Physics in medicine and biology最新文献

Objective.This study aimed to optimise Cone Beam Computed Tomography (CBCT) protocols for head and neck (H&N) radiotherapy treatments using a 3D printed anthropomorphic phantom. It focused on precise patient positioning in conventional treatment and adaptive radiotherapy (ART).Approach.Ten CBCT protocols were evaluated with the 3D-printed H&N anthropomorphic phantom, including one baseline protocol currently used at our centre and nine new protocols. Adjustments were made to milliamperage and exposure time to explore their impact on radiation dose and image quality. Additionally, the effect on image quality of varying the scatter correction parameter for each of the protocols was assessed. Each protocol was compared against a reference CT scan. Usability was assessed by three Clinical Scientists using a Likert scale, and statistical validation was performed on the findings.Main results. The work revealed variability in the effectiveness of protocols. Protocols optimised for lower radiation exposure maintained sufficient image quality for patient setup in a conventional radiotherapy pathway, suggesting the potential for reducing patient radiation dose by over 50% without compromising efficacy. Optimising ART protocols involves balancing accuracy across brain, bone, and soft tissue, as no single protocol or scatter correction parameter achieves optimal results for all simultaneously.Significance.This study underscores the importance of optimising CBCT protocols in H&N radiotherapy. Our findings highlight the potential to maintain the usability of CBCT for bony registration in patient setup while significantly reducing the radiation dose, emphasizing the significance of optimising imaging protocols for the task in hand (registering to soft tissue or bone) and aligning with the as low as reasonably achievable principle. More studies are needed to assess these protocols for ART, including CBCT dose measurements and CT comparisons. Furthermore, the novel 3D printed anthropomorphic phantom demonstrated to be a useful tool when optimising CBCT protocols.

Objective.Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours. Furthermore, ACQA can facilitate the contour selection process from various DLAS tools and/or deformable contour propagation from a prior treatment session. Here, we present the performance of novel DL-based 3D ACQA models for evaluating DLAS contours acquired during MRgOART.Approach.The ACQA model, based on a 3D convolutional neural network (CNN), was trained using pancreas and duodenum contours obtained from a research DLAS tool on abdominal MRIs acquired from a 1.5 T MR-Linac. The training dataset contained abdominal MR images, DL contours, and their corresponding quality ratings, from 103 datasets. The quality of DLAS contours was determined using an in-house contour classification tool, which categorizes contours as acceptable or edit-required based on the expected editing effort. The performance of the 3D ACQA model was evaluated using an independent dataset of 34 abdominal MRIs, utilizing confusion matrices for true and predicted classes.Main results.The ACQA predicted 'acceptable' and 'edit-required' contours at 72.2% (91/126) and 83.6% (726/868) accuracy for pancreas, and at 71.2% (79/111) and 89.6% (772/862) for duodenum contours, respectively. The model successfully identified false positive (extra) and false negative (missing) DLAS contours at 93.75% (15/16) and %99.7 (438/439) accuracy for pancreas, and at 95% (57/60) and 98.9% (91/99) for duodenum, respectively.Significance.We developed 3D-ACQA models capable of quickly evaluating the quality of DLAS pancreas and duodenum contours on abdominal MRI. These models can be integrated into clinical workflow, facilitating efficient and consistent contour evaluation process in MRgOART for abdominal malignancies.

Objective.Proton radiotherapy's efficacy relies on an accurate relative stopping power (RSP) map of the patient to optimise the treatment plan and minimize uncertainties. Currently, a conversion of a Hounsfield Units map obtained by a common x-ray computed tomography (CT) is used to compute the RSP. This conversion is one of the main limiting factors for proton radiotherapy. To bypass this conversion a direct RSP map could be obtained by performing a proton CT (pCT). The focal point of this article is to present a proof of concept of the potential of fast pixel technologies for proton tracking at clinical facilities.Approach.A two-layer tracker based on the TJ-Monopix1, a depleted monolithic active pixel sensor (DMAPS) chip initially designed for the ATLAS, was tested at the proton minibeam radiotherapy beamline at the Curie Institute. The chips were subjected to 100 MeV protons passing through the single slit collimator (0.4×20mm²) with fluxes up to1.3×107p s^-1 cm^-2. The performance of the proton tracker was evaluated with GEANT4 simulations.Main results.The beam profile and dispersion in air were characterized by an opening of 0.031 mm cm^-1, and aσx=0.172mm at the position of the slit. The results of the proton tracking show how the TJ-Monopix1 chip can effectively track protons in a clinical environment, achieving a tracking purity close to 70%, and a position resolution below 0.5 mm; confirming the chip's ability to handle high proton fluxes with a competitive performance.Significance.This work suggests that DMAPS technologies can be a cost-effective alternative for proton imaging. Additionally, the study identifies areas where further optimization of chip design is required to fully leverage these technologies for clinical ion imaging applications.

Objective.To present and validate a method to simulate from first principles the effect of oxygen on radiation-induced double-strand breaks (DSBs) using the Monte Carlo Track-structure code TOPAS-nBio.Approach.Two chemical models based on the oxygen fixation hypothesis (OFH) were developed in TOPAS-nBio by considering an oxygen adduct state of DNA and creating a competition kinetic mechanism between oxygen and the radioprotective molecule WR-1065. We named these models 'simple' and 'detailed' due to the way they handle the hydrogen abstraction pathways. We used the simple model to obtain additional information for the •OH-DNA hydrogen abstraction pathway probability for the detailed model. These models were calibrated and compared with published experimental data of linear and supercoiling fractions obtained with R6K plasmids, suspended in dioxane as a hydroxyl scavenger, and irradiated with¹³⁷Cs gamma-rays. The reaction rates for WR-1065 and O₂with DNA were taken from experimental works. Single-Strand Breaks (SSBs) and DSBs as a function of the dose for a range of oxygen concentrations [O₂] (0.021%-21%) were obtained. Finally, the hypoxia reduction factor (HRF) was obtained from DSBs.Main Results.Validation results followed the trend of the experimental within 12% for the supercoiled and linear plasmid fractions for both models. The HRF agreed with measurements obtained with¹³⁷Cs and 200-280 kVp x-ray within experimental uncertainties. However, the HRF at an oxygen concentration of 2.1% overestimated experimental results by a factor of 1.7 ± 0.1. Increasing the concentration of WR-1065 from 1 mM to 10-100 mM resulted in a HRF difference of 0.01, within the 8% statistical uncertainty between TOPAS-nBio and experimental data. This highlights the possibility of using these chemical models to recreate experimental HRF results.Significance.Results support the OFH as a leading cause of oxygen radio-sensitization effects given a competition between oxygen and chemical DNA repair molecules like WR-1065.

Objective.Ultrasound is the primary screening test for breast cancer. However, providing an interpretable auxiliary diagnosis of breast lesions is a challenging task. This study aims to develop an interpretable auxiliary diagnostic method to enhance usability in human-machine collaborative diagnosis.Approach.To address this issue, this study proposes the deep multi-stage reasoning method (DMSRM), which provides individual and overall breast imaging-reporting and data system (BI-RADS) assessment categories for breast lesions. In the first stage of the DMSRM, the individual BI-RADS assessment network (IBRANet) is designed to capture lesion features from breast ultrasound images. IBRANet performs individual BI-RADS assessments of breast lesions using ultrasound images, focusing on specific features such as margin, contour, echogenicity, calcification, and vascularity. In the second stage, evidence reasoning (ER) is employed to achieve uncertain information fusion and reach an overall BI-RADS assessment of the breast lesions.Main results.To evaluate the performance of DMSRM at each stage, two test sets are utilized: the first for individual BI-RADS assessment, containing 4322 ultrasound images; the second for overall BI-RADS assessment, containing 175 sets of ultrasound image pairs. In the individual BI-RADS assessment of margin, contour, echogenicity, calcification, and vascularity, IBRANet achieves accuracies of 0.9491, 0.9466, 0.9293, 0.9234, and 0.9625, respectively. In the overall BI-RADS assessment of lesions, the ER achieves an accuracy of 0.8502. Compared to independent diagnosis, the human-machine collaborative diagnosis results of three radiologists show increases in positive predictive value by 0.0158, 0.0427, and 0.0401, in sensitivity by 0.0400, 0.0600 and 0.0434, and in area under the curve by 0.0344, 0.0468, and 0.0255.Significance.This study proposes a DMSRM that enhances the transparency of the diagnostic reasoning process. Results indicate that DMSRM exhibits robust BI-RADS assessment capabilities and provides an interpretable reasoning process that better suits clinical needs.

Objective.With advancements in high-dose rate brachytherapy, the clinical translation of intensity modulated brachytherapy (IMBT) innovations necessitates utilization of model-based dose calculation algorithms (MBDCA) for accurate and rapid dose calculations. This study uniquely benchmarks a commercial MBDCA, BrachyVision ACUROS^TM(BVA), against Monte Carlo (MC) simulations, evaluating dose distributions for a novel IMBT applicator, termed as thesix-grooveDirection Modulated Brachytherapy (DMBT) tandem, expanding beyond previous focus on partially shielded vaginal cylinder applicators, through a novel methodology.Approach.The DMBT tandem applicator, made of a tungsten alloy with six evenly spaced grooves, was simulated using the GEANT4 MC code. Subsequently, two main scenarios were created using the BVA and reproduced by the MC simulations: 'Source at the Center of the Water Phantom (SACWP)' and 'Source at the Middle of the Applicator (SAMA)' for three cubical virtual water phantoms (20 cm)³, (30 cm)³, and (40 cm)³. A track length estimator was utilized for dose calculation and 2D/3D scoring were performed. The difference in isodose surfaces/lines (i.e. coverage) at each voxel,ΔD_{Isodose Levels/Lines}, was thus calculated for relevant normalization points (r_ref).Results.The coverage was comparable, based on 2D scoring, between the BVA and MC isodose surfaces/lines for the region of clinical relevance, (i.e. within 5 cm radius from the source) withΔD_{Isodose Lines}(r_ref: 1 cm from the source) falling within 2% for the two scenarios for all phantom sizes. For the phantom (20 cm)³,ΔD_{Isodose Levels}(3D scoring) recorded the range [-3.0% +6.5%] ([-7.4% +7.3%]) for 95% of the voxels of the same scoring volume for the SACWP (SAMA) scenario.Significance.The results indicated that the BVA could render comparable coverage as compared to the MC simulations in the region of clinical relevance for different phantom sizes.ΔD_{Isodose Lines}may offer an advantageous metric for evaluation of MBDCAs in clinical setting.

Objective.X-ray coronary angiograms (XCA) are widely used in diagnosing and treating cardiovascular diseases. Various structures with independent motion patterns in the background of XCA images and limitations in the dose of injected contrast agent have resulted in low-contrast XCA images. Background subtraction methods have been developed to enhance the visibility and contrast of coronary vessels in XCA sequences, consequently reducing the requirement for excessive contrast agent injections.Approach.The current study proposes an adaptive weighted total variation regularized online RPCA (WTV-ORPCA) method, which is a low-rank and sparse subspaces decomposition approach to subtract the background of XCA sequences. In the proposed method, the images undergo initial preprocessing using morphological operators to eliminate large-scale background structures and achieve image homogenization. Subsequently, the decomposition algorithm decomposes the preprocessed images into background and foreground subspaces. This step applies an adaptive weighted TV constraint to the foreground subspace to ensure the spatial coherency of the finally extracted coronary vessel images.Main results.To evaluate the effectiveness of the proposed background subtraction method, some qualitative and quantitative experiments are conducted on two clinical and synthetic low-contrast XCA datasets containing videos from 21 patients. The obtained results are compared with six state-of-the-art methods employing three different assessment criteria. By applying the proposed method to the clinical dataset, the mean values of the global contrast-to-noise ratio, local contrast-to-noise ratio, structural similarity index, and reconstruction error (RE) are obtained as5.976,3.173,0.987, and0.026, respectively. These criteria over the low-contrast synthetic dataset were4.851,2.942,0.958, and0.034, respectively.Significance.The findings demonstrate the superiority of the proposed method in improving the contrast and visibility of coronary vessels, preserving the integrity of the vessel structure, and minimizing REs without imposing excessive computational complexity.

Objective. This study presents a universal phantom for positron emission tomography (PET) that allows arbitrary static and dynamic activity distributions of various complexities to be generated using a single PET acquisition.Approach. We collected a high-statistics dataset (with a total of 22.4 × 10⁹prompt coincidences and an event density of 2.75 × 10⁶events mm^-3) by raster-scanning a single plane with a²²Na point source mounted on a robotic arm in the field-of-view of the uEXPLORER PET/CT scanner. The source position was determined from the reconstructed dynamic frames. Uniquely, true coincidences were separated from scattered and random events based on the distance between their line-of-response and the known source location. Finally, we randomly sampled the dataset to generate the desired activity distributions modeling several different phantoms.Main results. Overall, the target and the reconstructed phantom images had good agreement. The analysis of a simple geometric distribution showed high quantitative accuracy of the phantom, with mean error of <-3.0% relative to the ground truth for activity concentrations ranging from 5.3 to 47.7 kBq ml^-1. The model of a high-resolution¹⁸F-fluorodeoxyglucose distribution in the brain illustrates the usefulness of the technique in simulating realistic static neuroimaging studies. A dynamic¹⁸F-florbetaben study was modeled based on the time-activity curves of a human study and a segmented brain phantom with no coincidences repeating between frames. For all time points, the mean voxel-wise errors ranged from -4.4% to -0.7% in grey matter and from -3.9% to +2.8% in white matter.Significance. The proposed phantom technique is highly flexible and allows modeling of static and dynamic brain PET studies with high quantitative accuracy. It overcomes several key limitations of the existing phantoms and has many promising applications for the purposes of image reconstruction, data correction methods, and system performance evaluation, particularly for new high-performance dedicated brain PET scanners.

The ill-posed Positron emission tomography (PET) reconstruction problem usually results in limited resolution and significant noise. Recently, deep neural networks have been incorporated into PET iterative reconstruction framework to improve the image quality. In this paper, we propose a new neural network-based iterative reconstruction method by using weighted nuclear norm (WNN) maximization, which aims to recover the image details in the reconstruction process. The novelty of our method is the application of WNN maximization rather than WNN minimization in PET image reconstruction. Meanwhile, a neural network is used to control the noise originated from WNN maximization. Our method is evaluated on simulated and clinical datasets. The simulation results show that the proposed approach outperforms state-of-the-art neural network-based iterative methods by achieving the best contrast/noise tradeoff with a remarkable contrast improvement on the lesion contrast recovery. The study on clinical datasets also demonstrates that our method can recover lesions of different sizes while suppressing noise in various low-dose PET image reconstruction tasks. Our code is available athttps://github.com/Kuangxd/PETReconstruction.