Physics in medicine and biology最新文献

Objective.To develop a model that accurately describes the behavior of nanobubbles (NBs) under low-frequency ultrasound (US) insonation (<250 kHz), addressing the limitations of existing numerical models, such as the Marmottant model and Blake's Threshold model, in predicting NB behavior.Approach.A modified surface tension model, derived from empirical data, was introduced to capture the surface tension behavior of NBs as a function of bubble radius. This model was integrated into the Marmottant framework and combined with the Blake threshold to predict cavitation thresholds at low pressures, providing a comprehensive approach to understanding NB dynamics.Main results.Experimentally, inertial cavitation for NBs with a radius of 85 nm was observed at peak negative pressures of 200 kPa at 80 kHz and 1000 kPa at 250 kHz. The Marmottant model significantly overestimated these thresholds (1600 kPa). The modified surface tension model improved predictions at 250 kHz, while combining it with the Blake threshold accurately aligned cavitation thresholds at both frequencies (∼150 kPa at low pressures) with experimental results.Significance.This work bridges a critical gap in understanding the acoustic behavior of NBs at low US frequencies and offers a new theoretical framework for predicting cavitation thresholds of NBs at low US frequencies, advancing their application in biomedical US technologies.

Objective.The photo injector test facility at DESY in Zeuthen (PITZ) is building up an R&D platform, known as FLASHlab@PITZ, for systematically studying the FLASH effect in cancer treatment with its high-brightness electron beams, which can provide a uniquely large dose parameter range for radiation experiments. In this paper, we demonstrate the capabilities by experiments with a reduced parameter range on a startup beamline and study the potential performance of the full beamline by simulations.Approach.To measure the dose, Gafchromic films are installed both in front of and after the samples; Monte Carlo simulations are conducted to predict the dose distribution during beam preparation and help understand the dose distribution inside the sample. Plasmid DNA is irradiated under various doses at conventional and ultra-high dose rate (UHDR) to study the DNA damage by radiations. Start-to-end simulations are performed to verify the performance of the full beamline.Main results.On the startup beamline, reproducible irradiation has been established with optimized electron beams and the delivered dose distributions have been measured with Gafchromic films and compared to FLUKA simulations. The functionality of this setup has been further demonstrated in biochemical experiments at conventional dose rate of 0.05 Gy s^-1and UHDR of several 10⁵ Gy s^-1and a varying dose up to 60 Gy, with the UHDR experiments finished within a single RF pulse (less than 1 millisecond); the observed conformation yields of the irradiated plasmid DNA revealed its dose-dependent radiation damage. The upgrade to the full FLASHlab@PITZ beamline is justified by simulations with homogeneous radiation fields generated by both pencil beam scanning and scattering beams.Significance.With the demonstration of UHDR irradiation and the simulated performance of the new beamline, FLASHlab@PITZ will serve as a powerful platform for studying the FLASH effects in cancer treatment.

Objective.The clinical advantage of proton therapy, compared to other types of irradiations, lies in its reduced dose to normal tissue. Still, proton therapy faces challenges of normal tissue toxicity and radioresistant tumors. To combat these challenges, proton boron capture therapy (PBCT) and neutron capture enhanced particle therapy (NCEPT) were proposed to introduce high-LET radiation in the target volume.Approach. In this work, we performedin-vitroexperiments with a V79 cell line to validate PBCT and introduced a novel approach to use NCEPT in proton therapy. We quantified the effectiveness of PBCT and NCEPT with microdosimetric measurements, Monte-Carlo simulations and microdosimetric kinetic RBE model (MKM).Main results. No RBE increase was observed for PBCT. With the use of a tungsten spallation source, enough neutrons were generated in the incoming proton beam to measure significant neutron capture in the microdosimeter. However, no significant increase of RBE was detected when conventionalin vitroprotocol was followed. The resulting cell deactivation based RBE for NCEPT was found to be heavily dependent on which criteria was used to determine surviving colonies.Significance. PBCT and NCEPT are two proposed treatment modalities that may have the potential to expand the cases in which proton therapy can be beneficial. Understanding the scope of these treatment methods and developing measurement protocols to evaluate and understand their RBE impact are the first step to quantify their potential in clinical context.

Objective.State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study investigated whether excluding PM during training/fine-tuning improves the model's BCR discrimination performance, calibration, and robustness.Approach.First, the Original deep learning model (MIRAI), trained on the US (Massachusetts General Hospital) data, was validated, and the relative contribution of PM to BCR predictions was evaluated using saliency maps. Additionally, 23 792 mammograms from the Slovenian screening program were collected and two datasets were created, with and without screening positive exams. The original MIRAI was then fine-tuned on the training/fine-tuning set of Slovenian mammograms with and without PM, creating Fine-tuned MIRAI models. In total, four models (Original MIRAI with PM, Original MIRAI without PM, Fine-tuned MIRAI with PM, Fine-tuned MIRAI without PM) were compared on a test set in terms of discrimination performance for 1-5 Year BCR (evaluating area under the curve), calibration performance (measured with expected calibration error-ECE) and robustness to incremental PM removals/additions, and to incremental breast tissue removals.Results.The relative contribution of PM to the BCR prediction on the Original MIRAI model was low (∼5%); however, there were significant outliers where the relative contribution was more than 50%. The removal of PM did not impact the 1-5 Year BCR discrimination performance of the Original MIRAI (with screening positive exams: 0.77-0.91, without screening positive exams: 0.64-0.67). Fine-tuned MIRAI on mammograms with PM removed achieved significantly higher 1-5 Year BCR discrimination performance (with screening positive exams: 0.82-0.93, without screening positive exams: 0.71-0.79). After recalibration, all models had similar ECE (with screening positive exams: 0.04-0.05, without screening positive exams: 0.02-0.03).Significance.Improved BCR discrimination performance can be achieved when the model is trained/fine-tuned on mammograms with PM removed.

Objective. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment planning as separate tasks. Moreover in DL, the contouring and dose prediction tasks for automated treatment planning are done independently.Approach. In this study, we applied the multi-task learning (MTL) approach in order to seamlessly integrate automated contouring and voxel-based dose prediction tasks, as MTL can leverage common information between the two tasks and be able to increase the efficiency of the automated tasks. We developed our MTL framework using the two datasets: in-house prostate cancer dataset and the publicly available head and neck cancer dataset, OpenKBP.Main results. Compared to the sequential DL contouring and treatment planning tasks, our proposed method using MTL improved the mean absolute difference of dose volume histogram metrics of prostate and head and neck sites by 19.82% and 16.33%, respectively. Our MTL model for automated contouring and dose prediction tasks demonstrated enhanced dose prediction performance while maintaining or sometimes even improving the contouring accuracy. Compared to the baseline automated contouring model with the Dice score coefficients of 0.818 for prostate and 0.674 for head and neck datasets, our MTL approach achieved average scores of 0.824 and 0.716 for these datasets, respectively.Significance. Our study highlights the potential of the proposed automated contouring and planning using MTL to support the development of efficient and accurate automated treatment planning for radiotherapy.

Objective.Rigid patient motion can cause artifacts in single photon emission computed tomography (SPECT) images, compromising the diagnosis and treatment planning. Exponential data consistency conditions (eDCCs) are mathematical equations describing the redundancy of exponential SPECT measurements. It has been recently shown that eDCCs can be used to detect patient motion in SPECT projections. This study aimed at developing a fully data-driven method based on eDCCs to estimate and correct for translational motion in SPECT.Approach.If all activity is encompassed inside a convex regionKof constant attenuation, eDCCs can be derived from SPECT projections and can be used to verify the pairwise consistency of these projections. Our method assumes a single patient translation between two detector gantry positions. The proposed method estimates both the three-dimensional shift and the motion index, i.e. the index of the first gantry position after motion occurred. The estimation minimizes the eDCCs between the subset of projections before the motion index and the subset of motion-corrected projections after the motion index.Results.We evaluated the proposed method using Monte Carlo simulated and experimental data of a NEMA IEC phantom and simulated projections of a liver patient. The method's robustness was assessed by applying various motion vectors and motion indices. Motion detection and correction with eDCCs were sensitive to movements above 3 mm. The accuracy of the estimation was below the 2.39 mm pixel spacing with good precision in all studied cases. The proposed method led to a significant improvement in the quality of reconstructed SPECT images. The activity recovery coefficient relative to the SPECT image without motion was above 90% on average over the six spheres of the NEMA IEC phantom and 97% for the liver patient. For example, for a(2,2,2)cm translation in the middle of the liver acquisition, the activity recovery coefficient was improved from 35% (non-corrected projections) to 99% (motion-corrected projections).Significance.The study proposed and demonstrated the good performance of translational motion detection and correction with eDCCs in SPECT acquisition data.

Objective.The purpose of this study was to develop a robust deep learning approach trained with a smallin-vivoMRI dataset for multi-label segmentation of all eight carpal bones for therapy planning and wrist dynamic analysis.Approach.A small dataset of 15 3.0-T MRI scans from five health subjects was employed within this study. The MRI data was variable with respect to the field of view (FOV), wide range of image intensity, and joint pose. Atwo-stagesegmentation pipeline using modified 3D U-Net was proposed. In thefirst stage, a novel architecture, introduced as expansion transfer learning (ETL), cascades the use of a focused region of interest (ROI) cropped around ground truth for pretraining and a subsequent transfer by an expansion to the original FOV for a primary prediction. The bounding box around the ROI generated was utilized in thesecond stagefor high-accuracy, labeled segmentations of eight carpal bones. Different metrics including dice similarity coefficient (DSC), average surface distance (ASD) and hausdorff distance (HD) were used to evaluate performance between proposed and four state-of-the-art approaches.Main results.With an average DSC of 87.8 %, an ASD of 0.46 mm, an average HD of 2.42 mm in all datasets (96.1 %, 0.16 mm, 1.38 mm in 12 datasets after exclusion criteria, respectively), the proposed approach showed an overall strongest performance than comparisons.Significance.To our best knowledge, this is the first CNN-based multi-label segmentation approach for MRI human carpal bones. The ETL introduced in this work improved the ability to localize a small ROI in a large FOV. Overall, the interplay of atwo-stageapproach and ETL culminated in convincingly accurate segmentation scores despite a very small amount of image data.

Objective.Accurate prediction of thermal damage extent is essential for effective and precise thermal therapy, especially in brain laser interstitial thermal therapy (LITT). Immediate postoperative contrast-enhanced T1-weighted imaging (CE-T1WI) is the primary method for clinically assessingin vivothermal damage after image-guided LITT. CE-T1WI reveals a hyperintense enhancing rim surrounding the target lesion, which serves as a key radiological marker for evaluating the thermal damage extent. Although widely used in clinical practice, traditional thermal damage models rely on empirical parameters fromin vitroexperiments, which can lead to inaccurate predictions of thermal damagein vivo. Additionally, these models predict only two tissue states (damaged or undamaged), failing to capture three tissue states observed on post-CE-T1WI images, highlighting the need for improved thermal damage prediction methods.Approach.This study proposes a novel convolutional long short-term memory-based model that utilizes intraoperative temperature distribution history data measured by magnetic resonance temperature imaging (MRTI) during LITT to predict the enhancing rim on post-CE-T1WI images. This method was implemented and evaluated on retrospective data from 56 patients underwent brain LITT.Main results.The proposed model effectively predicts the enhancing rim on postoperative images, achieving an average dice similarity coefficient of 0.82 (±0.063) on the test dataset. Furthermore, it generates real-time predicted thermal damage area variation trends that closely resemble those of the traditional thermal damage model, suggesting potential for real-time prediction of thermal damage extent.Significance.This method could provide a valuable tool for visualizing and assessing intraoperative thermal damage extent.

Objective.This article explores a new graphics processing unit (GPU)-based techniques for efficient image reconstruction in organ-targeted positron emission tomography (PET) scanners with planar detectors.Approach.GPU-based reconstruction is applied to the Radialis low-dose organ-targeted PET technology, developed to overcome the issues of high exposure and limited spatial resolution inherent in traditional whole-body PET/CT (Computed Tomography) scans. The Radialis planar detector technology is based on four-side tileable sensor modules that can be seamlessly combined into a sensing area of the needed size, optimizing the axial field-of-view for specific organs, and maximizing geometric sensitivity. The article explores the transition from central processing unit-based maximum likelihood expectation maximization algorithms to a GPU-based counterpart, demonstrating a tenfold overall speedup in image reconstruction with a hundredfold improvement in iteration speed.Main results.Through standardized PET performance tests and clinical image analysis, this work demonstrates that GPU-based image reconstruction maintains diagnostic image quality while significantly reducing reconstruction times. The application of this technology, particularly in breast imaging using the Radialis low-dose positron emission mammography, significantly reduces exam times thus improving patient comfort and throughput in clinical settings.Significance.This study represents an important advancement in the clinical workflow of PET imaging, providing insights into optimizing reconstruction algorithms to effectively leverage the parallel processing capabilities of GPUs.