Physics in medicine and biology最新文献

Objective.Lung nodule appearance may provide prognostic information, as the presence of spiculation increases the suspicion of a nodule being cancerous. Spiculations can be quantified using morphological radiomics features extracted from CT images. Radiomics features can be affected by the acquisition parameters and scanner technologies; thus, it is essential to identify imaging conditions that provide reliable measurements, particularly for emerging technologies like photon-counting CT (PCCT). This study aimed to systematically quantify the effect of imaging parameters on the radiomics measurements using a virtual imaging trial (VIT) platform, and further verify the findings with human clinical data.Approach.The VIT utilized nine virtual patients, each with three 6 mm nodules of varying spiculations. The virtual patients were run through a validated CT simulator (DukeSim) to acquire images at three dose levels (CTDIvol = 2.85, 5.69, and 11.38 mGy) with a clinical energy-integrating CT and a PCCT. The acquired projection images were reconstructed using multiple slice thicknesses, kernels, and matrix sizes. The reconstructed images were processed to extract morphological features using three segmentation methods. The features were clustered into three broad type categories. Features extracted from the acquired CT images were compared to their corresponding ground truth values, across all imaging conditions.Main results.Among all imaging conditions, slice thickness had the greatest effect on the radiomics measurements. When the thickest slices were used, the coefficient of variation increased by [1.19%-9.66%] in the energy integrating CT images, and [3.94%-24.43%] in the PCCT images. For both scanners, varying the kernel sharpness and dose affected the radiomics measurements insignificantly, while pixel size and segmentation method were observed to have stronger effects. Under varying imaging conditions, the trends and magnitude of radiomics features measurements were coherent with virtual trial results.Significance.The findings stress the importance of choosing optimal reconstruction settings for radiomics extraction to achieve precise feature quantifications.

Objective. This study aims to determine the optimal structure of the Beam Shaping Assembly (BSA) for an accelerator-based boron neutron capture therapy (BNCT) facility. The aim is to maximize the possible depth of treatment for glioblastoma while ensuring that a treatment time constraint is not exceeded.Approach. To achieve this goal, we utilize a new optimization procedure known as topology optimization. This technique can accurately identify the most optimal structure of a nuclear device, in this case a BSA, to be identified among 9 × 10¹²⁰⁶possible structures for the example given in this study. The exploration of such a vast space of configurations is inaccessible to any other method available to date.Main results. The topology optimization generated Air-AlF₃-LiF-LiFPE BSA has an original structure that differs significantly from the structures previously tested by the BNCT community. This structure, which combines a ring collimator and a filter cone to mimic the effect of multi-field treatment, generates unprecedented treatment depths, with a treatable depth TD = 10.01 cm and an advantage depth AD = 12.48 cm (for 15 ppm of Boron-10 in blood, with a 3.5 tumor-to-blood Boron-10 concentration ratio), or TD = 10.30 cm and AD = 12.69 cm (for 18 ppm of Boron-10). These depths are much greater than any other design proposed to date by the community. The structure also verifies the latest proposed radiation protection constraints, which set limit values on its out-of-field leakages.Significance. The findings of this study indicate that topology optimization procedures are highly beneficial for the design of BSAs. In particular, the use of ring collimators could significantly improve the quality of BNCT treatments of brain tumors.

Objective. This study aims to enhance positron emission tomography (PET) imaging systems by developing a continuous depth-of-interaction (DOI) measurement technique using a single-ended readout. Our primary focus is on reducing the number of readout channels in the scintillation detectors while maintaining accurate DOI estimations, using a high-pass filter-based signal multiplexing technique combined with artificial neural networks (ANNs).Approach. Instead of reading out all 64 signals from an 8 × 8 silicon photomultiplier (SiPM) array for DOI estimation, the proposed method technique reduces the signals into just four channels by applying high-pass filters with different time constants. To recover the original signal amplitudes, an ANN is used to demultiplex the multiplexed signals. Specifically, the ANN processes the sampled waveforms of these four multiplexed signals and estimates the energy information of the original 8 × 8 SiPM channels. In this study, two DOI estimation strategies were explored for a continuous DOI (cDOI) PET detector utilizing triangular teeth-shaped reflectors: a 'single-step estimation' method directly estimating DOI from multiplexed signals, and a 'two-stage cascade estimation' method that first demultiplexes the signals and then estimates DOI. The performances of proposed strategies were validated using data irradiated at five steps (2, 6, 10, 14, and 18 mm).Results. The signal amplitude of row/column summed signals, which were recovered using the proposed ANN-based de-multiplexing, showed strong correlation with ground truth (e.g.R²= 0.98 for 125 MHz digitizer sampling rate). Moreover, both the single-step and two-stage estimation methods achieved high accuracy in DOI estimation, with an average DOI resolution of 4.86 and 5.11 mm respectively.Significance. This novel signal multiplexing technique significantly reduces the number of required readout channels, making cDOI PET more cost-effective.

Objective.This study proposes a real-time tumor position prediction-based multi-dimensional respiratory motion compensation puncture method to accurately track real-time lung tumors and achieve precise needle puncture.Approach.A hybrid model framework integrating prediction and correlation models is developed to enable real-time tumor localization. A Long Short-Term Memory neural network with bidirectional and attention modules (Bi-LSTM-ATT) is employed for predicting external respiratory signals. Subsequently, a backpropagation neural network is constructed to correlate these signals with tumor positions. Tumor trajectory decomposition and the determination of an optimal puncture window based on multiple criteria ensure accurate needle puncture.Main results.When the delay time of Bi-LSTM-ATT model is 500 ms, its RMSE, MAE, andR²are 0.0482 mm, 0.0414 mm, and 97.90% respectively. The correlation model locates lung tumors in 10 cases with a target registration error within 0.74 mm. The proposed puncture method achieves a puncture error ranging from 1.00 mm to 1.32 mm, with an average error of 1.2 mm.Significance.The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for percutaneous biopsy procedures within the lung.Clinical trial registrationClinical trial registration was not required for this research.

Objective. Accurate image reconstruction from data with truncation in x-ray computed tomography (CT) remains a topic of research interest; and the works reported previously in the literature focus largely on reconstructing an image only within the scanning field-of-view (FOV). We develop algorithms to invert the truncated data model for numerically accurate image reconstruction within the subject support or a region slightly smaller than the subject support.Methods. We formulate image reconstruction from data with truncation as an optimization program, which includes hybrid constraints on region-based image total-variation (TV) and imageℓ1-norm (L1) for effectively suppressing truncation artifacts. An algorithm, referred to as the TV-L1 algorithm, is developed for image reconstruction (i.e. inversion of the data model) from data with truncation through solving the optimization program.Results. We perform numerical studies to evaluate accuracy and stability of the TV-L1 algorithm by using simulated and real CT data. Accurate images can be obtained stably by use of the TV-L1 algorithm within the subject support, or a region substantially larger than the FOV, from data with truncation of varying degrees.Conclusions. The TV-L1 algorithm can invert the truncated data model to accurately and stably reconstruct images within the subject support, or a region slightly smaller than the subject support but substantially larger than the FOV.Significance. Accurate image reconstruction within the subject support, or a region substantially larger than the FOV, from data with truncation can be of theoretical and practical implication. The insights and TV-L1 algorithm may also be generalized to accurate image reconstruction from data with truncation in other tomographic imaging modalities.

Objective.Ultra-high field MRI with parallel transmission (pTx) provides a powerful neuroimaging tool with potential application in pediatrics. The use of pTx, however, necessitates a dedicated local specific absorption rate (SAR) management strategy, able to predict and monitor the peak local SAR (pSAR_10g). In this work, we address the pSAR_10gassessment for an in-house built 7 T 16Tx32Rx pediatric head coil, using the concept of virtual observation points (VOPs) for SAR estimation.Approach. We base our study on full-wave electromagnetic simulations performed on a database of 64 numerical anatomical head models of children aged between 4 and 16 years. We built VOPs on different subsets of this database ofN= 2 up to 30 models, and cross-validated the pSAR_10gprediction using non-intersecting subsets, each containing 30 models. We thereby propose a minimum anatomical safety factor (ASF) to apply to the VOP set to enforce safety, despite intersubject variability. Our analysis relies on the computation of the worst case SAR to VOP-SAR ratio, independent of the pTx RF excitation.Main results.The interpolation model provides that the minimum ASF decreases as1+5.37⋅N-0.75withN. Using all 64 models to build VOPs leads to an estimated ASF of 1.24 when considering the VOP validity for an infinite number of subjects.Significance.We propose a general simulation workflow to guide ASF estimation and quantify the trade-off between the number of numerical models available for VOP construction and the safety factor. The approach would apply to any simulation dataset and any pTx setup.

Objective.The increasing interest in hadron therapy has heightened the need for accurate and reliable methods to assess radiation quality and the biological effectiveness of particles used in treatment. Microdosimetry has emerged as a key tool for this, demonstrating its potential, reliability, and suitability. In this context, solid-state microdosimeters offer technological advantages over traditional tissue-equivalent proportional counters, and recent advancements have further improved their performance and reliability. However, one critical challenge in solid-state microdosimetry is the so-called 'border effect', which can impact measurement accuracy.Approach.In this study, the border effect in diamond microdosimeters was thoroughly studied using ion beam induced charge analysis. The research, relying on experiments conducted at the Surrey Ion Beam Centre, developed an effective method to characterise and quantify the border effect. Geant4 Monte Carlo simulations were also employed to assess the impact of the border effect under typical proton therapy conditions.Main results. The border effect in diamond microdosimeter was characterised and studied as a function of detector thickness, particle atomic number and particle range. A border effect model was developed and validated to reproduce the border effect in Monte Carlo simulations. The results of its application to the microdosimetry of a proton beam at different depths in water showed potential significant variations of up to 40% iny¯Fand 20% iny¯Dvalues.Significance.The results of this work highlight the importance of accurately characterising the border effect and encouraging further research to mitigate its influence on microdosimetry measurements.

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

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.