Physics in medicine and biology最新文献

Objective. Magnetic resonance imaging (MRI)-only proton therapy combines high soft tissue contrast with high-precision dose distributions. However, conventional dose calculation is impossible on MRI due to missing electron density information. This work investigated the feasibility of two fully deep learning (DL)-based MRI-only proton dose calculation pipelines for the pelvic region and their robustness to MRI intensity distortions.Approach. Two MRI-only proton dose calculation pipelines were established: A) the two-step pipeline converts MRI to synthetic computed tomography (sCT) and predicts proton dose distributions on sCT; B) the direct pipeline predicts proton dose distributions directly on MRI. MRI-CT pairs from 120 pelvis patients were considered. For modeling, 31 727 random pencil beams (PBs) and 13 430 PBs from 6 treatment plans (TPs) were calculated using Monte Carlo (MC) simulations. Performance of the pipelines was measured by comparing predicted and MC-simulated doses in terms of gamma pass rate (3 mm, 3%, dose threshold of 10%) and average relative error (ARE) for, both, individual PBs and TPs. For further understanding, an experiment was conducted to manually introduce intensity distortions to the input image and observe its influence on the predicted dose.Main results. Both pipelines showed high gamma pass rates (>99.2%). The two-step pipeline showed ARE of 0.11% and 2.63% for individual PBs and TP (planning target volume), respectively. For the direct prediction pipeline, larger ARE of 0.16% and up to 6.11% were observed for individual PBs and TP, respectively. The model predicting dose using MRI directly was robust against added MRI intensity distortions.Significance. DL-based MRI-only proton dose calculation was feasible in the pelvic region. The direct pipeline showed potential to learn the mapping between MRI image pattern and proton dose distribution, though, improvement in terms of information usage is warranted. The two-step pipeline is capable to predict proton dose distributions with low errors.

Objective. Bismuth germanate (BGO) has regained attention as a promising material for hybrid Cherenkov/scintillation time-of-flight positron emission tomography (TOF-PET). While excellent timing performance has been demonstrated in single-crystal studies using prompt Cherenkov photons, practical pixelated detector modules introduce appreciable inter-crystal scattering (InterCS) events that can degrade timing accuracy. The objective of this work was to experimentally investigate the impact ofInterCSon Cherenkov-based timing in pixelated BGO detectors and to identify optimal timestamp selection strategies.Approach. A dual-pixel BGO detector was constructed and coupled to a segmented SiPM readout to enable spatially resolved energy and timing measurements. Events were classified into full-energy deposition (FED); primary crystal 511 keV absorption),InterCS, andpenetrationcategories using energy-weighted positioning. This experimental classification was validated using GATE simulations, which further revealed that intra-crystal scattering (IntraCS) accounted for more than 25% of the events experimentally classified asFED. Multiple timestamp selection strategies were evaluated, and prompt photon statistics were quantified by integrating the first 1 ns of the timing waveform.Main results. ForInterCSevents, selecting the earlier of the two timestamps yielded a coincidence timing resolution of 221 ps FWHM (831 ps FWTM) measured in coincidence with a LYSO:(Ce, Mg) reference detector, compared to 184 ps FWHM (603 ps FWTM) forFEDevents. Energy-based timestamp selection was found to be suboptimal. Prompt photon analysis showed a measurable reduction in early photon yield forInterCSevents, with an average of 4.73 detected photons in the first 1 ns, compared to 5.76 forFEDevents.Significance. these results demonstrate thatInterCSintroduces systematic timing degradation in pixelated BGO Cherenkov TOF-PET detectors through energy redistribution and reduced prompt photon statistics. The findings highlight the necessity of time-aware, per-pixel timestamp selection strategies to preserve optimal timing performance in realistic BGO-based TOF-PET systems operating in the presence of Compton scattering.

Objective: Medical image registration serves as a cornerstone for precision diagnosis and treatment, particularly for dynamic organs like the lungs, where high deformability and complex motion patterns impose significant challenges. Traditional iterative methods are time-consuming, while existing deep learning approaches often struggle to synergistically optimize large deformation modeling and localized fine structure preservation due to limited receptive fields. This study aims to address the challenge of jointly handling large-scale and localized deformations in pulmonary imaging by developing a novel deep learning framework. Approach: We propose a Multi-Scale Hybrid Implicit-Explicit Registration Network (MS-HIENet), a mask-free end-to-end framework that integrates implicit neural representation (INR) and convolutional neural networks (CNN). The method employs a two-fold strategy: First, a multi-scale optimization mechanism where low-resolution layers leverage INR to capture global deformations and high-resolution layers utilize CNN to refine local anatomical structures, enabling coarse-to-fine hierarchical registration. Second, an INR-based coordinate-to-displacement implicit mapping framework is used to directly model continuous deformation fields, eliminating the dependency on mask annotations. Main results: Experimental results on the DIR-Lab dataset demonstrate that MS-HIENet achieves a mean Target Registration Error (TRE) of 1.00 mm, representing an average reduction of 29.5% compared to state-of-the-art deep learning methods. Ablation studies validate the effectiveness of the multi-scale collaboration and hybrid implicit-explicit representation, with the deformation field folding rate reaching an minimal level (mean:0.00017). Significance: The proposed method effectively bridges the gap between global deformation consistency and local anatomical precision. By combining the continuous modeling capabilities of INR with the local feature refinement of CNNs, MS-HIENet significantly enhances topological consistency and clinical applicability, offering a robust solution for high-precision lung image analysis.

Single-photon emission computed tomography (SPECT) is a pivotal molecular imaging technology in preclinical studies. Modern high-resolution systems require accurate system matrices for high-quality image reconstruction. Among available strategies, experimentally measured pointsource system responses offer the most accurate calibration. 99m Tc is the predominant radionuclide in SPECT, and high-activity 99m Tc-adsorbed resin microspheres have been widely used for geometric calibration or small-FOV system-matrix acquisition. However, the short (6.02 hrs) halflife of 99m Tc restricts measurement duration, complicating the long acquisitions needed to complete system-matrix sampling in high-resolution SPECT.In this study, we propose a generator-producible hybrid ⁹⁹Mo/⁹⁹ᵐTc point source using ⁹⁹Moadsorbable resin microspheres to provide high activity with an effectively longer usable half-life for extended acquisitions. We adsorbed ⁹⁹Mo onto AG1-X8 resin microspheres (0.37-mm diameter), achieving activities exceeding 10 mCi per bead. To evaluate potential contamination from ⁹⁹Mo high-energy emissions, we performed Monte Carlo simulations on two sub-millimeter animal SPECT platforms: a conventional multi-pinhole system and a novel self-collimating SPECT.Comparative analyses of point-source projections at both the field-of-view center and edge showed negligible impact of ⁹⁹Mo-derived high-energy photons on ⁹⁹ᵐTc system-matrix measurements. The source strength was sufficient to support a 100×100×100 system-matrix acquisition. In summary, we introduce a practical method for accurate, reproducible system-matrix calibration in state-of-art SPECT, facilitating the development of high-resolution systems with consistent imaging performance.

Objective.Spectral computed tomography (CT) data from photon-counting CT (PCCT) enables material decomposition. Mechanistic approaches such as maximum likelihood estimation are noise sensitive. Deep learning alternatives mitigate this issue, but their accuracy remains limited due to lack of incorporation of underlying physics principles and lack of ground truth data. This study aims to develop and validate a physics-informed deep-learning model, trained on validated simulated data, to decompose spectral CT images into density (ρ)and effective atomic number (Z_eff) maps.Methods.The training dataset included simulated abdominal PCCT scans from 32 human models with corresponding ground truth. The scans were obtained at two clinical dose levels, four detector energy thresholds, different iodinated contrast agent concentrations and reconstructed using three clinically-used kernels. A generative adversarial network (GAN) was trained with and without a physics-informed regularization loss to estimateρandZ_effmaps. Model performance was evaluated on 16 computational phantoms and validated on 6 clinical cases. A reader study was performed on 30 image slices to assess the comparative performance ofρandZ_effmaps to multi-rendered virtual monochromatic images (VMIs) for assessing liver lesion conspicuity.Main results.With physics-informed regularization, NRMSE of 1.29% and 0.68%, SSIM of 0.99 and 0.99, and PSNR of 29.8 dB and 29.04 dB were achieved. A maximum RMSE of 5.45% was achieved on clinical data. Reader study results showedρandZ_effimages had higher conspicuity scores compared to VMIs (median: 4.52 vs 4.13; 95% CIs: [4.19, 4.52] vs [4.01, 4.31]). The study showed equivalent conspicuity between VMIs and material images within a ±0.5 margin, though the small sample limits generalization.Significance.This study demonstrates the feasibility of material decomposition using a physics-informed GAN model trained on realistic simulated data. The maps provided equivalent conspicuity under a clinically acceptable margin, with a significantly small number of images for interpretation.

This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e. from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.

Quantification of protoporphyrin IX (PpIX) fluorescence in human brain tumours has the potential to significantly improve patient outcomes in neuro-oncology, but represents a formidable imaging challenge. Protoporphyrin is a biological molecule which interacts with the tissue micro-environment to form two photochemical states in glioma. Each exhibits markedly different quantum efficiencies, with distinct but overlapping emission spectra that also overlap with tissue autofluorescence. Fluorescence emission is known to be distorted by the intrinsic optical properties of tissue, coupled with marked intra-tumoural heterogeneity as a hallmark of glioma tumours. Existing quantitative fluorescence systems are developed and validated using simplified phantoms that do not simultaneously mimic the complex interactions between fluorophores and tissue optical properties or micro-environment. Consequently, existing systems risk introducing systematic errors into PpIX quantification when used in tissue. In this work, we introduce a novel pipeline for quantification of PpIX in glioma, which robustly differentiates both emission states from background autofluorescence without reliance on a priori spectral information, and accounts for variations in their quantum efficiency. Unmixed PpIX emission forms are then corrected for wavelength-dependent optical distortions and weighted for accurate quantification. Significantly, this pipeline is developed and validated using novel tissue-mimicking phantoms replicating the optical properties of glioma tissues and photochemical variability of PpIX fluorescence in glioma. Our workflow achieves strong correlation with ground-truth PpIX concentrations (R<2> = 0.918 ± 0.002), demonstrating its potential for robust, quantitative PpIX fluorescence imaging in clinical settings.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive technique used to characterize tissue microstructure by measuring the diffusion of water molecules. Conventional Q-space trajectory imaging (QTI) estimates diffusion using low-order moments; however, it often neglects higher-order moments, such as the skewness tensor, resulting in an incomplete representation of diffusion asymmetry and potential estimation bias. In this work, we propose Q-space trajectory imaging with Skewness Tensor Constraints (QTI-STC), a method that incorporates higher-order skewness tensors under positivity constraints to mitigate deviations in the estimation of lower-order moments caused by the omission of higher-order asymmetry information. Furthermore, we introduce linear trace-weighted (LF) and quadratic trace-weighted filters (QF) to enhance high-diffusion components while suppressing low-diffusion components. Extensive experiments conducted on public, noisy, and synthetic datasets demonstrate that our method yields estimates closer to the ground truth on synthetic data and exhibits superior robustness in noisy conditions.

Objective.Bone scan imaging for the detection of bone metastasis of breast cancer has been widely adopted; however, noise, anatomy superimposition, and small size for early lesions will severely affect its prediction performance. In this work, we propose a new framework with two major contributions to solve the main problems existing in current deep-learning-based approaches.Approach.In this study, we put forward a new model called the dual branch feature alignment network (DBFANet) for automated breast cancer bone metastases detection in bone scintigraphy. DBFA-net adopts a dual-branch CNN-Transformer structure: the CNN branch focuses on the local details, while the Transformer branch learns the global context. In addition, we design a feature alignment module (FRAT), which employs the bi-directional cross-attention mechanism for the complementary feature from two branches. Moreover, we propose an enhanced multi-scale attention module (EMSA) based on the squeeze-and-excitation (SE) block for stronger multi-scale lesion representations with less background noise suppression.Main results.We validated our proposed model based on a bone scintigraphy dataset containing 5092 images. In terms of bone metastasis prediction, DBFANet achieved an accuracy, precision, and recall value of 93.1%, 84.6%, and 84.7%, respectively, all superior to previous models (such as ResNet-50, EfficientNet-V2, and MaxViT). The ablation study has shown that both FRAT and EMSA have individual effectiveness and complementary benefits. Finally, additional external validation was performed on a publicly available bone scintigraphy dataset (BS-80K).Significance.DBFANet shows the highest detection performance for bone metastasis detection from multiview bone scintigraphy images with imbalanced classes and noise in the image, and the feature alignment with enhanced multiscale attention of DBFANet provides a useful and precise tool for bone metastasis diagnosis in a nuclear medicine imaging scenario.

Objective: 3D printing is increasingly used for radiotherapy quality assurance (QA) phantoms, yet, reproducing the structural heterogeneity and radiological properties of lung tissue remains challenging. This study evaluates thermoplastic polyurethane (TPU) for dynamic lung-mimicking phantoms, selected for its flexibility while preserving print-defined geometry, with a focus on radiological equivalence, directional anisotropy, and the detectability of sub-millimetre air channels.

Approach: Eleven TPU materials with various colours and Shore hardness (63-82) were printed into gyroid-patterned inserts of varying infill densities. Effective atomic number (Zeff) and relative electron density (RED) were determined using dual-energy computed tomography (CT). Anisotropy and internal air channels were assessed in five orientations using micro-CT, clinical CT, and flat-panel detector (FPD) imaging, and compared to a voxelised digital model derived from G-code toolpaths.

Main results: Measured Zeff ranged from 6.3 ± 0.6 to 11.1 ± 0.2, with pigment-driven variation up to 66% within identical material categories. Seven materials achieved lung mimicking Zeff (around 7.55). RED increased with infill and mimicked lung references (0.28-0.43) at moderate-infill. The low-infill insert contained 0.7 mm air channels visible in all modalities. The high-infill insert contained 0.3 mm channels, resolved accurately by the micro-CT and FPD but not reliably resolved by clinical CT due to resolution limitations. The digital model indicated diagonal anisotropy, micro-CT and FPD indicated near-isotropy in low and high infill, while CT showed apparent anisotropy due to its resolution limitations.

Significance: TPU-based gyroid phantoms are suitable lung-mimicking candidates for radiotherapy QA of imaging and dosimetry. Their periodic air-channels are reliably resolved by high-resolution imaging (micro-CT or FPD), but may be distorted by clinical CT, particularly in resolution-limited orientations, the digital model supports pre-print evaluation of these air-channels. Because undetected internal heterogeneities may affect dose calculation accuracy, high-resolution imaging when available, is preferred for assessing the internal structure of 3D-printed phantoms.