Physics in medicine and biology最新文献

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: This study evaluates the feasibility of using the high-energy particle accelerators LINAC4 at CERN and the Nuclotron at JINR for radiobiological experiments under ultra-high dose rate (UHDR) and FLASH-related irradiation conditions with proton and carbon ion beams.

Approach: Monte Carlo simulations were performed using the GEANT4 and FLUKA toolkits to model beam transport, dose deposition, and spatial dose characteristics of proton and carbon ion beams generated by the two facilities. Virtual irradiation setups were implemented using water phantoms and digital models of standard cell culture vessels.

Main results: The 160 MeV proton beam from LINAC4 and the 430 MeV/u carbon ion beam from the Nuclotron achieved high spatial precision and uniform dose distributions within approximately 5 mL water equivalent targets, including within the Bragg peak region. Owing to their pulsed beam structures, comprising millisecond-scale pulses with nanosecond-scale micro bunches, both accelerators can deliver several Gy within short irradiation intervals under UHDR conditions. This enables well-defined delivery relevant for in vitro FLASH studies. In contrast to collimated beams and reproducible temporal structures suitable for investigations aimed at elucidating the biological mechanisms underlying the FLASH effect, which require precise control over dose delivery.

Significance: These findings support the suitability of research-dedicated accelerator infrastructures such as LINAC4 and the Nuclotron for preclinical UHDR and FLASH-related radiobiological studies. Their ability to deliver pulsed, high-intensity hadron beams under controlled geometric and temporal conditions fulfils the key physical prerequisites for systematic in vitro investigations of UHDR and FLASH effects. By extending FLASH-oriented experimentation beyond clinical environments, this work provides a framework for studies addressing dose-threshold behaviours, tissue-specific responses, and the biological mechanisms underlying the FLASH effect.

Objective: Lutetium oxide (Lu₂O₃), with its high density (9.4 g/cm³), presents a compelling scintillation host for detecting 511 keV annihilation photons in positron emission tomography (PET). Despite its favorable density, the practical deployment of Lu₂O₃-based scintillators for PET has faced limitations due to difficulties in crystal growth and inappropriate decay time. Recent progress in ceramic processing has facilitated the development of transparent Lu₂O₃ ceramics, while targeted doping strategies have significantly improved their luminescence performance. This study evaluates the performance of Lu₂O₃:Yb and a newly developed ceramic scintillator of (Lu,Y)₂O₃:La, a modified Lu₂O₃-based compound incorporating yttrium (Y) and doped with lanthanum (La).

Approach: Various ceramic disks were fabricated and cut into 3 × 3 × 5 mm³ samples. The performance of both Lu₂O₃:Yb and (Lu,Y)₂O₃:La ceramic samples in terms of decay time, energy resolution, and coincidence timing resolution (CTR) was assessed. Decay time measurements were conducted using waveform data collected from samples mounted on an H10580 photomultiplier tube (PMT) and irradiated with 511 keV photons from a ²²Na source. Energy and coincidence timing resolutions were evaluated using both PMT and silicon photomultiplier (SiPM) setups, arranged in coincidence with a reference lutetium-yttrium oxyorthosilicate (LYSO) detector of the same size.

Main results: All three (Lu,Y)₂O₃:La ceramic scintillator samples exhibited a triple exponential decay profile and were dominated by a slow component ranging from 1379.3 to 1515.6 ns. The best energy resolution of 15.4% at 511 keV and the best CTR of 237.9 ps full width at half maximum (FWHM) were observed for the same sample. In contrast, a fast decay time of 1.6 ns was observed for the Lu₂O₃:Yb samples, which exhibited CTR values ranging from 237.9 ps to 261.4 ps FWHM, while the photopeak at 511 keV was difficult to distinguish. These CTR values were estimated between two identical ceramic samples, derived from coincidence measurements of each ceramic sample against the LYSO reference detector. The (Lu,Y)₂O₃:La samples achieved CTR values comparable to those of the Lu₂O₃:Yb samples, as their much higher light yield offsets the disadvantage associated with their slower decay time.

Significance: These results highlight the promising potential of the (Lu,Y)₂O₃:La ceramic scintillators for PET applications, especially for time-of-flight PET.

Objective: Quantitative imaging in positron emission tomography (PET) requires accurate, precise, and efficient scatter correction techniques. Conventional scatter estimation typically relies on tail-fitted single-scatter simulation (SSS). However, the accuracy of tail-fitted SSS is limited, for example, by mismatches between the attenuation image and the PET emission data or by the presence of activity outside the field of view (FOV). These shortcomings can be addressed using energy-based scatter estimation (EBSE), as recently proposed by Efthimiou and Hamill. The aim is to (1) improve the accuracy of EBSE by accounting for the line-of-response (LOR) dependence of the energy spectrum of unscattered photons, and (2) improve the computational speed of EBSE through better initialization and a more efficient optimization algorithm. Approach: The proposed improved EBSE method models the energy probability density function (PDF) of both single and multiple scattered photons, and incorporates a position-dependent (local) energy PDF for unscattered photons. These energy PDFs form the basis of two forward models used for scatter estimation based on 2D energy histograms. The performance of these models were evaluated using GATE Monte Carlo simulations and a NEMA phantom acquisition on a GE SIGNA PET/MR scanner. Furthermore, we assessed the stability of EBSE across the forward models by varying the number of counts in the 2D energy histograms via data mashing. Main results: EBSE outperformed tail-fitted SSS, particularly in regions near out-of-FOV activity. Our GATE simulations showed that incorporating a local energy PDF for unscattered photons improves off-center regional quantification by approximately 2% points. Additionally, improved initialization combined with the NEGML optimizer reduced the number of required EBSE iterations from 200 to 50, enabling execution on a mashed TOF sinogram in 12 minutes on a modern six-core CPU. Significance: The proposed method enhances both the accuracy and computational efficiency of EBSE, while clarifying scattered basis function limitations.

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.

Objective.The LET trilemma-an inherent conflict between target dose homogeneity, range robustness, and high dose-averaged linear energy transfer (LET_d)-poses a major challenge in treatment optimization. To ensure accurate beam delivery in multi-ion therapy, this study evaluated the effects of range and setup uncertainties on LET_d-optimized treatment plans and explored strategies to overcome this trilemma, framed within the phase I LET_descalation trial for head and neck cancers.Approach.Six head and neck cancer patients representing diverse tumors were selected. Multi-ion therapy plans using carbon-, oxygen-, and neon-ion beams were optimized to achieve a target LET_dof 90 keV μm^-1(the final LET_dlevel of the phase I trial). These plans were recalculated to incorporate systematic range uncertainty (±2.5%) and random daily setup variations (mean, 0.45 mm; standard deviation, 0.23 mm) across the 16 fractions, and their combined effects on the dose and LET_ddistributions were evaluated. Additionally, to explore strategies to enhance plan robustness, five modified plans were evaluated for one patient identified as particularly susceptible to these uncertainties.Main Results.Range uncertainty was the dominant contributor to degraded plan quality, substantially outweighing setup uncertainty. A small, centrally located tumor was most susceptible, exhibiting dose inhomogeneity of approximately 11%, while LET_dvariations were approximately 3 keV μm^-1. The most effective mitigation strategy involved replacing the original carbon-oxygen combination with oxygen ions for two beam ports, reducing dose inhomogeneity by more than 7% while maintaining normal tissue sparing adjacent to the target.Significance.Optimization toward achieving higher LET_dmakes treatment plans susceptible to range uncertainty, leading to dose degradation within small, deep-seated tumors. Employing heavier ions is an effective strategy to overcome this challenge, enabling robust target coverage by leveraging their inherently higher LET_dwhile sparing normal tissues. These findings provide a key rationale for ion selection in the design of robust multi-ion therapy.