Physics in medicine and biology最新文献

Objective: Whole-body Positron Emission Tomography (PET) imaging is often hindered by respiratory motion during acquisition, causing significant degradation in the quality of reconstructed activity images. An additional challenge in PET/CT imaging arises from the respiratory phase mismatch between CT-based attenuation correction and PET acquisition, leading to attenuation artifacts. To address these issues, we propose two new, purely data-driven methods for the joint estimation of activity, attenuation, and motion in respiratory self-gated time-of-flight (TOF) PET. These methods enable the reconstruction of a single activity image free from motion and attenuation artifacts. Approach: The proposed methods were evaluated using data from the anthropomorphic Wilhelm phantom acquired on a Siemens mCT PET/CT system, as well as three clinical [18F]FDG PET/CT datasets acquired on a GE DMI PET/CT system. Image quality was assessed visually to identify motion and attenuation artifacts. Lesion uptake values were quantitatively compared across reconstructions without motion modeling, with motion modeling but "static" attenuation correction, and with our proposed methods. Main results: For the Wilhelm phantom, the proposed methods delivered image quality closely matching the reference reconstruction from a static acquisition. The lesion-to-background contrast for a liver dome lesion improved from 2.0 (no motion correction) to 5.2 (using our proposed methods), matching the contrast from the static acquisition (5.2). In contrast, motion modeling with "static" attenuation correction yielded a lower contrast of 3.5. In patient datasets, the proposed methods successfully reduced motion artifacts in lung and liver lesions and mitigated attenuation artifacts, demonstrating superior lesion to background separation. Significance: Our proposed methods enable the reconstruction of a single, high-quality activity image that is motion-corrected and free from attenuation artifacts, without the need for external hardware.

Objective.Uveal melanomas and retinoblastomas can be treated with ophthalmic beta-emitting ruthenium-106/rhodium-106 applicators. The applicator manufacturer provides a datasheet of the dosimetric properties of each applicator set, but the source strengths and 3D dose distributions should be verified by the end user with independent measurements.Approach.The purpose of this work was to calibrate diamond detector against low energy electron beam and determine necessary correction factors in the geometry of ophthalmic applicators to be able to perform quality assurance (QA) measurements for the applicators. Two separate sets of applicators were evaluated.Main results.The results showed good agreement with manufacturers' specifications. An average agreement of 3% to the manufacturer's reference data was observed: measured dose rate/reference = 0.97 ± 0.04 (mean ± SD), range 0.90-1.05.Significance.It can be concluded that megavoltage electron beam is suitable for calibration of a diamond detector. After calibration, detector can be used for an absolute dose measurement of a ruthenium-106/rhodium-106 applicator with sufficient performance to detect deviations larger than 10% in the QA before clinical use.

Objective.To assess the performance of a probabilistic deep learning based algorithm for predicting inter-fraction anatomical changes in head and neck patients.Approach.A probabilistic daily anatomy model (DAM) for head and neck patients DAM (DAM_HN) is built on the variational autoencoder architecture. The model approximates the generative joint conditional probability distribution of the repeat computed tomography (rCT) images and their corresponding masks on the planning CT images (pCT) and their masks. The model outputs deformation vector fields, which are used to produce possible rCTs and associated masks. The dataset is composed of 93 patients (i.e. 315 pCT-rCT pairs), 9 (i.e. 27 pairs) of which were set aside for final testing. The performance of the model is assessed based on the reconstruction accuracy and the generative performance for the set aside patients.Main results.The model achieves a DICE score of 0.83 and an image similarity score normalized cross-correlation of 0.60 on the test set. The generated parotid glands, spinal cord and constrictor muscle volume change distributions and center of mass shift distributions were also assessed. For all organs, the medians of the distributions are close to the true ones, and the distributions are broad enough to encompass the real observed changes. Moreover, the generated images display anatomical changes in line with the literature reported ones, such as the medial shifts of the parotids glands.Significance.DAM_HNis capable of generating realistic anatomies observed during the course of the treatment and has applications in anatomical robust optimization, treatment planning based on plan library approaches and robustness evaluation against inter-fractional changes.

Objective.Metal artifacts seriously deteriorate CT image quality. Current metal artifacts reduction (MAR) methods suffer from insufficient correction or easily introduce secondary artifacts. To better suppress metal artifacts, we propose a sinogram completion approach extracting and utilizing useful information that contained in the corrupted metal trace projections.Approach.Our method mainly contains two stages: sinogram interpolation by an improved normalization technique for initial correction and physics-informed nonlinear sinogram decomposition for further improvement. In the first stage, different from the popular normalized metal artifact reduction method, we propose a more meaningful normalization scheme for the interpolation procedure. In the second stage, instead of performing a linear sinogram decomposition as done in the physics-informed sinogram completion method, we introduce a nonlinear decomposition model that can accurately separate the sinogram into metal and non-metal contributions by better modeling the physical scanning process. The interpolated sinogram and physics-informed correction compensate each other to reach the optimal correction results.Main results.Experimental results on simulated and real data indicate that, in terms of both structures preservation and detail recovery, the proposed physics-informed nonlinear sinogram completion method achieves very competitive performance for MAR compared to existing methods.Significance.According to our knowledge, it is for the first time that a nonlinear sinogram decomposition model is proposed in the literature for metal artifacts correction. It might motivate further research exploring this idea for various sinogram processing tasks.

Objective. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning has been widely explored for the metal artifact reduction (MAR) task. Nevertheless, paired metal artifact CT datasets suitable for training do not exist in reality. Although the synthetic CT image dataset provides additional training data, the trained networks still generalize poorly to real metal artifact data.Approach.A self-supervised U-shaped transformer network is proposed to focus on model generalizability enhancement in MAR tasks. This framework consists of a self-supervised mask reconstruction pre-text task and a down-stream task. In the pre-text task, the CT images are randomly corrupted by masks. They are recovered with themselves as the label, aiming at acquiring the artifacts and tissue structure of the actual physical situation. Down-stream task fine-tunes MAR target through labeled images. Utilizing the multi-layer long-range feature extraction capabilities of the Transformer efficiently captures features of metal artifacts. The incorporation of the MAR bottleneck allows for the distinction of metal artifact features through cross-channel self-attention.Main result. Experiments demonstrate that the framework maintains strong generalization ability in the MAR task, effectively preserving tissue details while suppressing metal artifacts. The results achieved a peak signal-to-noise ratio of 43.86 dB and a structural similarity index of 0.9863 while ensuring the efficiency of the model inference. In addition, the Dice coefficient and mean intersection over union are improved by 11.70% and 9.51% in the segmentation of the MAR image, respectively.Significance.The combination of unlabeled real-artifact CT images and labeled synthetic-artifact CT images facilitates a self-supervised learning process that positively contributes to model generalizability.

Objective.Commissioning an MR-linac treatment planning system requires output correction factors,kB→,Qclin,Qmsrfclin,fmsr, for detectors to accurately measure the linac's output at various field sizes. In this study,kB→,Qclin,Qmsrfclin,fmsrwas determined at the central axis using two methods: one that combines the corrections for the influence of the magnetic field and the small field in a single factor (kB→,Qclin,Qmsrfclin,fmsr), and a second that isolates the magnetic field's influence, allowing the use of output correction factors without a magnetic field,kQclin,Qmsrfclin,fmsr, from literature for determiningkB→,Qclin,Qmsrfclin,fmsr.Approach.To determinekB→,Qclin,Qmsrfclin,fmsrand examine its behaviour across different photon energies and magnetic flux densitiesBin small fields, measurements with an ionization chamber (0.07 cm³sensitive volume) and a solid-state detector were carried out at an experimental facility for both approaches. Changes in absorbed dose to water with field size were determined via Monte Carlo simulations. To evaluate clinical applicability, additional measurements were conducted on a 1.5 T MR-linac.Main results.Both methods determined comparablekB→,Qclin,Qmsrfclin,fmsrresults. For field sizes >3 × 3 cm²,Branging from -1.5 to 1.5 T and photon energies of 6 and 8 MV, no change ofkQclin,Qmsrfclin,fmsras a function of the magnetic field was observed. Comparison with measurement results from the 1.5 T MR-linac confirm this. For ⩽3 × 3 cm²,kB→,Qclin,Qmsrfclin,fmsrdepends on photon energy andB. For 1.5 T and 6 MV,BreduceskQclin,Qmsrfclin,fmsrup to 3% for the ionization chamber and up to 7% for the solid-state detector.Significance.kB→,Qclin,Qmsrfclin,fmsrwere successfully determined for two detectors, enabling their use at a 1.5 T MR-linac. For field sizes of >3 × 3 cm²,kB→,Qclin,Qmsrfclin,fmsris one for most detectors suitable for small field dosimetry for all available perpendicular MR-linac systems, as confirmed in the literature. For these field sizes and detectors, the correction factor accounting for the dosimeter response change in the reference field due to the magnetic field,kB→,Qmsrfmsr, can be used for cross-calibration. Therefore, future research may only focus on small field sizes.

Objective. The training of AI models for medical image diagnostics requires highly accurate, diverse, and large training datasets with annotations and pathologies. Unfortunately, due to privacy and other constraints the amount of medical image data available for AI training remains limited, and this scarcity is exacerbated by the high overhead required for annotation. We address this challenge by introducing a new controlled framework for the generation of synthetic images complete with annotations, incorporating multiple conditional specifications as inputs.Approach. Using lung CT as a case study, we employ a denoising diffusion probabilistic model to train an unconditional large-scale generative model. We extend this with a classifier-free sampling strategy to develop a robust generation framework. This approach enables the generation of constrained and annotated lung CT images that accurately depict anatomy, successfully deceiving experts into perceiving them as real. Most notably, we demonstrate the generalizability of our multi-conditioned sampling approach by producing images with specific pathologies, such as lung nodules at designated locations, within the constrained anatomy.Main results. Our experiments reveal that our proposed approach can effectively produce constrained, annotated and diverse lung CT images that maintain anatomical consistency and fidelity, even for annotations not present in the training datasets. Moreover, our results highlight the superior performance of controlled generative frameworks of this nature compared to nearly every state-of-the-art image generative model when trained on comparable large medical datasets. Finally, we highlight how our approach can be extended to other medical imaging domains, further underscoring the versatility of our method.Significance. The significance of our work lies in its robust approach for generating synthetic images with annotations, facilitating the creation of highly accurate and diverse training datasets for AI applications and its wider applicability to other imaging modalities in medical domains. Our demonstrated capability to faithfully represent anatomy and pathology in generated medical images holds significant potential for various medical imaging applications, with high promise to lead to improved diagnostic accuracy and patient care.

Objective.Achieving FLASH dose rate with pencil beam scanning intensity modulated proton therapy is challenging. However, utilizing a single energy layer with a ridge filter (RF) can maintain dose rate and conformality. Yet, changes in patient anatomy over the treatment course can render the RF obsolete. Unfortunately, creating a new RF is time-consuming, thus, incompatible with online adaptation. To address this, we propose to re-optimize the spot weights while keeping the same initial RF.Approach.Data from six head and neck cancer patients with a repeated computed tomography (CT₂) were used. FLASH treatment plans were generated with three methods on CT₂: 'full-adaptation' (FA), optimized from scratch with a new RF; 'spot-adaptation only' (SAO), re-using initial RF but adjusting plan spot weights; and 'no adaptation' (NoA) where the dose from initial plans on initial CT (CT₁) was recomputed on CT₂. The prescribed dose per fraction was 9 Gy. Different beam angles were tested for each CT₂(1 beam per fraction). The FA, SAO and NoA plans were then compared on CT₂.Main results.Fractions with SAO showed a median decrease of 0.05 Gy forD98% and a median increase of 0.03 Gy forD2% of CTV when compared to their homologous FA plans on nominal case. Median conformity number decreased by 0.03. Median max dose to spinal cord increased by 0.09 Gy. The largest median increase in mean dose to organs was 0.03 Gy to the mandible. The largest observed median difference in organs receiving a minimal dose rate of 40 Gy s^-1was 0.5% for the mandible. Up to 16 of the 20 evaluated SAO fractions were thus deemed clinically acceptable, with up to 8 NoA plans already acceptable before adaptation.Significance.Proposed SAO workflow showed that for most of our evaluated plans, daily reprinting of RF was not necessary.

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.Statistical properties of a CdTe photon-counting detector were simulated using a dedicated Monte Carlo model that includes spatial and spectral correlations. A measurement of the same properties was done to validate the simulation and gain further understanding of the detector.Approach.Photon histories were calculated using a Monte Carlo x-ray simulation program using energy dependent interaction probabilities of the incoming photons. Pulse forms corresponding to photon interaction locations were taken from a pre-calculated pulse shape lookup table and were inserted into simulated pulse trains. These pulse trains were evaluated. Measurements were done on a clinical CT scanner equipped with photon-counting detectors. The examined properties of the detector are detected counts, variances, variance-to-mean-ratios, as well as various spectral-spatial correlations connecting different thresholds in neighboring pixels.Main Results.The simulated data reproduced all trends observed in the statistics of the detector. Spectral correlations between threshold in one pixel showed an excellent agreement between simulation and measurement, both for low and higher fluxes. Spatial correlations between lower thresholds were slightly overestimated in simulations.Significance.The comparison of measured and simulated data shows that the simulation models the statistics of the detector well. This allows further investigation of the detector on a simulated basis and allows using the simulation to further optimize the detector design.