncertainty threshold is reached. Performance was evaluated in 10,000 virtual simulations and in human rMT and aMT measurements across two sessions per subject, including 3x5 cortical motor mapping to assess physiological spatial patterns. ResultsIn simulations, BUDAPEST achieved a mean absolute error of 1.9% MSO within ~10 pulses using a 2% uncertainty criterion while avoiding PEST misestimations. In human data, MT estimates were accurate within {+/-}4% MSO and robust to initialization; rMT showed strong session-to-session reliability (r = 0.78), whereas aMT exhibited greater variability. Motor mapping revealed coherent excitability gradients centered on the hotspot. ConclusionBUDAPEST enables rapid, reliable, and uncertainty-controlled MT estimation while reducing procedure time and participant burden. The accompanying GUI facilitates immediate adoption in research and clinical TMS environments. HighlightsO_LIIntroduces BUDAPEST, a Bayesian uncertainty-aware algorithm for rapid and reliable TMS motor threshold estimation. C_LIO_LIAchieves accurate MT estimates ({approx}2% MSO error) in ~10 pulses with user-controlled trade-offs between precision and procedure duration. C_LIO_LIDemonstrates robust performance in simulations and human data, with strong resting MT reliability and an open-source GUI enabling immediate adoption. C_LI