Physics in medicine and biology最新文献

Objective.Conventional temperature optimization in hyperthermia treatment planning aims to maximize tumour temperature (e.g.T90; the temperature reached in at least 90% of the tumour) while enforcing hard constraints on normal tissue temperature (max(T_tissue) ⩽45 °C). This method generally incorrectly assumes that tissue/perfusion properties are known, typically relying on average values from the literature. To enhance the reliability of temperature optimization in clinical applications, we developed new robust optimization strategies to reduce the impact of tissue/perfusion property uncertainties.Approach.Within the software package Plan2Heat, temperature calculations during optimization apply efficient superposition of precomputed distributions, represented by a temperature matrix (T-matrix). We extended this method using stochastic polynomial chaos expansion models to compute an averageT-matrix (T_avg) and a covariance matrixCto account for uncertainties in tissue/perfusion properties. Three new strategies were implemented usingT_avgandCduring optimization: (1)T_avg90 maximization, hard constraint on max(T_tissue), (2)T_avg90 maximization, hard constraint on max(T_tissue) variation, and (3) combinedT_avg90 maximization and variation minimization, hard constraint on max(T_tissue). Conventional and new optimization strategies were tested in a cervical cancer patient. 100 test cases were generated, randomly sampling tissue-property probability distributions. TumourT90 and hot spots (max(T_tissue) >45 °C) were evaluated for each sample.Main Results.Conventional optimization had 28 samples without hot spots, with a medianT90 of 39.7 °C. For strategies (1), (2) and (3), the number of samples without hot spots was increased to 33, 41 and 36, respectively. MedianT90 was reduced lightly, by ∼0.1 °C-0.3 °C, for strategies (1-3). Tissue volumes exceeding 45 °C and variation in max(T_tissue) were less for the novel strategies.Significance.Optimization strategies that account for tissue-property uncertainties demonstrated fewer, and reduced in volume, normal tissue hot spots, with only a marginal reduction in tumourT90. This implies a potential clinical utility in reducing the need for, or the impact of, device setting adjustments during hyperthermia treatment.

Objective.The propagation speed of a shear wave, whether externally or internally induced, in biological tissues is directly linked to the tissue's stiffness. The group shear wave speed (SWS) can be estimated using a class of time-of-flight (TOF) methods in the time-domain or phase speed-based methods in the frequency domain. However, these methods suffer from biased estimations or time-consuming computations, and they are especially prone to wave distortions inin vivocases. In this work, we present a parameter-free, robust, and efficient group SWS estimation method coined as Fourier energy spectrum centroid (FESC).Approach.The proposed FESC method is based on the center of mass inω-kspace. It was evaluated on data from computer simulations with additive Gaussian noise, a commercial elasticity phantom, anex vivopig liver, andin vivobiceps brachii muscles of three young healthy male subjects. The FESC method was compared with two 2D frequency-domain methods: Max-fre, which considers phase SWS at the peak ofk-space, and Fre-regre, which applies linear regression of phase SWS within a fixed bandwidth. Two additional benchmarks included time-domain methods based on cross-correlation (X-Corr) and radon sum transformation (RD).Main results.Statistical results showed that our FESC method and the RD method had comparable accuracy and robustness, outperforming the other benchmark methods. In the simulation and phantom studies, when the signal-to-noise ratio was higher than 25 dB, our FESC showed higher accuracy than RD. In thein vivostudy, our FESC method had better repeatability than RD. Furthermore, the proposed FESC method was 100 times faster than the runner-up method, X-Corr, and 3,000 times faster than the least efficient method, RD.Significance.All results indicated that our proposed Fourier-based method shows promise in reliably and efficiently providing reference values for group SWS in homogeneous bulk media.

Objective.Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures.Approach.Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established.Main results.Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules.Significance.This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.

Objective. Mathematical modeling can offer valuable insights into the behavior of biological systems upon treatment. Different mathematical models (empirical, semi-empirical, and mechanistic) have been designed to predict the efficacy of either hyperthermia (HT), radiotherapy (RT), or their combination. However, mathematical approaches capable of modeling cell survival from shared general principles for both mono-treatments alone and their co-application are rare. Moreover, some cell cultures show dose-dependent saturation in response to HT or RT, manifesting in survival curve flattenings. An advanced survival model must, therefore, appropriately reflect such behavior.Approach. We propose a mathematical approach to model the effect of both treatments based on the general principle of sublethal damage (SLD) accumulation for the induction of cell death and irreversible proliferation arrest. Our approach extends Jung's model on heat-induced cellular inactivation by incorporating dose-dependent recovery rates that delineate changes in SLD restoration.Main results. The resulting unified model (Umodel) accurately describes HT and RT survival outcomes, applies to simultaneous thermoradiotherapy modeling, and is particularly suited to reproduce survival curve flattening phenomena. We demonstrate the Umodel's robust performance (R2 0.95) based on numerous clonogenic cell survival data sets from the literature and our experimental studies.Significance. The proposed Umodel allows using a single unified mathematical function based on generalized principles of accumulation of SLD with implemented radiosensitization, regardless of the type of energy deposited and the mechanism of action. It can reproduce various patterns of clonogenic survival curves, including any flattening, thus encompassing the variability of cell reactions to therapy, thereby potentially better reflecting overall tumor responses. Our approach opens a range of options for further model developments and strategic therapy outcome predictions of sequential treatments applied in different orders and varying recovery intervals between them.

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 the photon fraction dose. To determine a patient's optimal proton and photon fraction doses, we maximize the target biologically effective dose (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.

The advent of ultra-high dose rate irradiation, known as FLASH radiation therapy, has shown promising potential in reducing toxicity while maintaining tumor control. However, the clinical translation of these benefits necessitates efficient treatment planning strategies. This study introduces a novel approach to optimize proton therapy for FLASH effects using traveling salesperson problem (TSP) heuristics. We applied these heuristics to optimize the arrangement of proton spots in treatment plans for 26 prostate cancer patients, comparing the performance against conventional sorting methods and global optimization techniques. Our results demonstrate that TSP-based heuristics significantly enhance FLASH coverage to the same extent as the global optimization technique, but with computation times reduced from hours to a few seconds. This approach offers a practical and scalable solution for enhancing the effectiveness of FLASH therapy, paving the way for more effective and personalized cancer treatments. Future work will focus on further optimizing run times and validating these methods in clinical settings.

Objective. In-beam positron emission tomography (PET) has important development prospects in real-time monitoring of proton therapy. However, in the beam-on operation, the high bursts of radiation events pose challenges to the performance of the PET system.Approach. In this study, we developed a dual-head in-beam PET system for proton therapy monitoring and evaluated its performance. The system has two PET detection heads, each with6×3Plug&Imaging (PnI) detection units. Each PnI unit consists of6×6lutetium-yttrium oxyorthosilicate crystal arrays. The size of each crystal strip is3.95×3.95×20 mm³, which is one-to-one coupled with a silicon photomultiplier. The overall size of the head is15.3×7.65 cm².Main results. The in-beam PET system achieved a single count rate of 48 Mcps at the activity of 144.9 MBq, an absolute sensitivity of 2.717%, and a spatial resolution of approximately 2.6 mm (full width at half maximum) at the center of the field-of-view. When imaging a Derenzo phantom, the system could resolve rods with a diameter of 2.0 mm. Time-dynamic [¹⁸F]-Fluorodeoxyglucose mouse imaging was performed, demonstrating the metabolic processes in the mouse. This shows that the in-beam PET system has the potential for biology-guided proton therapy. The in-beam PET system was used to monitor the range of a 130 MeV proton beam irradiating a polymethyl methacrylate (PMMA) phantom, with a beam intensity of6.0×109p s^-1and an irradiation duration of one minute. PET data were acquired only during the one-minute irradiation. We simulated the range shift by moving the PMMA and adding an air gap, showing that the error between the actual and the measured range is less than 1 mm.Significance. The results demonstrate that the system has a high count rate and the capability of range monitoring in beam-on operation, which is beneficial for achieving real-time range verification of proton beams in the future.

Objective.Low-dose computed tomography (LDCT) has gained significant attention in hospitals and clinics as a popular imaging modality for reducing the risk of x-ray radiation. However, reconstructed LDCT images often suffer from undesired noise and artifacts, which can negatively impact diagnostic accuracy. This study aims to develop a novel approach to improve LDCT imaging performance.Approach.A dual-domain Wasserstein generative adversarial network (DWGAN) with hybrid loss is proposed as an effective and integrated deep neural network (DNN) for LDCT imaging. The DWGAN comprises two key components: a generator (G) network and a discriminator (D) network. TheGnetwork is a dual-domain DNN designed to predict high-quality images by integrating three essential components: the projection-domain denoising module, filtered back-projection-based reconstruction layer, and image-domain enhancement module. TheDnetwork is a shallow convolutional neural network used to differentiate between real (label) and generated images. To prevent the reconstructed images from becoming excessively smooth and to preserve both structural and textural details, a hybrid loss function with weighting coefficients is incorporated into the DWGAN.Main results.Numerical experiments demonstrate that the proposed DWGAN can effectively suppress noise and better preserve image details compared with existing methods. Moreover, its application to head CT data confirms the superior performance of the DWGAN in restoring structural and textural details.Significance.The proposed DWGAN framework exhibits excellent performance in recovering structural and textural details in LDCT images. Furthermore, the framework can be applied to other tomographic imaging techniques that suffer from image distortion problems.

Objective.a new projector, orthogonal-distance ray-tracer varying-full width at half maximum (OD-RT-VF), was developed to model a shift-variant elliptical point-spread function (PSF) response to improve the image quality (IQ) of a preclinical dual-rotation PET system.Approach.the OD-RT-VF projector models different FWHM values of the PSF in multiple directions, using half-height and half-width tube-of-response (ToR) values. The OD-RT-VF method's performance was evaluated against the original OD-RT method and a ToR model with constant response. The evaluation involved simulations of NEMA NU 4-2008 IQ and Derenzo phantoms, as well as a real mouse injected with [¹⁸F]-NaF scanned with the easyPET.3D system.Main results.the OD-RT-VF method demonstrated superior image resolution and uniformity (11.9% vs 15.9%) compared to the OD-RT model. In micro-derenzo phantom simulations, it resolved rods down to 1.0 mm, outperforming the other methods. For IQ phantom simulations, the OD-RT-VF projector at convergency achieved hot rods recovery coefficients ranging from 22.4% to 93.3% and lower spillover ratios in cold regions of 0.22 and 0.33 for air and water, respectively. For bone radiotracer imaging, OD-RT-VF produced clearer images of major skeletal parts, with less noise compared to OD-RT and better resolution compared to ToR projectors.Significance.the study shows that the OD-RT-VF projector method enhances PET imaging by providing better resolution, uniformity, and IQ. This model, in addition to a list-mode and GPU-based reconstruction addressing the data sparsity of dual-rotation PET geometries, unlocks their imaging potential for small animal imaging.