Medical Physics

Ultra-high-dose-rate therapy enhances the protection of normal tissues and reduces side effects while effectively controlling tumors. This biological phenomenon is called the FLASH effect, and when observed, therapy is called FLASH Radiotherapy (FLASH-RT). Various hypotheses have been proposed to explain how ultra-high dose rates achieve these effects under different conditions, with the impact of tissue oxygen perfusion still needing further investigation. FLASH-RT involves brief exposure to radiation, which results in fewer heartbeats occurring during the irradiation period, which could lead to reduced tissue oxygen perfusion occurring during the treatment timeframe. Therefore, we developed a compartmental model to simulate oxygen transfer and its interaction with radiation. The proposed model consists of three compartments: 1) the heart and arteries; 2) the irradiated brains blood vessels and capillaries; and 3) the irradiated brain tissue. We employed a system of differential equations, incorporating experimental data from in vivo oxygen measurements using the Oxyphor probe in the brain, to fit the model parameters to the experimental results. This model shows how dose rate and oxygen perfusion could influence chemical processes such as lipid peroxidation, potentially leading to differential biological effects. Our analysis of lipid peroxidation as a function of dose rate revealed a sigmoidal dose-rate-response curve that correlates well with several published biological response datasets. Our results indicate that the differential chemical effects of FLASH-RT compared with conventional dose rates may depend on factors such as oxygen perfusion, consumption, and tissue oxygen tension. This suggests that the temporal dynamics of oxygen could play a crucial role in enhancing the therapeutic window for FLASH-RT treatments. Furthermore, it suggests that the magnitude of some observed FLASH effects may vary across tissues or tumors and across experimental models, given differential oxygen dynamics.

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

Purpose: Manual verification of AI-based auto-contouring is labor-intensive and prone to fatigue-related errors. This study developed the large language model (LLM)-based automated Quality Assurance (QA) for auto-contouring (LAQUA) system using a multimodal LLM, Gemini 2.5 Pro, and evaluated its feasibility as a clinical primary screening tool to streamline the QA workflow. Methods: Twenty male pelvic CT scans from an open dataset were utilized. Three distinct auto-contouring software packages (OncoStudio, RatoGuide prototype and syngo.via) were evaluated. Auto-contouring results for each slice were exported as PDF images with overlaid contours and input into Gemini 2.5 Pro. The LLM was instructed to rate the contour quality on a 5-point clinical scale (5: Optimal; 4: Acceptable; 3: Suboptimal; 2: Unacceptable; redraw from scratch; 1: Unacceptable; organ not detected). Using evaluations by two board-certified radiation oncologists as ground truth, Spearman's rank correlation coefficients ({rho}) and weighted kappa coefficients ({kappa}) were calculated. Additionally, to assess screening performance, sensitivity and specificity were calculated by dichotomizing the scores into "Pass" and "Fail" using two different cutoffs (scores [≥] 3 and [≥] 4 as "Pass"). Finally, the alignment of the rationales provided by the LLM with the auto-contouring quality was evaluated by two board-certified radiation oncologists. This was conducted using a Likert scale assessing four domains (error detection, hallucination, clinical relevance, and anatomical understanding), each scored out of 2 points. Results: The LAQUA system demonstrated moderate to strong agreement with expert judgments across all evaluated organs ({rho}: 0.567 - 0.835; quadratic weighted {kappa} : 0.639 - 0.804), with the rectum showing the highest correlation. Regarding screening performance, a cutoff of [≥]3 as "Pass" achieved the highest sensitivity and specificity in specific subgroups, but with wide 95% confidence intervals (CIs). A cutoff of [≥]4 as "Pass" narrowed the CIs, yielding the highest sensitivity in the rectum (0.976) and the highest specificity in the left femoral head (0.933). Qualitatively, the LLM's rationales achieved an overall mean score of 1.70 {+/-} 0.48 (out of 2), with 155 of 291 outputs receiving perfect scores across all criteria. Conclusions: The LAQUA system demonstrated substantial agreement with expert evaluations in AI-based auto-contouring quality assessment. While potential overestimation bias (risk of missing "Fail" cases) warrants caution, the observed sensitivity suggests its feasibility as a primary screening QA tool to efficiently filter acceptable contours, thereby reducing the clinical workload.

Automated and reliable image quality assessment (IQA) is essential for safe use of medical image synthesis in critical applications like adaptive radiotherapy, treatment planning, or missing-modality reconstruction, where unnoticed generative artifacts may adversely affect outcomes. We evaluated image-to-image translation quality by coupling large-scale expert visual quality assessment with explainable automated IQA modeling. Adversarial diffusion-based framework, SynDiff, was applied to four cross-modality synthesis tasks, including three inter-MR and a CBCT-to-CT translation. Using four-fold cross-validation, ten reference-based and eight no-reference IQA metrics were computed for all synthesized images. Visual IQA ratings were independently collected from thirteen expert raters using predetermined protocol and specialized image viewer enabling blinded, randomized six-point Likert scoring. Auto-Sklearn was employed to learn ensemble regression models mapping IQA metrics to visual consensus ratings, with separate models trained on reference-based and no-reference metrics. The models closely reproduced distribution and ordering of expert ratings, typically within +/- 0.5 Likert points. Reference-based models achieved higher agreement with visual ratings than no-reference models (R^2 0.75 vs. 0.59, resp.), although the latter remained unbiased and informative. Explainability analyses highlighted structure- and contrast-sensitive metrics as key predictors. Overall, the results demonstrate that ensemble regression models can provide transparent, scalable, and clinically meaningful quality control for generative medical imaging.

ObjectivesHierarchical Phase-Contrast Tomography (HiP-CT) enables non-destructive, multi-scale imaging of whole human organs. We describe how HiP-CT is utilized for biomedical research within the Human Organ Atlas Hub through three case studies: mapping the enteric nervous system (ENS) of the human colon, analysing myocardial and AV conduction architecture in Tetralogy of Fallot (TOF), and characterizing ductal organization in breast carcinoma. The challenges we faced with this novel biomedical data are discussed. MethodsWhole-organ and region-of-interest scans of three types of human organs were acquired at the European Synchrotron Radiation Facility (ESRF) with isotropic voxel sizes ranging from 20 {micro}m to 0.8 {micro}m. For the colon, voxel binning and RootPainter were employed to tackle data size to segment the ENS. For the heart, voxel-wise myocyte orientation mapping was calculated in terabyte-scale datasets with a high-performance computational framework (Cardiotensor). Breast carcinoma samples were correlated with histopathology for structure validation. ResultsHiP-CT revealed the large-scale organization of the ENS in the colon, enabling visualisation of the 3D structures of the ENS across the colon In TOF hearts, analysis uncovered abnormal myocardial structure and heterogeneous conduction system morphology. In breast carcinoma, HiP-CT resolved the full hierarchy of ductal structures and vascular relationships within tumour and peritumoral regions. ConclusionsHiP-CT provides unprecedented, hierarchical insight into intact human organ structure, bridging the gap between histology and radiology. Advances in knowledgeHiP-CT establishes a new ex vivo radiological modality capable of linking microscale pathology to whole-organ context, advancing translational research in neurogastroenterology, cardiology, and oncology

Head and neck cancer (HNC) requires accurate tumor delineation for effective radiotherapy planning. Manual segmentation of tumor regions is time-consuming and subject to considerable inter-observer variability. Although several automated approaches have been proposed, many rely on multimodal imaging such as PET/CT, which is expensive, less accessible in many clinical settings, and increases the burden on patients. In this work, we investigate a CT-only three-dimensional segmentation framework that provides a clinically practical and resource-efficient alternative. CT images of 136 head and neck cancer patients from the publicly available HN1 dataset in The Cancer Imaging Archive (TCIA) were used along with 30 additional cases from a private dataset collected at a tertiary care centre, Christian Medical College (CMC), Vellore, India. A fully automated segmentation model was developed to delineate the primary gross tumor volume (GTV) using the 3D nnU-Net framework. The models were trained using the HN1 dataset and an extended HN1+CMC dataset that included the additional private cases. Performance was evaluated using three-fold cross-validation with standard segmentation metrics including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and the 95th percentile Hausdorff Distance (HD95). The proposed CT-based model achieved a Global Dice of 0.63 and a Median Dice of 0.60 on the HN1 dataset. When the additional CMC cases were incorporated during training, the performance improved to a Global Dice of 0.65 and a Median Dice of 0.71. These results demonstrate that 3D nnU-Net can effectively segment head and neck tumors from CT images alone. The proposed CT-only approach provides a cost-effective and scalable solution that can support radiotherapy treatment planning and help reduce variability in clinical workflows.

BackgroundSpectral computed tomography (CT) is increasingly used for quantitative imaging, yet accurate prediction of spectral quantitative bias remains challenging and computationally expensive with conventional approaches. Bias manifests as systematic deviations in reconstructed quantities (e.g., Hounsfield units, iodine density) from their true physical values. It arises from a combination of model mismatch, hardware/processing imperfections, exam-dependent factors, and noise-induced effects amplified by nonlinear operations such as the logarithmic transformation and material decomposition. PurposeWe present a practical, projection-based statistical framework to estimate noise-induced spectral bias efficiently, without the runtime burden of Monte Carlo (MC) simulation. MethodsTo demonstrate the bias estimator, we modeled the central-ray of a clinical X-ray tube attenuating through a 300 mm patient-equivalent path with a 10 mm insert containing 10 mg/mL iodine. A 120 kVp tube voltage and tube currents from 100-350 mA were used. Ideal and realistic photon-counting detector responses were simulated across 50 bin threshold settings. Quantum Poisson noise was modeled, and Bayesian probabilities of material decomposition outputs centered on ground truth iodine and water bases were computed. Expected material decomposition outputs [Formula] were derived from a 2D probability map, and bias was measured. A simple Python Monte Carlo (MC) simulation served as a reference. ResultsThe proposed bias estimator closely matched MC-derived bias, with an average relative iodine bias percent difference between the estimators of 0.44% across all tube currents and bin thresholds. Average runtime of the bias estimator was only 0.5% of the MC simulation. Optimal thresholds for minimizing iodine noise (via the Cramer-Rao lower bound) differed from those minimizing iodine bias, highlighting key noise-bias tradeoffs. ConclusionEfficient spectral bias and noise estimation are essential for quantitative CT system design. This modular framework enables rapid, bias-aware optimization of spectral acquisition parameters and is adaptable to alternative spectral CT technologies beyond photon counting. Novelty and Significance of StudyPlease briefly (150 words or less) describe the novelty and/or significance of your study. Bias estimation is paramount for designing accurate spectral CT systems that deliver improved diagnostic performance. Traditional approaches rely on computationally intensive Monte Carlo simulations. We propose an efficient and practical bias estimator that uses Bayesian statistics and expected material decomposition values derived from a flexible, modular CT forward model. Unlike conventional Monte Carlo approaches, this framework enables rapid exploration of spectral design tradeoffs between bias and noise. We demonstrate both the accuracy and speed of this bias estimator relative to Monte Carlo approaches.

Background and PurposeThe use of MRI-based fat quantification can be applied to automatically identify red bone marrow which is highly sensitive to radiation and systemic therapies and could be used as an organ-of-interest for adaptive radiation therapy. Currently, the tradeoff of scan time and PDFF/R2* quantification accuracy from the 2-/3-/6-point methods, particularly for the time-constrained MR-Linac, remain unanswered. Therefore, the purpose of this study was to investigate the technical feasibility and quantitative performance of quantitative Dixon-based imaging for scanners within the radiation oncology department. Materials and MethodsA 2-/3-/6-point version of the quantitative Dixon sequence was developed and scanned on a 1.5T MR-Simulation, 3T MR-Simulation, and 1.5T MR-Linac scanner for five repetitions using the Calimetrix Model 725 PDFF-R2* phantom as a nominal reference for quantitative PDFF/R2* values. The image geometric distortion as well as the quantitative concordance, Bland-Altman agreement, repeatability, and reproducibility of both the PDFF/R2* values were determined. Each sequence was evaluated in both the pelvis and head and neck across both healthy volunteers and patients. ResultsThe most severe geometric distortion was less than 2 mm except for the 1.5T MR-Linac when using the 2-point Dixon sequence with distortions exceeding 5 mm. The 6-point Dixon sequence showed the highest concordance at above 0.97 across all scanners for both PDFF and R2* followed by the 3-point and 2-point sequence. The 2-point Dixon sequence exhibited significant PDFF biases particularly at the higher R2* values since it did not correct for it during reconstruction. For the Bland-Altman analysis, the 2-point Dixon sequence had the widest 95% limits of agreement followed by the 3-point and 6-point Dixon sequence with the narrowest bands. The goodness-of-fit is generally lowest at higher PDFF values and lower R2* values. Both repeatability and reproducibility were the lowest for the 6-point Dixon sequence. DiscussionThe 6-point quantitative Dixon sequence demonstrated superiority for the chosen evaluation metrics. The results of this work can be used to determine the threshold for true quantitative changes of PDFF/R2* while considering acquisition variabilities, enabling future biomarker studies and clinical trials. Further, this work provides validation for future investigations into quantitative bone marrow characterization. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/26347965v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@8b2139org.highwire.dtl.DTLVardef@322a97org.highwire.dtl.DTLVardef@18a3a46org.highwire.dtl.DTLVardef@1f7ef62_HPS_FORMAT_FIGEXP M_FIG C_FIG

Computed Tomography (CT) is one of the largest contributors to radiation exposure from medical imaging, which can induce DNA damage and increase cancer risk. Reducing CT radiation dose to improve patient safety inherently increases image noise and artifacts. Generative adversarial networks (GANs) have shown promise for unsupervised low-dose CT (LDCT) denoising. Building on this, RDBCycleGAN-CBAM, a CycleGAN-based model that integrates residual dense blocks (RDBs) and convolutional block attention modules (CBAM), was developed to effectively denoise quarter-dose CT images while preserving structural detail. The model was trained on unpaired quarter-dose and full-dose CT scans from the NIH-AAPM-Mayo dataset using adversarial (LSGAN), cycle-consistency, and identity losses. Evaluation on held-out test slices was performed using PSNR and SSIM as the primary image-quality metrics. The results demonstrate that the proposed RDBCycleGAN-CBAM method not only achieves higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values but also outperforms most existing deep learning-based methods, achieving mean improvements of +3.97 dB in PSNR and +0.053 in SSIM relative to quarter-dose inputs. Shapiro- Wilk tests for PSNR and SSIM motivated the use of the nonparametric Wilcoxon signed-rank test, by which highly significant improvements across both metrics (PSNR and SSIM) were demonstrated. The very large rank-biserial correlation values (1.0) indicate that nearly all test images experienced substantial quality improvement. Furthermore, the narrow bootstrap confidence intervals for the mean differences suggest that these improvements are consistent across the dataset. These advancements contribute to medical imaging by providing a viable, vendor-neutral tool for reducing patient radiation exposure without compromising diagnostic value.

Radiogenomics enables the non-invasive characterisation of the genomic and molecular properties of tumours, with epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) being one of the most investigated applications. In this study, we evaluate radiomics, contrastive learning, and convolutional deep learning approaches to predict the EGFR mutation status from chest Computed Tomography (CT) images using the TCIA Radiogenomics dataset (n=115). Our results, using 10-fold cross validation, demonstrate the capacity of imaging models to predict mutation status from CT data in a manner consistent with existing literature. Among the evaluated methods, models integrating radiomic with clinical features achieved the best performance, with an AUC of 0.790 and AUPRC of 0.517, outperforming both contrastive learning (AUC=0.787) and convolutional architectures (AUC=0.763). Beyond methodological comparisons, we discuss the challenges related to clinical translation. Specifically, we contrast radiogenomics with conventional tissue biopsies, and identify scenarios where radiogenomics might be useful, either independently or in conjunction with other existing diagnostic technologies. Together these findings evidence the potential utility of radiogenomics EGFR models and provide direct architecture comparisons on the same dataset.

Current standard of care imaging practices cannot reliably differentiate among certain renal tumors such as benign oncocytoma and clear cell renal cell carcinoma (RCC), and between low and high grade RCCs. Previous work has explored using deep learning, radiomics, and texture analysis to predict renal tumor subtypes and differentiate between low and high grade RCCs with mixed success. To further this work, large diverse datasets are needed to improve model performance and provide strong evaluation sets. In this work, a dataset of 831 multi-phase 3D CT exams was curated. Each exam contains up to three contrast-enhanced CT phases. Tumor outlines or bounding boxes were annotated and registered to the image volumes. The pathology results for each tumor and relevant patient metadata are also included.

Background and PurposeMagnetic resonance imaging (MRI) for radiation therapy treatment planning is currently being used in many anatomical sites to better visualize soft tissue landmarks, a technique known as an MRI simulation. A core component of modern MRI simulation configurations are the use of external laser positioning systems (ELPS) to help set up the patient. Though necessary for accurate and reproducible patient setup, the ELPS, if left on during imaging, may interfere negatively with image quality due to leaking electronic noise, of which MRI is sensitive to. It is currently unknown whether this leakage of electronic noise may further affect quantitative values derived from clinically employed relaxometric, diffusion, and fat fraction sequences. Therefore, in this study, we aim to characterize the impact of MRI simulation lasers on general image quality and quantitative imaging accuracy. Materials and MethodsFirst, a cine acquisition was used to visualize the real-time changes in image signal-to-noise ratio (SNR) from when the ELPS was deactivated to activated. To validate this effect quantitatively, the SNR was measured using the American College of Radiology (ACR) recommended protocol in a homogeneous phantom with the integrated body, 18-channel UltraFlex small, 18-channel UltraFlex large, 32-channel spine, and 16-channel shoulder coils. Next, a geometric distortion algorithm was tested in two vendor-provided phantoms while using the integrated body coil and the ACR Large Phantom protocol was tested. Finally, a series of quantitative MRI scans were performed using a CaliberMRI Model 137 Mini Hybrid phantom to validate quantitative T1, T2, and ADC while a Calimetrix PDFF-R2* phantom was used for quantitative PDFF and R2*. All scans were performed with both the ELPS both deactivated and activated. ResultsVisible electronic noise artifacts were seen when using the integrated body coil when the ELPS was activated on the cine acquisition which led to a four-fold decrease in SNR using the ACR protocol. This SNR drop was not seen when using the remaining tested coils. The automatic fiducial detection algorithm was affected negatively by ELPS activation leading to misidentification when identified perfectly with the ELPS deactivated. Degradation in image intensity uniformity, percent signal ghosting, and low contrast object detectability was seen during ACR Large Phantom testing using the 20-channel Head/Neck coil. Concordance across quantitative MRI values was similar when the ELPS was both deactivated and activated while a consistent increase in standard deviation inside the ADC vials was seen when the ELPS was activated. DiscussionThe extra noise induced from the activation of the ELPS during imaging should be avoided due to its potential to unnecessarily increase image noise. This is particularly true when conducting mandatory quality assurance testing for image quality and geometric distortion which utilize the integrated body coil which is most susceptible to ELPS-induced noise. Clear clinical guidelines should be implemented to make this issue known to the MRI technologists, physicists, and other relevant staff using an MRI with a supplementary ELPS for patient alignment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/26347809v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@dd725borg.highwire.dtl.DTLVardef@7ed081org.highwire.dtl.DTLVardef@1aac775org.highwire.dtl.DTLVardef@10ce397_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background and PurposeQuantitative relaxometry on the integrated MRI / linear accelerator (MR-Linac) at high isotropic resolution is currently limited due to prohibitively long scan times and limited field-of-views. Therefore, the purpose of this study was to assess the technical feasibility of the 3D-QALAS technique on the 1.5T MR-Linac which has the ability to acquire whole-brain 1 mm isotropic quantitative T1, T2, and PD maps along with multiple synthetic images in a 7 minute acquisition time. Materials and MethodsA 1 mm isotropic 3D-QALAS acquisition was scanned in both phantoms and a healthy volunteer on the 1.5T Elekta Unity MR-Linac device with scan times around seven minutes. A test-retest protocol across five independent sessions for the phantom was conducted. The correlation, repeatability, and reproducibility between measured and reference quantitative T1, T2, and PD values were determined in the phantom. Distortion was also studied. Vendor-provided reconstruction through SyMRI was performed to extract synthetic images and brain volume metric assessments on a healthy volunteer. ResultsThe slope and concordance between the measured and phantom reference values was 1.02 (1.00), 1.09 (0.90), and 0.99 (1.00) for T1, T2, and PD, respectively. Median distortion across the phantom remained below 2 mm. The repeatability and reproducibility coefficient-of-variation (CoV) was under 8% for all measured values. The measured brain volumes in the healthy volunteer was within expected age-adjusted reference values. DiscussionThe technical feasibility of using 3D-QALAS on the integrated 1.5T MR-Linac was confirmed. Applying this technique to the head and neck adaptive radiation therapy workflow will provide new opportunities to integrate quantitative imaging relaxometry biomarkers at 1 mm isotropic resolution. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/26347967v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1f43093org.highwire.dtl.DTLVardef@a1320eorg.highwire.dtl.DTLVardef@dd750eorg.highwire.dtl.DTLVardef@1300853_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundGene expression profiling in radiation-exposed blood is a valuable tool for biodosimetry and clinical research. Evaluating the bloods transcriptomic radiation response provides insight into absorbed dose, hematotoxicities, and immune reactions. However, detailed analysis using long-read RNA sequencing is currently limited in its diffusion, despite the potential additional insights that could be extracted, including novel isoform discovery and on-the-field gene expression studies, owing to its portability. ResultsIn this study, we utilized Oxford Nanopore Technologies long-read RNA sequencing on human whole-blood samples from three healthy donors 6 hours after exposure to 4 Gy of X-rays. Compared to sham-irradiated (0 Gy) blood, gene-level differential expression analysis identified 117 upregulated and 66 downregulated genes, including canonical DNA damage repair and inflammatory responses. At the transcript level, 102 transcripts were significantly upregulated, and 17 were downregulated, revealing isoform-specific regulation that was not captured at the gene level. Notably, IL32, which showed no significant change at the gene level, exhibited strong upregulation of two transcript isoforms, while WDR74, ITM2B, AK2, and RPS19 displayed changes in transcript usage following irradiation. Leveraging the power of long-read RNA sequencing, we further identified 26 novel transcript isoforms, expanding the catalog of radiation-responsive transcripts. ConclusionsThis is the first comprehensive study of long-read RNA-seq for transcriptomic profiling of human whole blood following ionizing radiation. These findings highlight the ability of long-read RNA sequencing to provide a more detailed view of radiation-induced transcriptomic alterations, underscoring its potential for biodosimetry and clinical applications.

Purpose: To develop a deep neural network-based, AIF-free, perfusion estimation method (QTMnet) for improved performance on glioma classification. Methods: A globally defined arterial input function (AIF) is needed to recover perfusion parameters in the two-compartment exchange model (2CXM). We have developed Quantitative Transport Mapping (QTM) to create an AIF-independent estimation method. QTM estimation can be formulated using deep neural networks trained on synthetic DCE-MRI data (QTMnet). Here, we provide a fluid mechanics-based DCE-MRI simulation with exchange between the capillaries and extravascular extracellular space. We implemented tumor ROI generation to morphologically characterize tissue perfusion. We compared our QTMnet implementation with 2CXM on 30 glioma human subjects, 15 of which had low-grade gliomas, and 15 with high-grade glioblastomas. Results: QTMnet outperforms (best AUC: 0.973) traditional 2CXM (best AUC: 0.911) in a glioma grading task. Conclusion: The AIF-independent QTMnet estimation provides a quantitative delineation between low-grade and high-grade gliomas.

BackgroundLattice radiotherapy (LRT) delivers heterogeneous dose distribution through a three-dimensional array of vertices within the tumor. It is typically applied in 1[~]5 fractions for patients with large tumor volumes. However, conventional LRT generally employs only a single vertex set, which may limit the biological advantages of this technique in multi-fraction treatments. PurposeThis study proposes a novel vertex arrangement strategy in LRT aimed at improving intratumoral dose homogeneity and enhancing coverage of high-dose regions through alternating irradiation of different vertex sets. Materials and methodsPatients with the gross tumor volume (GTV) between 300 cm3 to 2000 cm3 who received radiotherapy treatment at our institution were considered for inclusion. An "NaCl"-type structure was employed. Two sets of vertices ("Na"-type and "Cl"-type) were distributed within the tumor volume following a face-centered cubic (FCC) close-packed pattern analogous to the NaCl crystal structure. For each of the 10 patients with large tumor volumes (range: 319.23-1649.47 cc), two plans were generated: Plan A (optimized for "Na" vertices) and Plan B (optimized for "Cl" vertices). Each plan delivered 15 Gy per fraction to the vertices. Physical doses from Plans A and B were converted to EQD2 (/{beta} = 10 for GTV, /{beta} = 3 for normal tissues) and summed into three composite plans: A+A, A+B, and B+B. Plan quality was assessed using generalized equivalent uniform dose (EUD), homogeneity index (HI), D2, D98, and mean normal tissue dose (Dmean of NT). ResultsThe alternating composite plan (A+B) achieved significantly greater dose homogeneity compared to non-alternating plans (A+A and B+B), with a lower HI (1.23 {+/-} 0.08 vs. 1.70 {+/-} 0.08 and 1.70 {+/-} 0.09, p < 0.05) and higher EUD (3.76 {+/-} 0.38 Gy vs. 3.48 {+/-} 0.40 Gy and 3.42 {+/-} 0.25 Gy, p < 0.05). The low-dose metric D98 was also higher in A+B (4.23 {+/-} 0.27 Gy) than in A+A (3.92 {+/-} 0.25 Gy) and B+B (3.94 {+/-} 0.25 Gy). No significant difference was observed in NT Dmean among the three composite plans. ConclusionAlternating irradiation of two geometrically complementary vertex sets significantly improves dose coverage in high-dose regions and overall dose homogeneity without increasing normal tissue toxicity and potentially enhances therapeutic efficacy in spatially fractionated radiotherapy for large tumors.

BackgroundPatient radiation exposure in diagnostic radiology is an important concern for radiation protection and patient safety. Monitoring radiation dose levels during radiographic examinations is essential to ensure compliance with diagnostic reference levels (DRLs) and to optimize radiological practices. ObjectiveThe aim of this study was to evaluate patient radiation dose during conventional lumbar spine radiography and compare the obtained values with diagnostic reference levels. MethodsA descriptive cross-sectional multicenter study was conducted in four hospitals in the Sous Massa region, Morocco, between April and June 2017. Data were collected from 142 patients undergoing lumbar spine radiography examinations and from 20 radiology technicians. Exposure parameters including tube voltage, tube current, exposure time, focus-to-film distance, and field size were recorded. Entrance surface dose (ESD) was estimated using MICADO software, and dose area product (DAP) values were subsequently calculated. The 75th percentile values were determined and compared with diagnostic reference levels. ResultsThe regional 75th percentile ESD values were 5.33 mGy for the anteroposterior projection and 7.38 mGy for the lateral projection. Corresponding DAP values were 1840.9 mGy.cm2 and 2783.65 mGy.cm2, respectively. All obtained values were below the diagnostic reference levels used for comparison. However, variations between hospitals were observed, likely due to differences in imaging protocols and equipment. ConclusionRadiation doses associated with lumbar spine radiography in the evaluated hospitals were within acceptable limits according to diagnostic reference levels. Continuous monitoring of patient radiation exposure and optimization of radiographic techniques remain essential to ensure effective radiation protection.

Artificial intelligence (AI) based segmentation has many medical applications but limited curated datasets challenge model training; this study compares the impact of dataset annotation quality and quantity on whole liver AI segmentation performance. We obtained 3,089 abdominal computed tomography scans with whole-liver contours from MD Anderson Cancer Center (MDA) and a MICCAI challenge. A total of 249 scans were withheld for testing of which 30, MICCAI challenge data, were reserved for external validation. The remaining scans were divided into mixed-curation and highly-curated groups, randomly sampled into sub-datasets of various sizes, and used to train 3D nnU-Net segmentation models. Dice similarity coefficients (DSC), surface DSC with 2mm margins (SD 2mm), the 95th percentile of Hausdorff distance (HD95), and 2D axial slice DSC (Slice DSC) were used to evaluate model performance. The highly curated, 244-scan model (DSC=0.971, SD 2mm=0.958, HD95=2.98mm) performed insignificantly different on 3D evaluation metrics to the mixed-curation 2,840-scan model (DSC=0.971 [p>.999], SD 2mm=0.958 [p>.999], HD95=2.87mm [p>.999]). The 710-scan mixed-curation (Slice DSC=0.929) significantly outperformed the highly curated, 244-scan model (Slice DSC=0.923 [p=0.012]) on the 30 external scans. Highly curated datasets yielded equivalent performance to datasets that were a full order of magnitude larger. The benefits of larger, mixed-curation datasets are evidenced in model generalizability metrics and local improvements. In conclusion, tradeoffs between dataset quality and quantity for model training are nuanced and goal dependent.

PurposeMedical imaging typically generates 12- to 16-bit formats, yet conversion to 8-bit is often required. While deep learning has been widely explored in medical imaging, the influence of image bit depth on model performance is not fully understood. This study evaluates the impact of conversion from 16-bit to 8-bit for sex, age, and obesity classification using deep learning. Materials and methodsIn this retrospective, multi-institutional study, we analyzed 100,002 chest radiographs from 48,047 participants across three institutions. Three convolutional neural network architectures (ResNet52, EfficientNetB2, and ConvNeXtSmall) were trained on both 16-bit and 8-bit versions of the images. Model performance was evaluated using internal test datasets, randomly split multiple times, and an external test dataset. Statistical analysis included paired comparisons of area under the receiver operating characteristic curve (AUC-ROC) values, with Bonferroni correction for multiple comparisons. ResultsAcross all architectures and classification tasks, differences between 16-bit and 8-bit model performance were minimal (mean differences ranging from -0.218% to 0.184%). Statistical analyses revealed no significant differences in AUC-ROC values between bit depths for any model-task combination (all p-values > 0.05 after Bonferroni correction). Effect sizes were small to moderate (Cohens d ranging from -0.415 to 0.391). ConclusionReducing image bit depth from 16-bit to 8-bit does not significantly impact the performance of deep learning models in chest radiograph analysis. These findings suggest that 8-bit images can be used for deep learning applications in medical imaging without compromising model performance, potentially allowing for more efficient data storage and processing.

Access to positron emission tomography (PET) remains limited in rural and low-resource healthcare settings due to high infrastructure cost and radiotracer logistics. This restricts early oncologic screening in underserved populations. The study proposes a rural-optimized conditional diffusion framework for synthetic PET generation directly from widely available CT scans. The architecture employs a two-stage residual design consisting of a lightweight coarse predictor followed by computationally efficient diffusion refinement with reduced timesteps and deterministic sampling. A multi-objective SUV-aware loss ensures metabolic consistency. To emulate rural deployment conditions, this study simulates low-dose noise, Hounsfield unit miscalibration, and resolution degradation. Clinical validation demonstrates strong structural fidelity (SSIM 0.81) and stable SUVmean preservation. Domain-matched training achieves SUVmax error as low as 0.61. Cross-dataset analysis highlights the importance of SUV harmonization for robust rural deployment. This work presents a resource-sensitive AI frame-work supporting equitable oncology screening in rural healthcare systems. HighlightsO_LITwo-stage residual conditional diffusion for CT-to-PET translation. C_LIO_LISUV-aware multi-objective optimization preserves metabolic biomarkers. C_LIO_LIFew-shot adaptation improves cross-dataset SUV calibration. C_LI