Physics in medicine and biology最新文献

Objective. This study introduces the effective electric field (E_eff) as a novel observable for transcranial magnetic stimulation (TMS) numerical dosimetry.E_effrepresents the electric field component aligned with the local orientation of cortical and white matter (WM) neuronal elements. To assess the utility ofE_effas a predictive measure for TMS outcomes, we evaluated its correlation with TMS induced muscle responses and compared it against conventional observables, including the electric (E-)field magnitude, and its components normal and tangential to the cortical surface.Approach.Using a custom-made software for TMS dosimetry, theE_effis calculated combining TMS dosimetric results from an anisotropic head model with tractography data of gray and white matter (GM and WM). To test the hypothesis thatE_effhas a stronger correlation with muscle response, a proof-of-concept experiment was conducted. Seven TMS sessions, with different coil rotations, targeted the primary motor area of a healthy subject. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle.Main results.TheE_efftrend for the seven TMS coil rotations closely matched the measured MEP response, displaying an ascending pattern that peaked and then symmetrically declined. In contrast, theE-field magnitude and its components tangential (E_tan) and normal (E_norm) to the cortical surface were less responsive to coil orientation changes.E_effshowed a strong correlation with MEPs (r= 0.8), while the other observables had a weaker correlation (0.5 forE_normand below 0.2 forE-field magnitude andE_tan).Significance.This study is the first to evaluateE_eff, a novel component of the TMS inducedE-field. Derived using tractography data from both white and GM,E_effinherently captures axonal organization and local orientation. By demonstrating its correlation with MEPs, this work introducesE_effas a promising observable for future TMS dosimetric studies, with the potential to improve the precision of TMS applications.

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 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.

Cross-modal retrieval is crucial for improving clinical decision-making and report generation. However, current technologies mainly focus on linking single images with reports, ignoring the need to comprehensively observe multiple images in real clinical environments. Additionally, differences in imaging equipment, scanning parameters, geographic regions, and reporting styles in chest x-rays and reports cause inconsistent data distributions, which challenge model reliability and generalization. To address these challenges, we propose a study-level cross-modal retrieval task for chest x-rays and reports to better meet clinical needs. Our study-level approach involves cross-modal retrieval between multiple images and reports from patient exams. Given a set of study-level images or reports, our method retrieves relevant reports or images from a database, providing a more realistic reflection of clinical scenarios compared to traditional methods that link single images with reports. Furthermore, we introduce an adapter-based pre-training and fine-tuning method to enhance model generalization across diverse data distributions. Through comprehensive experiments, we demonstrate the advantages of our method in pre-training and fine-tuning. In the pre-training stage, we compare our method with the latest techniques, showing the effectiveness of integrating study-level image features using a vision transformer and aligning them with report features. In the fine-tuning stage, we compare the adapter-based fine-tuning method with the latest methods of full-parameter fine-tuning and conduct ablation studies with common head-based and full-parameter fine-tuning methods, proving our method's efficiency and significant potential for practical clinical applications. This study proposes a study-level cross-modal retrieval task for matching chest x-ray images and reports. By employing a pre-training and fine-tuning strategy with adapter modules, it addresses the issue of data distribution inconsistency and improves retrieval performance.

Objective. Dynamic positron emission tomography (dPET) is an important molecular imaging technology that is used for the clinical diagnosis, staging, and treatment of various human cancers. Higher temporal imaging resolutions are desired for the early stages of radioactive tracer metabolism. However, images reconstructed from raw data with shorter frame durations have lower image signal-to-noise ratios (SNRs) and unexpected spatial resolutions.Approach. To address these issues, this paper proposes a kinetic-induced voxel filtering technique for processing noisy and distorted dPET images. This method extracts the inherent motion information contained in the target PET image and effectively uses this information to construct an image filter for each PET image frame. To ensure that the filtered image remains undistorted, we integrate and reorganize the information from each frame along the temporal dimension. In addition, our method applies repeated filtering operations to the image to produce optimal denoising results.Main results. The effectiveness of the proposed method is validated on both simulated and clinical dPET data, with quantitative evaluations of dynamic images and pharmacokinetic parameter maps calculated via the peak SNR and mean structural similarity index measure. Compared with the state-of-the-art methods, our method achieves superior results in both qualitative and quantitative imaging scenarios.Significance. It exhibits commendable performance and high interpretability and is demonstrated to be both effective and feasible in high-temporal-resolution dynamic PET imaging tasks.

Background.Proton minibeam radiation therapy (pMBRT) is a spatially fractionated radiation therapy modality that uses a multi-slit collimator (MSC) to create submillimeter slit openings for spatial dose modulation. The pMBRT dose profile is characterized by highly heterogeneous dose in the plane perpendicular to the beam and rapidly changing depth dose profiles. Dose measurements are typically benchmarked against in-house Monte Carlo (MC) simulation tools. For preclinical and clinical translation, a treatment planning system (TPS) capable of accurately predicting pMBRT doses in tissue and accessible on a commercial platform is essential. This study focuses on the beam modeling and verification of pMBRT using the RayStation TPS, a critical step in advancing its clinical implementation.Methods.The pMBRT system was implemented in RayStation for the IBA Proteus®ONE single-room compact proton machine. The RayStation pMBRT model is an extension of the clinical beam model, allowing pMBRT dose calculations through the MSC using the existing clinical beam model. Adjustable MSC parameters include air gap, slit thickness, slit pitch, number of slits, slits direction and slit thickness. The pMBRT TPS was validated experimentally against measurements using six different collimators with various slit widths (0.4-1.4 mm) and center-to-center slit distances (2.8-4.0 mm). Each collimator comprised five non-divergent slits. Validation involved MatriXX measurements for average dose, Gafchromic film placed at varying depths to measure lateral dose profiles, and film placed along the beam axis to measure depth-dose curves in solid water phantoms. A single 150 MeV energy layer with a 0.5 cm spot spacing was used to create a uniform radiation map across the MSC field.Results.The comparison of average depth dose measurements with RayStation MC calculations showed a gamma passing rate better than 95% using 3 mm/3% criteria, except for the 0.4 mm slit width. After adjusting the slit width by 40-60μm to account for machining uncertainties, the gamma passing rate exceeded 95% under the same criteria. For the peaks and valleys of the percentage depth doses, agreement between RayStation and film measurements was above 90% using 2 mm/5% criteria, except in the high linear energy transfer region. Lateral profile comparisons at depths of 2, 6, and 10 cm demonstrated over 90% agreement for all curves using 0.2 mm/5% criteria.Conclusions.The pMBRT beam model for the Proteus®ONE-based system has been successfully implemented in RayStation TPS, with its initial accuracy validated experimentally. Further measurements, including additional energies and Spread Out Bragg Peaks, are required to complete the clinical commissioning process.

Objective.This study aimed to propose a method for obtaining anatomy-wise lung ventilation image (VI_aw) that enables functional assessment of lung parenchyma and tumor-blocked pulmonary segments. The VI_awwas 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 VI_svd. In the VI_svdof the 11 patients with tumor blockage of the airway, the functional value in tumor-blocked segments was set to 0 to generate the VI_aw. The lung was divided into regions of high functional volume (HFV), unrecoverable low functional volume (LFV), and recoverable LFV (rLFV, the region in the tumor-blocked segment with a high function value based on the VI_svd) 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 LFV 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 VI_awand reference ventilation images was 0.72 ± 0.05 for the patient with tumor blockage of the airway. TheV20 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 VI_awcan reach a moderate-strong correlation with reference ventilation images and thus can identify rLFV to support treatment planning to preserve lung function.

Objective.One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-consuming daily online replanning process. The current leaf sequencing method takes up to 10 min, 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 1.5 T MR-Linac were used. The architecture included: (1) a recurrent conditional generative adversarial network 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 (MUs) 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%/2 mm criteria) and 92.7 ± 1.6% (1%/1 mm 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 min to 8.3 min, an average reduction of 14% and up to 25% for individual plans. Execution time for one plan was less than 10 s 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 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 measured results are statistically significant, with ap-value<0.05. The dosimetric assessment confirmed that radiation treatment planning on Pseudo-CT achieved accuracy comparable to conventional CT.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.In preclinical research,in vivoimaging of mice and rats is more common than any other animal species, since their physiopathology is very well-known and many genetically altered disease models exist. Animal studies based on small rodents are usually performed using dedicated preclinical imaging systems with high spatial resolution. For studies that require animal models such as mini-pigs or New-Zealand White (NZW) rabbits, imaging systems with larger bore sizes are required. In case of hybrid imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI), clinical systems have to be used, as these animal models do not typically fit in preclinical simultaneous PET-MRI scanners.Approach.In this paper, we present initial imaging results obtained with the Hyperion II^DPET insert which can accommodate NZW rabbits when combined with a large volume MRI RF coil. First, we developed a rabbit-sized image quality phantom of comparable size to a NZW rabbit in order to evaluate the PET imaging performance of the insert under high count rates. For this phantom, radioactive spheres with inner diameters between 3.95 and7.86mm were visible in a warm background with a tracer activity ratio of 4.1 to 1 and with a total¹⁸F activity in the phantom of58MBq at measurement start. Second, we performed simultaneous PET-MR imaging of atherosclerotic plaques in a rabbitin vivousing a single injection containing¹⁸F-FDG for detection of inflammatory activity, and Gd-ESMA for visualization of the aortic vessel wall and plaques with MRI.Main results.The fused PET-MR images reveal¹⁸F-FDG uptake within an active plaques with plaque thicknesses in the sub-millimeter range. Histology showed colocalization of¹⁸F-FDG uptake with macrophages in the aortic vessel wall lesions.Significance.Our initial results demonstrate that this PET insert is a promising system for simultaneous high-resolution PET-MR atherosclerotic plaque imaging studies in NZW rabbits.

Objective.To develop and validate a novel multidimensional readout method that significantly reduces the number of readout channels (NRC) in PET detectors while maintaining high spatial and energy performance.Approach.We arranged a3×3×4SiPM array in multiple dimensions and employed row/column/layer summation with a resistor-based splitting circuit. We then applied denoising methods to enhance the peak-to-valley ratio in the decoding map, ensuring accurate crystal-position determination. Additionally, we investigated the system's energy response at 511 keV and evaluated the suitability for both clinical and research PET systems.Main results.The proposed multidimensional readout method achieved a favorable multiplexing ratio, lowering the total NRCs without compromising energy resolution at 511 keV. Our tests demonstrated that a SiPM bias voltage of 31 V effectively balances gain and saturation effects, resulting in reliable energy measurements.Significance.By reducing system complexity, cost, and power consumption, the multidimensional readout method presents a practical alternative to conventional readout schemes for PET and other large-scale sensor arrays. Additionally, the approach can manage simultaneous multi-layer hits by arranging detector layers and, when needed, uses ICS detection to correct for scatter events. Its adaptable architecture allows scaling to higher dimensions for broader applications (e.g. SPECT, CT, LiDAR). These features make it a valuable contribution toward more efficient, high-performance imaging technologies in both clinical and industrial settings.