Physics in medicine and biology最新文献

Objective.The tumor microenvironment characterized by heterogeneously organized vasculatures causes intra-tumoral heterogeneity of oxygen partial pressurepat the cellular level, which cannot be measured by current imaging techniques. The intra-tumoral cellularpheterogeneity may lead to a reduction of therapeutic effects of radiation. The purpose of this study was to investigate the effects of the heterogeneity on biological effectiveness of H-, He-, C-, O-, and Ne-ion beams for different oxygenation levels, prescribed dose levels, and cell types.Approach.The intra-tumoral cellularpdistributions were simulated with a numerical tumor model for average oxygen pressuresp¯tranging from 2.5 to 15 mmHg. The relative biological effectiveness (RBE)-weighted dose distributions of 3-15 Gy prescribed doses were planned for a cuboid target with the five ion species for constantp¯tvalues. Radiosensitivities of human salivary gland tumor (HSG) and Chinese hamster ovary (CHO) cells were investigated. The planned dose distributions were then recalculated by taking thepheterogeneity into account.Main results.Asp¯tdecreased and prescribed dose increased, the biological effectiveness of the ion beams decreased due to thepheterogeneity. The reduction in biological effectiveness was pronounced for lighter H- and He-ion beams compared to heavier C-, O-, and Ne-ion beams. The RBE-weighted dose in the target for HSG (CHO) cells decreased by 41.2% (44.3%) for the H-ion beam, while it decreased by 16.7% (14.7%) for the Ne-ion beam at a prescribed dose of 15 Gy under ap¯tof 2.5 mmHg.Significance.The intra-tumoral cellularpheterogeneity causes a significant reduction in biological effectiveness of ion beams. These effects should be considered in estimation of therapeutic outcomes.

Objective. Recently, there have been many advancements in deep unrolling methods for sparse-view computed tomography (SVCT) reconstruction. These methods combine model-based and deep learning-based reconstruction techniques, improving the interpretability and achieving significant results. However, they are often computationally expensive, particularly for clinical raw projection data with large sizes. This study aims to address this issue while maintaining the quality of the reconstructed image.Approach. The SVCT reconstruction task is decomposed into two subproblems using the proximal gradient method: optimizing dense-view sinograms and optimizing images. Then dense-view sinogram inpainting, image-residual learning, and image-refinement modules are performed at each iteration stage using deep neural networks. Unlike previous unrolling methods, the proposed method focuses on optimizing dense-view sinograms instead of full-view sinograms. This approach not only reduces computational resources and runtime but also minimizes the challenge for the network to perform sinogram inpainting when the sparse ratio is extremely small, thereby decreasing the propagation of estimation error from the sinogram domain to the image domain.Main results. The proposed method successfully reconstructs an image (512 × 512 pixels) from real-size (2304 × 736) projection data, with 3.39 M training parameters and an inference time of 0.09 s per slice on a GPU. The proposed method also achieves superior quantitative and qualitative results compared with state-of-the-art deep unrolling methods on datasets with sparse ratios of 1/12 and 1/18, especially in suppressing artifacts and preserving structural details. Additionally, results show that using dense-view sinogram inpainting not only accelerates the computational speed but also leads to faster network convergence and further improvements in reconstruction results.Significance. This research presents an efficient dual-domain deep unrolling technique that produces excellent results in SVCT reconstruction while requiring small computational resources. These findings have important implications for speeding up deep unrolling CT reconstruction methods and making them more practical for processing clinical CT projection data.

Objective: Accurate dose predictions are crucial to maximizing the benefits of carbon-ion therapy. Carbon beams incident on the human body cause nuclear interactions with tissues, resulting in changes in the constituent nuclides and leading to dose errors that are conventionally corrected using conventional single-energy computed tomography (SECT). Dual-energy computed tomography (DECT) has frequently been used for stopping power estimation in particle therapy and is well suited for correcting nuclear reactions because of its detailed body-tissue elemental information. This study proposes a correction method for the absolute dose in carbon-ion therapy that considers changes in nuclide composition resulting from nuclear reactions with body tissues, as a novel application of DECT. Approach: The change in dose associated with nuclear reactions is determined by correcting each integrated depth dose component of the carbon beam using a nuclear interaction correction factor. This factor is determined based on the stopping power, mass density, and nuclear interaction cross-section in body tissue. The stopping power and mass density were calculated using established methods, whereas the nuclear interaction cross-section was newly defined through a conversion equation derived from the effective atomic number. Main results: Nuclear interaction correction factors and corrected doses were determined for 85 body tissues with known compositions, comparing them with existing SECT-based methods. The root-mean-square errors of the SECT- and DECT-based nuclear interaction correction factors relative to theoretical values were 0.66% and 0.39%, respectively. Significance: This indicates a notable enhancement in the estimation accuracy with DECT. The dose calculations in uniform body tissues derived from SECT showed slight over-correction in adipose and bone tissues, whereas those based on DECT were almost consistent with theoretical values. Our proposed method demonstrates the potential of DECT for enhancing dose calculation accuracy in carbon-ion therapy, complementing its established role in stopping power estimation. .

Objective.Transcranial ultrasound is used in a variety of treatments, including neuromodulation, opening the blood-brain barrier, and high intensity focused ultrasound therapies. To ensure safety and efficacy of these treatments, numerical simulations of the ultrasound field within the brain are used for treatment planning and evaluation. This study investigates the accuracy of numerical modelling of the propagation of focused ultrasound through cranial bones.Approach.Holograms of acoustic fields after propagation through four human skull specimens were measured for frequencies ranging from 270 kHz to 1 MHz, using both quasi-continuous and pulsed modes. The open-source k-Wave toolbox was employed for simulations, using an equivalent-source hologram and a uniform bowl source with parameters that best matched the measured free-field pressure distribution.Main results.The average absolute error in k-Wave simulations with sound speed and density derived from CT scans compared to measurements was 15% for the spatial-peak acoustic pressure amplitude, 2.7 mm for the position of the focus, and 35% for the focal volume. Optimised uniform bowl sources achieved calculation accuracy comparable to that of the hologram sources.Significance.This method is demonstrated as a suitable tool for prediction of focal position, size and overall distribution of transcranial ultrasound fields. The accuracy of the shape and position of the focal region demonstrate the suitability of the sound speed and density mapping used here. However, large errors in pressure amplitude and transmission loss in some individual cases show that alternative methods for mapping individual skull attenuation are needed and the possibility of considerable errors in pressure amplitude should be taken into account when planning focused ultrasound studies or interventions in the human brain, and appropriate safety margins should be used.

Objective.Biodegradable cardiovascular stents made of thin, low atomic number metals (e.g. Zn, Mg, Fe) are now approved for clinical use. However, poor contrast under x-ray imaging leads to longer surgical times, high patient exposure, and sometimes stent misplacement. This study aimed at enhancing the visibility of low-Zmetal stents under x-ray imaging, by combining high-Zmetal coatings and beam filtration.Approach.Photon energy spectra from W-anode x-ray beams operated at 80 and 120 kVp, were generated by the SpekCalc and BEAMnrc softwares. The contrast produced by Fe stent struts (50-10μm W coatings), as well as dose and air kerma values (by BEAMnrc), were simulated. Several types of beam hardening filters (Sn: 0.1, 0.2 mm; Cu: 0.2, 0.7 mm) were also applied. Then, Fe foils (50µm) with W coatings (2-3µm-thick) were fabricated by magnetosputtering. These samples were x-ray visualized, for quantification of contrast between W-coated and uncoated Fe samples. Fe struts (50µm) were also coated with W (3.8 ± 0.2µm), and stent-like objects were x-ray visualized.Main results.Fe samples attenuate 6.4% (120 kVp) and 10.1% (80 kVp) spectra photons, and 25% and 34.5% for W-coated Fe samples (SpekCalc). BEAMnrc calculations revealed the highest contrast improvement in a 120 kVp beam (36.4%, and 38.5%) for W-coated and uncoated Fe samples with Sn (0.2 mm), and Cu + Sn (0.2 + 0.2 mm) filters. Experimentally, the highest contrasts between Fe and W-Fe foils, were obtained with 0.2 mm Sn (77 ± 7% contrast increase at 80 kV). The dose was also strongly reduced (70% and 75%, for 80 and 120 kVp beams). Finally, for 3D Fe stents visualized at 80 kVp, the highest CNR and CNRD values were achieved with 0.1 mm Sn (18.5 × and 20.1 mGy^-1; compared to 15.0 × and 12.0 mGy^-1in no-filter condition).Significance.The contrast of Fe-based stents in x-ray imaging is improved by addition of a thin layer of W and beam filtration with Sn. The precision and rapidity of biodegradable stents implantation would be improved thereby, as well as the dose to patients.

Objective. Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis.Approach. For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline.Main results. We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9,p≪0.0001in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner.Significance. Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

Objective.Reference dosimetry measurement in a pencil beam scanning system can exhibit dose fluctuation due to intra-spill spot positional drift. This results in a noisy reference dosimetry measurement against energy which could introduce errors in monitor unit calibration. The aim of this study is to investigate the impact of smoothing the reference dosimetry measurements on the type A uncertainty.Methods.The reference dosimetry measurement (Dw/MU)with a PTW 34045 advanced Markus chamber placed at 2 cm depth and a 10 × 10cm2scanned field are performed for 98 energy layers on five non-consecutive days using a water tank. The PTW 34089 large area ionization chamber (LAIC) is placed at the same depth and the charges are measured with a single spot irradiation (MspotLAIC). (Dw/MU)andMspotLAICare fitted with a linear and quadratic function to obtain a smooth plot of (Dw/MU)against the proton energy (reference dosimetry curve). Type A uncertainty of the measured reference dosimetry curve is compared against the de-noised fitted curve.Results.The repeatability of reference dosimetry measurement shows relative difference of up to 2.3% across the five days. The linear and quadratic fits between LAIC charges and the (Dw/MU)from PTW 34045 show a highR2values of more than 0.95. The maximum type A uncertainty of the de-noised reference dosimetry curve is lower (0.69% at 70.2 MeV) compared to the measured one (0.88% at 77.5 MeV). However, the average type A uncertainty of the denoised curve across all energies is higher compared to the measurements (0.50% versus 0.43%).Conclusion.We have presented the physical basis and procedure for fitting the charges measured with a LAIC to the reference dosimetry curve. The fitted reference dosimetry curve avoids large error in any energy layer but increases the average type A uncertainty across energies and should be used with caution.

Objective. Modeling of the collimator-detector response (CDR) in single photon emission computed tomography (SPECT) reconstruction enables improved resolution and accuracy, and is thus important for quantitative imaging applications such as dosimetry. The implementation of CDR modeling, however, can become a computational bottleneck when there are substantial components of septal penetration and scatter in the acquired data, since a direct convolution-based approach requires large 2D kernels. This work proposes a 1D convolution and rotation-based CDR model that reduces reconstruction times but maintains consistency with models that employ 2D convolutions. To enable open-source development and use of these models in image reconstruction, we release a SPECTPSFToolbox repository for the PyTomography project on GitHub.Approach. A 1D/rotation-based CDR model was formulated and subsequently fit to Monte Carlo (MC) point source data representative of¹⁷⁷Lu,¹³¹I, and²²⁵Ac imaging. Computation times of (i) the proposed 1D/rotation-based model and (ii) a traditional model that uses 2D convolutions were compared for typical SPECT matrix sizes. Both CDR models were then used in the reconstruction of MC, physical phantom, and patient data; the models were compared by quantifying total counts in hot regions of interest (ROIs) and activity contrast between hot ROIs and background regions.Results. For typical matrix sizes in SPECT reconstruction, application of the 1D/rotation-based model provides a two-fold computational speed-up over the 2D model when running on GPU. Only small differences between the 1D/rotation-based and 2D models (order of 1%) were obtained for count and contrast quantification in select ROIs.Significance. A technique for CDR modeling in SPECT was proposed that (i) significantly speeds up reconstruction times, and (ii) yields nearly identical reconstructions to traditional 2D convolution based CDR techniques. The released toolbox will permit open-source development of similar models for different isotopes and collimators.

Objective.Estimating the high-resolution (HR) blood flow velocity and pressure fields for the diagnosis and treatment of vascular diseases remains challenging.Approach. In this study, a physics-informed neural network (PINN) with a refined mapping capability was combined with ultrafast ultrasound image velocimetry (u-UIV) to predict HR hemodynamic parameters. Specifically, the Navier-Stokes equations were encoded into the PINN to dynamically optimize the network performance under physical constraints, and a refined mapping network was added at the input to achieve data refinement. During the prediction of HR ultrasound hemodynamic parameters, only the sparse spatial coordinates in the time series were input into the PINN, and the velocity vectors generated from the u-UIV were used together with physical residuals to enhance the physical correctness of HR predictions during the iterative process.Main results.The performance of the refined mapping network was validated via simulations, with a 1.9-fold increase in the radial resolution and a 2.5-fold increase in the axial resolution. HR velocity field estimation fromin vitroandin vivodata showed good agreement with theoretical values and u-UIV measurements, with micrometer-level spatial resolution (88µm×115µm for straight vessel, 75µm×120µm for stenotic vessel and 63µm × 79µm forin vivodata), while the pressure field could be inferred from physical laws.Significance.The proposed method performs well when few data samples are available and has the potential to assist in the clinical diagnosis of vascular diseases.

In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods have achieved revolutionary improvements in processing speed and automation, showing great potential, especially in unsupervised learning. This paper briefly introduces the core concepts of deep learning-based unsupervised image registration, followed by an in-depth discussion of innovative network architectures and a detailed review of these studies, highlighting their unique contributions. Additionally, this paper explores commonly used loss functions, datasets, and evaluation metrics. Finally, we discuss the main challenges faced by various categories and propose potential future research topics. This paper surveys the latest advancements in unsupervised deep neural network-based medical image registration methods, aiming to help active readers interested in this field gain a deep understanding of this exciting area.