Physics in medicine and biology最新文献

Objective.Photon-counting x-ray detectors (PCDs) can produce dual-energy (DE) x-ray images of lung cancer in a single x-ray exposure. It is important to understand the factors that affect contrast, noise and the contrast-to-noise ratio (CNR). This study quantifies the dependence of CNR on tube voltage, energy threshold and patient thickness in single exposure, DE, bone-suppressed thoracic imaging with PCDs, and elucidates how the fundamental processes inherent in x-ray detection by PCDs contribute to CNR degradation.Approach.We modeled the DE CNR for five theoretical PCDs, ranging from an ideal PCD that detects every primary photon in the correct energy bin while rejecting all scattered radiation to a non-ideal PCD that suffers from charge-sharing and electronic noise, and detects scatter. CNR was computed as a function of tube voltage and high energy threshold for average and larger-than-average patients. Model predictions were compared with experimental data extracted from images acquired using a cadmium telluride (CdTe) PCD with two energy bins and analog charge summing for charge-sharing suppression. The imaging phantom simulated attenuation, scatter and contrast in lung nodule imaging. We quantified CNR improvements achievable with anti-correlated noise reduction (ACNR) and measured the range of exposure rates over which pulse pile-up is negligible.Main Results.The realistic model predicted overall trends observed in the experimental data. CNR improvements with ACNR were approximately five-fold, and modeled CNR-enhancements were on average within 10% of experiment. CNR increased modestly (i.e.<20%) when increasing the tube voltage from 90 kV to 130 kV. Optimal energy thresholds ranged from 50 keV to 70 keV across all tube voltages and patient thicknesses with and without ACNR. Quantum efficiency, electronic noise, charge sharing and scatter degraded CNR by ~50%. Charge sharing and scatter had the largest effect on CNR, degrading it by ~30% and ~15% respectively. Dead-time losses were less than 5% for patient exposure rates within the range of clinical exposure rates.Significance.In this study, we (1) employed analytical and computational models to assess the impact of different factors on CNR in single-exposure DE imaging with PCDs, (2) evaluated the accuracy of these models in predicting experimental trends, (3) quantified improvements in CNR achievable through ACNR and (4) determined the range of patient exposure rates at which pulse pile-up can be considered negligible. To the best of our knowledge, this study represents the first systematic investigation of single-exposure DE imaging of lung nodules with PCDs.

Objective: Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. Approach. We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. Main results. Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). Significance. Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

Objective.The energy deposition of photons and protons differs. It depends on the position in the proton Bragg peak (BP) and the linear energy transfer (LET) leading to a variable relative biological effectiveness (RBE). Here, we investigate LET dependent alterations on metabolic viability and proliferation of sarcoma and endothelium cell lines following proton irradiation in comparison to photon exposure.Approach.Using a multi-step range shifter, each column of a 96-well plate was positioned in a different depth along four BP curves with increasing intensities. The high-throughput experimental setup covers dose, LET, and RBE changes seen in a treatment field. Photon irradiation was performed to calculate the RBE along the BP curve. Two biological information out of one experiment were extracted allowing a correlation between metabolic viability and proliferation of the cells.Main results.The metabolic viability and cellular proliferation were column-wise altered showing a depth-dose profile. Endothelium cell viability recovers within 96 h post BP irradiation while sarcoma cell viability remains reduced. Highest RBE values were observed at the BP distal fall-off regarding proliferation of the sarcoma and endothelial cells.Significance.The high-throughput experimental setup introduced here (I) covers dose, LET, and RBE changes seen in a treatment field, (II) measures short-term effects within 48 h to 96 h post irradiation, and (III) can additionally be transferred to various cell types without time consuming experimental adaptations. Traditionally, RBE values are calculated from clonogenic cell survival. Measured RBE profiles strongly depend on physical characteristics such as dose and LET and biological characteristics for example cell type and time point. Metabolic viability and proliferation proofed to be in a similar effect range compared to clonogenic survival results. Based on limited data of combined irradiation with doxorubicin, future experiments will test combined treatment with systemic therapies applied in clinics e.g. cyclin-dependent inhibitors.

Objective: Bacterial biofilms represent a major challenge for effective antibiotic therapy as they confer physical and functional changes that protect bacteria from their surrounding environment. In this work, focused ultrasound in combination with cavitation nuclei was used to disrupt biofilms of Staphylococcus aureus and Pseudomonas aeruginosa, both of which are on the World Health Organization's priority list for new antimicrobial research. Approach: Single species biofilms were exposed to ultrasound (0.5 MHz centre frequency, 0.5-1.5 MPa peak rarefactional pressure, 200 cycle pulses, 5 Hz repetition frequency, 30 s duration), in the presence of two different types of cavitation nuclei. Quantitative passive acoustic mapping (PAM) was used to monitor cavitation emissions during treatment using a calibrated linear array. Main Results: It was observed that the cumulative energy of acoustic emissions during treatment was positively correlated with biofilm disruption, with differences between bacterial species attributed to differences in biofilm morphology. PCaN provided increased biofilm reduction compared to microbubbles due in large part to their persistence over the duration of ultrasound exposure. There was also good correlation between the spatial distribution of cavitation as characterized by PAM and the extent of biofilm disruption observed with microscopy. Significance: Collectively, the results from this work indicate the potential broad applicability of cavitation for eliminating biofilms of priority pathogens and the opportunity presented by Passive Acoustic Mapping for real-time monitoring of antimicrobial processes.

Objective. Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ∼8) or becomes unfeasible in daily clinical practice when the number is sufficiently large (e.g., >100). Our proposed deep learning (DL)-based method addressed this issue by avoiding the intermediate dose calculation step and instead directly predicting the percentile dose distribution from the nominal dose distribution using a DL model. In this study, we sought to validate this DL-based statistical robustness evaluation method for efficient and accurate robustness quantification in head and neck (H&N) intensity-modulated proton therapy with diverse beam configurations and multifield optimization.Approach. A dense, dilated 3D U-net was trained to predict the 5th and 95th percentile dose distributions of uncertainty scenarios using the nominal dose and planning CT images. The data set comprised proton therapy plans for 582 H&N cancer patients. Ground truth percentile values were estimated for each patient through 600 dose recalculations, representing randomly sampled uncertainty scenarios. The comprehensive comparisons of different models were conducted for H&N cancer patients, considering those with and without a beam mask and diverse beam configurations, including varying beam angles, couch angles, and beam numbers. The performance of our model trained based on a mixture of patients with H&N and prostate cancer was also assessed in contrast with models trained based on data specific for patients with cancer at either site.Results. The DL-based model's predictions of percentile dose distributions exhibited excellent agreement with the ground truth dose distributions. The average gamma index with 2 mm/2%, consistently exceeded 97% for both 5th and 95th percentile dose volumes. Mean dose-volume histogram error analysis revealed that predictions from the combined training set yielded mean errors and standard deviations that were generally similar to those in the specific patient training data sets.Significance. Our proposed DL-based method for evaluation of the robustness of proton therapy plans provides precise, rapid predictions of percentile dose for a given confidence level regardless of the beam arrangement and cancer site. This versatility positions our model as a valuable tool for evaluating the robustness of proton therapy across various cancer sites.

Objective.The objective of this study is to explore the capabilities of photon-counting computed tomography (PCCT) in simultaneously imaging and differentiating materials with close atomic numbers, specifically barium (Z= 56) and iodine (Z= 53), which is challenging for conventional computed tomography (CT).Approach.Experiments were conducted using a bench-top PCCT system equipped with a cadmium zinc telluride detector. Various phantom setups and contrast agent concentrations (1%-5%) were employed, along with a biological sample. Energy thresholds were tuned to the K-edge absorption energies of barium (37.4 keV) and iodine (33.2 keV) to capture multi-energy CT images. K-edge decomposition was performed using K-edge subtraction and principal component analysis (PCA) techniques to differentiate and quantify the contrast agents.Main results.The PCCT system successfully differentiated and accurately quantified barium and iodine in both phantom combinations and a biological sample, achieving high correlations (R2≈1) between true and reconstructed concentrations. PCA outperformed K-edge subtraction, particularly in the presence of calcium, by providing superior differentiation between barium and iodine.Significance.This study demonstrates the potential of PCCT for reliable, detailed imaging in both clinical and research settings, particularly for contrast agents with similar atomic numbers. The results suggest that PCCT could offer significant improvements in imaging quality over conventional CT, especially in applications requiring precise material differentiation.

Objective.The beam switching time and fractional dose influence the FLASH effect. A single-beam-per-fraction (SBPF) scheme using uniform fractional dose (UFD) has been proposed for FLASH- radiotherapy (FLASH-RT) to eliminate the beam switching time. Based on SBPF schemes, a fractionation dose optimization algorithm is proposed to optimize non-UFD plans to maximize the fractionation effect and dose-dependent FLASH effect.Approach.The UFD plan, containing five 236 MeV transmission proton beams, was optimized for 11 patients with peripheral lung cancer, with each beam delivering a uniform dose of 11 Gy to the target. Meanwhile, the non-UFD plan was optimized using fractionation dose optimization. To compare the two plans, the equivalent dose to 2 Gy (EQD2) for the target and normal tissues was calculated with anα/βratio of 10 and 3, respectively. Both UFD and non-UFD plans ensured that the target received an EQD2 of 96.3 Gy. To investigate the overall improvement in normal tissue sparing with the non-UFD plan, the FLASH-enhanced EQD2 was calculated.Main results.The fractional doses in non-UFD plans ranged between 5.0 Gy and 24.2 Gy. No significant differences were found in EQD2_2%and EQD2_98%of targets between UFD and non-UFD plans. However, theD_95%of the target in non-UFD plans was significantly reduced by 15.1%. The sparing effect in non-UFD plans was significantly improved. The FLASH-enhanced EQD2_meanin normal tissue and ipsilateral lung was significantly reduced by 3.5% and 10.4%, respectively, in non-UFD plans. The overall improvement is attributed to both the FLASH and fractionation effects.Significance.The fractionation dose optimization can address the limitation of multiple-beam FLASH-RT and utilize the relationship between fractional dose and FLASH effect. Consequently, the non-UFD scheme results in further improvements in normal tissue sparing compared to the UFD scheme, attributed to enhanced fractionation and FLASH effects.

Objective. Aim of this work is to illustrate and experimentally validate a model to evaluate the dielectric properties of biological tissues on a wide frequency band using the magnetic resonance imaging (MRI) technique.Approach. The dielectric behaviour of biological tissues depends on frequency, according to the so-called relaxation mechanisms. The adopted model derives the dielectric properties of biological tissues in the frequency range 10 MHz-20 GHz considering the presence of two relaxation mechanisms whose parameters are determined from quantities derived from MRI acquisitions. In particular, the MRI derived quantities are the water content and the dielectric properties of the tissue under study at the frequency of the MR scanner.Main results.The model was first theoretically validated on muscle and fat using literature data in the frequency range 10 MHz-20 GHz. Results showed capabilities of reconstructing dielectric properties with errors within 16%. Then the model was applied to ex vivo muscle and liver tissues, comparing the MRI-derived properties with data measured by the open probe technique in the frequency range 10 MHz-3 GHz, showing promising results.Significance. The use of medical techniques based on the application of electromagnetic fields (EMFs) is significantly increasing. To provide safe and effective treatments, it is necessary to know how human tissues react to the applied EMF. Since this information is embedded in the dielectric properties of biological tissues, an accurate and precise dielectric characterization is needed. Biological tissues are heterogenous, and their characteristics depend on several factors. Consequently, it is necessary to characterize dielectric propertiesin vivofor each specific patient. While this aim cannot be reached with traditional measurement techniques, through the adopted model these properties can be reconstructedin vivoon a wide frequency band from non-invasive MRI acquisitions.

Objective.This work explores the enhancement of ionization clustering and its radial dependence around a gold nanoparticle (NP), indicative of the induction of DNA lesions, a potential trigger for cell-death.Approach.Monte Carlo track structure simulations were performed to determine (a) the spectral fluence of incident photons and electrons in water around a gold NP under charged particle equilibrium conditions and (b) the density of ionization clusters produced on average as well as conditional on the occurrence of at least one interaction in the NP using Associated Volume Clustering. Absorbed dose was determined for comparison with a recent benchmark intercomparison. Reported quantities are normalized to primary fluence, allowing to establish a connection to macroscopic dosimetric quantities.Main results.The modification of the electron spectral fluence by the gold NP is minor and mainly occurs at low energies. The net fluence of electrons emitted from the NP is dominated by electrons resulting from photon interactions. Similar to the known dose enhancement, increased ionization clustering is limited to a distance from the NP surface of up to200nm. The number of clusters per energy imparted is increased at distances of up to150nm, and accordingly the enhancement in clustering notably surpasses that of dose enhancement. Smaller NPs cause noticeable peaks in the conditional frequency of clusters between50nm-100nmfrom the NP surface.Significance.This work shows that low energy electrons emitted by NPs lead to an increase of ionization clustering in their vicinity exceeding that of energy imparted. While the electron component of the radiation field plays an important role in determining the background contribution to ionization clustering and energy imparted, the dosimetric effects of NPs are governed by the interplay of secondary electron production by photon interaction and their ability to leave the NP.