Physics in medicine and biology最新文献

Objective. Bismuth germanate (BGO) has regained attention as a promising material for hybrid Cherenkov/scintillation time-of-flight positron emission tomography (TOF-PET). While excellent timing performance has been demonstrated in single-crystal studies using prompt Cherenkov photons, practical pixelated detector modules introduce appreciable inter-crystal scattering (InterCS) events that can degrade timing accuracy. The objective of this work was to experimentally investigate the impact ofInterCSon Cherenkov-based timing in pixelated BGO detectors and to identify optimal timestamp selection strategies.Approach. A dual-pixel BGO detector was constructed and coupled to a segmented SiPM readout to enable spatially resolved energy and timing measurements. Events were classified into full-energy deposition (FED); primary crystal 511 keV absorption),InterCS, andpenetrationcategories using energy-weighted positioning. This experimental classification was validated using GATE simulations, which further revealed that intra-crystal scattering (IntraCS) accounted for more than 25% of the events experimentally classified asFED. Multiple timestamp selection strategies were evaluated, and prompt photon statistics were quantified by integrating the first 1 ns of the timing waveform.Main results. ForInterCSevents, selecting the earlier of the two timestamps yielded a coincidence timing resolution of 221 ps FWHM (831 ps FWTM) measured in coincidence with a LYSO:(Ce, Mg) reference detector, compared to 184 ps FWHM (603 ps FWTM) forFEDevents. Energy-based timestamp selection was found to be suboptimal. Prompt photon analysis showed a measurable reduction in early photon yield forInterCSevents, with an average of 4.73 detected photons in the first 1 ns, compared to 5.76 forFEDevents.Significance. these results demonstrate thatInterCSintroduces systematic timing degradation in pixelated BGO Cherenkov TOF-PET detectors through energy redistribution and reduced prompt photon statistics. The findings highlight the necessity of time-aware, per-pixel timestamp selection strategies to preserve optimal timing performance in realistic BGO-based TOF-PET systems operating in the presence of Compton scattering.

Objective.Spectral computed tomography (CT) data from photon-counting CT (PCCT) enables material decomposition. Mechanistic approaches such as maximum likelihood estimation are noise sensitive. Deep learning alternatives mitigate this issue, but their accuracy remains limited due to lack of incorporation of underlying physics principles and lack of ground truth data. This study aims to develop and validate a physics-informed deep-learning model, trained on validated simulated data, to decompose spectral CT images into density (ρ)and effective atomic number (Z_eff) maps.Methods.The training dataset included simulated abdominal PCCT scans from 32 human models with corresponding ground truth. The scans were obtained at two clinical dose levels, four detector energy thresholds, different iodinated contrast agent concentrations and reconstructed using three clinically-used kernels. A generative adversarial network (GAN) was trained with and without a physics-informed regularization loss to estimateρandZ_effmaps. Model performance was evaluated on 16 computational phantoms and validated on 6 clinical cases. A reader study was performed on 30 image slices to assess the comparative performance ofρandZ_effmaps to multi-rendered virtual monochromatic images (VMIs) for assessing liver lesion conspicuity.Main results.With physics-informed regularization, NRMSE of 1.29% and 0.68%, SSIM of 0.99 and 0.99, and PSNR of 29.8 dB and 29.04 dB were achieved. A maximum RMSE of 5.45% was achieved on clinical data. Reader study results showedρandZ_effimages had higher conspicuity scores compared to VMIs (median: 4.52 vs 4.13; 95% CIs: [4.19, 4.52] vs [4.01, 4.31]). The study showed equivalent conspicuity between VMIs and material images within a ±0.5 margin, though the small sample limits generalization.Significance.This study demonstrates the feasibility of material decomposition using a physics-informed GAN model trained on realistic simulated data. The maps provided equivalent conspicuity under a clinically acceptable margin, with a significantly small number of images for interpretation.

This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e. from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.

Objective.Bone scan imaging for the detection of bone metastasis of breast cancer has been widely adopted; however, noise, anatomy superimposition, and small size for early lesions will severely affect its prediction performance. In this work, we propose a new framework with two major contributions to solve the main problems existing in current deep-learning-based approaches.Approach.In this study, we put forward a new model called the dual branch feature alignment network (DBFANet) for automated breast cancer bone metastases detection in bone scintigraphy. DBFA-net adopts a dual-branch CNN-Transformer structure: the CNN branch focuses on the local details, while the Transformer branch learns the global context. In addition, we design a feature alignment module (FRAT), which employs the bi-directional cross-attention mechanism for the complementary feature from two branches. Moreover, we propose an enhanced multi-scale attention module (EMSA) based on the squeeze-and-excitation (SE) block for stronger multi-scale lesion representations with less background noise suppression.Main results.We validated our proposed model based on a bone scintigraphy dataset containing 5092 images. In terms of bone metastasis prediction, DBFANet achieved an accuracy, precision, and recall value of 93.1%, 84.6%, and 84.7%, respectively, all superior to previous models (such as ResNet-50, EfficientNet-V2, and MaxViT). The ablation study has shown that both FRAT and EMSA have individual effectiveness and complementary benefits. Finally, additional external validation was performed on a publicly available bone scintigraphy dataset (BS-80K).Significance.DBFANet shows the highest detection performance for bone metastasis detection from multiview bone scintigraphy images with imbalanced classes and noise in the image, and the feature alignment with enhanced multiscale attention of DBFANet provides a useful and precise tool for bone metastasis diagnosis in a nuclear medicine imaging scenario.

Objective.To investigate dynamic shuttle-mode xenon (Xe)-enhanced dual-energy CT (Xe-DECT) imaging for a regional assessment of ventilation inin vivorabbit lungs.Approach.Four mechanically ventilated rabbits were scanned during the washout of a 70% xenon in 30% oxygen gas mixture using dynamic shuttle-mode DECT at baseline and during methacholine (MCh)-induced bronchoconstriction (post-MCh). Material decomposition was applied to generate xenon and tissue density images (mg ml^-1). A tissue-based correction was used to isolate the xenon concentration (C_Xe) in the gas phase of the xenon density images. The resultantC_Xeimages were used to investigate regional ventilation defects (VDs) by comparing the VD fraction (VDF, expressed as percentage) between baseline and post-MCh conditions. Additionally, regional ventilation efficiency within the VDs and surrounding (non-VD) areas was quantified as specific ventilation (sV˙in min^-1). Ventilation was also qualitatively assessed by evaluating ventilation distributions during washout.Main results.MCh-induced bronchoconstriction resulted in an increase in VDF. The average VDF at baseline was 13.8 ± 8.5%, compared to an average post-MCh VDF of 29.6 ± 7.7%,p =0.026. The VDs at baseline did not reveal a reduced ventilation efficiency (sV˙VD:8.4 ± 2.7 min^-1), compared to non-VD areas (sV˙non-VD:7.0 ± 3.1 min^-1),p =0.306. In contrast, MCh-induced VDs were found to have a reduced ventilation efficiency (sV˙VD:4.9 ± 2.3 min^-1), compared to non-VD areas (sV˙non-VD: 6.4 ± 2.3 min^-1),p =0.004. Significance.Dynamic shuttle-mode Xe-DECT during washout enabled regional evaluation of ventilation in healthy and pathologicalin vivorabbit lungs. As traditional lung function tests offer only global assessments of respiratory impairment, there is a growing interest in pulmonary functional imaging to enable quantitative evaluation of regional lung function.

Objective.Elective nodal irradiation (ENI) is common clinical practice for many cancer sites including head-and-neck squamous cell carcinoma (HNSCC). ENI is performed to increase regional tumor control probability (TCP) but contributes to normal tissue complication probability (NTCP). We aim to improve the tradeoff between NTCP and regional TCP.Approach.Based on a previously developed model of lymphatic tumor progression for HNSCC, we estimate the probability of occult lymph node metastases in clinically negative lymph node levels (LNLs). We present a TCP model that predicts the regional TCP in the LNL irradiated with an arbitrary dose distribution. The TCP model is used for treatment plan optimization together with NTCP models.Main results.The approach is exemplified using three different HNSCC cases, considering the tradeoff between 1) xerostomia and ENI of contralateral LNL II, 2) dysphagia and ENI of LNL III, and 3) hypothyroidism and ENI of LNL IV. We show that NTCP may be lowered along with only minor reductions in regional TCP by compromising coverage of the LNL near relevant organs at risk.Significance.We present a method to control the trade-off between regional tumor control and risk of normal tissue complications in treatment plan optimization and demonstrate its application in a clinically relevant context.

Objective.An accurate and precise normalization procedure is essential to correct for variations in detector efficiency in reconstructed positron emission tomography (PET) images. Direct normalization is a conventional approach that requires a large number of counts per line of response from a known normalization source, which is time-consuming due to the need to acquire very high statistics with a reasonable source strength that does not saturate the system.Approach.To address the challenge of acquiring high signal-to-noise ratio (SNR) PET sensitivity maps efficiently, particularly with the often relatively low-count direct normalization data, this work develops a novel PET data processing and image reconstruction pipeline. This framework integrates sensitivity map features with generative modeling to synthesize high-quality maps, significantly reducing acquisition time while ensuring accurate and efficient normalization. Key contributions comprise a conditional attention-guided generative adversarial network that preserves the geometric and detector-specific characteristics of sensitivity maps, a robust assessment framework to verify synthesized map plausibility, and a comprehensive evaluation of the model's performance across a range of acquisition and scanner conditions.Main Results.Quantitative evaluations were performed by testing the model on totally unseen normalization data, acquired to reconstruct images of a Hoffman brain phantom, a contrast phantom, and a uniform cylinder phantom. This evaluation used high-count, low-count (1%-15% of high count scan), and synthetic high-count sensitivity maps. The Hoffman brain image volume normalized using a synthetic sensitivity map with 15% count statistics as input produced results that closely matched that using the high count normalization data, with peak SNR (PSNR), structural similarity index measure (SSIM), and normalized root mean square error (NRMSE) values (mean ± standard error) of 30.68 ± 0.31, 0.95 ± 0.00, and 0.35 ± 0.00, respectively. In comparison, the unprocessed sensitivity map with 15% count statistics yielded substantially worse PSNR, SSIM, and NRMSE values of 15.93 ± 0.43, 0.54 ± 0.01, and 1.84 ± 0.03, respectively.Significance.This novel, fast, and effective approach enables high SNR direct normalization of PET image volumes through deep learning using synthetic correction factors obtained from a short normalization scan.

Objective. Accurate segmentation of the prostate and dominant intraprostatic lesions (DILs) on magnetic resonance imaging (MRI) is important for prostate cancer radiation therapy treatment planning and targeted dose escalation. However, DIL segmentation remains challenging due to small datasets, institutional bias, and variable imaging protocols. Although the segment anything model (SAM) has shown promise in medical image segmentation, most prior work depends on manual prompts. This study developed a fully automated pipeline that combines localization with a fine-tuned SAM model to segment the prostate and DIL.Approach. Two datasets were utilized: the PI-CAI dataset, comprising 1476 patients, and the cancer imaging archive dataset, comprising 803 patients. The pipeline consisted of two stages: (1) a reinforcement learning-based localization network predicted bounding boxes as segmentation inputs, and (2) a fine-tuned SAM model performed segmentation. Model performance was evaluated using the dice similarity coefficient (DSC), intersection over union (IoU), and detection rates, with additional analysis based on lesion volumes.Main results. The proposed method achieved a mean and median DSC of 0.896 ± 0.070 and 0.915, and an IoU of 0.818 ± 0.100 and 0.844 for prostate segmentation. For DIL segmentation, the mean and median DSC were 0.592 ± 0.192 and 0.636, IoU of 0.446 ± 0.190 and 0.466, with a detection rate of 89%. Four DIL groups were created based on lesion volume percentile. The mean/median DSC and IoU for each volume group are as follows: 0.5-1.0 cubic centimeters (cc): 0.555 ± 0.201/0.562 & 0.414 ± 0.205/0.391; 1.0-1.8 cc: 0.603 ± 0.185/0.660 & 0.454 ± 0.180/0.492; 1.8-4.0 cc: 0.588 ± 0.183/0.627 & 0.439 ± 0.174/0.456; >4.0 cc: 0.621 ± 0.197/0.669 & 0.477 ± 0.197/0.503.Significance. This study presented a fully automated prostate and DIL segmentation framework on MRI by integrating a localization network with fine-tuned SAM. The method achieved robust performance across large multi-institutional datasets and diverse lesion shapes. It shows strong potential for application to clinical workflows for prostate cancer radiation therapy planning and treatment.

The high-throughput extraction of radiomics features from medical images for predictive modelling holds great promise to improve the clinical management of patients. Previous meta-analyses into the radiomics quality score (RQS) applied in the literature have shown that after more than a decade of investigation, issues with workflow standardisation, model reproducibility, validation, and data accessibility persist and impede the clinical translation of radiomics-based models. These systematic findings have informed a timely review of the best practices and pitfalls to avoid within radiomics and predictive modelling, with a focus on realistic radiomics modelling in the context of limited sample sizes. Each section covers a radiomics topic that encompasses one or more RQS criteria and is broken into subsections as follows: (1) a discussion of the background and recommendations on the respective topic, (2) key findings from our meta-analyses and discovered pitfalls, and (3) a succinct list of actionable items that reflect best practice. New and emerging quality appraisal tools and the future direction of radiomics are also discussed.

Objective.Online-adaptive, magnetic-resonance-(MR)-guided radiotherapy on a hybrid MR-linear accelerators enables stereotactic body radiotherapy (SBRT) of abdominal/pelvic tumors with large interfractional motion. However, overlaps between planning target volume (PTV) and dose-limiting organs at risk (OARs) often force compromises in PTV-coverage. Overlap-guided adaptive fractionation (AF) leverages daily variations in PTV/OAR overlap to improve PTV-coverage by administering variable fraction doses based on measured overlap volume. This study aims to assess the potential benefits of overlap-guided AF.Approach.We analyzed 58 patients with abdominal/pelvic tumors having received five-fraction MR-guided SBRT (>6 Gy/fraction), in whom PTV-overlap with at least one dose-limiting OAR (bowel, duodenum, stomach) occurred in⩾1 fraction. Dose-limiting OARs were constrained to 1cc⩽6 Gy per fraction, rendering overlapping PTV volumes underdosed. AF aims to reduce this underdosage by delivering higher doses to the PTV on days with less overlap volume, lower doses on days with more. PTV-coverage-gain compared to uniform fractionation was quantified by the area above the PTV dose-volume-histogram-curve and expressed in ccGy (1ccGy = 1cc receiving 1 Gy more). The optimal dose for each fraction was determined through dynamic programming by formulating AF as a Markov decision process.Main results.PTV/OAR overlap volume variation (standard deviation) varied substantially between patients (0.02-5.76cc). Algorithm-based calculations showed that 55 of 58 patients benefited in PTV-coverage from AF. Mean cohort benefit was 2.93ccGy (range -4.44 (disadvantage) to 22.42ccGy). Higher PTV/OAR overlap variation correlated with larger AF benefit.Significance.Overlap-guided AF for abdominal/pelvic SBRT is a promising strategy to improve PTV-coverage without compromising OAR sparing. Since the benefit of AF depends on PTV/OAR overlap variation-which is low in many patients-the mean cohort advantage is modest. However, well-selected patients with marked PTV/OAR overlap variation derive a relevant dosimetric benefit. Prospective studies are needed to evaluate AF feasibility and quantify clinical benefits.