Physics in medicine and biology最新文献

Objective: This study investigates the effects of various training protocols on enhancing the precision of MRI-only Pseudo-CT generation for radiation treatment planning and adaptation in head & neck cancer patients. It specifically tackles the challenge of differentiating bone from air, a limitation that frequently results in substantial deviations in the representation of bony structures on Pseudo-CT images.

Approach: The study included 25 patients, utilizing pre-treatment MRI-CT image pairs. Five cases were randomly selected for testing, with the remaining 20 used for model training and validation. A 3D U-Net deep learning model was employed, trained on patches of size 64³with an overlap of 32³. MRI scans were acquired using the Dixon gradient echo (GRE) technique, and various contrasts were explored to improve Pseudo-CT accuracy, including in-phase, water-only, and combined water-only and fat-only images. Additionally, bone extraction from the fat-only image was integrated as an additional channel to better capture bone structures on Pseudo-CTs. The evaluation involved both image quality and dosimetric metrics.

Main results: The generated Pseudo-CTs were compared with their corresponding registered target CTs. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the base model using combined water-only and fat-only images were 19.20 ± 5.30 HU and 57.24 ± 1.44 dB, respectively. Following the integration of an additional channel using a CT-guided bone segmentation, the model's performance improved, achieving MAE and PSNR of 18.32 ± 5.51 HU and 57.82 ± 1.31 dB, respectively. The dosimetric assessment confirmed that radiation treatment planning on Pseudo-CT achieved accuracy comparable to conventional CT. The measured results are statistically significant, with ap-value < 0.05.

Significance: This study demonstrates improved accuracy in bone representation on Pseudo-CTs achieved through a combination of water-only, fat-only and extracted bone images; thus, enhancing feasibility of MRI-based simulation for radiation treatment planning.

Objective.Compton cameras (CCs) are imaging devices that may improve observation of sources ofγphotons. The images are obtained by solving a difficult inverse problem. We present CoReSi, a Compton reconstruction and simulation software implemented in Python and powered by PyTorch to leverage multi-threading and to easily interface with image processing and deep learning algorithms. The code is mainly dedicated to medical imaging and near-field experiments where images are reconstructed in 3D.Approach.The code was developed over several years in C++, with the initial version being proprietary. We have since redesigned and translated it into Python, adding new features to improve its adaptability and performances. This paper reviews the literature on CC mathematical models, explains the implementation strategies we have adopted and presents the features of CoReSi.Main results.The code includes state-of-the-art mathematical models from the literature, from the simplest, which allow limited knowledge of the sources, to more sophisticated ones with a finer description of the physics involved. It offers flexibility in defining the geometry of the CC and the detector materials. Several identical cameras can be considered at arbitrary positions in space. The main functions of the code are dedicated to the computation of the system matrix, leading to the forward and backward projector operators. These are the cornerstones of any image reconstruction algorithm. A simplified Monte Carlo data simulation function is provided to facilitate code development and fast prototyping.Significance.As far as we know, there is no open source code for CC reconstruction, except for MEGAlib, which is mainly dedicated to astronomy applications. This code aims to facilitate research as more and more teams from different communities such as applied mathematics, electrical engineering, physics, medical physics get involved in CC studies. Implementation with PyTorch will also facilitate interfacing with deep learning algorithms.

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. If all activity is encompassed inside a convex region K of constant attenuation, eDCCs can be derived from SPECT projections and can be used to verify the pair-wise 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. 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). The study proposed and demonstrated the good performance of translational motion detection and correction with eDCCs in SPECT acquisition data.

Objective: One bottleneck of MRI-guided Online Adaptive Radiotherapy (MRoART) is the time-consuming daily online replanning process. The current leaf sequencing method takes up to 10 minutes, with potential dosimetric degradation and small segment openings that increase delivery time. This work aims to replace this process with a fast deep learning-based method to provide deliverable MLC sequences almost instantaneously, potentially accelerating and enhancing online adaption. Approach: Daily MRIs and plans from 242 daily fractions of 49 abdomen cancer patients on a 1.5T MR-Linac were used. The architecture included: 1) a recurrent conditional Generative Adversarial Network (rcGAN) model to predict segment shapes from a fluence map (FM), recurrently predicting each segment's shape; and 2) a linear matrix equation module to optimize the monitor units (MU) weights of segments. Multiple models with different segment numbers per beam (4-7) were trained. The final MLC sequences with the smallest relative absolute errors were selected. The predicted MLC sequence was imported into treatment planning system for dose calculation and compared with the original plans. Main results: The gamma passing rate for all fractions was 99.7±0.2% (2%/2mm criteria) and 92.7±1.6% (1%/1mm criteria). The average number of segments per beam in the proposed method was 6.0±0.6 compared to 7.5 ± 0.3 in the original plan. The average total MUs were reduced from 1641 ± 262 to 1569.5 ± 236.7 in the predicted plans. The estimated delivery time was reduced from 9.7 minutes to 8.3 minutes, an average reduction of 14% and up to 25% for individual plans. Execution time for one plan was less than 10 seconds using a GTX1660TIGPU. Significance: The developed models can quickly and accurately generate an optimized, deliverable leaf sequence from a FM with fewer segments. This can seamlessly integrate into the current online replanning workflow, greatly expediting the daily plan adaptation process. .

Objective.This study aims to develop and evaluate a fast and robust deep learning-based auto-segmentation approach for organs at risk in MRI-guided radiotherapy of pancreatic cancer to overcome the problems of time-intensive manual contouring in online adaptive workflows. The research focuses on implementing novel data augmentation techniques to address the challenges posed by limited datasets.Approach.This study was conducted in two phases. In phase I, we selected and customized the best-performing segmentation model among ResU-Net, SegResNet, and nnU-Net, using 43 balanced 3DVane images from 10 patients with 5-fold cross-validation. Phase II focused on optimizing the chosen model through two advanced data augmentation approaches to improve performance and generalizability by increasing the effective input dataset: (1) a novel structure-guided deformation-based augmentation approach (sgDefAug) and (2) a generative adversarial network-based method using a cycleGAN (GANAug). These were compared with comprehensive conventional augmentations (ConvAug). The approaches were evaluated using geometric (Dice score, average surface distance (ASD)) and dosimetric (D2% and D50% from dose-volume histograms) criteria.Main results.The nnU-Net framework demonstrated superior performance (mean Dice: 0.78 ± 0.10, mean ASD: 3.92 ± 1.94 mm) compared to other models. The sgDefAug and GANAug approaches significantly improved model performance over ConvAug, with sgDefAug demonstrating slightly superior results (mean Dice: 0.84 ± 0.09, mean ASD: 3.14 ± 1.79 mm). The proposed methodology produced auto-contours in under 30 s, with 75% of organs showing less than 1% difference in D2% and D50% dose criteria compared to ground truth.Significance.The integration of the nnU-Net framework with our proposed novel augmentation technique effectively addresses the challenges of limited datasets and stringent time constraints in online adaptive radiotherapy for pancreatic cancer. Our approach offers a promising solution for streamlining online adaptive workflows and represents a substantial step forward in the practical application of auto-segmentation techniques in clinical radiotherapy settings.

Objective. To develop an augmentation method that simulates cone-beam computed tomography (CBCT) related motion artifacts, which can be used to generate training-data to increase the performance of artificial intelligence models dedicated to auto-contouring tasks.Approach.The augmentation technique generates data that simulates artifacts typically present in CBCT imaging. The simulated pseudo-CBCT (pCBCT) is created using interleaved sequences of simulated breath-hold and free-breathing projections. Neural networks for auto-contouring of head and neck and bowel structures were trained with and without pCBCT data. Quantitative and qualitative assessment was done in two independent test sets containing CT and real CBCT data focus on four anatomical regions: head, neck, abdomen, and pelvis. Qualitative analyses were conducted by five clinical experts from three different healthcare institutions.Main results.The generated pCBCT images demonstrate realistic motion artifacts comparable to those observed in real CBCT data. Training a neural network with CT and pCBCT data improved Dice similarity coefficient (DSC) and average contour distance (ACD) results on CBCT test sets. The results were statistically significant (p-value ⩽.03) for bone-mandible (model without/with pCBCT: 0.91/0.92 DSC,p⩽ .01; 0.74/0.66 mm ACD,p⩽.01), brain (0.34/0.93 DSC,p⩽ 1 × 10^-5; 17.5/2.79 mm ACD,p= 1 × 10^-5), oral-cavity (0.81/0.83 DSC,p⩽.01; 5.11/4.61 mm ACD,p= .02), left-submandibular-gland (0.58/0.77 DSC,p⩽.001; 3.24/2.12 mm ACD,p⩽ .001), right-submandibular-gland (0.00/0.75 DSC,p⩽.1 × 10^-5; 17.5/2.26 mm ACD,p⩽ 1 × 10^-5), left-parotid (0.68/0.78 DSC,p⩽ .001; 3.34/2.58 mm ACD,p⩽.01), large-bowel (0.60/0.75 DSC,p⩽ .01; 6.14/4.56 mm ACD,p= .03) and small-bowel (3.08/2.65 mm ACD,p= .03). Visual evaluation showed fewer false positives, false negatives, and misclassifications in artifact-affected areas. Qualitative analyses demonstrated that, auto-generated contours are clinically acceptable in over 90% of cases for most structures, with only a few requiring adjustments.Significance.The inclusion of pCBCT improves the performance of trainable auto-contouring approaches, particularly in cases where the images are prone to severe artifacts.

Objective. Radiation dose and diagnostic image quality are opposing constraints in x-ray computed tomography (CT). Conventional methods do not fully account for organ-level radiation dose and noise when considering radiation risk and clinical task. In this work, we develop a pipeline to generate individualized organ-specific dose and noise at desired dose levels from clinical CT scans.Approach. To estimate organ-specific dose and noise, we compute dose maps, noise maps at desired dose levels and organ segmentations. In our pipeline, dose maps are generated using Monte Carlo simulation. The noise map is obtained by scaling the inserted noise in synthetic low-dose emulation in order to avoid anatomical structures, where the scaling coefficients are empirically calibrated. Organ segmentations are generated by a deep learning-based method (TotalSegmentator). The proposed noise model is evaluated on a clinical dataset of 12 CT scans, a phantom dataset of 3 uniform phantom scans, and a cross-site dataset of 26 scans. The accuracy of deep learning-based segmentations for organ-level dose and noise estimates was tested using a dataset of 41 cases with expert segmentations of six organs: lungs, liver, kidneys, bladder, spleen, and pancreas.Main results. The empirical noise model performs well, with an average RMSE approximately 1.5 HU and an average relative RMSE approximately 5% across different dose levels. The segmentation from TotalSegmentator yielded a mean Dice score of 0.8597 across the six organs (max = 0.9315 in liver, min = 0.6855 in pancreas). The resulting error in organ-level dose and noise estimation was less than 2% for most organs.Significance. The proposed pipeline can output individualized organ-specific dose and noise estimates accurately for personalized protocol evaluation and optimization. It is fully automated and can be scalable to large clinical datasets. This pipeline can be used to optimize image quality for specific organs and thus clinical tasks, without adversely affecting overall radiation dose.

As ultrasound-compatible flow phantoms are devised for performance testing and calibration, there is a practical need to obtain independent flow measurements for validation using a gold-standard technique such as particle image velocimetry (PIV). In this paper, we present the design of a new dual-modality flow phantom that allows ultrasound and PIV measurements to be simultaneously performed. Our phantom's tissue mimicking material is based on a novel hydrogel formula that uses propylene glycol to lower the freezing temperature of an ultrasound-compatible poly(vinyl) alcohol cryogel and, in turn, maintain the solution's optical transparency after thermocycling. The hydrogel's optical attenuation {1.56 dB cm^-1with 95% confidence interval (CI) of [1.512 1.608]}, refractive index {1.337, CI: [1.340 1.333]}, acoustic attenuation {0.038 dB/(cm × MHz^b), CI: [0.0368 0.0403]; frequency dependent factor of 1.321, CI: [1.296 1.346]}, and speed of sound {1523.6 m s^-1, CI: [1523.8 1523.4]} were found to be suitable for PIV and ultrasound flow measurements. As an application demonstration, a bimodal flow phantom with spiral lumen was fabricated and used in simultaneous flow measurements with PIV and ultrasound color flow imaging (CFI). Velocity fields and profiles were compared between the two modalities under a constant flow rate (2.5 ml s^-1). CFI was found to overestimate flow speed compared to the PIV measurements, with a 14%, 10%, and 6% difference between PIV and ultrasound for the 60°, 45°, and 30° angles measured. These results demonstrate the new phantom's feasibility in enabling performance validation of ultrasound flow mapping tools.

Objective.Deformable registration aims to achieve nonlinear alignment of image space by estimating a dense displacement field. It is commonly used as a preprocessing step in clinical and image analysis applications, such as surgical planning, diagnostic assistance, and surgical navigation. We aim to overcome these challenges: Deep learning-based registration methods often struggle with complex displacements and lack effective interaction between global and local feature information. They also neglect the spatial position matching process, leading to insufficient registration accuracy and reduced robustness when handling abnormal tissues.Approach.We propose a dual-branch interactive registration model architecture from the perspective of spatial matching. Implicit regularization is achieved through a consistency loss, enabling the network to balance high accuracy with a low folding rate. We introduced the dynamic matching module between the two branches of the registration, which generates learnable offsets based on all the tokens across the entire resolution range of the base branch features. Using trilinear interpolation, the model adjusts its feature expression range according to the learned offsets, capturing highly flexible positional differences. To facilitate the spatial matching process, we designed the gated mamba layer to globally model pixel-level features by associating all voxel information, while the detail enhancement module, which is based on channel and spatial attention, enhances the richness of local feature details.Main results.Our study explores the model's performance in single-modal and multi-modal image registration, including normal brain, brain tumor, and lung images. We propose unsupervised and semi-supervised registration modes and conduct extensive validation experiments. The results demonstrate that the model achieves state-of-the-art performance across multiple datasets.Significance.By introducing a novel perspective of position matching, the model achieves precise registration of various types of medical data, offering significant clinical value in medical applications.

Objective.Magnetic nanoparticle hyperthermia (MNH) emerges as a promising therapeutic strategy for cancer treatment, leveraging alternating magnetic fields (AMFs) to induce localized heating through magnetic nanoparticles. However, the interaction of AMFs with biological tissues leads to non-specific heating caused by eddy currents, triggering thermoregulatory responses and complex thermal gradients throughout the body of the patient. While previous studies have implemented the Atkinson-Brezovich limit to mitigate potential harm, recent research underscores discrepancies between this threshold and clinical outcomes, necessitating a re-evaluation of this safety limit. Therefore, in this study, through electromagnetic (EM) simulations, the complex interaction between AMFs and anatomical models was investigated.Approach.In particular, we considered a circular coil configuration placed at different positions along the craniocaudal axis of various anatomical human models. The excitation current was normalized, at different frequencies, to meet the basic restriction of local 10 g-averaged specific energy absorption rate (SAR) in the human models, as defined by the exposure guidelines of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the standard IEC 60601-2-33 of the International Electrotechnical Commission (IEC).Main results.The resulting permissible magnetic field strength values, for the reference levels set by the ICNIRP 2020 guidelines, emerged to be up to approximately 1.4 and 3 times less than that defined in the Atkinson-Brezovich limit. The widely used limit was found to align more closely with the first level of controlled operating mode defined in the IEC 60601-2-33 standard.Significance.The results indicate that the permissible magnetic field amplitude during MNH treatment should be much lower than that in the Atkinson-Brezovich limit. This study offers valuable insights into the role of computational simulations in advancing the potential to establish a reliable metric for safety evaluation and monitoring within the clinical framework of MNH.