Tomography, Volume 12, Issue 5 (May 2026) – 17 articles

Purpose: Myocardial iron overload has been demonstrated to have a heterogeneous distribution. A segmental T2* CMR approach, with correction factors applied to account for artifacts, has been demonstrated to be feasible and has permitted a reduction in cardiac morbidity and mortality, by better capturing the heterogeneous distribution of myocardial iron overload. To the best of our knowledge, commercially available software does not provide a segmental T2* technique. Our aims were to prospectively examine a large population of healthy volunteers, stratified by sex and age, using the Black Blood MEGE T2* mapping technique, to obtain normative values of the myocardium, to assess their relationship with physiological variables, and to fix correction factors for a segmental approach by using a commercially available software. Methods: Fifty healthy subjects (M:F = 1:1, 20–69 years) underwent CMR without a contrast agent. Segmental T2* values were obtained using cvi42 software; global values were the mean. Inter-study, and intra- and inter-operator reproducibility were assessed to confirm the stability of the acquired data. The association of T2* values with physiological characteristics, and myocardial wall thickness were assessed. The fluctuation of all segments versus the mid-septum was calculated to obtain a correction factor for each segment for the software used. Regional T2* differences were examined. A p-value <0.05 was considered statistically significant. Results: Twenty-five males and females, five for each decade (mean age 43 ± 13.8 years), were included. The native T2* values in all subjects averaged at 34.03 ± 6.65 ms (range 29.9–37.9 ms). Reproducibility analyses showed good correlations between the various datasets (ICC > 0.80). A weakly negative correlation was observed between age and T2* (p = 0.04). Segmental correction factors were developed and found to be significantly different from correction factors developed by non-commercially available software on non-state-of-the-art technology for sequences and scanners. Conclusions: Age-specific normative values and higher normal cut-off values than the conservative 20 ms are recommended to avoid systematic biases in the identification of pathological findings. Moreover, the correction factors developed by using the most reproducible Black Blood MEGE sequences and a commercially available software on a scanner of the current era could be a significant step toward spreading a more sensitive T2* segmental approach in the clinical arena worldwide. Full article

Background/Objectives: Early and accurate prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is vital for personalized breast cancer treatment. However, existing deep learning methods are hampered by tumor heterogeneity and semantic misalignment between high-dimensional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and low-dimensional clinical data, which limits pCR prediction performance and generalization. This study addresses these challenges via a novel multimodal network. Methods: We propose a Bidirectional Perceptual Multimodal Interaction Network (BPMINet) based on contrastive learning. BPMINet integrates pre-NAC DCE-MRI and clinical information through three core components: (1) we propose a bidirectional cross-modal attention (BiCMA) fusion mechanism to resolve semantic misalignment and facilitate effective multimodal feature fusion; (2) we design a multimodal contrast-aware feature enhancement (MCFE) module as a key component tightly integrated into the pCR-oriented contrastive learning framework, which serves to boost discriminative power for pCR prediction and improve generalization performance on hard-to-classify samples; (3) we adopt a dual-loss strategy to enable the collaborative optimization of discriminative feature representation and pCR prediction performance. Results: On two publicly available multicenter datasets, BPMINet outperformed all comparative methods across seven evaluation metrics: specifically, it surpassed the top-performing baseline by 5.17% in AUC and 5.24% in accuracy on the MAMA-MIA dataset. More notably, it achieved substantially larger gains of 11.72% in AUC and 7.38% in accuracy on the ISPY1 dataset. Conclusions: BPMINet achieves optimal pCR prediction performance, confirming its superiority and strong generalization ability for multimodal breast cancer pCR prediction. Full article

Objectives: This study investigated whether visual field abnormalities are present in eyes with suspected nasal optic disc hypoplasia (NOH) by using fundus photography and optical coherence tomography (OCT). Methods: NOH was diagnosed using the following criteria: (1) small optic disc, (2) nasal optic disc pallor or optic disc margin irregularity, (3) wedge-shaped temporal visual field defects extending from Mariotte’s blind spot, and (4) reduced nasal circumpapillary retinal nerve fiber layer (cpRNFL) thickness. Eyes fulfilling criteria 1, 2, and 4 without visual field abnormalities were classified as pseudo-NOH (pNOH), whereas eyes without visual field or cpRNFL abnormalities were considered normal. Nasal cpRNFL thickness was measured using OCT, radial peripapillary capillary (RPC) density was assessed using OCT angiography (OCTA), visual field testing was performed, and optic disc blood flow velocity was evaluated using the mean blur rate (MBR) and laser speckle flowgraphy (LSFG). Results: Seven eyes with NOH, 13 eyes with pNOH, and 24 normal right eyes were included. Nasal cpRNFL thickness and MBR were significantly reduced in both the NOH and pNOH groups compared with the normal group, with no significant difference between the NOH and pNOH groups. Nasal RPC density was significantly lower in the NOH group than in both the pNOH and normal groups, and no significant difference was observed between the pNOH and normal groups. Conclusions: Even when NOH was suspected from fundus, LSFG, and OCT C-scan findings, visual field abnormalities were not consistently present. Differences in RPC density measured using OCTA may have contributed to this variability. This study examined whether suspected nasal optic disc hypoplasia (NOH) is always associated with visual field defects. Using fundus imaging, OCT, OCT angiography, and laser speckle flowgraphy, we compared eyes with NOH, pseudo-NOH, and normal eyes. Although structural changes such as reduced nasal nerve fiber layer thickness and decreased blood flow were observed in both NOH and pseudo-NOH, visual field abnormalities were not consistently present. Notably, reduced radial peripapillary capillary density was specific to NOH, suggesting that vascular differences may explain variability in visual function. These findings highlight the importance of multimodal imaging in NOH evaluation. Full article

Background: Cage subsidence (CS) is among the main complications after oblique lumbar interbody fusion (OLIF) and may lead to the failure of indirect decompression. Accurate preoperative bone quality assessment is critical for risk stratification, yet the optimal imaging modality and diagnostic threshold remain unclear. Objectives: This study aimed to determine a quantitative computed tomography (QCT)-derived volumetric bone mineral density (vBMD) threshold for predicting CS after OLIF with posterior fixation. Methods: Patients undergoing OLIF with posterior fixation between July 2017 and March 2020 were retrospectively enrolled. Preoperative vBMD was measured using QCT as the average L2–L4 trabecular volumetric BMD. CS was defined as a loss of more than 2 mm of disk height on sagittal midline CT views between 3 days postoperatively and the last follow-up. Clinical and radiographic parameters including gender, age, body mass index, vBMD, number of operative levels, cage dimensions, disk height, segmental lordosis, intraoperative endplate injury, and fusion status were analyzed. Results: 86 patients (107 operative levels) with a mean follow-up of 20.6 months were included; 25 levels (23.4%) developed CS. Multivariate logistic regression identified vBMD (p < 0.001; OR 0.947; 95% CI 0.923–0.972) and intraoperative endplate injury (p = 0.031; OR 3.640; 95% CI 1.125–11.776) as independent risk factors. The area under the receiver operating characteristic curve (AUC) for vBMD was 0.847 (95% CI, 0.762–0.932), with an optimal threshold of 83.0 mg/cm³ (sensitivity 84.0%, specificity 76.8%). This threshold closely aligns with the American College of Radiology QCT criterion for osteoporosis (80 mg/cm³); however, given that it was derived from a single-center retrospective cohort, external validation in multi-center studies is warranted before broad clinical adoption. Fusion rates differed significantly between CS and non-CS groups (84.0% vs. 96.3%, p = 0.029). Conclusions: QCT-derived vBMD provides a phantom-calibrated, protocol-standardized metric for preoperative risk stratification of cage subsidence after OLIF. Full article

Objectives: Early identification of treatment response in brain metastases remains clinically challenging. This study explores tensor-valued diffusion MRI (dMRI), specifically q-space trajectory imaging (QTI), as a novel source of early imaging biomarkers during stereotactic radiotherapy (SRT). Methods: Twenty-six patients with brain metastases were enrolled; thirteen met quality and completeness criteria for QTI analysis (10 responders, three non-responders). MRI was acquired at four time points: before SRT, before final SRT fraction, and at 3 and 6 months post-SRT. QTI-derived metrics included mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (µFA), and isotropic (MKI) and anisotropic (MKA) diffusional variance. Parameter values within the tumour volume were compared pre- and during SRT and correlated with treatment response from standard MRI follow-up. Overall survival was assessed using Kaplan–Meier analysis. Results: Median survival was 12 months. QTI analysis was feasible with visible changes in the tumour tissue parameter maps over time. Statistically significant differences (p < 0.05) were found between responders and non-responders in FA before treatment. MKI in responders was significantly lower (p < 0.05) during SRT than before. Conclusions: This study presents a first exploration of QTI-derived parameters in a cohort of patients with brain metastases. We demonstrate feasibility and a scalable workflow, supporting further investigation in larger cohorts and in patients with larger or more stable lesions. Full article

Background/Objectives: Magnetic resonance imaging (MRI) provides a non-invasive means for a comprehensive assessment of the effect of radiation therapy (RT) on heart function. This study aims to determine RT induced cardiotoxicity in thoracic cancer patients using cardiac MRI. Methods: Cardiac MRI was performed at baseline and at six months post-treatment in patients undergoing standard-of-care RT for lung or esophageal cancers at a single institution. Parameters included regional myocardial strain in the longitudinal, circumferential, and radial directions as well as myocardium T1, T2, and extracellular-volume (ECV) maps. Cardiac segmental doses were extracted from the RT planning scans. The relationship between changes in segmental MRI parameters at six months and segmental heart RT dose were investigated. Results: Twelve patients underwent baseline MRI and four completed the follow-up MRI. Five of the segmental strain parameters showed notable changes between baseline and six-month follow-up. Increased doses in the heart base and apex were associated with moderate-to-large and mild deteriorations, respectively, in strain for all regions. Increased doses in the mid-ventricular regions were associated with improved strain in all regions. The segmental analysis revealed that myocardial regions nurtured by the left coronary artery are more negatively affected by radiation compared to those nurtured by the right coronary artery. Conclusions: Alterations in regional tissue and strain parameters on MRI vary according to local myocardial RT dose, suggesting there may be heterogeneity of radiation sensitivity for the heart substructures and regions. Changes in segmental strain parameters may reflect post-RT cardiac remodeling, but larger confirmatory studies are required. Full article

Coronary artery calcification, present in 20–30% of percutaneous coronary interventions (PCI), significantly impairs procedural success. Conventional angiography detects calcification in fewer than half of affected cases, while intravascular imaging—though precise—requires lesion crossability that cannot be guaranteed in up to 20% of severely calcified lesions. Cardiac CT (CCT) addresses both constraints by providing comprehensive, three-dimensional calcium characterization before the procedure begins, independent of wire crossability. This review details how specific CCT-derived parameters translate into procedural decisions. Calcium arc, depth, density, and longitudinal distribution each carry distinct implications for device selection: superficial high-density calcium favors atherectomy, while deep concentric patterns are better addressed by intravascular lithotripsy. Validated scoring systems—including the ABCD score—enable objective pre-procedural risk stratification. For chronic total occlusions, bifurcation lesions, ostial stenoses, and very long calcified segments, CCT provides lesion-specific information that supports stepwise strategy selection, equipment preparation, and anticipation of combined modification approaches. Importantly, CCT also identifies anatomical configurations—such as left main bifurcations or tortuous calcified segments—where specific device-related risks warrant particular caution. CCT and intravascular imaging serve complementary roles: CCT defines the strategic framework before the procedure, while intravascular imaging guides real-time execution and optimization. Limitations include operator-dependent interpretation, the absence of standardized protocols for translating calcium morphology into device selection, and the need to validate established Hounsfield unit thresholds in emerging photon-counting CT systems. Prospective randomized evidence comparing CCT-guided and intravascular imaging-guided strategies remains limited but is anticipated from ongoing trials. Full article

Background: Differentiating adrenal adenomas from non-adenomatous lesions remains a critical challenge in the management of adrenal incidentalomas. Conventional unenhanced CT relies on attenuation thresholds of 10 HU and 20 HU, which present trade-offs between sensitivity and specificity. Objectives: To evaluate the diagnostic performance of unenhanced Spectral CT using the attenuation difference between 40 keV and 140 keV virtual monoenergetic images for differentiating adrenal adenomas from non-adenomatous lesions. Methods: In this retrospective single-center study, 60 patients with adrenal lesions who underwent unenhanced dual-energy CT were included. Mean attenuation values were measured on conventional images and on virtual monoenergetic images at 40 keV and 140 keV. The spectral attenuation difference (Δ40–140 keV) was calculated. ROC analysis was performed to determine the optimal threshold and diagnostic performance. Additional analyses included DeLong comparison of correlated ROC curves and bootstrap resampling to estimate 95% confidence intervals for the area under the curve. Results: Forty-nine lesions were adenomas and eleven were non-adenomatous. The optimal threshold for Δ40–140 keV was −17 HU. When evaluated as a continuous variable, Δ40–140 keV yielded an area under the curve of 0.940 (95% confidence interval: 0.851–1.000), compared with 0.939 (95% confidence interval: 0.870–0.992) for conventional unenhanced attenuation. DeLong comparison showed no statistically significant difference between the two curves (p = 0.980). Diagnostic performance was as follows: HU ≤ 10 (AUC 0.816, diagnostic accuracy 0.70), HU ≤ 20 (AUC 0.883, diagnostic accuracy 0.87), and Δ40–140 keV ≤ −17 HU (AUC 0.940, diagnostic accuracy 0.90). The spectral attenuation difference demonstrated the highest overall diagnostic accuracy. Conclusions: Unenhanced Spectral CT using Δ40–140 keV improves discrimination between adrenal adenomas and non-adenomatous lesions compared with conventional attenuation thresholds. This technique may reduce indeterminate findings and limit the need for additional imaging. Full article

Background: Computed tomography (CT) is routinely used to estimate tumor size before lung resection, whereas pathologic examination provides the reference tissue-based measurement after surgery. This study aimed to compare CT-derived and pathologic tumor size and to evaluate correlation, agreement, proportional bias, clinically defined accuracy, and size-based T-category concordance, with particular attention to the effect of ground-glass opacity (GGO). Methods: This retrospective single-center study included 96 patients who underwent lung resection between January 2023 and December 2025 and had complete preoperative CT and pathologic tumor measurements. Maximum tumor diameter was defined as the largest of three orthogonal measurements for each modality. Correlation was assessed using Spearman’s rank correlation coefficient, reliability using the intraclass correlation coefficient (ICC), and agreement using Bland–Altman analysis. Proportional bias was evaluated by regression of the paired difference on the paired mean. Subgroup, size category, regression and size-based T-category concordance analyses were also performed. Results: CT and pathologic maximum diameters showed strong correlation (Spearman’s ρ = 0.952, p < 0.0001) and excellent reliability (ICC = 0.959, 95% CI, 0.939–0.973). The paired comparison was not statistically significant (p = 0.175), and the mean bias was −0.76 mm. However, the 95% limits of agreement ranged from −13.66 mm to +12.13 mm. Significant proportional bias was observed, with increasing CT underestimation as tumor size increased (slope = −0.093, p = 0.0014). In tumors with GGO, CT pathology differences shifted toward overestimation (+8.91 ± 7.30 mm vs. −1.64 ± 5.80 mm without GGO; p = 0.0003). Accuracy within ±5 mm and ±10 mm was 68.8% and 88.5%, respectively, but was lower in the GGO subgroup. CT-derived and pathology-derived size-based T-categories were concordant in 60 patients (62.5%), while pathology-based upstaging occurred in 23 patients (24.0%) and pathology-based downstaging in 13 patients (13.5%). Conclusions: CT-based tumor size showed strong overall correlation with pathologic measurements, but agreement at the individual patient level was more limited than correlation metrics alone would suggest. GGO and tumor size appeared to be important modifiers of measurement performance; however, the GGO-related findings should be interpreted cautiously because of the small subgroup size. These findings support cautious interpretation of CT-derived whole-lesion diameter, particularly in subsolid tumors and larger lesions. Full article

The sharp dose gradients that underpin the dosimetric advantage of particle therapy over photon therapy can be undermined by the interplay effects due to intra-fraction motion in modern pencil beam scanning systems. Fluoroscopy-Guided Particle Therapy (FGPT) offers a promising path to improved motion management through real-time tracking of tumors or surrogate signals. The advent of flat-panel detector (FPD)-based technology has enabled tighter integration of fluoroscopy/fluorography into treatment units and accelerated clinical adoption and research, with commercial systems such as Hitachi’s Real-time Gated Particle Therapy (RGPT) now available. However, the need for implanted fiducial markers, with the associated invasiveness and risk of complications, limits the utility of RGPT to a few anatomic sites in selected patients. The full potential of FGPT, therefore, depends on reliable marker-less tumor tracking, which remains challenging because soft-tissue targets are obscured by overlapping anatomy along the X-ray path, leading to reduced reliability of traditional image-registration algorithms in the projection domain. Recent advances in deep learning and AI-driven image registration have renewed hope for overcoming these barriers, enabling real-time marker-less tracking for particle therapy. This review outlines the evolution of fluoroscopy technology from image intensifier (II) to FPD-based systems, summarizes historical and recent vendor-supported FGPT strategies, and surveys emerging AI-based algorithms in the literature. A general review of machine learning-based image registration is provided, challenges in generalizability and interpretability are highlighted, and potential paths toward reliable, clinically deployable FGPT are discussed. Full article

Background/Objectives: Accurate grading of brain gliomas is important, and amide proton transfer-weighted (APTw) MRI shows promise for non-invasive tumor differentiation. This study aimed to perform a comprehensive review and meta-analyses to demonstrate heterogeneity in both the diagnostic accuracy and quantitative consistency of APTw MRI in distinguishing high-grade gliomas (HGGs) from low-grade gliomas (LGGs), highlight issues with reporting standards and identify sources of heterogeneity through meta-regression. Methods: A systematic literature search was conducted between 1 January 2013 and 18 January 2026, following PRISMA guidelines. Peer-reviewed articles in English reporting diagnostic accuracy/contrast values of APTw MRI and study parameters were included. Principal component analysis (PCA) was used to extract the principal components (PCs) of the chemical exchange saturation transfer (CEST) contrast mechanism. Random-effects meta-analyses and univariate meta-regression models using individual CEST parameters and three PCs were performed. Forest plots with pooled estimates were generated. Leave-one-out meta-analysis (LOOMA) and complete case analysis were performed to examine the effects of outliers and missing data, respectively. Results: A total of 31 studies were included. Meta-analyses of the AUC and mean difference demonstrated significant heterogeneity across the studies (I² = 73.9% & 78.2%, p < 0.001). The mean difference was moderated by one SD within the mean of the readout PC (p = 0.034) and the total PC (p = 0.02). The heterogeneity for the AUC and group mean difference was not substantially reduced by moderating nor LOOMA. The results of the meta-regression using all the data were similar to those using only data with no missing parameters. Conclusions: While APTw MRI shows promise for non-invasively distinguishing glioma grades, substantial heterogeneity in the study parameters limits generalizability. To improve consistency and comparability across studies, full reports of imaging parameters and standardization of APTw protocols are essential. Full article

Background: Cone Beam Computed Tomography (CBCT) is widely used in image-guided radiotherapy because it provides on-board volumetric imaging at relatively low doses, but its clinical utility for synthetic CT (sCT) generation remains limited by noise, scatter, artifacts, and reduced Hounsfield Unit (HU) fidelity. Conditional diffusion models (CDMs) have recently emerged as a promising alternative to earlier deep learning approaches because their iterative denoising process may better preserve anatomical structure and model uncertainty. Objective: This systematic review evaluates the use of conditional diffusion models for CBCT-to-CT synthesis, with particular attention to architectural strategies, reported quantitative outcomes, and potential clinical relevance. A systematic search was conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2013 and 2024. Eleven studies met the eligibility criteria and were analyzed to address three questions: (1) Which conditional diffusion strategies have been used? (2) What outcomes have been reported? and (3) What clinical implications have been discussed? Results: Across the included studies, CDMs frequently showed promising image quality performance, especially when incorporating anatomical priors, spatial-frequency guidance, hierarchical refinement, or latent representations. However, the evidence base remains small and highly heterogeneous with respect to anatomy, dimensionality, supervision strategy, and evaluation metrics, limiting the strength of direct comparative claims. The reviewed literature suggests that conditional diffusion models are a promising direction for CBCT-to-CT synthesis, but stronger dose-aware validation, standardized reporting, and broader multicenter evaluation are still needed before routine clinical deployment. This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO), under registration number CRD42024619240. Full article

Pharmacokinetic modeling in Positron Emission Tomography (PET) imaging has become a cornerstone in cancer research, offering insights into tumor development and progression. These models facilitate the quantification of radiotracer distribution and metabolism, enabling precise measurement of physiological parameters essential for cancer diagnosis, staging, and treatment monitoring. However, accurate pharmacokinetic modeling depends on reliable input function acquisition and partial volume correction techniques to minimize biases in quantitative PET metrics. This review provides a comprehensive overview of current methodologies and advancements in pharmacokinetic modeling for PET oncology imaging. We discuss techniques for acquiring input functions, including arterial, venous, and image-derived input functions (IDIFs), along with population-based input functions (PBIFs). Their strengths, limitations, and clinical applications are critically evaluated. Additionally, we examine quantitative methods such as partial volume correction (PVC) that mitigate the spatial resolution limitations of PET, improving radiotracer quantification in small or heterogeneous tumors. Furthermore, we explore advanced kinetic modeling techniques, including compartmental models, graphical approaches, and data-driven methods, highlighting recent innovations such as machine learning and Bayesian modeling. Key areas for future research in PET pharmacokinetic modeling include integrating hybrid imaging modalities, developing robust patient-specific input functions, and leveraging machine learning to streamline modeling processes. These advancements aim to enhance the precision and clinical utility of PET imaging in oncology, leading to more personalized cancer treatment strategies. Full article

Background/Objectives: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men globally. Radiation oncologists often find PCa tumor characterization and outcome prediction challenging. Therefore, the potential for artificial intelligence (AI) implementation in radiation oncology has increased in recent years. This systematic review aims to evaluate the efficacy of AI algorithms in characterizing PCa tumors and predicting post-therapy outcomes. Methods: A total of 2055 studies were identified through a comprehensive search across PubMed and Scopus, then exported to Covidence. Inclusion criteria focused on prospective and retrospective cohort studies as well as randomized clinical trials (RCTs) published between 2015 and 2024 that explored the implementation of AI in tumor characterization and outcome prediction of PCa. Two independent reviewers evaluated each paper, and evaluation metrics such as specificity, sensitivity, accuracy, and area under the curve (AUC) were analyzed. The Risk of Bias in Non-randomized Studies of Interventions, Version 2 (ROBINS-I V2) tool was used to assess the risk of bias (ROB). Results: Across the 19 studies analyzed, there was no significant difference in model performance between machine learning (ML) and deep learning (DL) models. AI models using multi-input strategies (e.g., radiomics with clinical markers) generally performed better than single-input models. Of the imaging modalities used for radiomic feature extraction, multiparametric MRI (mpMRI)-trained AI models consistently achieved the highest performance. Conclusions: AI displays considerable potential for integration into clinical workflows for PCa management. However, further studies utilizing larger datasets and external cohorts independent of the sample population are needed to validate clinical utility and improve model transparency for reliable implementation. Full article

Background and aim: Chest CT is a major contributor to population radiation exposure. Conventional, pooled diagnostic reference levels (DRLs) do not account for inter-individual variability in body habitus and are typically used retrospectively. We evaluated dose behavior in adult chest CT, derived BMI-stratified local DRLs, and developed models to enable AI-assisted, prescan dose prediction. Methods: Consecutive adult chest CT examinations from a single center were analyzed. Dose indices (CTDIvol, DLP) and patient factors (BMI, weight, height, age, sex; scan length and planned technical parameters where available) were extracted. DRLs were defined as the 75th percentile overall and within BMI categories (underweight, normal, overweight, and obese). Group differences were assessed using non-parametric tests; associations were examined using correlation analysis. Supervised learning (e.g., Random Forest, Gradient Boosting) was trained to predict CTDIvol and DLP from routinely available variables. Results: BMI-stratified DRLs increased monotonically with habitus: underweight 444.95 mGy·cm/9.60 mGy; normal 513.00/11.55; overweight 756.08/14.65; obese 931.60/20.25 (DLP/CTDIvol). Differences across BMI groups were significant for DLP (H = 31.53, p < 0.001) and CTDIvol (H = 33.61, p < 0.001). DLP correlated moderately with weight and BMI (r ≈ 0.54–0.56, p < 0.001), with a weaker association for age; height was not a meaningful predictor. No sex-based differences in CTDIvol or DLP were observed. Predictive models estimated CTDIvol and DLP with high performance (R² up to ~0.79 and ~0.77, respectively), enabling comparison of predicted dose against BMI-matched DRLs before acquisition. Conclusions: Size-aware, BMI-stratified DRLs provide clinically interpretable investigation levels that avoid pitfalls of pooled benchmarks. Coupled with robust prediction of individualized dose from routine variables, this framework supports a shift from retrospective audit to prospective, point-of-care dose governance and protocol optimization in chest CT. Full article

Background: Magnetic Particle Imaging (MPI) is an emerging biomedical imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIONs), providing high contrast, sensitivity, and quantification capabilities without ionizing radiation, making it particularly suitable for cancer diagnostics. Considerable engineering efforts are underway to translate MPI technology to clinical settings. Most of these MPI scanners feature a cylindrical bore geometry similar to that of other clinical imaging modalities, which limits their potential application primarily to head scanning. Methods: We have developed a single-sided MPI scanner designed to expand the modality’s applicability to other regions of the human body through a unique hardware design developed in our previous work. Imaging experiments were performed on an anatomical breast phantom containing implanted SPION point sources placed at anatomically plausible locations for breast tumors. These point sources served as artificial tumors for evaluating the system’s suitability for breast imaging applications. Results: The scanner successfully detected and clearly resolved the implanted SPION tumors in two orthogonal imaging planes. Tumor positioning was independently validated by ultrasound imaging, confirming MPI’s accurate localization. In addition, sensitivity measurements demonstrated a detection limit of 4.0 $\mathsf{\mu }\mathrm{g}$ of iron, below the estimated 4.8 $\mathsf{\mu }\mathrm{g}$ sensitivity threshold required for breast tumor detection with electronic depth scanning up to 3.5 cm deep. Conclusions: Together, these results demonstrate the capability of a single-sided MPI geometry for breast imaging applications. Imaging an anatomical breast-shaped volume presents significant challenges for MPI due to the size and accessibility constraints of conventional hardware. The results presented highlight the advantages of this approach and support its potential to extend MPI from small-animal imaging to clinically relevant applications. Full article

Background: As muscles atrophy, myocytes are replaced by fat and muscle strength diminishes, increasing thoracic supine kyphosis. Here, we investigate the relationship between muscle fat percentage (muscle–fat%) and thoracic supine kyphosis on CT. Methods: Thoracic Cobb angle was measured on supine CT scans from the AtlasDataset by four observers ($n=533$). Nine muscles were manually labeled on 100 scans (manual cohort). An nnU-Net model was trained on 80 cases with internal validation on 20 cases, then applied to segment the remaining 433 AtlasDataset scans (automated cohort). External segmentation benchmarking was performed on 30 cases from a separate open-source dataset. Associations between supine thoracic curvature and muscle–fat% were evaluated only in AtlasDataset. Results: Manual supine thoracic Cobb angle measurements demonstrated good inter-observer reproducibility (ICC(2,k) = 0.98) with a mean across-rater per-case SD of 3.4°. The nnU-Net achieved Dice scores >0.93 across all nine muscle groups on internal and external segmentation benchmarking. For both manual and automated cohorts, thoracic supine kyphosis correlated with muscle–fat% in the paraspinal (r = 0.35, 0.42), quadratus lumborum (r = 0.29, 0.33), vastus (r = 0.38, 0.32), psoas (r = 0.21, 0.23) and latissimus dorsi (r = 0.21, 0.17) muscles. Conclusions: Automated measurement of trunk muscle–fat% provides a reproducible imaging biomarker correlated with thoracic supine kyphosis on CT. Identifying fatty atrophy of core muscles may help identify potential targets for interventions in hyperkyphotic patients. Full article