Journal of Magnetic Resonance Imaging

Purpose: to introduce a novel magnetic resonance imaging (MRI) technique, MUSLIS MRI (muscle and lipid suppression), that generates two co-registered 3D sets of images in a single acquisition: one set with lipid signal suppression (MUSLIS1) and a second set with muscle signal suppression (MUSLIS2). Materials and Methods: Numerical simulations based on Bloch equations were performed to optimize the sequence parameters; the MP2RAGE sequence was employed for the implementation of MUSLIS imaging. MRI experiments were performed in vivo at 3 T. Quantitative measurements of signal contrast were carried out on MUSLIS1 and MUSLIS2. Various combination images and T1 parametric maps of the thighs were generated from the two co-registered 3D sets. Results and Discussion: Optimal sequence parameters to achieve lipid suppression in one dataset (MUSLIS1) and muscle suppression in the other dataset (MUSLIS2) were determined by numerical simulations. MUSLIS imaging was validated in vivo at 3 T: MUSLIS1 images and MUSLIS2 images displayed excellent lipid and muscle suppression, respectively. The values of the signal contrast between subcutaneous lipids and muscle, as well as between bone marrow lipids and muscle reached values close to 0.8. Thus, MUSLIS would allow for tissue characterization of lesion/masses and direct visualization of the adipose tissue. Combination images, generated from these two intrinsically co-registered sets of images (MUSLIS1 and MUSLIS2), displayed different contrasts and improved signal characteristics, such as mitigation of signal inhomogeneities. The development of MUSLIS imaging for musculoskeletal imaging followed the concept introduced with the FLAWS, an MRI sequence designed for brain MR imaging to generate two 3D sets of images, one with white matter suppression and the other with cerebrospinal fluid suppression. Similarly to FLAWS, which provides T1 maps of the brain, MUSLIS imaging yields T1 parametric maps of the musculoskeletal system. Furthermore, the unique characteristics of the combination images make MUSLIS imaging a potentially attractive technique at field strengths higher than 3 T. Conclusion: The proof-of-concept of a novel approach to musculoskeletal imaging, MUSLIS MRI, is proposed here and demonstrated by numerical simulations and MRI experiments performed in vivo at 3 T. Keywords: MRI, muscle, lipids, musculoskeletal imaging, high field MRI, fat suppression, FLAWS MRI, MP2RAGE, MUSLIS, inversion recovery, adipose tissue, bone marrow, subcutaneous fat, T1 mapping, 3 T, 7 T, neuromuscular disorders, metabolic diseases, muscular dystrophy.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Subject motion and gradient heating-induced frequency and phase offsets result in spectral misalignment during single-voxel MR spectroscopy (SVS) acquisitions. Several methods have been presented to align the spectra, but their performance on a 7T system is unclear. This study aimed to evaluate the practical importance of four retrospective correction methods, namely creatine fitting, residual water, spectral registration, and cross-correlation. SVS data were collected from 127 participants (88/39 female/male, 39{+/-}15 years) using a semi-localization by adiabatic selective refocusing sequence at 7T. Changes in the spectral linewidth, signal-to-noise ratio (SNR), similarity (mean similarity matrix (SImean)), and metabolite quantification (concentration, relative Cramer-Rao lower bounds (rCRLB)) after correction were evaluated. A Wilcoxon signed rank test was used to evaluate these changes. The p-values were adjusted using the false discovery rate (q<0.05) for multiple comparisons. For significant changes, the effect size was further calculated using the Rosenthal formula. The results demonstrated that all correction methods resulted in a significant decrease in the spectral linewidth and significant increases in the spectral SNR and SImean. The mean changes by using the four methods were 0.76-0.99 Hz for the spectral linewidth change (effect sizes:0.73-0.86), 5.79-6.95 for the SNR change (effect size:0.60-0.72), and 0.03-0.03 for the SImean change (effect size:0.87-0.87). The correction also significantly increased the estimates of the metabolite concentrations of PCr, Gln, Glu, GPC, Ins, NAA, Scyllo, and Tau (effect size:0.25-0.57), and significantly decreased the rCRLBs (effect size:0.26-0.42) of Scyllo, Tau, and Gly. These results indicate that each method provides practical benefits in improving the spectral linewidth, SNR, and similarity by showing a strong to very strong effect of the improvements (effect size>0.60). The correction only showed a weak to moderate effect of the changes in the metabolite quantification (effect size<0.60), suggesting that whether the correction results in more reliable metabolite quantification requires further validation.

Purpose: Proton density (PD) and macromolecular proton fraction (MPF) are fundamental MR parameters characterizing water and macromolecular content in tissues. Both parameters can be mapped using a time-efficient MRI protocol, though PD maps require additional corrections for the reception profile (RP) non-uniformity and global scaling. Existing PD mapping methods suffer from uncertainties related to PD normalization to reference tissue. This study aimed to develop a data-driven RP correction method for PD mapping based on MPF as an internal scale. Theory and Methods: One-parameter model linking PD and MPF was derived under assumption of proportionality between total macromolecular volume and proton concentration. This model with a uniform parameter value across brain tissues generates a surrogate PD map, which is used to estimate RP from an uncorrected PD map. The proposed algorithm was tested against a common PD mapping method utilizing bias-field correction and normalization to cerebrospinal fluid. Brain MRI data were acquired at 3T from seven healthy individuals. The protocol included 3D multi-echo T1-, PD-, and magnetization-transfer-weighted gradient-echo sequences. Parametric maps were reconstructed using the single-point MPF mapping algorithm with data-driven B1 correction. Results: MPF-based and bias-field corrections yielded similar PD measurements in white and gray matter with no significant bias and remarkably small variability (coefficients of variation <0.6%). Conclusions: MPF-based RP correction enables computationally simple and robust quantitative PD mapping at no additional cost if MPF maps are available. The main advantage of the proposed method is automatic elimination of global scaling that obviates the need for PD normalization.

Purpose: To quantify the sampling error of conventional 2D regions of interest (ROIs) compared with whole-organ 3D volumetric CT attenuation for opportunistic hepatic steatosis screening, and to evaluate its dependency on intraparenchymal spatial heterogeneity. Materials and Methods: In this retrospective computational study (CLAIM-compliant), 971 abdominal CT scans were analyzed across a multicenter derivation cohort (TotalSegmentator v2, n = 841) and an external validation cohort (Medical Segmentation Decathlon Task03, n = 130). Automated 3D liver segmentation was performed using nnU-Net, followed by an 8-mm spherical 3D erosion to eliminate peripheral partial volume effects and major hilar vessels. Three clinical sampling paradigms (1, 3, and 5 axial 2D ROIs of 15 mm diameter) were modeled through 500 stochastic Monte Carlo iterations per scan (485,500 total simulations). Absolute sampling error (|Delta HU| = |HU_ROI - HU_3D|) and diagnostic misclassification rates at the standard <= 40 HU steatosis threshold were computed. Results: In the derivation cohort, the conventional 3-ROI approach yielded a mean absolute error of 6.61 +/- 7.83 HU (median: 2.92 HU), significantly underperforming compared to the 5-ROI strategy (5.21 +/- 6.00 HU; Wilcoxon paired test, p < 0.001) but improving on the single-ROI approach (11.32 +/- 14.16 HU; p < 0.001). Sampling error correlated strongly with parenchymal spatial heterogeneity (Pearson r = 0.850, 95% CI: [0.830-0.868], p < 0.001). In borderline attenuation zones (35-45 HU), diagnostic misclassification reached 45.2% in heterogeneous livers. Findings were corroborated on external validation, confirming the reproducible decay of sampling error (|Delta HU| = 7.38 HU for 1 ROI vs. 5.14 HU for 3 ROI, p < 0.001). Conclusion: Manual 2D ROI sampling exhibits an intrinsic metrological uncertainty exceeding clinically relevant thresholds (5 HU) in heterogeneous livers, misclassifying nearly half of borderline cases. Opportunistic screening requires fully automated 3D volumetric extraction embedded into clinical PACS workflows.

PurposeWith growing interest in pediatric MRS to understand the neurochemistry of brain development, it is essential to establish pediatric-specific metabolite T1 and T2 relaxation times for reliable quantification of metabolites. Furthermore, characterization of age-related changes in relaxation times may provide insight into developmental differences in tissue microstructure and cellular composition. Therefore, the goals of this study were two-fold: (1) to estimate metabolite T1 and T2 relaxation times in a pediatric cohort; and (2) to assess linear relationships between age and relaxation times. MethodsTI- and TE-series data were acquired from the bilateral thalamus in 20 and 25 participants aged between 0 and 7 years, respectively. Data were processed and modelled in Osprey 2.5.0. Metabolite T1 and T2 relaxation times of total N-acetylaspartate at 2.0 ppm (tNAA2.0), total creatine at 3.0 ppm (tCr3.0), total creatine at 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI) and the combined glutamate and glutamine (Glx) were estimated by fitting the modelled signal amplitudes to TI-series inversion recovery signal and TE-series exponential decay equations, respectively. Linear relationships with age were tested for metabolite T1 and T2 relaxation times using Spearman correlations. ResultstNAA2.0 and Glx showed significant changes in T1 with age. tNAA2.0, tCr3.0, tCho and mI showed significant decrease in T2 with age. ConclusionSeveral metabolites showed significant changes in relaxation times with age in the thalamus region. Therefore, use of age-specific relaxation correction for pediatric studies using the linear equations provided is encouraged for reliable quantification of metabolites.

Purpose: To evaluate whether visually assessed high-quality prostate MRI, as defined by the Prostate Imaging Quality (PI-QUAL) scoring system, is sufficient to ensure consistent AI model performance, or whether hidden acquisition-related technical effects introduce variability that cannot be detected through visual inspection. Methods: This retrospective study included 568 bpMRI scans from seven institutions, classified using PI-QUAL v2 into high-quality (scores 2-3) and low-quality (score 1) groups. Quantitative image quality features were extracted using MRQy from T2w and ADC sequences. Three experiments were conducted on high-quality scans: (1) unsupervised clustering to identify hidden technical batch effects; (2) V-Net models (M) for prostate capsule segmentation on T2w using single-cluster and balanced multi-cluster training strategies; and (3) VoxelMorph-based deformable registration (M2) to align ADC maps to T2w images. Performance metrics included DSC, precision, recall, volume ratio, HD95, ASSD, and TRE. Kruskal-Wallis tests assessed cross-cluster variability, Two One-Sided Tests (TOST) evaluated performance equivalence, and bootstrap resampling quantified training efficiency. Results: Clustering identified four clusters among 277 high-quality bpMRI scans. Single-cluster models showed variable performance (DSC=0.41-0.85) with significant batch effects (p<0.001), even with 140 training samples. The balanced multi-cluster model (n=60) eliminated batch effects (p=0.075), achieved equivalent performance (p<0.001), and demonstrated 2.23-fold greater training efficiency despite using 2.3-fold fewer samples. Registration performance also varied significantly across clusters (TRE=0.86-5.47 mm, p<0.001). Conclusion: Acquisition-related batch effects significantly impact AI generalizability in prostate MRI, even among high-quality scans. Quantitative clustering enables detection of technical variations and supports diversity-aware training strategies that achieve equivalent performance with 2.3-fold fewer samples while eliminating batch effects.

Existing MRI-based markers have limited sensitivity and specificity for assessing myelin in the brain. This study investigated whether multi-parametric MRI could improve myelin mapping in the developing mouse brain. T1, T2, magnetization transfer, and diffusion MRI data were acquired at 7 Tesla from ex vivo myelin-associated oligodendrocyte basic protein (MOBP)-enhanced green fluorescent protein (eGFP) mouse brains at postnatal day 14 (P14), P35, and P56. Serial two-photon tomography data from the same mouse brains were used to quantify myelin content based on the MOBP signal intensity. Although most MR parameters demonstrated significant age-related changes, only a subset correlated strongly with MOBP signals. Partial least squares regression analysis of MRI and MOBP data from the corpus callosum and other brain regions demonstrated that the multi-parametric approach improved myelin mapping in the developing mouse brain. T2 was ranked as the highest contributor to myelin estimation across multiple brain regions.

In magnetic particle imaging (MPI), signal is generated from the non-linear magnetic response of superparamagnetic iron oxide (SPIO) nanoparticles. The optimization of SPIOs for MPI is an active area of investigation. Tracer performance can be evaluated by magnetic particle relaxometry (MPR) and image-based metrics. This study characterized and compared the performance of ten commercially available tracers, spanning a broad range of magnetic core sizes, hydrodynamic diameters and surface coatings, using the peak signal intensity measured from MPR, and the total signal and maximum signal intensity measured from 2D MPI images. Synomag-D tracers exhibited the highest MPR peak signal intensities and highest maximum signal per {micro}g iron. ProMag, a micron-sized iron oxide particle (MPIO), yielded a low MPR peak signal intensity but had the highest total MPI signal per {micro}g of iron. A strong correlation was observed between MPR peak signal intensity and MPI maximum signal intensity (R2=0.94). In contrast, there was a weak correlation between MPR peak signal intensity and MPI total signal intensity (R2=0.38), though this correlation improved when MPIOs were excluded from the analysis (R2=0.91). While MPR generally predicts the tracer performance, it does not completely replicate the complex imaging conditions. As such, comprehensive tracer evaluation requires combining both MPR and image-based metrics.

There is increasing demand for quantitative measurements in neuroimaging research, including absolute quantification of regional myelin density. Fast whole-brain macromolecular proton fraction (MPF) imaging enables acquisition and reconstruction of quantitative maps of MPF, a biomarker of myelin, and has been used in many research applications. To date, no standard protocols have been published for parcellating MPF maps. FreeSurfer is a widely used software package for automated brain parcellation. It is designed for use with high-resolution T1-weighted anatomical images, typically acquired with an MPRAGE sequence. MPF maps inherently provide high contrast between white and gray matter and can potentially be used as input images for parcellation, enabling simultaneous quantitative tissue characterization and volumetric assessment. However, it is unclear how the use of MPF maps as input images with FreeSurfer software affect reliability and repeatability of regional MPF and volume estimates. We evaluated three FreeSurfer parcellation workflows for this purpose. The workflows differed in the amount of neuroimaging data required and computational intensity. MPF and MPRAGE data were acquired in two separate sessions for 11 adults. Reliability was evaluated using mean relative differences in estimates, Bland-Altman analysis, and intra-class correlation coefficients (ICCs). Repeatability was assessed using within-subject coefficients of variation (CVws). All three workflows produced similarly high within-subject repeatability across scans. However, reliability in regional MPF estimates was lower in gray matter than in white matter for workflows that used MPF maps as input. Workflows that use MPF maps as FreeSurfer input may be adequate for estimating mean MPF in GM and WM of cortical parcels and in subcortical regions. However, the MPRAGE-based workflow is recommended when reliable individual-level estimates or volume estimates are required.

The shift towards open science in preclinical research requires high-quality, comparable data adhering to FAIR principles, yet rigorous quality assurance (QA) and quality control (QC) frameworks remain less established in preclinical magnetic resonance imaging (MRI) than in clinical imaging. To address this limitation, a multicenter study was conducted across 21 international laboratories using standardized commercial liquid phantoms for mouse and rat MRI setups and a harmonized acquisition protocol. Data processing was centralized using AIDAqc, an automated pipeline extracting quantitative metrics including signal-to-noise ratio (SNR), temporal SNR (tSNR), ghost-to-signal ratio (GSR), and motion-equivalent temporal instability, combined with five complementary outlier-detection algorithms. The evaluated datasets encompassed magnetic field strengths from 3.0 to 16.4 T and heterogeneous coil and acquisition configurations. In mouse phantom data, SNR and tSNR were significantly lower at 3 T than at 7 and 9.4 T, whereas artifact-related metrics did not differ significantly between field-strength groups. In rat phantom data, differences between field strengths were less pronounced. At 7 T, substantial dataset-specific differences were observed in anatomical and functional quality metrics, also among datasets using the similar coil configuration. Multicenter reference values for frequently represented 7 T configurations were established, including SNR/tSNR of 48 {+/-} 3/48 {+/-} 1 dB for mouse surface-coil datasets and 49 {+/-} 3/44 {+/-} 4 dB for rat array-coil datasets. Longitudinal anatomical SNR showed generally low variability, whereas ghosting was more variable across time. Automated outlier detection additionally identified transient acquisition instabilities that were not always apparent by visual inspection. Together, these findings show that field strength and nominal coil configuration alone do not adequately characterize MRI performance and support standardized, longitudinal phantom-based QC combined with automated analysis as a practical approach for improving the reliability, transparency, and interoperability of multicenter preclinical MRI data.

Background: Intramuscular fat and fibrous tissue deposition increase with ageing, disuse, and disease and contribute to impaired muscle function and reduced 'muscle quality'. Magnetic resonance imaging (MRI) and biopsy provide reference measures of muscle composition, but their cost, invasiveness, and limited accessibility restrict routine use. B-mode ultrasonography has emerged as a practical, low-cost alternative, with ultrasound-derived echo intensity widely interpreted as a surrogate measure (index) of intramuscular fat, fibrosis, and 'muscle quality'. However, such interpretation warrants further scrutiny. Aim: To quantify the relationship between ultrasound-derived echo intensity and reference measures of intramuscular fat and fibrous tissue and determine whether these relationships vary across muscles or according to anatomical and methodological factors. Methods: A systematic search of six databases was conducted (last search: August 2026). Human and animal studies reporting correlations between echo intensity and measures of intramuscular fat or fibrous tissue using MRI, histology, or chemical analysis were synthesised using multilevel random-effects meta-analysis with study-level cluster-robust inference. Correlations were Fisher z-transformed and back-transformed for interpretation. Exploratory analyses examined fat compartment, muscle, muscle architecture, health status, study model, and distance-correction procedures. Results: Twenty-eight studies met the eligibility criteria. For intramuscular fat, 204 effects from 23 independent studies yielded a weak-to-moderate pooled correlation with echo intensity (r=0.54, 95% CI: 0.45-0.61; 95% PI: - 0.02-0.84; P<0.01), with substantial heterogeneity (I^2=74.5%). The wide prediction interval suggests the true correlation could range from negligible to strong. No significant moderation effects were observed for study model (animal vs. human: r=0.46 vs. 0.55; P=0.26), health status (clinical vs. healthy: r=0.64 vs. 0.49; P=0.11), probe-to-muscle distance correction (corrected vs. raw echo intensity: r=0.57 vs. 0.53; P=0.25), or muscle architecture (non-pennate vs. pennate: r=0.63 vs. 0.53; P=0.56), although some categories contained few studies and effects. Model-based inference indicated significant moderation effect of lipid compartment (intramyocellular lipid vs. total intramuscular fat vs. extramyocellular lipid: r=0.10 vs. 0.57 vs. 0.49; P<0.001) but this effect was not significant under cluster-robust inference (P=0.50). For fibrous tissue, six effects from four studies yielded a weak, non-significant pooled correlation (r=0.34, 95% CI -0.39, 0.81], 95% PI [-0.85, 0.96]; P=0.24). Conclusions: Echo intensity is weakly-to-moderately associated with intramuscular fat, and this varied substantially across studies and muscles, whereas evidence for intramuscular fibrous tissue is limited and uncertain. These findings provide limited support for interpreting echo intensity as a surrogate measure or an index of intramuscular fat or fibrosis or, on this basis, as an index of 'muscle quality'.

To compare virtual noncontrast (VNC) error between deep-silicon photon-counting CT (PCCT) and dual-energy energy-integrating detector CT (EID-CT) in a matched phantom and characterize tissue-specific error in humans. This prospective exploratory study included a matched phantom experiment and pilot human evaluation. Prototype projection-domain water-density maps served as the VNC surrogate. Twenty-four vials containing 0.1--20 mg I/mL in deionized water or simulated blood were scanned with PCCT and EID-CT. Three men underwent true noncontrast and contrast-enhanced PCCT; 80 matched measurements were analyzed. Paired t tests and mixed-effects models assessed system differences and iodine associations. Three participants (mean age, 67 {+/-} 10 years; all men) were included. Mean VNC error was 29.1 HU lower with PCCT than EID-CT (95% CI, 15.5--42.7 HU; {rho} < 0.001). Error was not associated with iodine concentration for PCCT (slope, 0.03 HU per mg I/mL; 95% CI, -0.92 to 0.98; {rho} = 0.95) but increased for EID-CT (slope, 3.51 HU per mg I/mL; 95% CI, 2.56--4.46 HU; {rho} < 0.001). The slopes differed by 3.48 HU per mg I/mL ({rho} < 0.001). In humans, errors in five non-blood-pool tissues were within 5 HU. Error was not associated with iodine concentration among 61 non-blood-pool measurements (adjusted slope, -0.17 HU per mg I/mL; 95% CI, -1.22 to 0.88; {rho} = 0.75). PCCT provided VNC measurements with errors closer to zero and less dependent on iodine than EID-CT in the phantom. In humans, error was generally small in non-blood-pool tissues and was not associated with iodine concentration.

Introduction: Resection margins in oral squamous cell carcinoma (OSCC) are frequently inadequate. This study investigated the accuracy and diagnostic performance of ex-vivo 7T MRI against histology. Methods: Fifty OSCC specimens underwent ex-vivo 7T MRI. Tumours were segmented on corresponding histology and twice on MRI: blinded to histology (radiologist segmentation; clinical practice) and using histology (histology-MR segmentation; MR-based ground truth). Resection margins were measured in five regions and compared with histology. Results: Anterior and posterior margins were generally smaller on MRI than on histology (maximum mean difference -2.5 mm), whereas cranial, caudal and deep margins were generally larger on MRI (mean differences <1 mm). For close margins (<5 mm), sensitivity was 95% for histology-MR segmentations and 91% for radiologist segmentations, with 75% specificity for both. Conclusion: Ex-vivo 7T MRI accurately measures resection margins and detects close margins with high sensitivity. A future clinical trial will determine whether intraoperative implementation reduces inadequate margins and adjuvant treatment.

Objective: To develop and validate a dual-probe Diffuse Correlation Spectroscopy (DCS) system for non-invasive and simultaneous, monitoring of cerebral blood flow (CBF) in the bilateral Middle Cerebral Artery (MCA) territories, and expanding the utility of conventional DCS limited to cortical-volume-based CBF measurements to vessel-specific cerebral perfusion monitoring. Methods: A dual-probe DCS system was designed for non-invasive monitoring of MCA-specific perfusion. Probe placement and protocol optimization study has been performed using anatomical landmarks, motor and speech activation tasks in healthy volunteers. System stability and repeatability were further evaluated in a pilot cohort of 30 healthy (age, 25{+/-}7 years) participants using optimized probe position and protocol. A bilateral MCA ischemic Lacunar Infract stroke case report also validated the feasibility of the system in clinical settings. Results: Measurements demonstrated superior sensitivity towards MCA-territory perfusion at targeted probe locations compared to off-MCA positions. In pilot cohort, significant increase of 30.34 {+/-} 21.56% and 36.48 {+/-} 21.22% in rCBF corresponding to hand squeeze and speech task respectively showed reproducible physiological responsiveness of the system (p<0.001). Measurement done on a patient with bilateral MCA ischemic Lacunar Infract stroke showed a significant change of 30% during speech for both the MCAs but no significant change is observed for hand squeeze tasks. Conclusion: The custom built dual-probe DCS system enables non-invasive, operator-independent, targeted and continuous monitoring of rCBF within bilateral MCA territories. Significance: This approach enables the potential use of DCS system for bilateral and vessel-specific monitoring of cerebral perfusion in the MCA territories.

To evaluate whether eight-bin deep-silicon photon-counting CT can quantify iodine and gadolinium separately and distinguish radiopaque embolic beads from vascular contrast in tumor phantoms. In this phantom study, two-input decomposition of scanner-generated iodine and water images was compared with three- and five-material decomposition of five reconstructed energy-bin images in 56 common test vials, including nine simulated-blood preparations. One tantalum and one bismuth vial were also tested with five-material decomposition. A separately calibrated two-input method using scanner-generated 70-keV and iodine images was applied to four static 3D-printed tumor vascular models: iodine-only and gadolinium-only controls and two models with iodine-containing beads and gadolinium-filled channels. Concentration errors were summarized by bias and root mean square error (RMSE). For iodine, RMSE was 0.40, 0.73, and 1.12 mg I/mL with two-input, three-material, and five-material decomposition, respectively; corresponding gadolinium RMSEs were 0.57, 0.55, and 0.46 mg Gd/mL. Two-input biases were +0.01 mg I/mL and +0.29 mg Gd/mL. Five-material decomposition distinguished tantalum and bismuth in the separate test vials. Beads and vascular gadolinium were spatially separated in the tumor models. In the two bead-containing models, gadolinium measured 32.56 {+/-} 8.02 and 37.02 {+/-} 7.93 mg Gd/mL (mean {+/-} spatial SD), compared with the prepared 35.00 mg Gd/mL. Deep-silicon photon-counting CT separately visualized radiopaque embolic beads and quantified vascular gadolinium from a single acquisition. Recalibrated scanner-generated images supported quantitative dual-contrast imaging, while research energy-bin decomposition distinguished additional materials.

BACKGROUND Primary sclerosing cholangitis (PSC) is characterized by multifocal biliary strictures on MRI/MRCP. However,we have observed that young PSC patients often displayed a marked intrahepatic bile duct visibility,sometimes with gallbladder enlargement, before developing biliary strictures. This study aimed to evaluatewhether this imaging pattern was associated with subsequent diagnosis of PSC. PATIENTS AND METHODS This retrospective two-center study included patients younger than 21 years who had a baseline liverMRI/MRCP, without biliary strictures. The endpoint was a definite diagnosis of large duct PSC during follow-up. The association of baseline imaging findings with the endpoint was evaluated by multivariable logisticregression, Cox models, and Kaplan-Meier analyses. RESULTS Ninety-four patients were included: 54% were women, the mean age was 16.7 years, and 23% had IBD. Overa mean follow-up of 7.75 years, 21 patients were subsequently diagnosed with large duct PSC after a mediantime of 5.60 years. Marked intrahepatic bile duct visibility and gallbladder enlargement displayed goodperformance to predict the subsequent diagnosis of PSC (AUC, 0.77 and 0.69, respectively). The presence ofboth signs displayed a specificity of 1.00. In multivariable analyses, marked intrahepatic bile duct visibility andgallbladder enlargement were independently associated with subsequent PSC diagnosis (OR 17.00 (95% CI,3.10-93.24; p = 0.001) and 16.08 (95% CI, 2.29-113.02; p = 0.005) respectively) and shorter time to diagnosis(HR 9.42 (95% CI, 2.50-35.59; p < 0.001) and 7.59 (95% CI, 2.03-28.41; p = 0.003) respectively). Survivalanalyses confirmed the association between these two imaging signs and the subsequent PSC diagnosis (HR7.78 (95% CI, 2.62-23.16; p < 0.001) and 3.85 (95% CI, 1.64-9.08; p < 0.001) respectively). CONCLUSIONS In young patients without biliary strictures, marked intrahepatic bile duct visibility and gallbladder enlargementon MRI/MRCP were strongly associated with subsequent diagnosis of large duct PSC.

Abstract: Purpose: Superparamagnetic iron oxide nanoparticles (SPION) are widely used as MRI contrast agents for cell tracking and molecular imaging. However, their detection using conventional T2/T2* weighted and susceptibility-weighted MRI relies on negative contrast (signal voids), which lacks specificity because similar hypointense signals can arise from other sources of magnetic susceptibility. This study introduces Frequency Shift Imaging (FSI), a positive-contrast technique based on an adiabatic Shinnar-Le Roux (SLR) self-refocused pulse that addresses the limited specificity of conventional SPION imaging. Methods: An adiabatic self-refocused RF pulse was designed using the SLR algorithm for frequency-selective positive-contrast imaging. The optimized pulse had a spectral bandwidth of 370 Hz, a peak RF amplitude of 14 T, and a duration of 15 ms. FSI data were acquired on a 7T MRI scanner. Phantom experiments used agarose-embedded 30 nm cell-labeled SPION at 0%, 25%, 75%, and 100% concentrations (pure). In vivo validation was performed following intramuscular SPION injection into the hind limb of a mouse. FSI images were compared with conventional acquisitions. Results: Quantitative phantom analysis showed normalized FSI signal intensities of 25.02% and 75.16% for samples containing 25% and 75% labeled cells, respectively, demonstrating an approximately proportional relationship between FSI signal and labeled-cell concentration. In vivo, FSI generated localized hyperintense positive contrast at the SPIO injection site, providing improved conspicuity and localization compared with the corresponding negative-contrast signal void. Discussion and Conclusion: FSI enables robust, B1-insensitive, positive-contrast SPION imaging at 7T with reduced echo time and SAR compared to paired adiabatic approaches, supporting its potential for quantitative cell tracking and molecular imaging applications.

Automatic segmentation of medical images could accelerate clinical and research workflows, yet existing tools are rarely compared independently on shared benchmarks, making it difficult to assess genuine progress. This is particularly consequential for limb muscle segmentation in magnetic resonance imaging (MRI), which underpins volumetric analysis, radiomics, and fat fraction quantification used as biomarkers in neuromuscular disease -- applications where segmentation errors can directly affect interpretation. Despite the availability of multiple open-source segmentation tools spanning diverse architectures, no independent head-to-head comparison has appeared in the literature. We address this gap by evaluating eight tools, six of which run independently, and the other two of which are meant to be auxiliary algorithms once muscles have been somehow initially labeled. We evaluated these tools on three-dimensional MRI volumes across three base datasets and then later against derived datasets, assessing both quantitative metrics and qualitative usability. Our results reveal substantial performance variation across methods and datasets, with many methods showing reduced accuracy on pathological cases. Domain-specific models trained on large datasets consistently outperformed foundation models and newer general-purpose architectures. Our results reveal practical trade-offs between accuracy, generalizability, and ease of use that are critical for clinical adoption, while raising concerns for deployment on underrepresented populations and pathologies.

Purpose: Posterior pharyngeal wall (PPW) thickening is a common structural manifestation of treatment-related internal lymphedema in patients with head and neck cancer and can be evaluated using lateral videofluoroscopic images acquired during modified barium swallow (MBS) studies. However, no standardized method currently exists for quantitative measurement. Methods: This two-phase methodological study first conducted a scoping review to characterize existing approaches to PPW thickness measurement and identify the principal factors contributing to variation in PPW thickness measurement. Eight studies were identified with variation observed across these measurement factors. An empirical reliability study was then performed using 107 MBS studies from 93 Head and Neck Cancer patients. Two independent raters annotated 10 anatomical landmarks on pre-swallow and post-swallow resting frames, and a computer-assisted algorithm quantitatively generated seven candidate PPW thickness measurements based on different vertebral reference points and measurement directions. Reliability was assessed intraclass correlation coefficients (ICCs). Results: Inter-rater reliability was highest for the measurement originating from the midpoint of C3 and extending perpendicular to the anterior C3 surface (ICC, 0.78; 95% CI, 0.72-0.83). Among the normalization references, C2-C4 vertebral length demonstrated greater reliability than C3 vertebral length, with an ICC of 0.88 (95% CI, 0.85-0.91). No significant differences were observed between pre-swallow and post-swallow PPW thickness measurements across the evaluated approaches. Conclusions: These findings provide a methodological foundation for more standardized and reliable quantification of PPW thickness, with potential to expand the role of MBS from the functional evaluation of swallowing to include quantitative characterization of pharyngeal structural abnormalities.