Physics in medicine and biology最新文献

Objective.To develop a computational tool that converts biological images into geometries compatible with computational software dedicated to the Monte Carlo simulation of radiation transport (TOPAS), and subsequent biological tissue responses (CompuCell3D). The depiction of individual biological entities from segmentation images is essential in computational radiobiological modeling for two reasons: image pixels or voxels representing a biological structure, like a cell, should behave as a single entity when simulating biological processes, and the action of radiation in tissues is described by the association of biological endpoints to physical quantities, as radiation dose, scored the entire group of voxels assembling a cell.Approach.The tool is capable of cropping and resizing the images and performing clustering of image voxels to create independent entities (clusters) by assigning a unique identifier to these voxels conforming to the same cluster. The clustering algorithm is based on the adjacency of voxels with image values above an intensity threshold to others already assigned to a cluster. The performance of the tool to generate geometries that reproduced original images was evaluated by the dice similarity coefficient (DSC), and by the number of individual entities in both geometries. A set of tests consisting of segmentation images of cultured neuroblastoma cells, two cell nucleus populations, and the vasculature of a mouse brain were used.Main results.The DSC was 1.0 in all images, indicating that original and generated geometries were identical, and the number of individual entities in both geometries agreed, proving the ability of the tool to cluster voxels effectively following user-defined specifications. The potential of this tool in computational radiobiological modeling, was shown by evaluating the spatial distribution of DNA double-strand-breaks after microbeam irradiation in a segmentation image of a cell culture.Significance.This tool enables the use of realistic biological geometries in computational radiobiological studies.

Objective.Magnetic particle imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatiotemporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which the use of regularization methods can address. Multi-contrast MPI reconstructs the signal from different tracer materials or environments separately, resulting in multi-channel images that enable quantification of, for example, temperature or viscosity. Single- and multi-contrast MPI reconstructions produce different kinds of artifacts. The objective of this work is threefold: first, to present the concept of multi-contrast specific MPI channel leakage artifacts; second, to ascertain the source of these leakage artifacts; and third, to introduce a method for their reduction.Approach.A definition for leakage artifacts is established, and a quantification method is proposed. A comprehensive analysis is conducted to establish a connection between the properties of the multi-contrast MPI system matrix and the leakage artifacts. Moreover, a two-step measurement and reconstruction method is introduced to reduce channel leakage artifacts between multi-contrast MPI channels.Main results.The severity of these artifacts correlates with the system matrix shape and condition number and depends on the similarity of the corresponding frequency components. Using the proposed two-step method on both semi-simulated and measured data a significant leakage reduction and speed up the convergence of the multi-contrast MPI reconstruction was observed.Significance.The multi-contrast system matrix analysis we conducted is essential for understanding the source of the channel leakage artifacts and finding methods to reduce them. Our proposed two-step method is expected to improve the potential for real-time multi-contrast MPI applications.

Objective.A reliable, calibrated, non-invasive thermometry method is essential for thermal therapies to monitor and control the treatment. Ultrasound (US) is an effective thermometry modality due to its relatively high sensitivity to temperature changes, and fast data acquisition and processing capabilities.Approach.In this work, the change in backscattered energy (CBE) was used to control the tissue temperature non-invasively using a real-time proportional-integral-derivative (PID) controller. A clinical high-frequency US scanner was used to acquire radio-frequency echo data fromex vivoporcine tissue samples andin vivomice hind leg tissue while the tissue was treated with mild hyperthermia by a focused US applicator. The PID controller maintained the focal temperature at approximately 40 °C for about 4 min.Main results.The results show that the US thermometry based on CBE estimated by a high-frequency US scanner can produce 2D temperature maps of a localized heating region and to estimate the focal temperature during mild hyperthermia treatments. The CBE estimated temperature varied by an average of ±0.85 °C and ±0.97 °C, compared to a calibrated thermocouple, inex vivoandin vivostudies, respectively. The mean absolute deviations of CBE thermometry during the controlled hyperthermia treatment were ±0.45 °C and ±0.54 °C inex vivoandin vivo,respectively.Significance.It is concluded that non-invasive US thermometry via backscattered energies at high frequencies can be used for real-time monitoring and control of hyperthermia treatments with acceptable accuracy. This provides a foundation for an US mediated drug delivery system.

Objective.Normal tissue complication probability (NTCP) modelling is rapidly embracing deep learning (DL) methods, acknowledging the importance of spatial dose information. Finding effective ways to combine information from radiation dose distribution maps (dosiomics) and clinical data involves technical challenges and requires domain knowledge. We propose different multi-modality data fusion strategies to facilitate future DL-based NTCP studies.Approach.Early, joint and late DL multi-modality fusion strategies were compared using clinical and mandibular radiation dose distribution volumes. These were contrasted with single-modality models: a random forest trained on non-image data (clinical, demographic and dose-volume metrics) and a 3D DenseNet-40 trained on image data (mandibular dose distribution maps). The study involved a matched cohort of 92 osteoradionecrosis cases and 92 controls from a single institution.Main results.The late fusion model exhibited superior discrimination and calibration performance, while the join fusion achieved a more balanced distribution of the predicted probabilities. Discrimination performance did not significantly differ between strategies. Late fusion, though less technically complex, lacks crucial inter-modality interactions for NTCP modelling. In contrast, joint fusion, despite its complexity, resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation.Significance.This study is a pioneering effort in comparing different strategies for including image data into DL-based NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. Multiple data fusion strategies should be compared and reported in multi-modality NTCP modelling using DL.

Objective.To demonstrate the feasibility of integrating fully-automated online adaptive proton therapy strategies (OAPT) within a commercially available treatment planning system and underscore what limits their clinical implementation. These strategies leverage existing deformable image registration (DIR) algorithms and state-of-the-art deep learning (DL) networks for organ segmentation and proton dose prediction.Approach.Four OAPT strategies featuring automatic segmentation and robust optimization were evaluated on a cohort of 17 patients, each undergoing a repeat CT scan. (1) DEF-INIT combines deformably registered contours with template-based optimization. (2) DL-INIT, (3) DL-DEF, and (4) DL-DL employ a nnU-Net DL network for organ segmentation and a controlling ROIs-guided DIR algorithm for internal clinical target volume (iCTV) segmentation. DL-INIT uses this segmentation alongside template-based optimization, DL-DEF integrates it with a dose-mimicking (DM) step using a reference deformed dose, and DL-DL merges it with DM on a reference DL-predicted dose. All strategies were evaluated on manual contours and contours used for optimization and compared with manually adapted plans. Key dose volume metrics like iCTV D98% are reported.Main results.iCTV D98% was comparable in manually adapted plans and for all strategies in nominal cases but dropped to 20 Gy in worst-case scenarios for a few patients per strategy, highlighting the need to correct segmentation errors in the target volume. Evaluations on optimization contours showed minimal relative error, with some outliers, particularly in template-based strategies (DEF-INIT and DL-INIT). DL-DEF achieves a good trade-off between speed and dosimetric quality, showing a passing rate (iCTV D98% > 94%) of 90% when evaluated against 2, 4 and 5 mm setup error and of 88% when evaluated against 7 mm setup error. While template-based methods are more rigid, DL-DEF and DL-DL have potential for further enhancements with proper DM algorithm tuning.Significance.Among investigated strategies, DL-DEF and DL-DL demonstrated promising within 10 min OAPT implementation results and significant potential for improvements.

Objective.In proton therapy, range uncertainties prevent optimal benefit from the superior depth-dose characteristics of proton beams over conventional photon-based radiotherapy. To reduce these uncertainties we recently proposed the use of phase-change ultrasound contrast agents as an affordable and effective range verification tool. In particular, superheated nanodroplets can convert into echogenic microbubbles upon proton irradiation, whereby the resulting ultrasound contrast relates to the proton range with high reproducibility. Here, we provide a firstin vivoproof-of-concept of this technology.Approach.First, thein vitrobiocompatibility of radiation-sensitive poly(vinyl alcohol) perfluorobutane nanodroplets was investigated using several colorimetric assays. Then,in vivoultrasound contrast was characterized using acoustic droplet vaporization (ADV) and later using proton beam irradiations at varying energies (49.7 MeV and 62 MeV) in healthy Sprague Dawley rats. A preliminary evaluation of thein vivobiocompatibility was performed using ADV and a combination of physiology monitoring and histology.Main results.Nanodroplets were non-toxic over a wide concentration range (<1 mM). In healthy rats, intravenously injected nanodroplets primarily accumulated in the organs of the reticuloendothelial system, where the lifetime of the generated ultrasound contrast (<30 min) was compatible with a typical radiotherapy fraction (<5 min). Spontaneous droplet vaporization did not result in significant background signals. Online ultrasound imaging of the liver of droplet-injected rats demonstrated an energy-dependent proton response, which can be tuned by varying the nanodroplet concentration. However, caution is warranted when deciding on the exact nanodroplet dose regimen as a mild physiological response (drop in cardiac rate, granuloma formation) was observed after ADV.Significance.These findings underline the potential of phase-change ultrasound contrast agents forin vivoproton range verification and provide the next step towards eventual clinical applications.

Magnetic resonance elastography (MRE) is a non-invasive method for determining the mechanical response of tissues using applied harmonic deformation and motion-sensitive MRI. MRE studies of the human brain are typically performed at conventional field strengths, with a few attempts at the ultra-high field strength, 7T, reporting increased spatial resolution with partial brain coverage. Achieving high-resolution human brain scans using 7T MRE presents unique challenges of decreased octahedral shear strain-based signal-to-noise ratio (OSS-SNR) and lower shear wave motion sensitivity. In this study, we establish high resolution MRE at 7T with a custom 2D multi-slice single-shot spin-echo echo-planar imaging sequence, using the Gadgetron advanced image reconstruction framework, applying Marchenko-Pastur Principal component analysis denoising, and using nonlinear viscoelastic inversion. These techniques allowed us to calculate the viscoelastic properties of the whole human brain at 1.1 mm isotropic imaging resolution with high OSS-SNR and repeatability. Using phantom models and 7T MRE data of eighteen healthy volunteers, we demonstrate the robustness and accuracy of our method at high-resolution while quantifying the feasible tradeoff between resolution, OSS-SNR, and scan time. Using these post-processing techniques, we significantly increased OSS-SNR at 1.1 mm resolution with whole-brain coverage by approximately 4-fold and generated elastograms with high anatomical detail. Performing high-resolution MRE at 7T on the human brain can provide information on different substructures within brain tissue based on their mechanical properties, which can then be used to diagnose pathologies (e.g. Alzheimer's disease), indicate disease progression, or better investigate neurodegeneration effects or other relevant brain disorders,in vivo.

Objective: Megavoltage cone-beam CT (MV-CBCT) is advantageous in metal artifact reduction during Image-Guided Radiotherapy (IGRT), although it is limited by poor soft tissue contrast. This study proposed and evaluated a novel hybrid modality dual-energy (DE) imaging method combining the complementary advantages of kV-CT and MV-CBCT. Approach: The kV-CT and MV-CBCT images were acquired on a planning CT scanner and a Halcyon linear accelerator respectively. After rigid registration, images of basis materials were generated using the iterative decomposition method in the volumetric images. The decomposition accuracy was quantitatively evaluated on a Gammex 1472 phantom. The performance of contrast enhancement and metal artifact reduction in virtual monochromatic images were evaluated on both phantom and patient studies. Main results: Using the proposed method, the mean percentage errors for RED and SPR were 0.90% and 0.81%, outperforming the clinical single-energy mapping method with mean errors of 1.28% and 1.07%, respectively. The contrasts of soft-tissue insets were enhanced by a factor of 2~3 at 40 keV compared to kV-CT. The standard deviation in the metal artifact area was reduced by ~67%, from 42 HU (kV-CT) to 14 HU (150 keV monochromatic). The head and neck patient test showed that the percent error of soft-tissue RED in the metal artifact area was reduced from 18.1% (HU-RED conversion) to less than 1.0% (the proposed method), which was equivalent to the maximum dosimetric difference of 28.7% based on the patient-specific plan. Significance: Without hardware modification or extra imaging dose, the proposed hybrid modality method enabled kV-MV DE imaging, providing improved accuracy of quantitative analysis, soft-tissue contrast and metal artifact suppression for more accurate IGRT. .

Objective.Heterostructured scintillators offer a promising solution to balance the sensitivity and timing in TOF-PET detectors. These scintillators utilize alternating layers of materials with complementary properties to optimize performance. However, the layering compromises time resolution due to light transport issues. This study explores double-sided readout-enabling improved light collection and Depth-of-Interaction (DOI) information retrieval-to mitigate this effect and enhance the timing capabilities of heterostructures.Approach.The time resolution and DOI performances of 3 × 3 × 20 mm³BGO&EJ232 heterostructures were assessed in a single and double-sided readout (SSR and DSR, respectively) configuration using high-frequency electronics.Main results.Selective analysis of photopeak events yielded a DOI resolution of 6.4 ± 0.04 mm. Notably, the Coincidence Time Resolution (CTR) improved from 262 ± 8 ps (SSR) to 174 ± 6 ps (DSR) when measured in coincidence with a fast reference detector. Additionally, symmetrical configuration of two identical heterostructures in coincidence was tested, yielding in DSR a CTR of 254 ± 8 ps for all photopeak events and 107 ± 5 ps for the fastest events.Significance.By using high-frequency double-sided readout, we could measure DOI resolution and improve the time resolution of heterostructures of up to 40%. The DOI information resulted intrinsically captured in the average between the timestamps of the two SiPMs, without requiring any further correction.

Objective: Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cramér-Rao Lower Bound (CRLB) than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs). This work explores charge sharing compensation using local spatial coincidence counter information for MEICC detectors through a deep-learning method. Approach: By analyzing the impact of charge sharing on photon count detection, we designed our network with a focus on individual pixels. Employing MEICC data of patches centered on POIs as input, we utilized local information for effective charge sharing compensation. The output was VMAI at different energies to address real detector issues without knowledge of primary counts. To achieve data diversity, a fast and online data generation method was proposed to provide adequate training data. A new loss function was introduced to reduce bias for training with high-noise data. The proposed method was validated by Monte Carlo (MC) simulation data for MEICC detectors that were compared with conventional PCDs. Main-Results: For conventional data as a reference, networks trained on low-noise data yielded results with a minimal bias (about 0.7%) compared with > 3% for the polynomial fitting method. The results of networks trained on high-noise data exhibited a slightly increased bias (about 1.3%) but a significantly reduced standard deviation (STD) and normalized root mean square error (NRMSE). The simulation study of the MEICC detector demonstrated superior compared to the conventional detector across all the metrics. Specifically, for both networks trained on high-noise and low-noise data, their biases were reduced to about 1% and 0.6%, respectively. Meanwhile, the results from a MEICC detector were of about 10% lower noise than a conventional detector. Moreover, an ablation study showed that the additional loss function on bias was beneficial for training on high-noise data. Significance: We demonstrated that a network-based method could utilize local information in PCDs effectively by patch-based learning to reduce the impact of charge sharing. MEICC detectors provide very valuable local spatial information by additional coincidence counters. Compared with MEICC detectors, conventional PCDs only have limited local spatial information for charge sharing compensation, resulting in higher bias and standard deviation in VMAI estimation with the same patch strategy. .