Physics in Medicine & Biology

Objective. The International Commission on Radiological Protection (ICRP) decided to develop pregnant-female reference computational phantoms, including the maternal and fetal phantoms, through its 2007 general recommendations. Acknowledging the advantages of the mesh geometry, the ICRP decided to develop the pregnant-female mesh-type reference computational phantoms (MRCPs) for 8-, 10-, 15-, 20-, 25-, 30-, 35-, and 38-week fetal ages directly in the mesh format. As part of this process, the present study developed the mesh-type fetal phantoms. Approach. The reference blood-inclusive organ masses, elemental compositions, and densities were established based on various scientific literatures. Then, the phantoms were developed in accordance with the established reference dataset while reflecting the anatomical features of the developing fetus, such as fetal-age-specific anthropometric parameters, bone ossification, and contents formation time. Main results. The phantoms were developed in the tetrahedral-mesh format and can be implemented in the general-purpose Monte Carlo codes (i.e., Geant4, PHITS, MCNP6, and EGSnrc) without the necessity of the voxelization process. To explore the dosimetric impact of the developed phantoms, photon specific absorbed fractions (SAFs) were computed for energies between 10-2 to 101 MeV for the fetal liver and spleen as source regions and self-irradiation and cross-irradiation to the fetal brain, lungs, and urinary bladder wall as target regions. The SAFs showed the fetal-age-dependent dose trends (i.e., SAF decreases with increasing fetal age) due to organ masses increases via fetal growth. Significance. The mesh-type fetal phantoms, as part of the ICRP pregnant-female MRCPs, will be used to calculate reference dose coefficients for fetal members of the public for both the current and future ICRP general recommendations.

Objective. Spectral CT and material decomposition methods are crucial for precise material identification and quantitative composition analysis in preclinical research and clinical diagnosis. The empirical material decomposition method is widely used for its straightforward modeling approach, independence from spectral and detector response knowledge, and operational convenience. However, this method has limited decomposition accuracy and its precision depends on the choice of calibration phantoms. Approach. To address these issues, we propose an empirical correction decomposition method (ECDM). The innovation of this method lies in its ability to conveniently estimate and correct empirical decomposition errors using a specially designed calibration phantom. First, the specially designed calibration phantom for ECDM undergoes empirical decomposition initially to establish the relationship between decomposition errors and decomposition values. Then, ECDM estimates and corrects the error of empirical decomposition values. Main results. In the phantom experiments, ECDM improves the decomposition accuracy of empirical methods, effectively reducing the different decomposition errors caused by four different sizes of calibration phantoms from a maximum of 144\% to within 25\%. In the mouse experiments, ECDM achieves accurate quantification of contrast agents in biological tissues, outperforming the other two methods. The absolute error percentages of ECDM in the decomposition results of the two standard iodine solutions are both less than 5\%. Significance. ECDM significantly improves decomposition accuracy and reduces the impact of the size of the empirical calibration phantom. Overall, our method based on spectral CT is very convenient and practical for the quantitative measurement in biomedical applications.

Objective: Nuclear fragmentation generates a diverse dosimetric environment in the path of 12C ion beams. Concise parametrization of the beam’s composition is paramount for determining key correction factors in clinical dosimetry. This study sets out to provide such a parametrization based on detailed Monte Carlo simulations of clinically relevant 12C beams. Special attention was paid to the products of nuclear fragmentations and their importance in determining the stopping power ratios. Approach: Using the Monte Carlo simulation package GATE, the spectral fluence of all primary and secondary particles in water were computed at different depths for selected clinically relevant incident energies. Collision-stopping power data was taken from the ICRU90, SRIM and MSTAR database, as well as from previous publications. Main results: The choice of stopping power data was shown to have a bigger impact on the resulting stopping power ratio than the choice of physics lists for the simulations. Significance: A comprehensive analysis of the relationship between fragmentation and dosimetric data has been provided. This study compared different methods for determining spectral fluence-based stopping power ratios, which is essential for accurate ion beam dosimetry.

Objective: For calculating shielding in X-ray rooms, it is often assumed that the beams impinge perpendicularly on the protective barriers. This is not always true, but this premise simplifies the calculations and enhances protection by being a conservative calculation. In this work, a method for calculating radiation transmission through planar shielding that considers the obliquity of the incident beam is presented. Approach: The output of the method produces energy spectra according to the direction of radiation impinging on the shielding. Four angles of incidence on the barrier are considered, along with monoenergetic pencil beams with energies ranging from 10 to 150 keV and two materials: lead and concrete. The direction of emerging photons is discretized into 49 different direction vectors. Monte Carlo calculations are performed for thicknesses of 0.1, 0.5, and 1.0 mm of lead, and 1, 5, 10, and 15 cm of concrete. Additionally, a multilayer iterative method is implemented for calculating attenuation of other thicknesses. Main results: The distribution of radiant energy according to the coordinates of its directional vector illustrates the effect of the obliquity of the incidence and the significance of the shielding material employed. In the case of concrete, the dispersion of radiation away from the original direction of incidence is much more pronounced than in the case of lead at energies below its K-edge. The multilayer iterative method provides highly accurate values of transmitted radiant energy in both monoenergetic and polyenergetic beams, for both lead and concrete, across the various studied incidence directions. Significance: Considering the direction of the photons reaching a shield and the direction of the photons passing through it allows multilayer composite shielding calculations to closely approximate the calculation made for the composite shielding.

The clinical decision to establish if a patient with carotid disease should undergo surgical intervention is primarily based on the percent stenosis. Whilst this applies for high-grade stenosed vessels (>70%), it falls short for other cases. Due to the heterogeneity of plaque tissue, probing the mechanics of the tissue would likely provide further insights into why some plaques are more prone to rupture. Mechanical characterization of such tissue is nontrivial, however, due to the difficulties in collecting fresh, intact plaque tissue and using physiologically relevant mechanical testing of such material. The use of polyvinyl alcohol (PVA) cryogel is thus highly convenient because of its acoustic properties and tunable mechanical properties. Methods: The aim of this study is to demonstrate the potential of polyvinyl alcohol phantoms to simulate atherosclerotic features. In addition, a testing and simulation framework is developed for full PVA vessel material characterization using ring tensile testing and inflation testing combined with non-invasive ultrasound imaging and computational modelling. Results: Strain stiffening behavior was observed in PVA through ring tensile tests, particularly at high (n=6) freeze-thaw cycles. Inflation testing of bi-layered phantoms featuring lipid pool inclusions demonstrated high strains at shoulder regions. The application of an inverse finite element framework successfully recovered boundaries and determined the shear moduli for the PVA wall to lie within the range 27 kPa to 53 kPa. Conclusion: The imaging-modelling framework presented facilitates the use and characterization of arterial mimicking phantoms to further explore plaque rupture. It also shows translational potential for non-invasive mechanical characterization of atherosclerotic plaques to improve the identification of clinically relevant metrics of plaque vulnerability.

Endometriosis, affecting about 10% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery or the analysis of T1/T2 MRI images, with the latter being quicker and cheaper but less accurate. A key diagnostic sign of endometriosis is the obliteration of the Pouch of Douglas (POD). However, even experienced clinicians struggle with accurately classifying POD obliteration from MRI images, which complicates the training of reliable AI models. In this paper, we introduce the Human-AI Collaborative Multi-modal Multi-rater Learning (HAICOMM) methodology to address the challenge above. HAICOMM is the first method that explores three important aspects of this problem: 1) multi-rater learning to extract a cleaner label from the multiple "noisy" labels available per training sample; 2) multi-modal learning to leverage the presence of T1/T2 MRI images for training and testing; and 3) human-AI collaboration to build a system that leverages the predictions from clinicians and the AI model to provide more accurate classification than standalone clinicians and AI models. Presenting results on the multi-rater T1/T2 MRI endometriosis dataset that we collected to validate our methodology, the proposed HAICOMM model outperforms an ensemble of clinicians, noisy-label learning models, and multi-rater learning methods.

Plane wave imaging (PWI) is fast, but limited by poor imaging quality. Coherent plane wave compounding (CPWC) improves image quality but decrease frame rate. In this study, we propose a modified CycleGAN model that combines a residual attention module with a space-frequency dual-domain discriminator, termed RADD-CycleGAN, to rapidly reconstruct high-quality ultrasound images. To enhance the ability to reconstruct image details, we specially design a process of hybrid dynamic and static channel selection followed by the frequency domain discriminator. The low-quality images are generated by the 3-angle CPWC, while the high-quality images are generated as real images (ground truth) by the 75-angle CPWC. The training set includes unpaired images, whereas the images in the test set are paired to verify the validity and superiority of the proposed model. Finally, we respectively design ablation and comparison experiments to evaluate the model performance. Compared with the basic CycleGAN, our proposed method reaches a better performance, with a 7.8% increase in the peak signal-to-noise ratio (PSNR) and a 22.2% increase in the structural similarity index measure (SSIM). The experimental results show that our method achieves the best unsupervised reconstruction from low quality images in comparison with several state-of-the-art methods.

Objective. Channelized Hotelling model observers are efficient at simulating the human observer visual performance in medical imaging detection tasks. However, channelized Hotelling observers (CHO) are subject to statistical biases from zero-signal and finite-sample effects. The point estimate of the d′ value is also not always symmetric with exact confidence interval (CI) bounds determined for the infinitely trained CHO. A method for correcting these statistical biases and CI asymmetry is studied. Approach. CHO d′ values and CI bounds with hold-out and resubstitution methods were computed for a range of 200 × 200 pixels images from 20 to 10 000 images for 10, 40 and 96 channels from a set of 20 000 images with gaussian coloured simulated noise and simulated signal. The median of the non-central F cumulative distribution (F′), which is the CHO underlying statistical behaviour for the resubstitution method, was computed, and compared to d′ values and CI bounds. A set of experimental data was used to evaluate F′ median values. Main results. The F′ median allows to get accurate corrected simulated d′ values down to zero-signals. For small d′ values, the variation of d′ values with the inverse of number of images is not linear while the F′ median allows a good correction in such conditions. The F′ median is also inherently symmetric with regards to the CI. With experimental data, F′ median values in a range of about 1–10 d′ values were within −0.8% to 4.7% of linearly extrapolated values at an infinite number of images. Significance. The F′ median correction is an effective simultaneous correction of the zero-signal statistical bias and finite-sample statistical bias, and of CI asymmetry of CHO.

Objective. Achieving ultra-precise temporal resolution in ionizing radiation detection is essential, particularly in positron emission tomography, where precise timing enhances signal-to-noise ratios and may enable reconstruction-less imaging. A promising approach involves utilizing ultrafast modulation of the complex refractive index, where sending probe pulses to the detection crystals will result in changes in picoseconds (ps), and thus a sub—10 ps coincidence time resolution can be realized. Towards this goal, here, we aim to first measure the ps changes in probe pulses using an ionizing radiation source with high time resolution. Approach. We used relativistic, ultrafast electrons to induce complex refractive index and use probe pulses in the near-infrared (800 nm) and terahertz (THz, 300 µm) regimes to test the hypothesized wavelength-squared increase in absorption coefficient in the Drude free-carrier absorption model. We measured BGO, ZnSe, BaF2, ZnS, PBG, and PWO with 1 mm thickness to control the deposited energy of the 3 MeV electrons, simulating ionization energy of the 511 keV photons. Main results. Both with the 800 nm and THz probe pulses, transmission decreased across most samples, indicating the free carrier absorption, with an induced signal change of 11% in BaF2, but without the predicted Drude modulation increase. To understand this discrepancy, we simulated ionization tracks and examined the geometry of the free carrier distribution, attributing the mismatch in THz modulations to the sub-wavelength diameter of trajectories, despite the lengths reaching 500 µm to 1 mm. Additionally, thin samples truncated the final segments of the ionization tracks, and the measured initial segments have larger inter-inelastic collision distances due to lower stopping power (dE/dx) for high-energy electrons, exacerbating diffraction-limited resolution. Significance. Our work offers insights into ultrafast radiation detection using complex refractive index modulation and highlights critical considerations in sample preparation, probe wavelength, and probe-charge carrier coupling scenarios.

Objective: Currently, treatment planning in cancer hadrontherapy relies on dose-volume criteria and physical quantities constraints. However, incorporating biologically related models of Tumor Control Probability (TCP) and of Normal Tissue Complication Probability (NTCP) would help further minimizing adverse tissue reactions, and would allow achieving a more patient-specific strategy. The aim of this work was therefore the development of a mechanistic approach to predict NTCP for late tissue reactions following ion irradiation. Approach: A dataset on the tolerance of the rat spinal cord was considered, providing NTCP (for paresis of at least grade II) experimental data following irradiation by photons, protons, helium and carbon ions, under different fractionation schemes. The photon data were fit by a mechanistic NTCP model with four parameters, called Critical Element Model; this allowed fixing the two parameters that only depend on the tissue features. Afterwards, the two parameters depending on radiation quality were predicted by applying the BIANCA biophysical model, for each ion type and dose-averaged LET value. Main results: The predicted NTCP curves for ion irradiation were tested against the ion experimental data, by Chi-Square and p-value calculations. The model passed a significance test at 1% for all the datasets, and 5% for 13 out of 16 datasets, thus showing a good predictive power. The RBE was also calculated and compared with the data for the endpoint of NTCP equal to 50%, and a considerable discrepancy with the commonly calculated RBE for cell survival was shown. Significance: This study highlights the importance of considering the endpoint of interest when computing the RBE, through the application of a NTCP model, and it represents a first step towards the development of an approach to improve treatment plan optimization in therapy. To this aim, the approach needs to be extended to other endpoints and to be applied to patients’ data.

Objective. To develop a nonlinear, model-based parameter estimation method directly from incomplete measurements in k − w space for robust spectral analysis in highly accelerated chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI). Approach. A CEST-specific, separable nonlinear model, which describes spectral decomposition using multi-pool Lorentzian functions (conventional magnetization transfer (MT), direct saturation of water signals (DS), amide, amine, and nuclear Overhauser effect) derived from the steady-state Bloch McConnel equation, is incorporated into a measurement model in CEST MRI. Furthermore, signal drop in saturation transfer experiments is formulated by an additional, separable nonlinear spectral prior indicating that the symmetric z-spectra synthesized using conventional MT and DS always remain higher or equal to the whole z-spectra with all pools. Given the above considerations, linear and nonlinear parameters in the proposed method are estimated in an alternating fashion directly from highly incomplete measurements in k − w space by solving a constrained optimization problem with the physics-constrained spectral priors while imposing additional sparsity priors on spatial parameter maps. Main results. Compared with conventional methods, the proposed method yields clearer delineation of tumor-specific CEST maps without apparent artifact and noise. Significance. We successfully demonstrated the feasibility of the proposed method for CEST MRI with highly incomplete measurements thus enabling high-resolution whole brain CEST MRI in clinically reasonable imaging time.

Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufacturers incorporate various post-acquisition image-processing techniques into the scanner’s computer software tools for different post-processing tasks. These tools provide a final image of adequate quality and essential features for accurate clinical reporting and predictive interpretation for better treatment planning. Different post-acquisition image-processing tasks for MRI quality enhancement include noise removal, motion artifact reduction, magnetic bias field correction, and eddy electric current effect removal. Recently, deep learning (DL) methods have shown great success in many research fields, including image and video applications. DL-based data-driven feature-learning approaches have great potential for MR image denoising and image-quality-degrading artifact correction. Recent studies have demonstrated significant improvements in image-analysis tasks using DL-based convolutional neural network techniques. The promising capabilities and performance of DL techniques in various problem-solving domains have motivated researchers to adapt DL methods to medical image analysis and quality enhancement tasks. This paper presents a comprehensive review of DL-based state-of-the-art MRI quality enhancement and artifact removal methods for regenerating high-quality images while preserving essential anatomical and physiological feature maps without destroying important image information. Existing research gaps and future directions have also been provided by highlighting potential research areas for future developments, along with their importance and advantages in medical imaging.

Objective This study aims to determine a margin and robustness setting for treating locally advanced cervical cancer (LACC) with a library-of-plan based online-adaptive intensity-modulated proton therapy (IMPT). Approach We analyzed 13 LACC patients with delineated planning and weekly repeat CT scans. For each patient, 120 IMPT treatment of 25 fractions were simulated with a library-of-plans approach. Six different robustness settings (2 to 7 mm set-up robustness (SR) plus 3% range robustness (RR)) were used to create those 120 IMPT plan. Each fraction was simulated with a weekly repeat CT scan, combined with the sampling of inter- and intrafraction treatment uncertainties. The fraction doses were accumulated to obtain a treatment dose to the target volumes, distinguishing between the low-risk clinical target volume (CTV-T-LR) and the elective CTV (CTV-E). If one of the two targets obtained an adequate coverage for more than 90% of the treatments, different anisotropic margins were sampled on top of the robustness setting to the other target to obtain the Pareto-optimal margin in terms of adequate coverage versus increase in target volume. Main results The percentage of treatments that reach the dose criterion V42.75Gy>95% for the CTV-T-LR was 22.3%, 28.5%, 51.2%, 73.1%, 85.3%, and 90.0% for 2, 3, 4, 5, 6, and 7 mm SR plus 3% RR and for the CTV-E, this percentage was 60.4%, 73.8%, 86.5%, 92.3%, 96.9%, and 98.5%. The Pareto-optimal margin combined with a 5mm/3% robustness setting for the CTV-T-LR with an adequate coverage for >90% of the treatment was given by {0, 1, 0, 3, 3, 0} mm in the left, right, anterior, posterior, cranial, caudal direction. Significance Our study evaluated combinations of robustness and anisotropic margin settings for IMPT for LACC. With 5 mm SR and 3% RR for CTV-E and CTV-T-LR plus a margin to the CTV-T-LR of {0, 1, 0, 3, 3, 0} mm in LRAPCC ensured an adequate coverage for >90% of the simulated IMPT treatments.

Objective. Spot size stability is crucial in pencil beam scanning (PBS) proton therapy, and variations in spot size can disrupt dose distributions. Recently, a novel proton beam delivery method known as DynamicARC PBS scanning has been introduced. The current study investigates the dosimetric impact of spot size errors in DynamicARC proton therapy for head and neck (HNC), prostate, and lung cancers. Approach. Robustly optimized DynamicARC proton therapy plans were created for HNC (n = 4), prostate (n = 4), and lung (n = 4) cancer patients. Spot size errors of ±10%, ±15%, and ±20% were introduced, and their effects on target coverage (D95% and D99%), homogeneity index (HI), and organ-at-risk doses were analyzed across different cancer sites. Main Results. HNC and lung cancer plans showed greater vulnerability to spot size errors, with reductions in target coverage of up to 4.8% under −20% spot size errors. Dose homogeneity was also more affected in these cases, with HI degrading by 0.12 in lung cancer. Prostate cancer demonstrated greater resilience to spot size variations, even under errors of ±20%. For spot size errors ±10%, the oral cavity, parotid glands, and constrictor muscles experienced Dmean deviations within ±1.2%, while deviations were limited to ±0.5% for D10% of the bladder and rectum and ±0.3% for V20 Gy(RBE) of the lungs. The robustness analysis indicated that lung cancer plans were most susceptible to robustness reductions caused by spot size errors, while HNC plans demonstrated moderate sensitivity. Conversely, prostate cancer plans demonstrated high robustness, experiencing only minimal reductions in target coverage. Significance. While the ±10% spot size tolerance is appropriate in majority of the cases, lung cancer plans may require more stringent criteria. As DynamicARC becomes clinically available, measuring spot size errors in practice will be essential to validate these findings and refine tolerance thresholds for clinical use.

Objective. Linear energy transfer (LET) verification was conducted using a silicon-on-insulator (SOI) microdosimeter during the commissioning of LET-optimized carbon-ion radiotherapy (CIRT). This advanced treatment technique is expected to improve local control rates, especially in hypoxic tumors. Approach. An SOI microdosimeter with a cylindrical sensitive volume of 30 μm diameter and 5 μm thickness was used. Simple cubic plans and patient plans using the carbon-ion beams were created by treatment planning system, and the calculated LETd values were compared with the measured LETd values obtained by the SOI microdosimeter. Main results. Reasonable agreement between the measured and calculated LETd was seen in the plateau region of depth LETd profile, whereas the measured LETd were below the calculated LETd in the peak region, specifically where LETd exceeds 75 keV μm⁻¹. The discrepancy in the peak region may arise from the uncertainties in the calibration process of the SOI microdosimeter. Excluding the peak region, the average ratio and standard deviation between measured and calculated LETd values were 0.996 and 7%, respectively. Significance. This verification results in the initiation of clinical trials for LET-optimized CIRT at QST Hospital, National Institutes for Quantum Science and Technology.

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). All TParch and TPclin plans were acceptable, but planning aims were more frequently attained with patient-tailored configurations (115/132 versus 100/132 instances). Median CTVIR D98 and bladder D2cm3 doses significantly improved ( p< 0.001 and p< 0.01 respectively) in TParch plans in comparison with TPclin plans, 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: 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.

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

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.

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. 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 - 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. 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.

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 (0.14² mm²) from both lower-resolution (0.28² mm²) and higher-resolution (0.14² mm²) 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