Physics in Medicine & Biology

BackgroundRealistic PET/CT phantoms are essential for system evaluation, protocol optimization, and validation of advanced reconstruction methods. However, existing phantoms are often limited by simplified geometries, spatially uniform activity patterns, and complex preparation procedures. PurposeTo develop and evaluate PixelPrintPET, a 3D printing-based method for fabricating anatomically realistic PET/CT phantoms with spatially heterogeneous radiotracer distributions and a single-solution filling workflow that avoids physical compartmentalization. MethodsPixelPrintPET generates voxel-based printing instructions that encode spatially varying infill, which is realized during printing through modulation of filament extrusion, enabling heterogeneous activity distributions without compartmentalization of radioactivity at different activity concentrations. Calibration phantoms and anatomically structured phantoms were designed and printed using high-flow polylactic acid (PLA), with anatomical inputs derived from either digital atlas-based models or patient imaging data. The printed phantoms were subsequently filled by immersion in a radioactive solution, allowing activity distribution to be controlled by the internal porous structure. A bottom-up filling procedure with reduced surface tension was developed to ensure uniform infiltration and minimize air entrapment. Phantoms were imaged on the PennPET Explorer PET/CT system, and quantitative performance was evaluated using contrast recovery coefficient (CRC), target-to-background ratio (TBR), and comparisons with simulated or patient-derived reference data. ResultsA strong linear relationship between infill ratio and normalized signal (R2 = 0.998) was demonstrated by the calibration phantom, enabling reliable mapping between structure and activity. Additionally, air entrapment was minimized to less than 1% of the total phantom volume. In the contrast recovery phantom, CRC values were consistent with measurements using traditional phantoms. The brain phantom reproduced atlas-derived contrast patterns, with gray-to-white matter differences within 5% after accounting for resolution and other system effects. The patient-based thorax phantom showed high reproducibility across repeated scans, with differences within 3%, and closely matched the input patient image with regional differences within 10% in all regions except the lung. ConclusionsPixelPrintPET enables the fabrication of realistic, reproducible, and versatile PET/CT phantoms with a voxel-level control of the activity distribution. This approach provides a practical solution for generating patient-specific and application-specific phantoms, with the potential to accelerate system validation, protocol development, and clinical translation of advanced PET/CT technologies.

PurposeImaging of the central lymphatic system enables characterization of patient-specific lymphatic anatomy and accurate localization of leaks. Advancements in CT technology, particularly spectral CT, can enhance CT lymphangiography (CTL) with improved visualization and quantification. This study aimed to assess the feasibility of spectral CTL in both static and dynamic scans. Materials and Methods50% diluted iodinated contrast was injected into the bilateral superficial inguinal lymph nodes of a pig. The pig was scanned with a dual-layer spectral CT every 60 seconds for 10 minutes. To optimize contrast and visualize peristalsis, a second animal was injected with 25% and 10% diluted contrast and scanned dynamically 4 and 6.25 minutes after contrast injection. Conventional images and iodine maps were reconstructed to calculate the contrast-to-noise ratio (CNR). Additionally, the iodine density was measured adjacent to the lymphovenous junction to show fluctuations from peristalsis and contrast washout. ResultsIodine maps, compared to conventional images, separated the contrast-filled central lymphatic system from surrounding soft tissue and increased CNR to 895 compared to 43 with conventional images. 25% diluted contrast provided the best balance between visualization and quantification of the central lymphatic system, showing high and low iodine density regions corresponding to peristalsis. Iodine density peaked at 15.4 {+/-} 0.6 mg/mL and decreased to 2.0 {+/-} 0.1 mg/mL at 10.5 minutes. ConclusionSpectral CTL not only improves visualization of the central lymphatic system compared to CTL but also provides quantitative information for physiological characterization of lymphatic disease that can enhance current subjective assessment. Research highlights- Iodine maps from spectral CT lymphangiography separated contrast-filled lymphatic structures from surrounding soft tissue and provided better contrast-to-noise compared to conventional images. - Spectral CT lymphangiography enabled quantification of contrast in the central lymphatic system that demonstrated contrast washout and may be utilized for physiological characterization of disease. - Dynamic spectral CT imaging of the lymphatic system visually showed peristalsis in the thoracic duct and was further reflected in quantitative iodine density measurements.

Whole-body SPECT bone scintigraphy reflects skeletal metabolic activity throughout the body and plays an indispensable role in the screening, treatment evaluation, and prognostic assessment of bone metastases in tumors. However, the automatic detection and segmentation of hypermetabolic bone lesions remain challenging due to low contrast, limited spatial resolution, and complex lesion distributions. In this study, we proposed Bone-Segnet, a dual-view guided automatic segmentation network for hypermetabolic bone lesions that integrated multi-scale feature modeling, global context modeling, and view-conditioned modulation. Pixel-level annotated anterior and posterior whole-body bone scintigraphy images were used for model training and prediction. The proposed network enhanced the recognition of low-contrast and small-scale lesions through small-lesion enhancement and multi-scale contextual modeling. A Transformer module was further introduced to strengthen global feature representation, while cross-view collaborative modeling was achieved by incorporating the complementary characteristics of anterior and posterior imaging. Experimental results demonstrated that the proposed method outperformed existing approaches across multiple evaluation metrics, with the Dice score improving from 0.7440 to 0.8750, indicating a substantial improvement in segmentation performance. Further quantitative analysis based on the segmentation results revealed significant differences among disease types in lesion count, pixel burden, and spatial distribution patterns, reflecting the heterogeneity of disease-related skeletal metabolic activity. Overall, the proposed method improved automatic lesion segmentation performance and enabled quantitative analysis of lesion burden and spatial distribution patterns, providing objective data support for the assessment of related diseases. Index Terms--Whole-body SPECT, bone lesion segmentation, dual-view modeling, quantitative analysis.

Computed tomography is the largest contributor to population radiation dose from medical imaging, yet no diagnostic reference levels (DRLs) have been published from Kosovo or the Western Balkans. This retrospective audit analyzed all CT examinations performed on a 128- slice scanner at the University Clinical Centre of Kosovo between January and March 2026. After exclusions, 1,535 acquisitions from 1,092 patients across nine examination categories were analyzed. Local DRLs were defined as the 75th percentile and compared against German (BfS 2022) and Turkish (Kahraman et al., 2024) reference values. Head CT (n = 590) demonstrated CTDIvol 4.7% below the BfS DRL yet scan length 98.5% above the orientation value (median 25.8 vs 13 cm). Abdomen-pelvis CTDIvol matched the BfS reference while scan length exceeded it by 28%. Coronary CTA showed CTDIvol +377%, consistent with retrospective ECG gating. Excess scan length, not CTDIvol, is the major driver of elevated dose at this institution. The identified excesses are correctable through technologist landmarking training, protocol review, and enabling iterative reconstruction.

Tumor pseudo-progression (PsP) refers to an initial increase in tumor size or the appearance of new lesions. These pseudo-progressive lesions are predominantly composed of infiltrative inflammatory cells, such as macrophages. This phenomenon commonly occurs in patients undergoing radiation therapy or immunotherapy and typically indicates a positive treatment response. However, it often leads to premature treatment cessation due to misinterpretation as disease progression. Non-invasive imaging biomarkers capable of distinguishing pseudo-progression from true progression would greatly aid in treatment decision-making. In our preliminary study, we explored the potential of gadoterate meglumine (Gd-DOTA, a macrocyclic Gd-contrast) in combination with amine chemical-exchange saturation transfer (amine-CEST) imaging to differentiate tumor from radiation necrosis by assessing Gd-DOTA uptake by infiltrating immune cells, such as macrophages. To evaluate whether amine-CEST, in combination with Gd-DOTA, can differentiate macrophages from cancer cells, we incubated them with Gd-DOTA for 30 minutes. Subsequently, the cells were processed, and amine-CEST imaging was performed on a 9.4 Tesla preclinical scanner. Upon treatment with Gd-DOTA, we did not observe a significant change in amine-CEST contrast in F98 cells compared with untreated cells, whereas treated macrophages exhibited a marked decrease (~40%) in amine-CEST signal compared with untreated macrophages. This reduction in signal was attributed to the uptake of Gd-DOTA by macrophages, which notably shortened water T1 relaxation, thereby quenching the amine-CEST signal. Conversely, cancer cells showed no appreciable change in the amine-CEST signal, indicating no Gd-DOTA uptake. Furthermore, to validate that T1 shortening influences amine-CEST signal, cancer cells were also treated with manganese chloride (MnCl2) for 30 minutes. The uptake of MnCl2 by cancer cells similarly induced T1 shortening, as observed in macrophages, resulting in a decrease in the amine-CEST signal from these cells. Next, we performed the amin-CEST imaging on F98 tumor-bearing rats and radiation necrotic rats. Post-injection with Gd-DOTA showed no appreciable change in the amine-CEST contrast in the tumor-bearing rat, whereas a significant decrease in contrast was observed in the radiation necrotic rat. This further demonstrates that no change in the amine-CEST contrast in tumor-bearing rats is due to cancer cells failing to take up Gd-DOTA. The decrease in amine-CEST contrast in radiation-treated rats reflects the uptake of Gd-DOTA by macrophages infiltrating the radiation-necrotic regions. This straightforward imaging approach holds promise for clinical translation. It offers a novel method for characterizing pseudo-progressive lesions and monitoring diverse treatment responses in cancer patients using standard clinical scanners.

PurposeTo address the limitations of single-distance, 1D performance metrics in RF coil design. This work introduces a multi-objective, volume-of-interest (VOI) based analysis to systematically characterize the trade-offs between power efficiency, pSAR efficiency, and homogeneity as a function of dipole length (l) and distance-to-load (d) for multiple dipole geometries and target anatomies. MethodsElectromagnetic simulations of straight and end-meandered dipole antennas were performed with varying lengths (100-500 mm) and distance-to-load (1-81 mm) over three anatomical targets (prostate, kidney, heart). Homogeneity, power efficiency, pSAR efficiency, and load sensitivity performance metrics were calculated within each anatomical VOI. Inter-element coupling at variable d was assessed in a 3-element array, and a subset of single-element simulations was experimentally validated using B1+ mapping. ResultsA fundamental trade-off was found between power efficiency and pSAR efficiency. Optimal power efficiency was achieved with shorter dipoles (150 mm < l < 300 mm) closer to the sample (d < 30 mm), while optimal pSAR efficiency and homogeneity were achieved with longer dipoles at further from the sample (d > 60 mm). Inter-element coupling increased with distance-to-load but could be managed by increasing element spacing. Experimental measurements were in good agreement with simulation trends. ConclusionIncreasing distance-to-load to 40-60 mm, compared with commonly used distances of 20-30 mm, offers a practical strategy for improving pSAR efficiency and homogeneity with a minimal decrease in power efficiency. This work provides a quantitative analysis that enables RF coil designers to make informed, data-driven decisions when developing next-generation body arrays and suggests that unshielded end-meandered dipoles could be an optimal transmit element geometry.

Developing parsimonious, mechanism-aware quantitative models that predict how biological effectiveness changes with different modifiers remains, in general, an unsolved problem. Advances in radiobiological research have created a large knowledge base of first-principles mechanistic models of radiation response that, in principle, could accurately predict radiosensitivity across different experimental and clinical conditions. However, in practice these mechanistic models come with an overabundance of parameters, the majority of which are practically unidentifiable and, moreover, likely unnecessary if one simply wishes to predict how radiosensitivity changes for some specific modifier of interest. Nevertheless, determining which few details in the full mechanistic model are relevant for a given purpose, as well as how to remove any other extraneous details, remains a highly non-trivial task. In this study, we demonstrate the potential of model reduction, starting from a detailed mechanistic description, as a systematic strategy for deriving parsimonious, experimentally falsifiable radiobiological descriptors. As a proof-of-concept demonstration, we apply the Manifold Boundary Approximation Method (MBAM) to a Mechanistic Model of DNA Repair and Survival (MEDRAS), for the problem of cell survival prediction following an acute exposure. Our findings reveal that the complete MEDRAS model for an arbitrary mixed-quality exposure can be structurally simplified to a reduced three-parameter model for an effective uniform-quality, named MEDRAS-LPL. Additional MBAM analysis on MEDRAS-LPL identifies two boundaries in parameter space, corresponding to sparsely ionizing and densely ionizing radiation. Mapping of MEDRAS-LPL parameter space on to effective LQ space further demonstrates that parameters close to the sparsely ionizing boundary line up with expectations from the theory of dual radiation, while parameters close to the densely ionizing boundary line up with expectations from a purely linear model based on a target-theory description. Moreover, our formalism predicts enhanced synergistic interactions between sparsely ionizing and densely ionizing radiation beyond the Zaider Rossi model (ZRM) paradigm, in line with empirical observations. The results highlight the potential for using reduced-order models not only for predictive applications but also for generating novel hypotheses that can inform future experimental designs and optimization strategies in radiobiology.

Pressure injuries represent a significant healthcare challenge requiring early detection to prevent severe complications. While thermal imaging shows promise for detecting early pressure-related temperature changes, its robustness across varying imaging conditions and diverse patient populations remains unclear. This study systematically evaluated how imaging protocol variations (lighting, distance, positioning, camera type) and participant skin tone influence classification model performance for thermal cooling detection. Using a controlled cooling protocol to simulate early pressure injury temperature changes, we collected 1,680 images from 35 diverse participants across 12 imaging protocol variations. We compared two approaches: three deep learning models (MobileNetV2, InceptionNetV3, ResNet50) and a threshold-based approach using an optimal fixed threshold temperature differential. Deep learning models outperformed the threshold-based approach, achieving 98.6-99.6% accuracy compared to 95.6%, with superior performance across all imaging protocols and skin tone groups. Threshold-based approach showed camera-dependent misclassification patterns across skin tones. On the high-resolution FLIR E8XT, the MST 7-10 group had 8 of 11 misclassifications. This pattern shifted on the low-resolution FLIR ONE Pro, where the intermediate skin tone group (MST 6) had 22 of 44 total misclassifications.In contrast, deep learning models maintained consistent performance across all skin tone groups and imaging protocols. Visualization analysis of the deep learning models suggested that these models focused on thermal gradients at cooling region boundaries, suggesting that spatial temperature gradients, not single-value thresholds, are critical for accurate detection. These findings suggest the potential of deep learning-based approaches to maintain robust, equitable performance across diverse skin tones and imaging conditions.

Objective:This study aims to compare the advantages and disadvantages of DLIR and adaptive statistical iterative reconstruction-V (ASIR-V) in thin-slice (2.5 mm) CT images of hepatic lesions characterized by high and low contrast. Additionally, the study seeks to determine the optimal DLIR strength for the evaluation of liver lesions. Methods:A retrospective analysis was performed on 90 patients who underwent abdominal contrast-enhanced CT scans. Group A comprised 48 patients with low-contrast lesions, while Group B included 42 patients with high-contrast lesions. The acquired images were reconstructed using post-processing DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strengths, all with a slice thickness of 2.5 mm (subgroups A1-A3, B1-B3). Furthermore, images were reconstructed with ASIR-V at 50% strength at slice thicknesses of 2.5 mm and 5 mm (subgroups A4/B4 and A5/B5, respectively). CT values and standard deviations (SD) of the liver and lesions were measured, and the corresponding signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The edge rise slope (ERS) was determined using ImageJ software by measuring CT values along a line from the liver parenchyma to the lesion. Objective metrics were compared using one-way ANOVA, with independent samples t-tests applied for inter-group differences. Subjective scoring, which encompassed noise level, diagnostic confidence, and lesion margin delineation, was conducted by two radiologists, with differences analyzed using the Kappa test. Results: Objective evaluation revealed a progressive decrease in lesion SD and a progressive increase in SNR and CNR from subgroups A1/B1 to A3/B3. The SD of Group A2 decreased by 57.4% compared to A4, while the SNR and CNR of A2 icreased by 19.3% and 24.6% compared to A4. Although subgroup B2 had a lower SNR than B5, the difference was not statistically significant. SNR and CNR in B2 increased by 24.1% and 11.9%, respectively, compared to B4. ERS gradually decreased from A1/B1 to A3/B3. ERS values in A2 and B2 increased by 27.0% and 39.4%, respectively, relative to A5 and B5. Although A3 had a lower ERS than A1 and A2, all DLIR subgroups exhibited higher ERS than A5; similar trends were observed in Group B. Subjective evaluation indicated good inter-reader agreement (Kappa > 0.61, p < 0.05). As DLIR strength increased, noise scores rose progressively in both groups. However, noise in A2 and B2 was lower than in A4/A5 and B4/B5. Diagnostic confidence and lesion margin delineation scores were highest in A2 and B2, while all subjective scores were lowest in A5 and B5. Discussion: Most prior studies evaluated the liver, vessels, or confirmed that image quality can be guaranteed at low doses. However, there are few studies on specific individual lesions. Therefore, this study aims to investigate specific individual lesions. The details and detection rate were analyzed separately to confirm the clinical acceptability of 2.5-mm DLIR image in different contrast lesions. Conclusion: For both high- and low-contrast hepatic lesions, DLIR provides superior image quality compared to ASIR-V, with the 2.5mm DLIR-M setting being optimal. DLIR-M reduces image noise, improves spatial resolution, and produces images more suitable for diagnostic purposes.

Cancer stem cells (CSCs) exhibit increased resistance to radiotherapy, contributing to tumor recurrence and progression. While CSCs are known for their intrinsic resistance, the role of their spatial organization remains poorly understood. We extend a computational model of tumorsphere growth to investigate how the spatial distribution of CSCs influences radiation response. The model explicitly tracks cell lineages and spatial positions, revealing a preferential accumulation of CSCs in the spheroid interior. Because radiosensitivity increases with oxygen availability, and oxygen levels are lowest in the tumor core, this spatial organization confers a protective advantage to the CSC population. We find that this effect is negligible in small, well-oxygenated tumorspheres but becomes pronounced as growth leads to the emergence of hypoxic regions. To isolate the role of spatial structure, we compare these results with control simulations in which CSC positions are randomly reassigned. In these synthetic configurations, CSC survival under irradiation is markedly reduced, demonstrating that spatial localization is a key determinant of radioresistance. This effect persists even after the onset of central necrosis, suggesting that the "spatial niche" of CSCs is a critical target for improving therapeutic outcomes. Author SummaryCancer stem cells are known to survive radiotherapy better than other cancer cells, often leading to tumor recurrence. While this resistance is usually attributed to intrinsic biological differences between cells, in this study we show that their physical location within the tumor plays a critical and previously underestimated role. We developed a three-dimensional computer model that simulates the growth of a tumorsphere from a single cancer stem cell. Because oxygen levels influence how sensitive cells are to radiation, our model tracks the position of each cell and calculates the oxygen distribution. We found that cancer stem cells naturally accumulate in the poorly oxygenated spheroid core, where radiation is less effective. To confirm that this location directly causes their survival advantage, we performed a "digital experiment": We artificially redistributed the same cells randomly throughout the tumorsphere before applying simulated radiation. In this random configuration, cancer stem cell survival dropped significantly. Our results show that radioresistance is not only an intrinsic cell property, but also a consequence of the spatial structure of the tumor. This finding suggests that future therapies could be improved by targeting not only the stem cells themselves, but also the protective hypoxic niches where they reside.

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

ObjectivesTo characterize longitudinal age-related changes in abdominal organ volumes using CT volumetry and to model nonlinear trajectories across multiple organs. Materials & MethodsThis retrospective single-center study included adults who underwent whole-body screening low-dose CT between 2006 and 2017. Subjects with at least eight examinations during a follow-up period of at least 78 months were included. After applying exclusion criteria, 700 participants with 6,739 CT series were analyzed. Non-contrast CT images were processed using automated organ segmentation, and volumes of the liver, pancreas, spleen, and kidneys were quantified. Longitudinal changes were modeled using generalized additive mixed models with sex-specific smooth functions of age and subject-level random effects. Age-dependent rates of change were estimated from model derivatives. ResultsA total of 700 participants (mean age, 56.9 {+/-} 9.8 years, 29.6% women) were evaluated. Liver, pancreas, and kidney volumes showed mild increases or plateaued at approximately 40-60 years of age, depending on the organ, and were followed by gradual declines with advancing age, whereas splenic volume showed a progressive decrease across the age range. These patterns showed nonlinear age dependence. The transition from positive to negative change rates tended to occur earlier in women than in men for several organs, particularly the liver and kidneys. ConclusionLongitudinal CT analysis demonstrated nonlinear age-related changes in abdominal organ volumes, with organ-specific trajectories and sex-related differences in the timing and magnitude of volume changes. QuestionHow do abdominal organ volumes change longitudinally with age, and can their trajectories be characterized for each organ? FindingsLongitudinal CT analysis demonstrated nonlinear, organ-specific volume trajectories, with transitions from stability to decline around 40-60 years and earlier transitions in women than men. Clinical RelevanceLongitudinal reference patterns of abdominal organ volumes on CT improve the interpretation of age-related changes and support more accurate differentiation between physiological variation and disease-related volume alterations.

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

PurposeLow-dose radiation-induced adaptive response (LDRIAR) is well documented, but its role in early DNA damage signalling remains unclear. This study aimed to investigate whether adaptive response influences initial DNA double-strand break (DSB) recognition, as reflected by {gamma}H2AX foci formation, and to evaluate its time-dependent expression in human lymphocytes. Materials and MethodsPeripheral blood lymphocytes from three healthy donors were exposed to a priming dose followed by a challenging dose at defined time intervals. DNA damage was assessed using {gamma}H2AX foci analysis, comparing acute and split-dose exposures in both PHA-stimulated (large) and non-stimulated (small) lymphocytes. ResultsA clear time-dependent adaptive response was observed. No significant reduction in {gamma}H2AX foci was detected at 1 h (p > 0.05). At 2 h, a significant decrease was observed ([~]7-8% in large and [~]13% in small lymphocytes; p < 0.01), which increased at 4 h ([~]12% and [~]22%, respectively; p < 0.001). The maximal response occurred at 15 h, with reductions of [~]40- 43% in large and [~]27% in small lymphocytes (p < 0.001). Small lymphocytes exhibited an earlier response, while large lymphocytes showed a greater magnitude at later time points. The temporal trend was consistent across donors, with minor variability at later intervals. ConclusionsThe findings demonstrate that LDRIAR is reflected at the level of DNA damage signalling and follows a defined temporal pattern with cell-type specificity. This suggests that adaptive response may influence early DSB-associated processes, contributing to a better understanding of radiation response mechanisms in radiobiology.

Background and purposePrecise manual annotation of the left ventricular myocardial (LVM) wall is essential for cardiac substructure research, wall-specific radiation dosimetry, and segmentation model development. However, existing radiotherapy-oriented atlases and conventional CT viewing planes lack an explicit framework for reproducible, wall-level LVM delineation. To address this gap, we developed an anatomy-guided manual segmentation protocol for delineating the five LVM walls on non-contrast-enhanced CT (NECT) or contrast-enhanced CT (CECT) scans. Materials and methodsThis protocol was developed using 60 chest CT scans from two prospective cohorts at Heidelberg University Hospital, including 50 CECTs from IMRT-MC2 cohort and 10 NECTs from MAGELLAN cohort. Manual contouring was performed in 3D Slicer. Segmentation rules were established through review by a radiation oncologist and a cardiology expert, based on the American Heart Association 17-segment model, and were tested on additional CT scans before final protocol definition. ResultsThe protocol centers on three geometric steps: (1) defining the LV long axis using the endocardial apex and the center of the mitral annulus; (2) constructing an apical delimitation plane based on LV geometry; and (3) partitioning wall regions via intersections of the right ventricular and LV cavity centers in the short-axis view. This workflow enables structured segmentation of the anterior, septal, lateral, inferior, and apical LVM walls, supporting anatomically coherent 3D reconstruction. ConclusionThis study provides contouring steps and a representative atlas as a methodological basis for standardized annotation, with potential applications in dose-mapping cardiotoxicity analysis and deep-learning modeling for radiotherapy.

PurposeTo present the design and validation of a lowcost, microcontrollerbased gas delivery system that automates fixed inspired respiratory stimuli for MRI experiments. MethodsThe system uses three solenoid valves controlled by an Arduinobased circuit to switch between premixed medical gas cylinders according to predefined timing protocols. By using the MRI scanner external timing signal, gas delivery can be synchronised with image acquisition. Both a permanently installed configuration and a portable enclosure were constructed using commercially available components, with a total material cost of approximately {pound}650. The system was integrated with a singleuse breathing circuit and evaluated using hypercapnic and hyperoxic stimulus paradigms. Endtidal oxygen and carbon dioxide were measured using a respiratory gas analyser and physiological responses were assessed using BOLD MRI at 3 T. ResultsThe system delivered reliable, repeatable gas transitions during MRItriggered protocols. During hypercapnia (n{square}={square}15), the mean increase in endtidal carbon dioxide was 8.7{square}{+/-}{square}1.8{square}mmHg from a baseline of 32.2{square}{+/-}{square}3.1{square}mmHg, producing a mean grey matter BOLD signal increase of 3.2{+/-}1.7%. During hyperoxia (n{square}={square}15), the mean increase in endtidal oxygen was 292.3{square}{+/-}{square}59.0{square}mmHg from a baseline of 114.5{square}{+/-}{square}10.7{square}mmHg, with an associated BOLD signal change of 1.2{+/-}1.7%. Across both protocols respiratory and BOLD responses were consistent across participants. ConclusionThis microcontrollerbased system provides an inexpensive and reliable method for administering fixed inspired respiratory stimuli with automated MRI synchronisation. It offers an intermediate option between simple manual systems and higher cost commercial gas blenders, making it well suited for technical and methodological studies in cerebrovascular reactivity, hyperoxiaBOLD and related applications.

PurposeHigh-density RF receive arrays are required to realize the inherently available SNR and parallel imaging advantages at ultrahigh field strengths, which are essential for high-resolution functional and anatomical brain MRI. This study aims to systematically assess the impacts of often-overlooked parasitic losses associated with various RF coil components, as these losses can degrade the realized SNR and cause significant deviation from the ultimate intrinsic SNR (uiSNR; the theoretical upper bound of available SNR). In addition, we seek to detail engineering solutions to each of these loss mechanisms in pursuit of achieving a higher fraction of the uiSNR limit. MethodsA 16-channel loop-folded dipole transceiver array was developed for 10.5T human head applications and paired with a fully-updated 64-channel receive-only loop array. The optimization of the receive array considered several factors, including (but not limited to) coil dimensions to accommodate a larger population, the size and number of loops to enhance SNR and parallel imaging performance, and circuit design strategies to minimize parasitic losses. The SNR and parallel imaging performance of the receive array were quantitatively assessed by comparison with the uiSNR, as well as existing high-channel-count receive arrays at 7T and 10.5T. Finally, the complete 16-channel transmit, 80-channel receive coil array was safety validated for human use and employed for high-resolution functional and anatomical MRI at 10.5T. ResultsInitial results show that the 80-channel array, featuring larger loops in an overlapped layout with optimized circuitry, significantly improves the SNR and approaches the uiSNR limit in a large fraction of the head, while maintaining or enhancing the parallel imaging performance compared to previously used non-overlap layout. ConclusionThis study suggests that, although the traditionally used high-channel-count loop receive array technology can approach the uiSNR limit in the >10T regime, meticulous design optimization--including systematic assessment and minimization of parasitic losses--has become increasingly critical for achieving this goal in this new field-strength territory.

Ionizing radiation induces molecular responses that may be used to estimate exposure when physical dosimeters are unavailable. Here we present two large-scale proteomics datasets generated from mouse dorsal skin punch samples collected following controlled X-ray exposures spanning multiple doses, dose rates, and post-exposure time points. Experiment 1 comprised 96 samples (including 16 reference samples) collected 6 days after exposure to 0-75 cGy delivered at either 30 or 300 cGy/min. Experiment 2 comprised 936 samples (including 236 reference samples) exposed to 0-100 cGy at either 3 or 28 cGy/min dose rates and harvested between 7 and 150 days post-exposure. All samples were processed using a standardized workflow involving automated bead-based digestion and data-independent acquisition mass spectrometry. The datasets include multiple pooled reference sample types, process controls, and system suitability standards ensuring high quality data. All data presented are available via ProteomeXchange at several levels of processing, from raw files through normalized peptide- and protein-level abundance matrices suitable for biomarker discovery and machine learning applications. This dataset will facilitate generation of new insights into the biological changes and molecular signatures resulting from X-ray exposure in mice and may also help inform future studies in humans.

BackgroundPrevious studies evaluated large language model (LLM) performance on the American Society of Nuclear Cardiology (ASNC) Board Preparation Exam. Without domain-specific context, the best model (GPT-4o) achieved 63.1%, below the estimated 65% passing threshold and the 78% mean score of human fellows-in-training (FITs). Providing textbook context improved GPT-4o to 73.8% on text-only questions, but still fell short of human trainees. Whether next-generation LLMs with retrieval-augmented generation (RAG) can exceed this gap is unknown. MethodsClaude Opus 4.7 and GPT-5.5 were administered all 168 questions (141 text-only, 27 image-based) from the 2023 ASNC Board Preparation Exam across 5 iterations each, using RAG with a nuclear cardiology textbook, companion atlas, and ASNC clinical guidelines. Claude used local FAISS-based semantic retrieval; GPT-5.5 used Azures cloud-hosted vector store. Performance was compared to prior LLM results and 13 human FITs. ResultsAcross 5 iterations, Claude Opus 4.7 achieved a mean accuracy of 86.3% {+/-} 1.4% (text 88.8%, image 73.3%). GPT-5.5 achieved 86.7% {+/-} 2.2% (text 88.5%, image 77.0%) but refused a mean of 12.2 questions (7.3%) per iteration due to safety filters. Both models surpassed the human FIT mean (78.0%) and the estimated passing threshold. Compared to GPT-4o without context (63.1%), this represents a 23-percentage-point improvement in 18 months. ConclusionNext-generation LLMs with RAG now surpass average human trainee performance on nuclear cardiology board preparation questions, suggesting significant potential as educational tools and knowledge-reference aids in cardiovascular imaging. Condensed AbstractAcross 5 iterations each, Claude Opus 4.7 and GPT-5.5 with retrieval-augmented generation achieved mean accuracies of 86.3% and 86.7% on the 2023 ASNC Board Preparation Exam (168 questions), both surpassing the mean human fellow-in-training score of 78%. GPT-5.5 refused a mean of 12.2 questions (7.3%) per iteration due to safety filters. These results represent a 23-percentage-point improvement over the best prior LLM without context (63.1%), demonstrating that RAG-enhanced LLMs have reached human-level proficiency in nuclear cardiology knowledge. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/26352768v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@5f2465org.highwire.dtl.DTLVardef@4e80d3org.highwire.dtl.DTLVardef@1ebbb93org.highwire.dtl.DTLVardef@167d3c1_HPS_FORMAT_FIGEXP M_FIG C_FIG Overview of the three-study research arc evaluating LLM performance on the 2023 ASNC Board Preparation Exam. Study 1 (2024) tested four LLMs without context (best: GPT-4o, 63.1%). Study 2 (2025) added textbook context to GPT-4o (73.8%). Study 3 (2026, current) evaluated Claude Opus 4.7 and GPT-5.5 with retrieval-augmented generation across 5 iterations each (mean 86.3% and 86.7%, respectively), both surpassing the human fellow-in-training mean of 78%. Right panel shows the performance scale with key thresholds.

Objectives: AI-based reconstructions can reduce MRI acquisition times and/or improve image quality. Guidelines recommend clinical evaluations and post-deployment monitoring of these novel methods, however, there has been little investigation of the clinical resources required for such assessments. The aim of this study was to evaluate the healthcare resource utilisation and potential savings associated with AI-based reconstructions in rectal MRI. Methods: A retrospective economic costing analysis was conducted from the NHS healthcare perspective. Resource utilisation data were extracted from the Electronic Patient Records for 9 healthy volunteer scans and 104 rectal MRI examinations evaluating an AI-based reconstruction. The resource profile included the MRI scan and the staff time required for data acquisition and analysis. Results: The clinical evaluation of the AI-based reconstruction cost {pound}15,023. Deployment of the AI-based reconstruction reduced the length of an MRI rectum scan by 22 minutes, theoretically saving approximately {pound}3,437 per month. Addition of post-deployment quality control scans reduced this monthly saving to {pound}2,636. If the quality control scans were evaluated using radiologists rather than image quality metrics, monthly savings would be approximately {pound}2,541. With ongoing quality control, the clinical evaluation cost would be recouped between 5.8 and 6 months, compared with 4.4 months without ongoing quality control. Conclusions: Deploying AI-based reconstructions can yield cost savings through reduced scanning times. Quality control tests using image quality metrics would save radiological burden and reduce costs compared with conducting repeated image scoring by radiologists.