3D-printing inherently MRI-visible accessories in aiding MRI-guided biopsies.

BACKGROUND: 3D printers have gained prominence in rapid prototyping and viable in creating dimensionally accurate objects that are both safe within a Magnetic Resonance Imaging (MRI) environment and visible in MRI scans. A challenge when making MRI-visible objects using 3D printing is that hard plastics are invisible in standard MRI scans, while fluids are not. So typically, a hollow object will be printed and filled with a liquid that will be visible in MRI scans. This poses an engineering challenge however since objects created using traditional Fused Deposition Modeling (FDM) 3D-printing techniques are prone to leakage. Digital Light Processing (DLP) is a relatively modern and affordable 3D-printing technique using UV-hardened resin, capable of creating objects that are inherently liquid-tight. When printing hollow parts using DLP printers, one typically requires adding drainage holes for uncured liquid resin to escape during the printing process. If this is not done liquid resin will remain inside the object, which in our application is the desired outcome. PURPOSE: We devised a method to produce an inherently MRI-visible accessory using DLP technology with low dimensional tolerance to facilitate MRI-guided breast biopsies. METHODS: By hollowing out the object without adding drainage holes and tuning printing parameters such as z-lift distance to retain as much uncured liquid resin inside as possible through surface tension, objects that are inherently visible in MRI scans can be created without further post-processing treatment. RESULTS: Objects created through our method are simple and inexpensive to recreate, have minimal manufacturing steps, and are shown to be dimensionally exact and inherently MRI visible to be directly used in various applications without further treatment. CONCLUSION: Our proposed method of manufacturing objects that are inherently both MRI safe, and MRI visible. The proposed process is simple and does not require additional materials and tools beyond a DLP 3D-printer. With only an inexpensive DLP 3D-printer kit and basic cleaning and sanitation materials found in the hospital, we have demonstrated the viability of our process by successfully creating an object containing fine structures with low spatial tolerances used for MRI-guided breast biopsies.

Accuracy of 3D real-time MRI temperature mapping in gel phantoms during microwave heating.

BACKGROUND: Interventional magnetic resonance imaging (MRI) can provide a comprehensive setting for microwave ablation of tumors with real-time monitoring of the energy delivery using MRI-based temperature mapping. The purpose of this study was to quantify the accuracy of three-dimensional (3D) real-time MRI temperature mapping during microwave heating in vitro by comparing MRI thermometry data to reference data measured by fiber-optical thermometry. METHODS: Nine phantom experiments were evaluated in agar-based gel phantoms using an in-room MR-conditional microwave system and MRI thermometry. MRI measurements were performed for 700 s (25 slices; temporal resolution 2 s). The temperature was monitored with two fiber-optical temperature sensors approximately 5 mm and 10 mm distant from the microwave antenna. Temperature curves of the sensors were compared to MRI temperature data of single-voxel regions of interest (ROIs) at the sensor tips; the accuracy of MRI thermometry was assessed as the root-mean-squared (RMS)-averaged temperature difference. Eighteen neighboring voxels around the original ROI were also evaluated and the voxel with the smallest temperature difference was additionally selected for further evaluation. RESULTS: The maximum temperature changes measured by the fiber-optical sensors ranged from 7.3 K to 50.7 K. The median RMS-averaged temperature differences in the originally selected voxels ranged from 1.4 K to 3.4 K. When evaluating the minimum-difference voxel from the neighborhood, the temperature differences ranged from 0.5 K to 0.9 K. The microwave antenna and the MRI-conditional in-room microwave generator did not induce relevant radiofrequency artifacts. CONCLUSION: Accurate 3D real-time MRI temperature mapping during microwave heating with very low RMS-averaged temperature errors below 1 K is feasible in gel phantoms. RELEVANCE STATEMENT: Accurate MRI-based volumetric real-time monitoring of temperature distribution and thermal dose is highly relevant in clinical MRI-based interventions and can be expected to improve local tumor control, as well as procedural safety by extending the limits of thermal (e.g., microwave) ablation of tumors in the liver and in other organs. KEY POINTS: Interventional MRI can provide a comprehensive setting for the microwave ablation of tumors. MRI can monitor the microwave ablation using real-time MRI-based temperature mapping. 3D real-time MRI temperature mapping during microwave heating is feasible. Measured temperature errors were below 1 °C in gel phantoms. The active in-room microwave generator did not induce any relevant radiofrequency artifacts.

Estimation of the Proton Resonance Frequency Coefficient in Agar-based Phantoms.

Aim: Agar-based phantoms are popular in high intensity focused ultrasound (HIFU) studies, with magnetic resonance imaging (MRI) preferred for guidance since it provides temperature monitoring by proton resonance frequency (PRF) shift magnetic resonance (MR) thermometry. MR thermometry monitoring depends on several factors, thus, herein, the PRF coefficient of agar phantoms was estimated. Materials and Methods: Seven phantoms were developed with varied agar (2, 4, or 6% w/v) or constant agar (6% w/v) and varied silica concentrations (2, 4, 6, or 8% w/v) to assess the effect of the concentration on the PRF coefficient. Each phantom was sonicated using varied acoustical power for a 30 s duration in both a laboratory setting and inside a 3T MRI scanner. PRF coefficients were estimated through linear trends between phase shift acquired using gradient sequences and thermocouple-based temperatures changes. Results: Linear regression (R 2 = 0.9707-0.9991) demonstrated a proportional dependency of phase shift with temperature change, resulting in PRF coefficients between -0.00336 ± 0.00029 and -0.00934 ± 0.00050 ppm/°C for the various phantom recipes. Weak negative linear correlations of the PRF coefficient were observed with increased agar. With silica concentrations, the negative linear correlation was strong. For all phantoms, calibrated PRF coefficients resulted in 1.01-3.01-fold higher temperature changes compared to the values calculated using a literature PRF coefficient. Conclusions: Phantoms developed with a 6% w/v agar concentration and doped with 0%-8% w/v silica best resemble tissue PRF coefficients and should be preferred in HIFU studies. The estimated PRF coefficients can result in enhanced MR thermometry monitoring and evaluation of HIFU protocols.

Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.

OBJECTIVE: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. MATERIALS AND METHODS: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. RESULTS: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. CONCLUSION: Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

Corrigendum: Comparison of 3DCRT and IMRT out-of-field doses in pediatric patients using Monte Carlo simulations with treatment planning system calculations and measurements.

[This corrects the article DOI: 10.3389/fonc.2022.879167.].

Dataset of received optical power on pork meat for optical in-body communications studies.

The utilization of actual biological tissue (e.g., pork meat samples) and tissue-mimicking phantoms for optical-based in-body data and energy transfer studies is crucial. Near-infrared (NIR) light, a part of the light spectrum that falls between visible light and infrared, is highly advantageous as a carrier for data transmission due to its superior ability to penetrate biological tissue, for instance, the human body. Using pork meat samples as a propagation medium for prolonged experiments is challenging due to the deterioration of meat quality caused by drying in the temperature chamber. Typically, a controlled-temperature chamber can be utilized to warm the tissue samples to 37 °C. Some experiments need to be carried out over long periods, in some cases exceeding one hour, including the demonstration of transmitting large-size data (e.g., high-definition images or videos) in real-time through biological tissue using NIR LED. Moreover, for statistical analysis, some experiments need to be repeated, therefore degradation of the tissue sample should be avoided. Furthermore, experiments may also encompass investigations into optical wireless power transfer (OWPT) conducted on biological tissues under NIR illumination and employing energy harvester-based commercial photovoltaic cells (PV) at the receiving ends, which would require a long time to charge the storage (e.g., battery or supercapacitor) fully. Using phantoms for such an experiment is also not straightforward, requiring careful consideration, such as standardization issues. One possible approach to address this challenge is to conduct experiments in a free-space environment (e.g., sample-free) while guaranteeing that the optical power received in free-space is equivalent to that obtained through biological tissue. This can be achieved by carefully controlling the LED's current and arranging the optical channel's distance to achieve comparable results. The received optical power is the primary parameter for comparing free-space and biological tissue setups. This dataset provides settings for NIR LEDs ( P m a x = 375 mW and λ = 810 nm), allowing in-body communication experiments in a free-space environment. The LED's current settings in this dataset (free-space) are equivalent in comparison to those used in a test-bed using biological tissue with 5 (five) different variations of LED currents (i.e., 500 mA, 400 mA, 300 mA, 200 mA, and 100 mA). The dataset consists of six pork meat samples with different thicknesses and fat-muscle layer compositions, resulting in 36 data points. This dataset holds significant potential for reuse in any biomedical research, particularly in the fields of in-body communication and energy transfer utilizing light.

Customization of Computed Tomography Radio-Opacity in 3D-Printed Contrast-Injectable Tumor Phantoms.

Medical Imaging Phantoms (MIPs) calibrate imaging devices, train medical professionals, and can help procedural planning. Traditional MIPs are costly and limited in customization. Additive manufacturing allows for customizable, patient-specific phantoms. This study examines the CT attenuation characteristics of contrast-injectable, chambered 3D-printed phantoms to optimize tissue-mimicking capabilities. A MIP was constructed from a CT of a complex pelvic tumor near the iliac bifurcation. A 3D reconstruction of these structures composed of three chambers (aorta, inferior vena cava, tumor) with ports for contrast injection was 3D printed. Desired attenuations were 200 HU (arterial I), 150 HU (venous I), 40 HU (tumor I), 150 HU (arterial II), 90 HU (venous II), and 400 HU (tumor II). Solutions of Optiray 350 and water were injected, and the phantom was scanned on CT. Attenuations were measured using ROIs. Mean attenuation for the six phases was as follows: 37.49 HU for tumor I, 200.50 HU for venous I, 227.92 HU for arterial I, 326.20 HU for tumor II, 91.32 HU for venous II, and 132.08 HU for arterial II. Although the percent differences between observed and goal attenuation were high, the observed relative HU differences between phases were similar to goal HU differences. The observed attenuations reflected the relative concentrations of contrast solutions used, exhibiting a strong positive correlation with contrast concentration. The contrast-injectable tumor phantom exhibited a useful physiologic range of attenuation values, enabling the modification of tissue-mimicking 3D-printed phantoms even after the manufacturing process.

Accuracy of ultra-high resolution and virtual non-calcium reconstruction algorithm for stenosis evaluation with photon-counting CT: results from a dynamic phantom study.

BACKGROUND: We compared ultra-high resolution (UHR), standard resolution (SR), and virtual non-calcium (VNCa) reconstruction for coronary artery stenosis evaluation using photon-counting computed tomography (PC-CT). METHODS: One vessel phantom (4-mm diameter) containing solid calcified lesions with 25% and 50% stenoses inside a thorax phantom with motion simulation underwent PC-CT using UHR (0.2-mm slice thickness) and SR (0.6-mm slice thickness) at heart rates of 60 beats per minute (bpm), 80 bpm, and 100 bpm. A paired t-test or Wilcoxon test with Bonferroni correction was used. RESULTS: For 50% stenosis, differences in percent mean diameter stenosis between UHR and SR at 60 bpm (51.0 vs 60.3), 80 bpm (51.7 vs 59.6), and 100 bpm (53.7 vs 59.0) (p ≤ 0.011), as well as between VNCa and SR at 60 bpm (50.6 vs 60.3), 80 bpm (51.5 vs 59.6), and 100 bpm (53.7 vs 59.0) were significant (p ≤ 0.011), while differences between UHR and VNCa at all heart rates (p ≥ 0.327) were not significant. For 25% stenosis, differences between UHR and SR at 60 bpm (28.0 vs 33.7), 80 bpm (28.4 vs 34.3), and VNCa vs SR at 60 bpm (29.1 vs 33.7) were significant (p ≤ 0.015), while differences for UHR vs SR at 100 bpm (29.9 vs 34.0), as well as for VNCa vs SR at 80 bpm (30.7 vs 34.3) and 100 bpm (33.1 vs 34.0) were not significant (p ≥ 0.028). CONCLUSION: Stenosis quantification accuracy with PC-CT improved using either UHR acquisition or VNCa reconstruction. RELEVANCE STATEMENT: PC-CT offers to scan with UHR mode and the reconstruction of VNCa images both of them could provide improved coronary stenosis quantification at increased heart rates, allowing a more accurate stenosis grading at low and high heart rates compared to SR. KEY POINTS: Evaluation of coronary stenosis with conventional CT is challenging at high heart rates. PC-CT allows for scanning with ECG-gated UHR and SR modes. UHR and VNCa images were compared in a dynamic phantom. UHR improves stenosis quantification up to 100 bpm. VNCa reconstruction improves stenosis evaluation up to 80 bpm.

Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging.

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used.

Development of a patient-specific model of the human coronary system for percutaneous transluminal coronary angioplasty balloon catheter training and testing.

BACKGROUND: To treat stenosed coronary arteries, percutaneous transluminal coronary angioplasty (PTCA) balloon catheters must combine pushability, trackability, crossability, and rewrap behavior. The existing anatomic track model (ASTM F2394) for catheter testing lacks 3D morphology, vessel tortuosity, and compliance, making evaluating performance characteristics difficult. This study aimed to develop a three-dimensional patient-specific phantom (3DPSP) for device testing and safe training for interventional cardiologists. METHODS: A range of silicone materials with different shore hardnesses (00-30-45 A) and wall thicknesses (0.5 mm, 1 mm, 2 mm) were tested to determine compliance for creating coronary vessel phantoms. Compliance was assessed using optical coherence tomography (OCT) and compared to values in the literature. Stenosis was induced using multilayer casting and brushing methods, with gypsum added for calcification. The radial tensile properties of the samples were investigated, and the relationship between Young's modulus and compliance was determined. Various methods have been introduced to approximate the friction between silicone and real coronary vessel walls. Computerized tomography (CT) scans were used to obtain patient-specific anatomy from the femoral artery to the coronary arteries. Artery lumens were segmented from the CT scans to create dissolvable 3D-printed core models. RESULTS: A 15A shore hardness silicone yielded an experimental compliance of 12.3-22.4 m m 2 mmHg · 10 3 for stenosed tubes and 14.7-57.9 m m 2 mmHg · 10 3 for uniform tubes, aligning closely with the literature data (6.28-40.88 m m 2 mmHg · 10 3 ). The Young's modulus ranged from 43.2 to 75.5 kPa and 56.6-67.9 kPa for the uniform and calcified materials, respectively. The dependency of the compliance on the wall thickness, Young's modulus, and inner diameter could be shown. Introducing a lubricant reduced the silicone friction coefficient from 0.52 to 0.13. The 3DPSP was successfully fabricated, and comparative analyses were conducted among eight commercially available catheters. CONCLUSION: This study presents a novel method for crafting 3DPSPs with realistic mechanical and frictional properties. The proposed approach enables the creation of comprehensive and anatomically precise setups spanning the right femoral artery to the coronary arteries, highlighting the importance of such realistic environments for advancing medical device development and fostering safe training conditions.

Non-contact elasticity contrast imaging using photon counting.

Significance: Tissues' biomechanical properties, such as elasticity, are related to tissue health. Optical coherence elastography produces images of tissues based on their elasticity, but its performance is constrained by the laser power used, working distance, and excitation methods. Aim: We develop a new method to reconstruct the elasticity contrast image over a long working distance, with only low-intensity illumination, and by non-contact acoustic wave excitation. Approach: We combine single-photon vibrometry and quantum parametric mode sorting (QPMS) to measure the oscillating backscattered signals at a single-photon level and derive the phantoms' relative elasticity. Results: We test our system on tissue-mimicking phantoms consisting of contrast sections with different concentrations and thus stiffness. Our results show that as the driving acoustic frequency is swept, the phantoms' vibrational responses are mapped onto the photon-counting histograms from which their mechanical properties-including elasticity-can be derived. Through lateral and longitudinal laser scanning at a fixed frequency, a contrast image based on samples' elasticity can be reliably reconstructed upon photon level signals. Conclusions: We demonstrated the reliability of QPMS-based elasticity contrast imaging of agar phantoms in a long working distance, low-intensity environment. This technique has the potential for in-depth images of real biological tissue and provides a new approach to elastography research and applications.

How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T.

Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.

Detectability of intracranial vessel wall atherosclerosis using black-blood spectral CT: a phantom and clinical study.

BACKGROUND: Computed tomography (CT) is the usual modality for diagnosing stroke, but conventional CT angiography reconstructions have limitations. METHODS: A phantom with tubes of known diameters and wall thickness was scanned for wall detectability, wall thickness, and contrast-to-noise ratio (CNR) on conventional and spectral black-blood (SBB) images. The clinical study included 34 stroke patients. Diagnostic certainty and conspicuity of normal/abnormal intracranial vessels using SBB were compared to conventional. Sensitivity/specificity/accuracy of SBB and conventional were compared for plaque detectability. CNR of the wall/lumen and quantitative comparison of remodeling index, plaque burden, and eccentricity were obtained for SBB imaging and high-resolution magnetic resonance imaging (hrMRI). RESULTS: The phantom study showed improved detectability of tube walls using SBB (108/108, 100% versus conventional 81/108, 75%, p < 0.001). CNRs were 75.9 ± 62.6 (mean ± standard deviation) for wall/lumen and 22.0 ± 17.1 for wall/water using SBB and 26.4 ± 15.3 and 101.6 ± 62.5 using conventional. Clinical study demonstrated (i) improved certainty and conspicuity of the vessels using SBB versus conventional (certainty, median score 3 versus 0; conspicuity, median score 3 versus 1 (p < 0.001)), (ii) improved sensitivity/specificity/accuracy of plaque (≥ 1.0 mm) detectability (0.944/0.981/0.962 versus 0.239/0.743/0.495) (p < 0.001), (iii) higher wall/lumen CNR of SBB of (78.3 ± 50.4/79.3 ± 96.7) versus hrMRI (18.9 ± 8.4/24.1 ± 14.1) (p < 0.001), and (iv) excellent reproducibility of remodeling index, plaque burden, and eccentricity using SBB versus hrMRI (intraclass correlation coefficient 0.85-0.94). CONCLUSIONS: SBB can enhance the detectability of intracranial plaques with an accuracy similar to that of hrMRI. RELEVANCE STATEMENT: This new spectral black-blood technique for the detection and characterization of intracranial vessel atherosclerotic disease could be a time-saving and cost-effective diagnostic step for clinical stroke patients. It may also facilitate prevention strategies for atherosclerosis. KEY POINTS: • Blooming artifacts can blur vessel wall morphology on conventional CT angiography. • Spectral black-blood (SBB) images are generated from material decomposition from spectral CT. • SBB images reduce blooming artifacts and noise and accurately detect small plaques.

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach.

Objective: Radio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when the tissue and probe are fixed. We previously proposed the innovative RFTSDP method in which the RF data are recorded while stimulating the tissue. Applying stimulation can unveil the mechanical characteristics of the tissue in RF echo. Materials and methods: In this study, an apparatus was developed to induce vibrations at different frequencies to the medium. Data were collected from four PVA phantoms simulating the nonlinear behaviors of healthy, fibroadenoma, cyst, and cancerous breast tissues. Raw focused, raw, and beamformed ultrafast data were collected under conditions of no stimulation, constant force, and various vibrational stimulations using the Supersonic Imagine Aixplorer clinical/research ultrasound imaging system. Time domain (TD), spectral, and nonlinear features were extracted from each RF TS. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms were employed for classification. Results: The optimal outcome was achieved using the SVM classifier considering 19 features extracted from beamformed ultrafast data recorded while applying vibration at the frequency of 65 Hz. The classification accuracy, specificity, and precision were 98.44 ± 0.20 %, 99.49 ± 0.01 %, and 98.53 ± 0.04 %, respectively. Applying RFTSDP, a notable 24.45 % improvement in accuracy was observed compared to the case of fixed probe assessing the recorded raw focused data. Conclusions: External vibration at an appropriate frequency, as applied in RFTSDP, incorporates beneficial information about the medium and its dynamic characteristics into the RF TS, which can improve tissue characterization.

Paediatric Anatomical Models in Radiotherapy Applications.

Anatomical models have key applications in radiotherapy, notably to help understand the relationship between radiation dose and risk of developing side effects. This review analyses whether age-specific computational phantoms, developed from healthy subjects and paediatric cancer patient data, are adequate to model a paediatric population. The phantoms used in the study were International Commission on Radiological Protection (ICRP), 4D extended cardiac torso (XCAT) and Radiotherapy Paediatric Atlas (RT-PAL), which were also compared to literature data. Organ volume data for 19 organs was collected for all phantoms and literature. ICRP was treated as the reference for comparison, and percentage difference (P.D) for the other phantoms were calculated relative to ICRP. Overall comparisons were made for each age category (1, 5, 10, 15) and each organ. Statistical analysis was performed using Microsoft Excel (version 16.59). The smallest P.D to ICRP was for Literature (-17.4%), closely followed by XCAT (26.6%). The largest was for RT-PAL (88.1%). The rectum had the largest average P.D (1,049.2%) and the large bowel had the smallest (2.0%). The P.D was 122.6% at age 1 but this decreased to 43.5% by age 15. Linear regression analysis showed a correlation between organ volume and age to be the strongest for ICRP (R2 = 0.943) and weakest for XCAT (R2 = 0.676). The phantoms are similar enough to ICRP for potential use in modelling paediatric populations. ICRP and XCAT could be used to model a healthy population, whereas RT-PAL could be used for a population undergoing/after radiotherapy.

A framework for in-field and out-of-field patient specific secondary cancer risk estimates from treatment plans using the TOPAS Monte Carlo system.

Objective. To allow the estimation of secondary cancer risks from radiation therapy treatment plans in a comprehensive and user-friendly Monte Carlo (MC) framework.Method. Patient planning computed tomography scans were extended superior-inferior using the International Commission on Radiological Protection's Publication 145 computational mesh phantoms and skeletal matching. Dose distributions were calculated with the TOPAS MC system using novel mesh capabilities and the digital imaging and communications in medicine radiotherapy extension interface. Finally, in-field and out-of-field cancer risk was calculated using both sarcoma and carcinoma risk models with two alternative parameter sets.Result. The TOPAS MC framework was extended to facilitate epidemiological studies on radiation-induced cancer risk. The framework is efficient and allows automated analysis of large datasets. Out-of-field organ dose was small compared to in-field dose, but the risk estimates indicate a non-negligible contribution to the total radiation induced cancer risk.Significance. This work equips the TOPAS MC system with anatomical extension, mesh geometry, and cancer risk model capabilities that make state-of-the-art out-of-field dose calculation and risk estimation accessible to a large pool of users. Furthermore, these capabilities will facilitate further refinement of risk models and sensitivity analysis of patient specific treatment options.

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study.

OBJECTIVES: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. METHODS: A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). RESULTS: The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. CONCLUSION: The radiomics features were non-reproducible with significant variability in values among different CT techniques. CLINICAL RELEVANCE STATEMENT: Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. KEY POINTS: CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.

BACKGROUND: Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT. METHODS: Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed. RESULTS: DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058). CONCLUSION: DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used. RELEVANCE STATEMENT: Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction. KEY POINTS: Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.

TEMPy: a toolkit for the modeling of weighted tissue equivalent material in diagnostic imaging.

Objective.Accurate simulation of human tissues is imperative for advancements in diagnostic imaging, particularly in the fields of dosimetry and image quality evaluation. Developing Tissue Equivalent Materials (TEMs) with radiological characteristics akin to those of human tissues is essential for ensuring the reliability and relevance of imaging studies. This study presents the development of a mathematical model and a new toolkit (TEMPy) for obtaining the best composition of materials that mimic the radiological characteristics of human tissues. The model and the toolkit are described, along with an example showcasing its application to obtain desired TEMs.Approach.The methodology consisted of fitting volume fractions of the components of TEM in order to determine its linear attenuation coefficient as close as possible to the linear attenuation coefficient of the reference material. The fitting procedure adopted a modified Least Square Method including a weight function. This function reflects the contribution of the x-ray spectra in the suitable energy range of interest. TEMPy can also be used to estimate the effective atomic number and electron density of the resulting TEM.Main results.TEMPy was used to obtain the chemical composition of materials equivalent to water and soft tissue, in the energy range used in x-ray imaging (10 -150 keV) and for breast tissue using the energy range (5-40 keV). The maximum relative difference between the linear attenuation coefficients of the developed and reference materials was ±5% in the considered energy ranges.Significance.TEMPy facilitates the formulation of TEMs with radiological properties closely mimicking those of real tissues, aiding in the preparation of physical anthropomorphic or geometric phantoms for various applications. The toolkit is freely available to interested readers.

PhoenixMR: A GPU-based MRI simulation framework with runtime-dynamic code execution.

BACKGROUND: Simulations of physical processes and behavior can provide unique insights and understanding of real-world problems. Magnetic Resonance Imaging (MRI) is an imaging technique with several components of complexity. Several of these components have been characterized and simulated in the past. However, several computational challenges prevent simulations from being simultaneously fast, flexible, and accurate. PURPOSE: The simulation of MRI experiments is underutilized by medical physicists and researchers using currently available simulators due to reasons including speed, accuracy, and extensibility constraints. This paper introduces an innovative MRI simulation engine and framework that aims to overcome these issues making available realistic and fast MRI simulation. METHODS: Using the CUDA C/C++ programing language, an MRI simulation engine (PhoenixMR), incorporating a Turing-complete virtual machine (VM) to simulate abstract spatiotemporal complexities, was developed. This engine solves a set of time-discrete Bloch equations using the symmetric operator splitting technique. An extensible front-end framework package (written in Python) aids the use of PhoenixMR to simplify simulation development. RESULTS: The PhoenixMR library and front-end codes have been developed and tested. A set of example simulations were performed to demonstrate the ease of use and flexibility of simulation components such as geometrical setup, pulse sequence design, phantom design, and so forth. Initial validation of PhoenixMR is performed by comparing its accuracy and performance against a widely used MRI simulator using identical simulation parameters. Validation results show PhoenixMR simulations are three orders of magnitude faster. There is also strong agreement between models. CONCLUSIONS: A novel MRI simulation platform called PhoenixMR has been introduced. This research tool is designed to be usable by physicists and engineers interested in performing MRI simulations. Examples are shown demonstrating the accuracy, flexibility, and usability of PhoenixMR in several key areas of MRI simulation.