Physics in medicine and biology最新文献

Objective.This study aims to develop a multi-modality-guided dose prediction (MMDP)-based auto-planning algorithm for functional lung avoidance radiotherapy (FLART) guided by voxel-wise lung function images.Approach.The proposed auto-planning algorithm consists of a novel MMDP model and a function-guided dose mimicking algorithm. The MMDP model features extracting complementary features from multi-modality images for predicting dose distributions close to FLART plans. An instance-weighting anatomy-to-function training strategy is tailored to enhance prediction accuracy. A function-guided voxel-wise dose mimicking algorithm is developed to convert predicted dose into FLART (MMDP-FLART) plans. We retrospectively collected data from 163 lung cancer patients across three institutions, comprising 114/28 cases for training/validation and 21 cases with SPECT ventilation (V) images for testing. Furthermore, we prospectively collected 33 cases with SPECT perfusion (Q) images for evaluation. MMDP-FLART plans were compared against conventional radiotherapy (ConvRT) and FLART plans manually created by senior clinicians.Main results.MMDP achieved accurate dose predictions, with median prediction errors for all assessed dose-volume histogram (DVH) metrics within ±1 Gy/±1%. The MMDP model reduced prediction absolute errors for functionally weighted mean lung dose (fMLD) by 12.77% compared to an anatomy-guided dose prediction model and the instance-weighting anatomy-to-function training strategy reduced prediction absolute errors for fMLD by 22.64%. Compared to manual ConvRT plans, MMDP-FLART plans effectively reduced fMLD by 0.80 Gy (11.9%,p< 0.01) and 0.46 Gy (6.0%,p< 0.01) on SPECT V and Q datasets respectively. Compared to manual FLART plans, MMDP-FLART plans exhibited lower and comparable fMLD on SPECT V and Q datasets respectively with lower dose to heart and esophagus.Significance. The MMDP model with instance-weighting anatomy-to-function training can achieve accurate dose prediction for FLART. The MMDP-based auto-planning algorithm can produce FLART plans leveraging voxel-wise lung function information from V/Q images. It shows promise in promoting FLART planning efficiency, consistency, and quality.

Objective.We evaluated different breakpoint (BP) strategies and the impact of scan path optimization on dose accuracy, beam interruptions, and delivery efficiency in proton dose-driven continuous scanning (DDCS). Our goal is to provide insights for the effective clinical implementation of DDCS.Approach.Proton pencil beam scanning plans were retrospectively simulated for DDCS with beam current optimized for the shortest beam delivery time (BDT). Five BP strategies were evaluated: three spot distance (SD)-based (SD1, SD1.5, SD2) using SD thresholds, and two SR-based (SR1, SR0) using the ratio of MU delivered at the planned spot to that delivered in transit. Simulations included three scan paths (default, length-optimized, time-optimized). Comparative analysis included BP fraction (beam interruptions), dose accuracy, and BDT.Main results.SD-based approaches achieved excellent dosimetric accuracy, with 2%/2 mm Gamma pass rates >98% and CTV DVH RMSE <1% across all BP thresholds and scan paths. SD2 with length-optimized path minimized BPs (median 1.1%, range 0%-6.7%) while maintaining high dose accuracy, making it the preferred choice when minimizing dose deviations and BPs is the priority. SR-based approaches had shorter BDTs, maintaining >95% Gamma pass rates and <2% CTV DVH RMSE with optimized scan path. SR0 with time-optimized path is suitable when BDT is critical. Scan path optimization reduced BPs for SD-based methods and improved dose accuracy for SR-based methods. If only the default serpentine path is available, caution is required for lung treatments to ensure clinically acceptable dose with SR-based methods.Significance.Dose accuracy can be maintained without reducing the beam current optimized for BDT in DDCS. SD- and SR-based methods show complementary strengths: SD2 with a length-optimized path minimizes dose deviations and BPs, whereas SR0 with time-optimized path offers shorter BDT and maintaining acceptable dose deviations. These findings provide guidance for implementing proton DDCS to balance dose accuracy, beam interruptions, and delivery efficiency according to clinical needs.

Objective.The aim of this study is to investigate the relation between two figures of merit for the low contrast resolution of computed tomography (CT) imaging systems, with the perspective of its use for acceptance and constancy testing.Approach.We use simulated data as well as 29 CT image datasets of the MITA body phantom CCT189 obtained using a previously published protocol, including CT devices from five different manufacturers and various image reconstruction methods. From these data, the detectability indexd' is determined using the channelised Hotelling observer (CHO), which requires hundreds of images per setting. We compared' to the minimum detectable contrast (MDC), a statistically defined measure of low contrast detectability, that can be determined using only few images per setting.Main results.For the CHO with circular symmetric DDOG (dense difference of Gaussians) channels,d' is proportional to the inverse of the product of MDC and the diameter of the object to be detected. The proportionality factor depends strongly on the texture of the noise.Significance.The findings provide the basis for the development of an acceptance and constancy test for CT low contrast resolution, making use ofd'CHO and MDC.

Purpose.Diffusion-weighted imaging (DWI) has significant value in disease diagnosis and treatment response monitoring, but its inherent low signal-to-noise ratio (SNR) severely affects image quality and quantification accuracy. Existing denoising techniques often blur important tissue boundary information when suppressing noise.Methods.This study proposes a band-limited implicit neural representation (BL-INR) framework for DWI denoising. The method introduces BL positional encoding based on the frequency response characteristics of the sinc function to restrict INR models from learning high-frequency noise while maintaining strong signal representation capabilities. Furthermore, multi-b-value DWI and structural MRI from the same patient are integrated as anatomical priors, exploiting the correlation between true signals and the statistical independence of noise to achieve effective denoising.Main Results.In clinical DWI data evaluation across four anatomical regions (brain, head and neck, abdomen, and pelvis), BL-INR's visualization results were superior to existing methods. Under extremely low SNR conditions (SNR = 1) in simulated noise experiments, BL-INR achieved a peak SNR of 35.44 and structural similarity index of 0.933, significantly outperforming other methods. Phantom denoising results showed that BL-INR achieved an average apparent diffusion coefficient value error of only4.57×10-5 mm² s^-1, the smallest among all methods.Significance.BL-INR provides a novel approach for DWI denoising by limiting the frequency of INR input positional encoding. Its self-supervised learning characteristics require no paired training data and allow convenient clinical application. The method enables the derivation of accurate diffusion parameters, providing a reliable foundation for DWI-based quantitative analysis with significant clinical application value.

Objectives.Particle therapy is an advanced radiotherapy technique primarily using protons and carbon ions, with helium and oxygen ions also being considered for clinical applications. A critical concern in ion therapy is the production of secondary neutrons due to nuclear reactions, which may contribute to unwanted dose deposition within the patient.Approach.This study investigates neutron production for different ion species and energies, providing essential data for assessing secondary neutron exposure. Protons, helium, carbon, and oxygen ions were used to irradiate a PMMA phantom at two energies, corresponding to penetration depths of approximately 5 cm and 14 cm in water. The secondary neutron fluence and ambient dose equivalent (H*(10)) were measured using an ERBSS at four angular positions (0°, 45°, 90°, and 135°) relative to the beam direction.Main results.Results showed significant differences in neutron production depending on ion species and energy. The neutron ambient dose equivalent per primary ion in the beam direction varied by a factor of about 50 across the different ion species. When normalized to the absorbed dose in the pristine Bragg peak, variations of up to a factor of 10 were observed between proton and oxygen ions. However, at off-axis positions, neutron ambient dose equivalent per absorbed dose was relatively similar across ion species, even lower for ions heavier than protons when normalizing to the biologically effective treatment dose.Significance.This study presents the first measurement-based comparative analysis of fluence energy distributions and neutron equivalent doses for protons, helium, carbon, and oxygen ions in a synchrotron-based clinical facility for monoenergetic beams. These findings are highly relevant for evaluating secondary neutron exposure in particle therapy and optimizing treatment strategies to reduce long term-risks of radiation induced second cancers.

Objective. Cherenkov signatures from a bismuth germanate (BGO) crystal open the possibility of establishing BGO as a promising material for time-of-flight positron emission tomography (TOF-PET) detectors, particularly if the first Cherenkov photons can be uniquely timestamped. To maximize the utility of Cherenkov signatures, we employed an optical photon counting detector concept based on a thick, semi-monolithic BGO crystal coupled to a silicon photomultiplier (SiPM) array that provides digital photon timestamps from each SiPM channel. We characterized a prototype detector to demonstrate this concept and explored the use of rich spatiotemporal information of photon transport kinetics.Approach. The detector was built using a 42.68 × 2 × 20 mm³BGO crystal and a 16 × 1 array of 2 × 2 mm³SiPMs with a 2.68 mm pitch. A 16-channel low-noise high-frequency signal processing chain with fast comparators generated digital photon signals, which were recorded using waveform digitizers. Three-dimensional (3D) position calibration and first photon delay distribution (FPDD) construction provided the basis for data-driven methods to improve time resolution and estimate the probability of Cherenkov detection for each event.Main results. With a sufficient number of SiPM channels and 1.8 ns signal shaping, approximately 77% of events were uniquely timestamped with the first photon. FPDD clearly captured photon arrival properties, parameterized with the Cherenkov and scintillation contributions. A coincidence time resolution with a reference detector of 172 ps full width at half maximum was achieved by FPDD-based correction of 3D position dependence. Parameters investigated for Cherenkov detection probability estimation showed consistent correlation with time resolution.Significance. The results demonstrated the feasibility of a photon counting BGO detector for TOF-PET with both promising timing and positioning performance. The abundance of photon information provides a strong basis for further performance gains through data-driven Cherenkov identification and advanced event-by-event corrections.

Objective.Ultrasound shear wave imaging enables noninvasive, quantitative assessment of tissue pathology with mechanical elasticity measurements. However, shear wave elastography (SWE) reconstructions are challenged by noise sensitivity, inefficient multi-push strategies for scalable region of interest coverage, and limited annotated data, leading to suboptimal reconstruction and unreliable inclusion segmentation.Approach.In this work, we present a novel two-stage deep learning framework that addresses these limitations through a convolutional neural network (CNN)-based multi-nested-LSTM reconstruction network followed by a compound-loss-driven CNN-denoiser. The reconstruction stage begins with a ResNet3D-encoder that extracts spatiotemporal features from sequential multi-push acoustic radiation force data. These features are temporally windowed with Nested CNN-LSTM, converted from 3D to 2D with temporal attention module, and enhanced by fast Fourier transform-based frequency attention. The resulting 2D maps are subsequently decoded into primary 2D elasticity reconstructions. To mitigate data-scarcity and improve generalization, a patch-based training regime is also proposed. The second stage introduces a dual-decoder denoising network that separately processes inclusion and background stiffness features, followed by a fusion module that produces a denoised modulus map and a segmentation mask. A multi-objective compound loss is designed to accommodate the denoising, fusing, and mask generation. The method is validated on sequential multi-push (simulation and experimental) SWE motion data with multiple overlapping regions.Results.The method was tested on simulated and CIRS phantom datasets with four overlapping push regions, yielding 26.33 dB peak-signal-to-noise-ratio (PSNR), 30.73 dB contrast-to-noise-ratio (CNR), and 0.813 intersection over union (IoU) in simulation, and 22.44 dB PSNR, 36.88 dB CNR, and 0.781 IoU experimentally. Evaluation on anex vivoswine liver confirmed elasticity estimates within reported biological stiffness ranges. Compared to DSWE-Net and spatio-temporal CNNs, our approach shows superior reconstruction, segmentation, and noise insensitivity.Significance.This framework provides a robust approach to SWE reconstruction and inclusion segmentation, demonstrating strong potential for clinical translation.

Objective.The delineation of the clinical target volume (CTV) in radiotherapy is fundamentally uncertain due to the invisibility of microscopic disease on medical images. The ICRU 83 report acknowledges this by proposing a probabilistic interpretation of the CTV, but it does not define how to compute the probability of microscopic tumor presence (MTP) in tissue. This work addresses this gap by introducing a novel stochastic model that estimates the probability of MTP at the voxel level based on local spatial correlations in the voxels' neighborhood.Approach.We developed two first-principles stochastic models to simulate MTP under different assumptions, incorporating spatial correlation between neighboring voxels. The constant marginal probability (CMP) model assumes spatially uniform MTP and is suited for tumors without radial dependence on the distance from the gross tumor volume (GTV). The variable marginal probability (VMP) model introduces radial dependence, modeling decreasing MTP with distance from the GTV. The CMP model was evaluated on prostate cancer data, while the VMP model was assessed using breast and lung cancer data.Results.Both models accurately reproduced the fraction of times that MTP is present. In the prostate case, the CMP model estimated a marginal probability of MTP of 0.03, consistent with a literature report that indicates an average total microscopic tumor volume of approximately583mm3across patients. The VMP model successfully replicated the radial distribution of tumor islets, achieving mean absolute errors of 0.01 mm and 0.011 mm for breast and lung cancer distance distributions, respectively. However, not all MTP characteristics could be fully captured by the models, and in some cases discrepancies with population based tumor characteristics remain.Significance.This work introduces a statistically consistent framework that enables a probabilistic definition of the CTV. The proposed models provide a new way to capture key aspects of microscopic disease spread by introducing local voxel correlations.

Respiratory motion tracking is critical for optimizing thoracoabdominal radiotherapy accuracy but remains constrained by the system latency of medical linear accelerators. Neural signals that precede the emergence of respiratory motion have the potential to mitigate this system latency issue in respiratory motion tracking radiotherapy. However, the real-time decoding of respiratory-related neural signals is challenging, creating translational bottlenecks that surpass the technical barriers encountered in conventional imaging-based tracking systems. This prospective review aims to provide an overview of the technical challenges and potential solutions for translating neural signals-based respiratory motion tracking into clinical practice.