Physics in medicine and biology最新文献

Objective: The arrangement of chromosomes in the cell nucleus has implications for cell radiosensitivity. The development of new tools to utilize Hi-C chromosome conformation data in nanoscale radiation track structure simulations allows for in silico investigation of this phenomenon. We have developed a framework employing Hi-C-based cell nucleus models in Monte Carlo radiation simulations, in conjunction with mechanistic models of DNA repair, to predict not only the initial radiation-induced DNA damage, but also the repair outcomes resulting from this damage, allowing us to investigate the role chromosome conformation plays in the biological outcome of radiation exposure. Approach: In this study, we used this framework to generate cell nucleus models based on Hi-C data from fibroblast and lymphoblastoid cells and explore the effects of cell type-specific chromosome structure on radiation response. The models were used to simulate external beam irradiation including DNA damage and subsequent DNA repair. The kinetics of the simulated DNA repair were compared with previous results. Main Results: We found that the fibroblast models resulted in a higher rate of inter-chromosome misrepair than the lymphoblastoid model, despite having similar amounts of initial DNA damage and total misrepairs for each irradiation scenario. Significance: This framework represents a step forward in radiobiological modeling and simulation allowing for more realistic investigation of radiosensitivity in different types of cells.

Objective: Liver cancer patients may benefit from proton therapy through increase of the tumor control probability (TCP). However, proton therapy is a limited resource and may not be available for all patients. We consider combined proton-photon liver SBRT treatments (CPPT) where only some fractions are delivered with protons. It is investigated how limited proton fractions can be used best for individual patients and optimally allocated within a patient group. Approach: Photon and proton treatment plans were created for five liver cancer patients. In CPPT, limited proton fractions may be optimally exploited by increasing the fraction dose compared to photon fraction dose. To determine a patient's optimal proton and photon fraction dose, we maximize the target BED while constraining the mean normal liver BED, which leads to an up- or downscaling of the proton and photon plan, respectively. The resulting CPPT balances the benefits of fractionation in the normal liver versus exploiting the superior proton dose distributions. After converting the target BED to TCP, the optimal number of proton fractions per patient is determined by maximizing the overall TCP of the patient group. Main results: For the individual patient, a CPPT treatment that delivers a higher fraction dose with protons than photons allows for dose escalation in the target compared to delivering the same proton and photon fraction dose. On the level of a patient group, CPPT may allow to distribute limited proton slots over several patients. Through an optimal use and allocation of proton fractions, CPPT may increase the average patient group TCP compared to a proton patient selection strategy where patients receive single-modality proton or photon treatments. Significance: Limited proton resources can be optimally exploited via CPPT by increasing the target dose in proton fractions and allocating available proton slots to patients with the highest TCP increase. .

Objective.Mild cognitive impairment (MCI) is a precursor stage of dementia characterized by mild cognitive decline in one or more cognitive domains, without meeting the criteria for dementia. MCI is considered a prodromal form of Alzheimer's disease (AD). Early identification of MCI is crucial for both intervention and prevention of AD. To accurately identify MCI, a novel multimodal 3D imaging data integration graph convolutional network (GCN) model is designed in this paper.Approach.The proposed model utilizes 3D-VGGNet to extract three-dimensional features from multimodal imaging data (such as structural magnetic resonance imaging and fluorodeoxyglucose positron emission tomography), which are then fused into feature vectors as the node features of a population graph. Non-imaging features of participants are combined with the multimodal imaging data to construct a population sparse graph. Additionally, in order to optimize the connectivity of the graph, we employed the pairwise attribute estimation （PAE） method to compute the edge weights based on non-imaging data, thereby enhancing the effectiveness of the graph structure. Subsequently, a population-based GCN integrates the structural and functional features of different modal images into the features of each participant for MCI classification.Main results.Experiments on the AD Neuroimaging Initiative demonstrated accuracies of 98.57%, 96.03%, and 96.83% for the normal controls (NC)-early MCI (EMCI), NC-late MCI (LMCI), and EMCI-LMCI classification tasks, respectively. The AUC, specificity, sensitivity, and F1-score are also superior to state-of-the-art models, demonstrating the effectiveness of the proposed model. Furthermore, the proposed model is applied to the ABIDE dataset for autism diagnosis, achieving an accuracy of 91.43% and outperforming the state-of-the-art models, indicating excellent generalization capabilities of the proposed model.Significance.This study demonstratesthe proposed model's ability to integrate multimodal imaging data and its excellent ability to recognize MCI. This will help achieve early warning for AD and intelligent diagnosis of other brain neurodegenerative diseases.

Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufacturers incorporate various post-acquisition image-processing techniques into the scanner's computer software tools for different post-processing tasks. These tools provide a final image of adequate quality and essential features for accurate clinical reporting and predictive interpretation for better treatment planning. Different post-acquisition image-processing tasks for MRI quality enhancement include noise removal, motion artifact reduction, magnetic bias field correction, and eddy electric current effect removal. Recently, deep learning (DL) methods have shown great success in many research fields, including image and video applications. DL-based data-driven feature-learning approaches have great potential for MR image denoising and image-quality-degrading artifact correction. Recent studies have demonstrated significant improvements in image-analysis tasks using DL-based convolutional neural network (CNN) techniques. The promising capabilities and performance of DL techniques in various problem-solving domains have motivated researchers to adapt DL methods to medical image analysis and quality enhancement tasks. This paper presents a comprehensive review of DL-based state-of-the-art MRI quality enhancement and artifact removal methods for regenerating high-quality images while preserving essential anatomical and physiological feature maps without destroying important image information. Existing research gaps and future directions have also been provided by highlighting potential research areas for future developments, along with their importance and advantages in medical imaging.

Objective: Motivated by the diagnostic value of tissue viscosity beyond elasticity, the goal of this work is to develop robust methodologies based on shear wave elastography (SWE) to reconstruct combined elasticity and viscosity maps of soft tissues out of the measurement plane.

Approach: Building on recent advancements in full-waveform inversion (FWI) in reconstructing elasticity maps beyond the measurement plane, we proposed to reconstruct a complete viscoelasticity map by novel combination of three ideas: (a) multiresolution imaging, where lower frequency content is used to reconstruct low resolution map, which is then utilized as a starting point for higher resolution reconstruction by including higher frequency content; (b) acquiring SWE data on multiple planes from multiple pushes, one at a time, and then simultaneously using all the data to invert for a single viscoelasticity map; (c) sequential reconstruction where combined viscoelasticity reconstruction is followed by fixing the elasticity map (and thus kinematics), and repeating the reconstruction but just for the viscosity map.

Main results: We examine the proposed methodology using synthetic SWE data to reconstruct the viscoelastic properties of both homogeneous and heterogeneous tumor-like inclusions with shear modulus ranging from 3 to 20 kPa, and viscosity ranging from 1 to 3 Pa.s. Final validation is performed in silico, where the annular inclusion is reconstructed using noisy data with varying signal-to-noise ratios (SNR) of 30, 20 and 10 dB. While elasticity images are reasonably reconstructed even for poor SNR of 10 dB, viscosity imaging seem to require better SNR.

Significance: This work, analogous to reconstructing 3D images from 2D measurements, offers a feasibility study for achieving 3D viscoelasticity reconstructions using conventional ultrasound scanners, potentially leading to biomarkers with greater specificity compared to currently available 2D elasticity images.

Objective: Cerebral arterial and venous flow (A/V) classification is a key parameter for understanding dynamic changes in neonatal brain perfusion. Currently, transfontanellar ultrasound Doppler imaging is the reference clinical technique able to discriminate between A/V using vascular indices such as resistivity index (RI) or pulsatility index (PI). However, under conditions of slow arterial and venular flow, small signal fluctuations can lead to potential misclassifications of vessels. Recently, ultrafast ultrasound imaging has paved the way for better sensitivity and spatial resolution. Here, we show that A/V classification can be performed robustly using ultrafast Doppler spectrogram. Approach: The overall classification steps are as follows: for any pixel within a vessel, a normalized Doppler spectrogram (NDS) is computed that allows for normalized correlation analysis with ground-truth signals that were established semi-automatically based on anatomical/physiological references. Furthermore, A/V classification is performed by computing Pearson correlation coefficient between NDS in ground-truth domains and the individual pixel's NDS inside vessels and finding an optimal threshold. Main Results: When applied to human newborns (n= 40), the overall accuracy, sensitivity, and specificity were found to be 88.5% ± 6.7%, 88.5% ± 6.5%, and 87.0% ± 8.8% respectively. We also examined strategies to fully automate this process, leading to a moderate decrease of 1%-3% in the same metrics. Additionally, when compared to the main clinical metrics such as RI, and PI, the receiver operating characteristic curves exhibited higher areas under the curve; on average by +36% (p < 0.0001) in the full imaging sector, +35% (p = 0.0116) in the cortical regions, +53% (p < 0.0001) in the basal ganglia, +28% (p = 0.0051) in the cingulate gyrus, and +35% (p < 0.0001) in the remaining brain structures. Significance: Our findings suggest that the proposed NDS-based approach can distinguish between A/V when studying cerebral perfusion in neonates. .

Objective.Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely analyzed. This study aims to comprehend human planner's interaction with the AI planning tool and incorporate the analysis to improve the existing AI tool.Approach.An in-house AI tool for whole breast radiation therapy planning was deployed in our institution since 2019, among which 522 patients were included in this study. The AI tool automatically generates fluence maps of the tangential beams to create anAI plan. Human planner makes fluence edits deemed necessary and after attending physician approval for treatment, it is recorded asfinal plan. Manual modification value maps were collected, which is the difference between theAI-planand thefinal plan. Subsequently, a human-AI interaction (HAI) model using full scale connected U-Net was trained to learn such interactions and perform plan enhancements. The trained HAI model automatically modifies theAI planto generate AI-modified plans (AI-m plan), simulating human editing. Its performance was evaluated against originalAI-planandfinal plan. Main results. AI-m planshowed statistically significant improvement in hotspot control over theAI plan, with an average of 25.2cc volume reduction in breast V105% (p= 0.011) and 0.805% decrease in Dmax (p< .001). It also maintained the same planning target volume (PTV) coverage as thefinal plan, demonstrating the model has captured the clinic focus of improving PTV hot spots without degrading coverage.Significance.The proposed HAI model has demonstrated capability of further enhancing theAI planvia modeling human-AI tool interactions. This study shows analysis of human interaction with the AI planning tool is a significant step to improve the AI tool.

Purpose.To provide a novel and personalized method (FLIP, FLowand Irradiation Personalized) using patient-specific circulating blood flows and individualized time-dependent irradiation distributions, to quantify the dose delivered to blood in large vessels during proton or photon external beam radiotherapy.Methods.Patient-specific data were obtained from ten cancer patients undergoing radiotherapy, including the blood velocity field in large vessels and the temporal irradiation scheme using photons or protons. The large vessels and the corresponding blood flow velocities are obtained from phase-contrast MRI sequences. The blood dose is obtained discretizing the fluid into individual blood particles (BPs). A Lagrangian approach was applied to simulate the BPs trajectories along the vascular velocity field flowlines. Beam delivery dynamics was obtained from beam delivery machine measurements. The whole IS is split into a sequence of successive IEs, each one with its constant dose rate, as well as its corresponding initial and final time. Calculating the dose rate and knowing the spatiotemporal distribution of BPs, the dose is computed by accumulating the energy received by each BP as the time-dependent irradiation beams take place during the treatment.Results.Blood dose volume histograms from proton therapy and photon radiotherapy patients were assessed. The irradiation times distribution is obtained for BPs in both modalities. Two dosimetric parameters are presented: (i)D_3%, representing the minimum dose received by the 3% of BPs receiving the highest doses, and (ii)V_{0.5 Gy}, denoting the blood volume percentage that has received at least 0.5 Gy.Conclusion.A novel methodology is proposed for quantifying the circulating blood dose along large vessels. This methodology involves the use of patient-specific vasculature, blood flow velocity field, and dose delivery dynamics recovered from the irradiation machine. Relevant parameters that affect the dose received, as the distance between large vessels and CTV, are identified.

Objective.Advanced pinhole collimation geometries optimized for preclinical high-energyɣimaging facilitate applications such asɑandßemitter imaging, simultaneous multi-isotope PET and PET/SPECT, and positron range-free PET. These geometries replace each pinhole with a group of clustered pinholes (CPs) featuring smaller individual pinhole opening angles (POAs), enabling sub-mm resolution imaging up to ∼1 MeV. Further narrowing POAs while retaining field-of-view (FOV) may enhance high-energy imaging but faces geometrical constraints. Here, we detail how the novel twisted CPs (TCPs) address this challenge.Approach.We compared TCP and CP collimator sensitivity at equal system resolution (SR) and SR at matched sensitivity by tuning pinhole diameters for¹⁸F (511 keV) and⁸⁹Zr (909 keV). Additionally, simulated Derenzo phantoms at low activity (LA: 12 MBq ml^-1) and high activity (HA: 190 MBq ml^-1) levels, along with uniformity images, were compared to assess image resolution and uniformity.Main results.At equal SR, TCP increased average central FOV sensitivity by 15.6% for¹⁸F and 29.4% for⁸⁹Zr compared to CP. Image resolution was comparable, except for⁸⁹Zr at LA, where TCP resolved 0.80 mm diameter rods compared to 0.90 mm for CP. Image uniformity was equivalent for¹⁸F, while for⁸⁹Zr TCP granted a 10.4% improvement. For collimators with matched sensitivity, TCP improved SR by 6.6% for¹⁸F and 17.7% for⁸⁹Zr while also enhancing image resolution; for¹⁸F, rods distinguished were 0.65 mm (CP) and 0.60 mm (TCP) for HA, and 0.70 mm (CP and TCP) for LA. For⁸⁹Zr, image resolutions were 0.75 mm (CP) and 0.65 mm (TCP) for HA, and 0.90 mm (CP) and 0.80 mm (TCP) for LA. Image uniformity with TCP decreased by 18.3% for¹⁸F but improved by 20.1% for⁸⁹Zr.Significance.This study suggests that the TCP design has potential to improve high-energyɣimaging.

Objective: To evaluate spatially fractionated radiation therapy (SFRT) for very-high-energy electrons (VHEEs) delivered with pencil beam scanning.

Approach: Radiochromic film was irradiated at the CERN Linear Electron Accelerator for Research (CLEAR) using 194 MeV electrons with a step-and-shoot technique, moving films within a water tank. Peak-to-valley dose ratios (PVDRs), depths of convergence (PVDR≤1.1), peak doses, and valley doses assessed SFRT dose distribution quality. A Monte Carlo (MI) model of the pencil beams was developed using TOPAS and applied to a five-beam VHEE SFRT treatment for a canine glioma patient, compared to a clinical 6 MV VMAT plan. The plans were evaluated based on dose-volume histograms, mean dose, and maximum dose to the planning target volume (PTV) and organs at risk (OARs). Main Results: Experimental PVDR values were maximized at 15.5 ± 0.1 at 12 mm depth for 5 mm spot spacing. A depth of convergence of 76.5 mm, 70.7 mm, and 56.6 mm was found for 5 mm, 4 mm, and 3 mm beamlet spacings, respectively. MC simulations and experiments showed good agreement, with maximum relative dose differences of 2% in percentage depth dose curves and less than 3% in beam profiles. Simulated PVDR values reached 180 ± 4, potentially achievable with reduced leakage dose. VHEE SFRT plans for the canine glioma patient showed a decrease in mean dose (>16%) to OARs while increasing the PTV mean dose by up to 15%. Lowering beam energy enhanced PTV dose homogeneity and reduced OAR maximum doses.

Significance: The presented work demonstrates that pencil beam scanning SFRT with VHEEs could treat deep-seated tumors such as head and neck cancer or lung lesions, though small beam size and leakage dose may limit the achievable PVDR. .