Physics in medicine and biology最新文献

Motivated by elastography that utilizes tissue mechanical properties as biomarkers for liver disease, with the eventual objective of quantitatively linking histopathology and bulk mechanical properties, we develop a micromechanical modeling approach to capture the effects of fat and collagen deposition in the liver. Specifically, we utilize computational homogenization to convert the microstructural changes in hepatic lobule to the effective viscoelastic modulus of the liver tissue, i.e. predict the bulk material properties by analyzing the deformation of repeating unit cell. The lipid and collagen deposition is simulated with the help of ad hoc algorithms informed by histological observations. Collagen deposition is directly included in the computational model, while composite material theory is used to convert fat content to the microscopic mechanical properties, which in turn is included in the computational model. The results illustrate the model's ability to capture the effect of both fat and collagen deposition on the viscoelastic moduli and represents a step towards linking histopathological changes in the liver to its bulk mechanical properties, which can eventually provide insights for accurate diagnosis with elastography.

Objective.Magnetic particle imaging (MPI) is an emerging tomographic 'hot spot' imaging modality with potential to visualize superparamagnetic iron oxide nanoparticle tracer distributions with high sensitivity and quantitative accuracy. MPI shares many similarities with positron emission tomography (PET), where the partial volume effect (PVE) can result in signal under- and over-quantification due to spill-over of signal arising from limited resolution. While the PVE has been alluded to in the MPI literature it has not been previously studied nor characterized. The objective of this study was to systematically characterize this PVE in MPI.Approach.This contribution characterizes the PVE using models of varying size and shape filled with a uniform concentration of tracer. The effect of object size on signal distribution was analyzed after application of a new image post-processing filter.Main results.As object size increased, signal distribution increased to a maximum signal value independent of object geometry and proportional to tracer concentration. Furthermore, for small objects with characteristic dimensions below the resolution of the tracer at the scanning conditions used, signal suppression was observed. These results are consistent with foundational observations of PVE in PET, suggesting that approaches to overcome the PVE in PET may be applicable to MPI.Significance.This finding has significant impact on the MPI field by demonstrating the presence of the PVE phenomenon that can directly influence imaging results.

Deep learning-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 deep learning (DL), the contouring and dose prediction tasks for automated treatment planning are done independently. 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 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. 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. 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 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 performed in-vitro experiments 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 conventional invitro protocol 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.As proton arc therapy (PAT) approaches clinical implementation, optimizing treatment plans for this innovative delivery modality remains challenging, especially in addressing arc delivery time. Existing algorithms for minimizing delivery time are either optimal but computationally demanding or fast but at the expense of sacrificing many degrees of freedom. In this study, we introduce a flexible method for pre-selecting energy layers (EL) in PAT treatment planning before the actual robust spot weight optimization.Approach.Our EL pre-selection method employs metaheuristics to minimize a bi-objective function, considering a dynamic delivery time proxy and tumor geometrical coverage penalized as a function of selected organs-at-risk crossing. It is capable of parallelizing multiple instances of the problem. We evaluate the method using three different treatment sites, providing a comprehensive dosimetric analysis benchmarked against dynamic proton arc plans generated with early energy layer selection and spot assignment (ELSA) and IMPT plans in RayStation TPS.Result.The algorithm efficiently generates Pareto-optimal EL pre-selections in approximately 5 min. Subsequent PAT treatment plans derived from these selections and optimized within the TPS, demonstrate high-quality target coverage, achieving a high conformity index, and effective sparing of organs at risk. These plans meet clinical goals while achieving a 20%-40% reduction in delivery time compared to ELSA plans.Significance.The proposed algorithm offers speed and efficiency, producing high-quality PAT plans by placing proton arc sectors to efficiently reduce delivery time while maintaining good target coverage and healthy tissues sparing.

Objective: Computed tomography (CT) is a crucial medical imaging technique which uses X-ray radiation to identify cancer tissues. Since radiation poses a significant health risk, low dose acquisition procedures need to be adopted. However, low-dose CT (LDCT) can cause higher noise and artifacts which massively degrade the diagnosis.

Approach: To denoise LDCT images more effectively, this paper proposes a deep learning method based on U-Net with multiple lightweight attention-based modules and residual reinforcement (MLAR-UNet), We integrate a U-Net architecture with several advanced modules, including Convolutional Block Attention Module (CBAM), Cross Residual Module (CR), Attention Cross Reinforcement Module (ACRM), and Convolution and Transformer Cross Attention Module (CTCAM). Among these modules, CBAM applies channel and spatial attention mechanisms to enhance local feature representation. However, serious detail loss caused by incorrect embedding of CBAM for LDCT denoising is verified in this study. To relieve this, we introduce CR to reduce information loss in deeper layers, preserving features more effectively. To address the excessive local attention of CBAM, we design ACRM, which incorporates Transformer to adjust the attention weights. Furthermore, we design CTCAM, which leverages a complex combination of Transformer and convolution to capture multi-scale information and compute more accurate attention weights.

Results: Experiments verify the embedding rationality and validity of each module and show that the proposed MLAR-UNet denoises LDCT images more effectively and preserves more details than many state-of-the-art (SOTA) methods on clinical chest and abdominal CT datasets.

Significance: The proposed MLAR-UNet not only demonstrates superior LDCT image denoising capability but also highlights the strong detail comprehension and negligible overheads of our designed ACRM and CTCAM. These findings provide a novel approach to integrating Transformer more efficiently in image processing.

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 (AFOV) for specific organs, and maximizing geometric sensitivity. The article explores the transition from Central Processing Unit (CPU)-based Maximum Likelihood Expectation Maximization (MLEM) 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 (LD-PEM), 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. .

Objective.Gold nanoparticles (GNPs) are widely used for biological research and applications. The in-vivo concentration of GNPs is usually low due to biological safety concerns, thus posing a challenge for imaging. This work investigates on optimal energy threshold selection in photon-counting detector(PCD)-based CT (PCCT) for the quantification of low-concentration GNPs.Approach.We derived the mathematical expression of the upper bound of the material decomposition error in the gold image. Comprehensive simulations were implemented for cylindrical phantom with inserts of different GNP concentrations. CT scans of this phantom were simulated with a 140 kVp x-ray beam under a realistic pre-clinical CT dose range. The PCD energy thresholds from 30 to 110 keV were enumerated for 2,3-channel PCCT and the optimal energy thresholds were determined by searching for the lowest decomposition error.Main results.The optimal energy threshold(s) to minimize the decomposition error in gold image was 44 keV for the 2-channel PCCT and{34,40}keV for the 3-channel case. Numerical results also validated the derived upper bounds of the decomposition error.Significance.This work addressed the need for selecting appropriate energy thresholds for accurate quantification of contrast agent distributions in pre-clinical PCCT. Both the analytical expression of the upper bound of material decomposition error and simulation results showed that the balanced consideration on photon counting noise levels and the numerical properties of the decomposition matrix is required in selecting the appropriate energy thresholds to achieve the most accurate material decomposition.

Objective: This study aimed to propose a method for obtaining anatomy-wise lung ventilation image (VIaw) that enables functional assessment of lung parenchyma and tumor-blocked pulmonary segments. The VIaw was used to define multiple functional volumes of the lung and thereby support radiation treatment planning. Approach: A super-voxel-based method was employed for functional assessment of lung parenchyma to generate VIsvd. In the VIsvd of the 11 patients with tumor blockage of the airway, the functional value in tumor-blocked segments was set to 0 to generate the VIaw. The lung was divided into regions of high functional volume (HFV), unrecoverable low functional volume, and recoverable low functional volume (rLFV, the region in the tumor-blocked segment with a high function value based on the VIsvd) to design three intensity-modulated photon plans for five patients. These plans were an anatomical-lung-guided plan (aPlan), a functional-lung-guided plan (fPlan), and a recoverable functional-lung-guided plan (rfPlan) where the latter protected both HFV and rLFV. Main results: The low functional volume in the reference ventilation images and the tumor-blocked segments had a high overlap similarity coefficient value of 0.90 ± 0.07. The mean Spearman correlation between the VIaw and reference ventilation images was 0.72 ± 0.05 for the patient with tumor blockage of the airway. The V20 and mean dose of rLFV in rfPlan were lower than those in aPlan by 12.1% ± 8.4% and 13.0% ± 6.4%, respectively, and lower than those in fPlan by 14.9% ± 9.8% and 15.9% ± 6.5%, respectively. Significance: The VIaw can reach a moderate-strong correlation with reference ventilation images and thus can identify rLFV to support treatment planning to preserve lung function. .

Multiplexed positron emission tomography (mPET) imaging allows simultaneous observation of physiological and pathological information from multiple tracers in a single PET scan. Although supervised deep learning has demonstrated superior performance in mPET image separation compared to purely model-based methods, acquiring large amounts of paired single-tracer data and multi-tracer data for training poses a practical challenge and needs extended scan durations for patients. In addition, the generalisation ability of the supervised learning framework is a concern, as the patient being scanned and their tracer kinetics may potentially fall outside the training distribution. In this work, we propose a self-supervised learning framework based on the deep image prior (DIP) for mPET image separation using just one dataset. In particular, we integrate the multi-tracer compartmental model into the DIP framework to estimate the parametric maps of each tracer from the measured dynamic dual-tracer activity images. Consequently, the separated dynamic single-tracer activity images can be recovered from the estimated tracer-specific parametric maps. In the proposed method, dynamic dual-tracer activity images are used as the training label, and the static dual-tracer image (reconstructed from the same patient data from the start to the end of acquisition) is used as the network input. The performance of the proposed method was evaluated on a simulated brain phantom for dynamic dual-tracer [¹⁸F]FDG+[¹¹C]MET activity image separation and parametric map estimation. The results demonstrate that the proposed method outperforms the conventional voxel-wise multi-tracer compartmental modeling method (vMTCM) and the two-step method DIP-Dn+vMTCM (where dynamic dual-tracer activity images are first denoised using a U-net within the DIP framework, followed by vMTCM separation) in terms of lower bias and standard deviation in the separated single-tracer images and also for the estimated parametric maps for each tracer, at both voxel and ROI levels.