Journal of Magnetic Resonance Imaging

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.

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.

BackgroundAccurate differentiation of benign and malignant focal liver lesions (FLLs) is essential for clinical decision-making. Magnetic resonance elastography (MRE) and diffusion-weighted imaging (DWI) are advanced MRI techniques used for noninvasive lesion characterization, but their comparative diagnostic performance has not been definitively established. ObjectiveTo systematically compare the diagnostic accuracy of MRE and DWI for distinguishing benign from malignant FLLs. MethodsA systematic review and meta-analysis were conducted following PRISMA guidelines. PubMed, Embase, and Scopus were searched through July 2025 for studies directly comparing MRE and DWI in the same patient cohorts with focal liver lesions, using histopathology or validated imaging follow-up as the reference standard. Sensitivity, specificity, and area under the curve (AUC) were pooled using bivariate random-effects models, with paired analysis to compare modalities. Results219 patients with 284 focal liver lesions were analyzed. MRE demonstrated higher pooled sensitivity (93.8%, 95% CI: 85.6-97.5) and specificity (89.9%, 95% CI: 74.6-96.4) than DWI (sensitivity 86.2%, 95% CI: 80.5-90.5; specificity 83.4%, 95% CI: 74.3-89.8). MRE also had a higher AUC (0.97 vs. 0.88). Likelihood ratio analysis indicated MREs stronger ability to both confirm and exclude malignancy. Paired meta-analysis confirmed a statistically significant increase in sensitivity for MRE (relative sensitivity 1.09; p = 0.018), with no significant difference in specificity. ConclusionMRE demonstrates superior sensitivity and overall diagnostic accuracy compared to DWI for differentiating benign and malignant FLLs. Further large-scale prospective studies are needed to confirm these results and determine optimal cutoff values to guide clinical decision-making.

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.

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.

ObjectiveTo evaluate the dose efficiency of cadmium-zinc-telluride (CZT) based photon-counting CT (PCCT) compared to energy-integrating detector CT (EID-CT) across phantom sizes. MethodsA patient-specific 3D-printed pancreas phantom and a phantom with tissue mimicking inserts were placed in extension rings corresponding to the 50th, 75th, 85th, and 95th percentile adult waist circumferences. Phantoms were scanned on both PCCT and EID-CT with CTDIvol ranging from 0.5 to 19.4 mGy. Noise was measured in both phantoms to evaluate dose efficiency. Non-Poisson noise at low doses (<2 mGy) was quantified using root mean square error from linear fits of the noise-dose relationship. Potential dose reduction was then assessed by matching noise levels between scanners across phantom sizes. ResultsPCCT demonstrated reduced noise compared to EID-CT across all phantom sizes and doses with average noise reductions of 22%, 23%, 25%, and 28% for the 50th, 75th, 85th, and 95th percentile phantoms, respectively. Noise reduction intensified at lower doses and larger phantom sizes, reaching 88 HU at 1 mGy for the 95th percentile phantom. Non-Poisson noise decreased significantly with PCCT compared to EID-CT for all phantom sizes (p < 0.013). At matched noise levels, PCCT enabled dose reductions of 33% and 44% for the 50th and 95th percentile phantoms, respectively. ConclusionsPCCT exhibited superior dose efficiency compared to EID-CT across a range of phantom sizes. The enhanced dose efficiency enables both noise reduction and potential dose reduction for the imaging of obese patients and low-dose imaging applications. Key PointsO_ST_ABSQuestionC_ST_ABSPhoton-counting CT (PCCT) enables improved quantum detection and eliminates electronic noise, but its benefits have not been evaluated for obese patient sizes. FindingsCompared to EID-CT, PCCT enhanced dose efficiency with noise reduction and potential dose reduction across all phantom sizes and doses. Clinical RelevanceThe improved dose efficiency of PCCT facilitates noise reduction that enables diagnostic image quality, and thereby the diagnostic accuracy, for obese patients.

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.

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.

ObjectiveTo examine the association between lumbo-pelvic angles (LPAs) and Magnetic Resonance Imaging (MRI) detected discopathy in adults with low back pain (LBP) in the Gaza Strip and to establish local reference values for LPA measurements. MethodologyProspective cross-sectional study, 200 adults with LBP referred for lumbosacral MRI at two major hospitals in Gaza Strip. 1.5T MRI scanners were used, and Lumbar lordosis angle (LLA), sacral kyphosis angle (SKA) and sacral table angle (STA) were measured on mid-sagittal T2 images. Discopathy characteristics were recorded, and disability was assessed using the Oswestry Disability Index. ResultOf the 200 participants (mean age 45.7{+/-}13.6 years; 52.5% male), discopathy was most common at L4/L5 (89.5%), L5/S1 (67%) and L3/L4 (40.5%). LPAs were not significantly associated with discopathy involvement, type or severity, except for SKA discopathy severity at L3/4 (p=0.044). LPA measures were consistent across age groups, though LLA and STA were lower in males (p<0.001 and p=0.008), and obese individuals had higher LLA than those of normal weight (p=0.004). Reference LPA values were established stratified by LBP duration, in acute, subacute and chronic LBP, indicating a negative correlation between LLA and SKA in moderate and chronic duration. ConclusionIn adults with LBP in Gaza Strip, MRI-derived LPA showed limited association with lumbar discopathy characteristics, pain duration or disability. Although small differences related to gender, BMI and a single disc level were observed, overall associations were weak. The study establishes population-specific reference values for LPAs, which should be interpreted cautiously within a broader clinical context.

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).

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.

BackgroundLarge-scale CT-based reference standards for abdominal organ volume, incorporating age, sex, and body size, are limited. PurposeTo establish sex- and age-specific reference distributions for major abdominal organ volumes on non-contrast abdominopelvic CT in a nationwide Japanese cohort to provide a foundation for automated clinical assessment and dose optimization. Materials and MethodsIn this retrospective, multicenter study, using the Japan Medical Image Database, we identified all non-contrast abdominopelvic CT examinations performed in 2024. Unique adults with available data on age, sex, height, and weight were included in this study. The final sample comprised 49,764 examinations (26,456 men and 23,308 women) conducted at nine institutions. Automated segmentation (TotalSegmentator v2.10.0) was used to produce organ volumes, excluding hollow viscera. The sex-specific 10th, 25th, 50th, 75th, and 90th percentiles were calculated. Age-volume relationships of body surface area (BSA)-normalized volumes (mL/m2) were modeled using natural cubic splines (four degrees of freedom) separately by sex. ResultsMedian (mL) male vs female volumes were as follows: liver, 1194.7 vs 1024.0; pancreas, 63.6 vs 52.2; spleen, 118.1 vs 95.1; kidneys (total), 268.3 vs 221.2; adrenals (total), 6.6 vs 4.2; iliopsoas (total), 483.4 vs 317.7; prostate, 24.9 (men only). Age-volume relationships of BSA-normalized volumes showed convex patterns for the liver, pancreas, and kidneys in both sexes and for male adrenal glands; lower values in older age groups for the spleen and iliopsoas in both sexes; and higher values in older age groups for the prostate and female adrenal glands. ConclusionThis nationwide Japanese CT cohort provides sex- and age-resolved volumetric reference standards. These standards enable objective identification of abnormalities, support personalized medicine, and facilitate automated AI-based reporting to reduce radiologist workload and optimize radiation dose protocols. Key ResultsO_LIMedian volumes (men vs women, mL): liver 1195/1024; pancreas 64/52; spleen 118/95; kidneys 268/221; adrenals 6.6/4.2; iliopsoas 483/318; prostate 25. C_LIO_LIBody surface area-normalized age-volume relationships were convex for liver, pancreas, and kidneys in both sexes and for male adrenal glands. C_LIO_LISpleen and iliopsoas declined monotonically with age in both sexes, whereas prostate and female adrenal glands increased monotonically. C_LI

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

IntroductionIdiopathic normal pressure hydrocephalus (iNPH) is a partially reversible neurological disorder in which imaging biomarkers support diagnosis and surgical decision-making. The callosal angle (CA) is one of the most robust radiological markers of iNPH and has also been associated with postoperative shunt outcome. However, several manual measurement variants exist and artificial intelligence (AI)-based tools now enable automatic CA measurement. Materials and MethodsIn total 71 patients (40 with confirmed iNPH and 31 controls) were included. Six predefined manual methods for measuring CA were applied to preoperative 3D T1-weighted MRI and evaluated for diagnostic performance and interobserver agreement. An AI-derived automatic CA (cMRI from Combinostics) was included as a seventh method and compared with the traditional manual method (perpendicular to the bicommissural plane and through the posterior commissure). Automatic measurements were additionally assessed in pre- and postoperative scans to evaluate robustness against shunt-related artifacts. ResultsAll seven CA variants significantly differentiated iNPH patients from controls (p < 0.05). The traditional method showed the highest discriminative performance (AUC = 0.986, SE = 0.012), while alternative planes demonstrated slightly lower accuracy (AUC range = 0.957-0.978). Interobserver agreement for manual measurements was good to excellent (ICC = 0.687-0.977). Automatic CA measurements showed excellent correlation with the traditional method, preoperative ICC = 0.92; postoperative ICC = 0.96. ConclusionAlthough several CA positions perform comparably, the traditional method remains marginally superior and is best supported by the literature. Automated CA measurements closely match expert manual assessment in pre- and postoperative imaging, supporting clinical implementation.

PurposePortal hypertension, a major complication of chronic liver disease, leads to significant morbidity and mortality. While portal vein diameter measured on imaging has long been proposed as a non-invasive marker of portal hypertension, normative CT-based reference values and population-level associations remain incompletely characterized. Here, we aim to define contemporary reference values for portal vein diameter on clinically obtained CT and evaluate its associations with demographic, clinical, and imaging factors, as well as its diagnostic performance for portal hypertension. MethodsWe conducted a retrospective analysis of 20,225 clinically obtained CT scans at a single academic medical center. The main portal vein was automatically segmented using Total Segmentator, and maximum diameter extracted using the Vascular Modeling Toolkit. Associations with demographic and imaging factors were evaluated using linear mixed-effects models; prevalent liver disease and portal hypertension using logistic regression; risk of incident ascites and esophageal varices among participants with liver disease using Cox regression; and invasive hepatic venous pressures using correlation analysis and linear regression. ResultsThe mean portal vein diameter was 12.4 mm (95% CI, 12.37-12.45). Larger diameter was independently associated with male sex (+1.4 mm), higher BMI (+0.11 mm/kg/m2), greater height (+0.04 mm/cm), and older age (+0.05 mm/10 years) (all p <0.001), and was substantially larger on contrast-enhanced abdomen/pelvis CT (+2.4 mm, p <0.001). Each 1-mm increase in portal vein diameter was associated with higher odds of prevalent liver disease (OR 1.06; 95% CI, 1.04-1.08) and portal hypertension (OR 1.18; 95% CI, 1.12-1.28). Among individuals with liver disease, greater diameter predicted higher risk of incident esophageal varices (baseline diameter HR 1.50; 95% CI, 1.14-2.08) and ascites (HR per mm increase in diameter 1.06; 95% CI, 1.003-1.12). However, portal vein diameter demonstrated weak to no association with invasively measured hepatic venous pressures. ConclusionIn this large, EHR-linked imaging cohort, the mean portal vein diameter on CT was 12.4 mm and varied with demographic and imaging factors. Larger diameter was associated with liver disease, portal hypertension, and subsequent development of varices and ascites, supporting use of portal vein diameter as a pragmatic screening or enrichment tool within multimodal clinical frameworks. Key ResultsO_LIMean portal vein diameter on routine clinical CT was 12.4 mm (95% CI, 12.37-12.45) and varied with sex, height, BMI, exam type, contrast use, and clinical setting. C_LIO_LIEach 1-mm increase in portal vein diameter was associated with higher odds of prevalent liver disease (OR 1.06) and portal hypertension (OR 1.18). C_LIO_LIAmong individuals with liver disease, larger portal vein diameter predicted higher risk of incident esophageal varices and ascites, independent of demographic and imaging factors. C_LI

BackgroundDisc degeneration is the primary cause of low back pain, although the disc itself is not usually the source of the pain. Instead, it can lead to various clinically significant conditions that cause pain. However, there are no objective measures of the disc degeneration. PurposeLack of objective measures of disc degeneration may sometimes cause uncertainties in treatment decisions. Currently disc degeneration is graded by visual assessment of MRI, which often leads to uncertainty and disagreements. Therefore, the objective of this study was to develop a simple, efficient, accurate, and objective diagnostic tool for assessing disc degeneration. Study typeProspective (data acquired on site) and retrospective (data from online repository). PopulationLumbar spine MRI data from 277 participants are used. 208 of those were from an online repository and 69 were from our site. Field strength/Sequence3.0T; T2 weighted 2D and 3D fast spin echo pulse sequences. AssessmentA fully automated method is implemented where selected radiomics features are calculated from T2 weighted MRI and used for classification of the disc degeneration grade. Binary disc masks are generated using nnU-Net and radiomics features are extracted using Pyradiomics. Optimal preprocessing approaches are explored to obtain reliable feature calculations from repeated scans. Several advanced decision tree classification methods were also tested. Statistical testsF1 accuracy score, Area Under the Curve, confidence interval. ResultsXGBoost was in good agreement with the rater and the important features used in classification were in accord with expected changes in discs. Data conclusionAutomated evaluation of disc degeneration streamlines the physicians workflow and reduces uncertainties. Using radiomics features enables explainability and provides simple and robust training for machine learning approaches. Level of evidence2 Technical Efficacy3

3D-DXA reconstructs DXA hip scans to 3-dimensional images allowing measurement of trabecular and cortical bone parameters. Given the higher image quality of GE Healthcare iDXA than GE Healthcare Prodigy, it could be hypothesized that the reconstruction might differ, thereby affecting 3D-DXA results. The aim of the study was to assess agreement and precision of 3D-DXA cortical and trabecular femur parameters between Prodigy and iDXA densitometers in adult subjects. The study cohort was composed of 391 men and women recruited from 3 clinical centers (USA and Brazil). All subjects were scanned on either Prodigy or iDXA scanners. Short-term precision was assessed on two Prodigy and two iDXA densitometers. 3D-DXA analyses were performed using 3D-Shaper software version 2.14. Agreement between densitometers was assessed by regression and Bland-Altman analyses. Short-term precision was determined following International Society for Clinical Densitometry recommendations. Strong agreements for 3D-DXA parameters were obtained between devices regardless of the center or the DXA device model (all R2 > 0.96). Bland-Altman analyses demonstrated statistically (p < 0.05), but not clinically, significant difference between both aBMD and 3D-DXA measurements obtained using Prodigy and iDXA scanners. Short-term precision of areal BMD and 3D-DXA parameters was similar between densitometers. This study demonstrated excellent 3D-DXA measurement agreement and similar precision between iDXA and Prodigy densitometers. These data provide evidence that no adjustments are required when using 3D-Shaper software on iDXA or Prodigy instruments. Mini AbstractWe assessed agreement and precision of 3D-DXA parameters between GE Healthcare Prodigy and iDXA densitometers in adults. Strong agreement was observed between devices, and short-term precision was comparable. Findings indicate that no adjustment is needed when using 3D-DXA with GE Healthcare densitometers.

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.

BackgroundKidney volumetry derived from CT has been proposed as a surrogate of renal function in living kidney donor evaluation. However, clinical integration has been limited by reader-dependent workflows and semiautomatic methods susceptible to image quality. PurposeTo evaluate whether fully automated CT-based segmentation of renal cortex, medulla and total parenchymal volume provides reproducible volumetric biomarkers associated with global and split renal function in living kidney donor candidates. Materials and MethodsIn this retrospective single-center study, 461 living kidney donor candidates (2003-2021) underwent contrast-enhanced abdominal CT. A convolutional neural network was trained to automatically segment cortical, medullary, and total parenchymal volumes on arterial-phase images. Segmentation performance was evaluated against manual reference annotations. Volumes were indexed to body surface area. Associations with eGFR, 24-hour creatinine clearance, cystatin C, and tubular clearance were assessed using Spearman correlation coefficient ({rho}), and side-specific volume fractions were compared with scintigraphy -derived split function. ResultsAutomated segmentation achieved excellent agreement with expert reference segmentations (Dice 0.95 for cortex; 0.90 for medulla). eGFR correlated moderately with cortical ({rho} = 0.46) and total parenchymal volume ({rho} = 0.45), and modestly with medullary volume ({rho} = 0.30). Similar associations were observed for other global measures, with the strongest correlation for cortical volume and tubular clearance ({rho} = 0.53). Side-specific volume fractions correlated with scintigraphy-derived split renal function ({rho} = 0.49-0.56; all p < 0.001). ConclusionAutomated CT-based renal subcompartment segmentation provides reproducible volumetric biomarkers within routine donor evaluation. Cortical volume performs comparably to total parenchymal volume and tracks split renal function at the cohort level, suggesting potential utility in donor assessment.