Physics in medicine and biology最新文献

An analysis of the methodology used by the authors of the commented article is presented and errors related to data preparation are pointed out.

Objective: In breast diagnostic imaging, the morphological variability of breast tumors and the inherent ambiguity of ultrasound images pose significant challenges. Moreover, multi-task computer-aided diagnosis systems in breast imaging may overlook inherent relationships between pixel-wise segmentation and categorical classification tasks. Approach. In this paper, we propose a multi-task learning network with deep inter-task interactions that exploits the inherently relations between two tasks. First, we fuse self-task attention and cross-task attention mechanisms to explore the two types of interaction information, location and semantic, between tasks. In addition, a feature aggregation block is developed based on the channel attention mechanism, which reduces the semantic differences between the decoder and the encoder. To exploit inter-task further, our network uses an circle training strategy to refine heterogeneous feature with the help of segmentation maps obtained from previous training. Main results. The experimental results show that our method achieved excellent performance on the BUSI and BUS-B datasets, with DSCs of 81.95% and 86.41% for segmentation tasks, and F1 scores of 82.13% and 69.01% for classification tasks, respectively. Significance. The proposed multi-task interaction learning not only enhances the performance of all tasks related to breast tumor segmentation and classification but also promotes research in multi-task learning, providing further insights for clinical applications.}.

Objective: This study introduces the effective electric field (Eeff) as a novel observable for transcranial magnetic stimulation (TMS) numerical dosimetry. Eeff represents the electric field component aligned with the local orientation of cortical and white matter neuronal elements. To assess the utility of Eeff as 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, the Eeff is calculated combining TMS dosimetric results from an anisotropic head model with tractography data of grey and white matter. To test the hypothesis that Eeff has 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: The Eeff trend for the seven TMS coil rotations closely matched the measured MEP response, displaying an ascending pattern that peaked and then symmetrically declined. In contrast, the E-field magnitude and its components tangential (Etan) and normal (Enorm) to the cortical surface were less responsive to coil orientation changes. Eeff showed a strong correlation with MEPs (r = 0.8), while the other observables had a weaker correlation (0.5 for Enorm and below 0.2 for E-field magnitude and Etan). Significance: This study is the first to evaluate Eeff, a novel component of the TMS induced E-field. Derived using tractography data from both white and grey matter, Eeff inherently captures axonal organization and local orientation. By demonstrating its correlation with MEPs, this work introduces Eeff as a promising observable for future TMS dosimetric studies, with the potential to improve the precision of TMS applications.

Background: Proton minibeam radiation therapy (pMBRT) dose profile is characterized by highly heterogeneous dose in the plane perpendicular to the beam direction and rapidly changing depth dose profiles. Typically, dose measurements are benchmarked against in-house Monte Carlo simulation tools. It is essential to have a treatment planning system (TPS) that can accurately predict pMBRT doses in tissue and be available via commercial platform for preclinical and clinical use. Methods: The pMBRT beam model was implemented in RayStation for the IBA Proteus®ONE single-room compact proton machine. The RayStation pMBRT beam model is an add-on to the clinically used beam model. The adjustable parameters include air gap, slit thickness, slit pitch, number of slits, slits direction and slit thickness. The pMBRT TPS is validated experimentally against measurements. Six different collimators with various slit widths and center-to-center slit distance are used. The slit width varies from 0.4 mm to 1.4 mm, and the center to center (c-t-c) distance varies from 2.8 mm to 4.0 mm. The slits are non-divergent with a total of 5 slits. Results: When comparing the average depth dose measurements against the RayStation dose MC calculation, the agreement is better than a 95% gamma passing rate using 3mm/3% criteria except the 0.4 mm slit width. However, after we adjusted the slit width by 40 - 60 μm to account for machining uncertainty, the agreement again exceeds a 95% gamma passing rate using 3mm/3% criteria. When comparing the PDDs of the peaks and valleys between RayStation and film measurements, the agreement is above 90% using 2mm/5% criteria. When comparing later profiles at various depths, the agreement is above 90% for all curves using 0.2mm/5%. Conclusions: The pMBRT beam modeling has been successfully established for our Proteus®ONE-based pMBRT system using the RayStation TPS, with demonstrated accuracy through experimental validation.

FLASH radiotherapy employs ultra-high dose rates of>40Gy s^-1, which may reduce normal tissue complication as compared to conventional dose rate treatments, while still ensuring the same level of tumour control. The potential benefit this can offer to patients has been the cause of great interest within the radiation oncology community, but this has not translated to a direct understanding of the FLASH effect. The oxygen depletion and inter-track interaction hypotheses are currently the leading explanations as to the mechanisms behind FLASH, but these are still not well understood, with many questions remaining about the exact underpinnings of FLASH and the treatment parameters required to optimally induce it. Monte Carlo simulations may hold the key to unlocking the mystery behind FLASH, allowing for analysis of the underpinning mechanisms at a fundamental level, where the interactions between individual radiation particles, DNA strands and chemical species can be studied. Currently, however, there is still a great deal of disagreement in simulation findings and the importance of the different mechanisms they support. This review discusses current studies into the mechanisms of FLASH using the Monte Carlo method. The simulation parameters and results for all major investigations are provided. Discussion primarily revolves around the oxygen depletion and inter-track interactions hypotheses, though other, more novel, theories are also mentioned. A general list of recommendations for future simulations is provided, informed by the articles discussed. This review highlights some of the useful parameters and simulation methodologies that may be required to finally understand the FLASH effect.

Objective.The optical absorption properties of biological tissues in photoacoustic (PA) tomography are typically quantified by inverting acoustic measurements. Conventional approaches to solving the inverse problem of forward optical models often involve iterative optimization. However, these methods are hindered by several challenges, including high computational demands, the need for regularization, and sensitivity to both the accuracy of the forward model and the completeness of the measurement data. The aim of this study is to introduce a novel learned iterative method for recovering spatially varying optical absorption coefficients (OACs) from PA pressure measurements.Approach.The study introduces a deep learning-based approach that employs the proximal gradient descent mechanism to achieve optical inversion. The proposed framework consists of multiple cascaded structural units, which iteratively update the absorption coefficients through a learning process, unit by unit.Main results.The proposed method was validated through simulations, phantom experiments, andin vivostudies. Comparative analyses demonstrated that the proposed approach outperforms traditional nonlearning and learning-based methods, achieving at least 12.85% improvement in relative errors, 3.50% improvement in peak signal-to-noise ratios, and 3.53% improvement in structural similarity in reconstructing the OAC distribution.Significance.This method significantly improves the accuracy and efficiency of quantitative PA tomography. By addressing key challenges such as computational demand and sensitivity to the accuracy of the forward model and the completeness of the measurement data, the proposed framework offers a more reliable and efficient alternative to traditional methods, with potential applications in medical imaging and diagnostics.

Objective.Magnetic particle imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a Plug-and-Play approach based on a generic zero-shot denoiser with anℓ1-prior.Approach.We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, ART, DIP and the previous PP-MPI, which is a Plug-and-Play method with denoiser trained on MPI-friendly data.Main results.We derive a Plug-and-Play reconstruction method based on a generic zero-shot denoiser. Addressing (hyper)parameter selection, we perform an extended parameter search on a hybrid validation dataset we produced and apply the derived parameters for reconstruction on the 3D Open MPI Dataset. We offer a quantitative and qualitative evaluation of the zero-shot Plug-and-Play approach on the 3D Open MPI dataset with the validated parameters. Moreover, we show the quality of the approach with different levels of preprocessing of the data.Significance.The proposed method employs a zero-shot denoiser which has not been trained for the MPI reconstruction task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.

Internationally, reference dosimetry for clinical proton beams largely follows the guidelines published by the International Atomic Energy Agency (IAEA TRS-398 Rev. 1, 2024). This approach yields a relative standard uncertainty of 1.7% (k=1) on the absorbed dose to water determined under reference conditions. The new IPEM code of practice presented here, enables the relative standard uncertainty on the absorbed dose to water measured under reference conditions to be reduced to 1.0% (k=1). This improvement is based on the absorbed dose to water calibration service for proton beams provided by the National Physical Laboratory (NPL), the UK's primary standards laboratory. This significantly reduced uncertainty is achieved through the use of a primary standard level graphite calorimeter to derive absorbed dose to water directly in the clinical department's beam. This eliminates the need for beam quality correction factors (k_(Q,Q_0 )) as required by the IAEA TRS-398 approach. The portable primary standard level graphite calorimeter, developed over a number of years at the NPL, is sufficiently robust to be useable in the proton beams of clinical facilities both in the UK and overseas. The new code of practice involves performing reference dosimetry measurements directly traceable to the primary standard level graphite calorimeter in a clinical proton beam. Calibration of an ionisation chamber is performed in the centre of a standard test volume (STV) of dose, defined here to be a 10 x 10 x 10 cm volume in water, centred at a depth of 15 cm. Further STVs at reduced and increased depths are also utilised. The designated ionisation chambers are Roos-type plane-parallel chambers. This article provides all the necessary background material, formalism, and specifications of reference conditions required to implement reference dosimetry according to this new code of practice. The Annexes provide a detailed review of ion recombination and how this should be assessed (Annex A1) and detailed work instructions for creating and delivering the standard test volumes (Annex A2). .

Objective.Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (<500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a frameworkdynamicreconstruction andmotionestimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.Approach.DREME incorporates a deep learning-based real-time CBCT imaging and motion estimation method into a dynamic CBCT reconstruction framework. The reconstruction framework reconstructs a dynamic sequence of CBCTs in a data-driven manner from a standard pre-treatment scan, without requiring patient-specific prior knowledge. Meanwhile, a convolutional neural network-based motion encoder is jointly trained during the reconstruction to learn motion-related features relevant for real-time motion estimation, based on a single arbitrarily-angled x-ray projection. DREME was tested on digital phantom simulations and real patient studies.Main Results.DREME accurately solved 3D respiration-induced anatomical motion in real time (∼1.5 ms inference time for each x-ray projection). For the digital phantom studies, it achieved an average lung tumor center-of-mass localization error of 1.2 ± 0.9 mm (Mean ± SD). For the patient studies, it achieved a real-time tumor localization accuracy of 1.6 ± 1.6 mm in the projection domain.Significance.DREME achieves CBCT and volumetric motion estimation in real time from a single x-ray projection at arbitrary angles, paving the way for future clinical applications in intra-fractional motion management. In addition, it can be used for dose tracking and treatment assessment, when combined with real-time dose calculation.