Physics in medicine and biology最新文献

Purpose.Patient-tailored intracavitary/interstitial (IC/IS) brachytherapy (BT) applicators may increase dose conformity in cervical cancer patients. Current configuration planning methods in these custom applicators rely on manual specification or a small set of (straight) needles. This work introduces and validates a two-stage approach for establishing channel configurations in the 3D printed patient-tailored ARCHITECT applicator.Methods.For each patient, the patient-tailored applicator shape was based on the first BT application with a commercial applicator and integrated connectors to a commercial (Geneva) intrauterine tube and two lunar ring channels. First, a large candidate set was generated of channels that steer the needle to desired poses in the target region and are contained in the applicator. The channels' centrelines were represented by Bézier curves. Channels running between straight target segments and entry points were optimised and refined to ensure (dynamic) feasibility. Second, channel configurations were selected using geometric coverage optimisation. This workflow was applied to establish patient-tailored geometries for twenty-two patients previously treated using the Venezia applicator. Treatment plans were automatically generated using the in-house developed algorithm BiCycle. Plans for the clinically used configuration,TPclin, and patient-tailored configuration,TParch, were compared.Results.Channel configurations could be generated in clinically feasible time (median: 2651 s, range 1826-3812 s). AllTParchandTPclinplans were acceptable, but planning aims were more frequently attained with patient-tailored configurations (115/132 versus 100/132 instances). Median CTV_IRD98and bladderD2cm3doses significantly improved (p<0.001 andp<0.01 respectively) inTParchplans in comparison withTPclinplans, and in approximately half of the patients dosimetric indices improved.Conclusion.Automated patient-tailored BT channel configuration planning for 3D printed applicators is clinically feasible. A treatment planning study showed that all plans met planning limits for the patient-tailored configurations, and in selected cases improved the plan quality in comparison with commercial applicator configurations.

Objective: The need for increased sensitivity in magnetic resonance imaging (MRI) is crucial for its advancement as an imaging modality. The development of passive Lenz Resonators for effective RF magnetic field focusing will improve MRI sensitivity via local amplification of MRI signal, thereby leading to more efficient diagnosis and patient treatment.

Approach: While there are methods for amplifying the signal from specific nuclei in MRI, such as hyperpolarization, a general solution will be more advantageous and would work in combination with these preexisting methods. While the Lenz Lens proposed such a general solution based on Lenz's law and the reciprocity principle, it came at the cost of limited signal enhancement. In this work, the first-in-kind prototype Lenz Resonator was conceived and examined as a general frequency-selective passive flux-focusing element for significant MRI signal enhancement. A 3.0 T Philips Achieva MRI was used to compare the signal from a phantom in the presence of Lenz Lenses, Lenz Resonators, and control trials with neither component.

Main results: An MRI investigation demonstrated an experimental amplification of the signal-to-noise ratio up to 80% using an MRI insert of two coaxial Lenz Resonators due to superior B1 magnetic field focusing. The resonators displayed consistent amplification, nearly independent of their x-position within the MRI bore.

Significance: This behavior demonstrates the feasibility of imaging large objects of varying shapes without penalties for signal amplification using Lenz Resonators. The Lenz Resonators versatility in geometrical design and consistent signal amplifying abilities between pulse sequences should allow for the development of Lenz Resonators suitable for most commonly used MRI setups.

Objective: Conventionally, if two metabolic processes are of interest for image analysis, two separate, sequential PET scans are performed. However, sequential PET scans cannot simultaneously display the metabolic targets. The concurrent study of two simultaneous PET scans could provide new insights into the causes of diseases. Approach: In this work, we propose a reconstruction algorithm for the simultaneous injection of a β+-emitter emitting only annihilation photons and a β+-γ-emitter emitting annihilation photons and an additional prompt γ-photon. As in previous works, the γ-photon is used to identify events originating from the β+-γ-emitter. However, due to e.g. attenuation, the γ-photon is often not detected and not all events can correctly be associated with the β+-γ-emitter as they are detected as double coincidences. In contrast to previous works, we estimate this number of double coincidences with origin in the β+-γ, emitter including the attenuation of the prompt γ, and incorporate this estimation in the forward-projection of the ML-EM algorithm. For evaluation, we simulate different scenarios with varying objects and attenuation maps. The nuclide 18F serves as β+-emitter, while 44Sc functions as β+-γ emitter. The performance of the algorithm is assessed by calculating the residual error of the β+-γ-emitter in the reconstructed β+-emitter image. Additionally, the intensity values in the simulated cylinders of the ground truth (GT) and the reconstructed images are compared. Main Results: The remaining activity in the β+-emitter image varied from 0.4% to 3.7%. The absolute percentage difference between GT and reconstructed intensity for the pure β+ emitter images was found to be between 3.0 and 7.4% for all cases. The absolute percentage difference between GT and reconstructed intensity for the β+-γ emitter images ranged from 8.7 to 10.4% for all simulated cases. Significance: These results demonstrate that our approach can reconstruct two separate images with a good quantitation accurac.

Objective: Diffusion equation imaging processing is promising to enhance images showing lesions of bone metastasis (LBM). The Perona-Malik diffusion (PMD) model, which has been widely used and studied, is an anisotropic diffusion processing method to denoise or extract objects from an image effectively. However, the smoothing characteristics of PMD or its related method hinder extraction and enhancement of soft tissue regions of medical image such as computed tomography (CT), typically leaving an indistinct region with ambient tissues. Moreover, PMD expands the border region of the objects. A novel diffusion methodology must be used to enhance the LBM region effectively. Approach. For this study, we originally developed a diffusion equation quantification (DEQ) method that uses a filter function to selectively provide modulated diffusion according to the original locations of objects in an image. The structural similarity index measure (SSIM) and Lie derivative image analysis (LDIA) L-value map were used to evaluate image quality and processing. Main results. We determined superellipse function with its order n=4 for the LBM region. DEQ was found to be more effective at contrasting LBM for various LBM CT images than PMD or its improved models. DEQ yields enhancement agreeing with the indications of positron emission tomography despite complex lesions of bone metastasis comprising osteoblastic, osteoclastic, mixed tissues, and metal artifacts, which is innovative. Moreover, DEQ retained high quality of image (SSIM > 0.95), and achieved a low mean value of the L-value (< 0.001), indicative of our intended selective diffusion compared to other PMD models. Significance. Our method improved the visibility of mixed tissue lesions, which can assist computer visional framework and can help radiologists to produce accurate diagnose of LBM regions which are frequently overlooked in radiology findings because of the various degrees of visibility in CT images.

Purpose: To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system (TPS) to automatically generate intensity modulated radiation therapy (IMRT) treatment plans for head-and-neck (HN) cancer with consistent organ-at-risk (OAR) sparing performance. Methods: With IRB approval, one hundred and twenty HN patients receiving IMRT were included. The DRL agent was trained with 20 patients. During each inverse optimization process, the intermediate dosimetric endpoints' value, dose volume constraints value and structure objective function loss were collected as the DRL states. By adjusting the objective constraints as actions, the agent learned to seek optimal rewards by balancing OAR sparing and planning target volume (PTV) coverage. Reward computed from current dose-volume-histogram (DVH) endpoints and clinical objectives were sent back to the agent to update action policy during model training. The trained agent was evaluated with the rest 100 patients. Results: The DRL agent was able to generate a clinically acceptable IMRT plan within 12.4±3.1 minutes without human intervention. DRL plans showed lower PTV maximum dose (109.2%) compared to clinical plans (112.4%) (p<.05). Average median dose of left parotid, right parotid, oral cavity, larynx, pharynx of DRL plans were 15.6Gy, 12.2Gy, 25.7Gy, 27.3Gy and 32.1Gy respectively, comparable to 17.1 Gy,15.7Gy, 24.4Gy, 23.7Gy and 35.5Gy of corresponding clinical plans. The maximum dose of cord+5mm, brainstem and mandible were also comparable between the two groups. In addition, DRL plans demonstrated reduced variability, as evidenced by smaller 95% confidence intervals. The total MU of the DRL plans was 1611 vs 1870 (p<.05) of clinical plans. The results signaled the DRL's consistent planning strategy compared to the planners' occasional back-and-forth decision-making during planning. Conclusion: The proposed deep reinforcement learning (DRL) agent is capable of efficiently generating HN IMRT plans with consistent quality. .

Objective - Microdosimetry is gaining increasing interest in particle therapy. Thanks to the advancements in microdosimeter technologies and the increasing number of experimental studies carried out in hadron therapy frameworks, it is proving to be a reliable experimental technique for radiation quality characterisation, quality assurance, and radiobiology studies. However, considering the variety of detectors used for microdosimetry, it is important to ensure the consistency of microdosimetric results measured with different types of microdosimeters. Approach - This work presents a novel multi-thickness microdosimeter and a methodology to characterise the radiation quality of a clinical carbon-ion beam. The novel device is a diamond detector made of three sensitive volumes (SV) of different thicknesses: 2, 6 and 12 μm. The SVs, which operate simultaneously, were accurately aligned and laterally positioned within 3mm. This allignement allowed for a comparison of the results with a negligible impact of the SVs alignment and their lateral positioning, ensuring the homogeneity of the measured radiation quality. An experimental campaign was carried out at MedAustron using a carbon-ion beam of typical clinical energy (284.7MeV/u). Main results - The measurement results allowed for a meticulous interpretation of its radiation quality, highlighting the effect of the SV thickness. The consistency of the microdosimetric spectra measured by detectors of different thicknesses is discussed by critically analysing the spectra and the differences observed. Significance - The methodology presented will be highly valuable for future experiments investigating the effects of the target volume size in radiobiology and could be easily adapted to the other particles employed in hadron therapy for clinical (i.e. protons) and for research purposes (e.g. helium, lithium and oxygen ions).

Objective: Contrast agents in CT scans can compromise the accuracy of dose calculations in radiation therapy planning, especially for particle therapy. This often requires an additional non-contrast CT scan, increasing radiation exposure and introducing potential registration errors. Our goal is to resolve these issues by accurately estimating radiotherapy parameters from dual virtual non-contrast (dual-VNC) images generated by contrast-enhanced dual-energy CT (DECT) scans, while accounting for noise and variability in tissue composition. Approach: A new Bayesian model is introduced to estimate dual-VNC Hounsfield units from contrast-enhanced DECT data. The model defines a prior distribution that describes tissue variations in terms of elemental compositions and mass densities. Multiple reference tissues are used to estimate variations across human tissues. A likelihood distribution is also defined to model the noise contained in CT data. The model is thoroughly validated in a simulated environment including 12 virtual patients under low and high iodine uptake scenarios, while incorporating noise and beam hardening effects. The eigentissue decomposition (ETD) technique is used to derive elemental compositions and parameters critical for radiotherapy from the dual-VNC images, such as electron density (ρ_e), particle stopping power (SPR), and photon energy absorption coefficient (EAC) Main results: The proposed method yields accurate voxelwise estimations for ρ_e, SPR, and EAC, with root mean square errors of 3.09%, 3.14%, and 1.34% for highly-enhanced tissues, compared to 5.93%, 6.39%, and 17.11% when the presence of contrast agent is ignored. It also demonstrates robustness to systematic shifts in tissue composition and bandwidth variations in the prior distribution, resulting in overall uncertainties down to 1.13%, 1.33%, and 0.86% for ρ_e, SPR, and EAC in soft tissues; 1.17%, 1.32%, and 1.34% in enhanced soft tissues; and 4.34%, 4.00%, and 2.50% in bone. Significance: The proposed method accurately derives radiotherapy parameters from contrast-enhanced DECT data and demonstrates robustness against systematic errors in reference data, highlighting its potential for clinical use.

The study of microcirculation can reveal important information related to pathology. Focusing on alterations that are represented by an obstruction of blood flow in microcirculatory regions may provide an insight into vascular biomarkers. The current in silico study assesses the capability of contrast enhanced ultrasound (CEUS) and super-resolution ultrasound imaging (SRU) flow-quantification to study occlusive actions in a microvascular bed, particularly the ability to characterise known and model induced flow behaviours. The aim is to investigate theoretical limits with the use of CEUS and SRU in order to propose realistic biomarker targets relevant for clinical diagnosis. Results from CEUS flow parameters display limitations congruent with prior investigations. Conventional resolution limits lead to signals dominated by large vessels, making discrimination of microvasculature specific signals difficult. Additionally, some occlusions lead to weakened parametric correlation against flow rate in the remainder of the network. Loss of correlation is dependent on the degree to which flow is redistributed, with comparatively minor redistribution correlating in accordance with ground truth measurements for change in mean transit time,dMTT(CEUS,R = 0.85; GT,R = 0.82) and change in peak intensity,dIp(CEUS,R = 0.87; GT,R = 0.96). Major redistributions, however, result in a loss of correlation, demonstrating that the effectiveness of time-intensity curve parameters is influenced by the site of occlusion. Conversely, results from SRU processing provides accurate depiction of the anatomy and dynamics present in the vascular bed, that extends to individual microvessels. Correspondence between model vessel structure displayed in SRU maps with the ground truth was>91%for cases of minor and major flow redistributions. In conclusion, SRU appears to be a highly promising technology in the quantification of subtle flow phenomena due ischaemia induced vascular flow redistribution.

Objective.This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity computed tomography (CT) images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiation while maintaining treatment accuracy and accelerating MR image acquisition. The primary focus is to determine the extent to which low-resolution MR images can be utilized to generate high-quality CT images through a systematic study of spatial resolution-dependent magnetic resonance imaging (MRI)-to-CT image conversion.Approach.Paired MRI-CT images were acquired from healthy control and tumor models, generated by injecting MDA-MB-231 and 4T1 tumor cells into the mammary fat pad of nude and BALB/c mice to ensure model diversification. To explore various MRI resolutions, we downscaled the highest-resolution MR image into three lower resolutions. Using a customized U-Net model, we automated region of interest masking for both MRI and CT modalities with precise alignment, achieved through three-dimensional affine paired MRI-CT registrations. Then our customized models, Nested U-Net GAN and Attention U-Net GAN, were employed to translate low-resolution MR images into high-resolution CT images, followed by evaluation with separate testing datasets.Main Results.Our approach successfully generated high-quality CT images (0.14²mm²) from both lower-resolution (0.28²mm²) and higher-resolution (0.14²mm²) MR images, with no statistically significant differences between them, effectively doubling the speed of MR image acquisition. Our customized GANs successfully preserved anatomical details, addressing the typical loss issue seen in other MRI-CT translation techniques across all resolutions of MR image inputs.Significance.This study demonstrates the potential of using low-resolution MR images to generate high-quality CT images, thereby reducing radiation exposure and expediting MRI acquisition while maintaining accuracy for radiotherapy.

We previously developed a FLASH planning framework for streamlined pin-ridge-filter (pin-RF) design, demonstrating its feasibility for single-energy proton FLASH planning. In this study, we refined the pin-RF design for easy assembly using reusable modules, focusing on its application in liver stereotactic ablative body radiotherapy (SABR). This framework generates an intermediate intensity-modulated proton therapy (IMPT) plan and translates it into step widths and thicknesses of pin-RFs for a single-energy FLASH plan. Parameters like energy spacing, monitor unit limit, and spot quantity were adjusted during IMPT planning, resulting in pin-RFs assembled using predefined modules with widths from 1 to 6 mm, each with a water-equivalent-thickness of 5 mm. This approach was validated on three liver SABR cases. FLASH doses, quantified using the FLASH effectiveness model at 1 to 5 Gy thresholds, were compared to conventional IMPT (IMPT-CONV) doses to assess clinical benefits. The highest demand for 6 mm width modules, moderate for 2-4 mm, and minimal for 1- and 5-mm modules were shown across all cases. At lower dose thresholds, the two-beam case reduced indicators including liver V21Gy and skin Dmax by >19.4%, while the three-beam cases showed reductions ≤11.4%, indicating the need for higher fractional beam doses for an enhanced FLASH effect. Positive clinical benefits were seen only in the two-beam case at the 5 Gy threshold. At the 1 Gy threshold, the two-beam FLASH plan outperformed the IMPT-CONV plan, reducing dose indicators for all relevant normal tissues by up to 31.2%. In contrast, the three-beam cases showed negative clinical benefits, with skin Dmax and liver V21Gy increasing by up to 17.4% due to lower fractional beam doses and closer beam arrangements. This study evaluated the feasibility of modularizing streamlined pin-RFs in single-energy proton FLASH planning for liver SABR, offering guidance on optimal module composition and strategies to enhance FLASH planning.