Simulated Phantom Research Articles

Angle of polarized light (AOP) Property for optical classification of the crosslinked polymer

Light-matter interaction has been profoundly studied for sample material classification. However, the optical classification of the sample through the polarized light-matter interaction remains underexplored. It is limited to the measurement of intensity instead of the angle of polarized light (AOP) for its degree of polarization. Measurement of the degree of polarization within a material or a medium becomes easier with a simple, low-cost and direct measurement without the need of any probing or labelling agent. Thus, this investigation was conducted mainly to determine the angle of polarized light (AOP) property of the crosslinked polymer using our proposed polarization measurement technique as an alternative approach of the material classification. The angle of polarized light (AOP) of each polymer was determined in combination property of polarization by absorption, transmission, and scattering. Our proposed scattered angle (ס=90°, 100°, 110°, and 120°) successfully measured the AOP of each polymer that can be classified into two groups. Group 1 represents the AOP value (aopt1-aopn) for a test sample oft1 = 3.1 %, 3.2, and 3.3 % with comparison to the normal sample (n = 3.0 %) and Group 2 represents the AOP value (aopt2-aopn) for the test sample oft2 = 3.4 %, 3.6 % and 3.7 % with comparison to the normal sample (n = 3.0 %). Our study proved a direct, easy, and simple method of determining the degree of polarization of the polymers without the need of complex formulation and labelling protocol. Therefore, this work may enhance the investigation of the optical properties of the agarose-based tissue-mimicking phantom (AGTMP) for modeling or simulation of the real biological sample in the future. Our polarization measures are worthy of further explored and implemented in current optical imaging techniques or sensing platform for optical classification of the biomaterials.

Twisted clustered pinhole collimation for improved high-energy preclinical SPECT/PET

Objective.Advanced pinhole collimation geometries optimized for preclinical high-energyɣimaging facilitate applications such asɑandßemitter imaging, simultaneous multi-isotope PET and PET/SPECT, and positron range-free PET. These geometries replace each pinhole with a group of clustered pinholes (CPs) featuring smaller individual pinhole opening angles (POAs), enabling sub-mm resolution imaging up to ∼1 MeV. Further narrowing POAs while retaining field-of-view (FOV) may enhance high-energy imaging but faces geometrical constraints. Here, we detail how the novel twisted CPs (TCPs) address this challenge.Approach.We compared TCP and CP collimator sensitivity at equal system resolution (SR) and SR at matched sensitivity by tuning pinhole diameters for18F (511 keV) and89Zr (909 keV). Additionally, simulated Derenzo phantoms at low activity (LA: 12 MBq ml-1) and high activity (HA: 190 MBq ml-1) levels, along with uniformity images, were compared to assess image resolution and uniformity.Main results.At equal SR, TCP increased average central FOV sensitivity by 15.6% for18F and 29.4% for89Zr compared to CP. Image resolution was comparable, except for89Zr at LA, where TCP resolved 0.80 mm diameter rods compared to 0.90 mm for CP. Image uniformity was equivalent for18F, while for89Zr TCP granted a 10.4% improvement. For collimators with matched sensitivity, TCP improved SR by 6.6% for18F and 17.7% for89Zr while also enhancing image resolution; for18F, rods distinguished were 0.65 mm (CP) and 0.60 mm (TCP) for HA, and 0.70 mm (CP and TCP) for LA. For89Zr, image resolutions were 0.75 mm (CP) and 0.65 mm (TCP) for HA, and 0.90 mm (CP) and 0.80 mm (TCP) for LA. Image uniformity with TCP decreased by 18.3% for18F but improved by 20.1% for89Zr.Significance.This study suggests that the TCP design has potential to improve high-energyɣimaging.

Towards high-performance deep learning architecture and hardware accelerator design for robust analysis in diffuse correlation spectroscopy

This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to generate autocorrelation functions (ACFs) to train the DL network. We assessed the accuracy of the proposed DL using simulated and milk phantom data. Compared to convolutional neural networks (CNN), our lightweight DL architecture achieves 66.7% and 18.5% improvement in MSE for BFi and the coherence factor β, using synthetic data evaluation. The accuracy of rBFi over different algorithms was also investigated. We further simplified the DL computing primitives using subtraction for feature extraction, considering further hardware implementation. We extensively explored computing parallelism and fixed-point quantization within the DL architecture. With the DL model's compact size, we employed unrolling and pipelining optimizations for computation-intensive for-loops in the DL model while storing all learned parameters in on-chip BRAMs. We also achieved pixel-wise parallelism, enabling simultaneous, real-time processing of 10 and 15 autocorrelation functions on Zynq-7000 and Zynq-UltraScale+ field programmable gate array (FPGA), respectively. Unlike existing FPGA accelerators that produce BFi and the β from autocorrelation functions on standalone hardware, our approach is an encapsulated, end-to-end on-chip conversion process from intensity photon data to the temporal intensity ACF and subsequently reconstructing BFi and β. This hardware platform achieves an on-chip solution to replace post-processing and miniaturize modern DCS systems that use single-photon cameras. We also comprehensively compared the computational efficiency of our FPGA accelerator to CPU and GPU solutions.

Optimal weighting strategies for maximizing contrast-to-noise ratio in photon counting CT images.

Photon counting detectors (PCDs) with energy discrimination capabilities have the potential to generate grayscale CT images with improved contrast-to-noise ratio (CNR) through optimal weighting of their spectral measurements. This study evaluates the CNR performance of grayscale CT projections and images generated from spectral measurements of PCDs using three energy-weighting strategies: pre-log weighting, post-log weighting, and material decomposition (MD) weighting. This study provides the expressions of optimal weights and maximum achievable CNR of these energy-weighting strategies, which only require the knowledge of detected bin counts and do not require information of PCD energy responses or imaging techniques. We defined and solved a generalized eigenvalue problem to obtain the maximum achievable CNR in the projection domain for low-contrast tasks using three energy-weighting strategies: pre-log weighting (weighted sum of energy bin counts), post-log weighting (weighted sum of line integrals), and MD weighting (weighted sum of basis material thicknesses, which is equivalent to virtual monoenergetic images [VMIs]). These expressions only contain energy bin counts from PCD measurements. We used a realistic PCD energy response model to simulate the detected bin counts and conducted Monte Carlo simulations of different contrast tasks and phantoms to evaluate the projection- and image-domain CNR performance of these energy-weighting strategies. Additionally, the total counts method (a special case of pre-log weighting with unity weights) was included for comparison. We also conducted Gammex head and body phantom scans on an edge-on-irradiated silicon PCCT prototype to evaluate the image-domain CNR performance of these energy-weighting strategies. The results show that pre-log, post-log, and MD weighting strategies generate approximately equal projection-domain maximum achievable CNR, with a difference of less than 2%, and outperform the total counts method. These three energy-weighting strategies also generate approximately equal image-domain maximum CNR when the contrast task is located at the center of a homogeneous phantom. Pre-log weighting generates the highest image-domain CNR for an off-center contrast task location or inhomogeneous phantoms while also outperforming the total counts method. We derived the expression of projection-domain maximum achievable CNR using three energy-weighting strategies. Our results suggest that using pre-log weighting strategies enables fast grayscale CT image generation with high CNR from spectral PCD measurements for inhomogeneous phantoms and off-center region of interests (ROIs).

Unsupervised Denoising in Spectral CT: Multi-Dimensional U-Net for Energy Channel Regularisation

Spectral Computed Tomography (CT) is a versatile imaging technique widely utilized in industry, medicine, and scientific research. This technique allows us to observe the energy-dependent X-ray attenuation throughout an object by using Photon Counting Detector (PCD) technology. However, a major drawback of spectral CT is the increase in noise due to a lower achievable photon count when using more energy channels. This challenge often complicates quantitative material identification, which is a major application of the technology. In this study, we investigate the Noise2Inverse image denoising approach for noise removal in spectral computed tomography. Our unsupervised deep learning-based model uses a multi-dimensional U-Net paired with a block-based training approach modified for additional energy-channel regularization. We conducted experiments using two simulated spectral CT phantoms, each with a unique shape and material composition, and a real scan of a biological sample containing a characteristic K-edge. orangeMeasuring the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for the simulated data and the contrast-to-noise ratio (CNR) for the real-world data, our approach not only outperforms previously used methods—namely the unsupervised Low2High method and the total variation-constrained iterative reconstruction method—but also does not require complex parameter tuning.

Enhancing ultrasonic attenuation images through multi-frequency coupling with total nuclear variation.

Quantitative ultrasound is a non-invasive image modality that numerically characterizes tissues for medical diagnosis using acoustical parameters, such as the attenuation coefficient slope. A previous study introduced the total variation spectral log difference (TVSLD) method, which denoises spectral log ratios on a single-channel basis without inter-channel coupling. Therefore, this work proposes a multi-frequency joint framework by coupling information across frequency channels exploiting structural similarities among the spectral ratios to increase the quality of the attenuation images. A modification based on the total nuclear variation (TNV) was considered. Metrics were compared to the TVSLD method with simulated and experimental phantoms and two samples of fibroadenoma in vivo breast tissue. The TNV demonstrated superior performance, yielding enhanced attenuation coefficient slope maps with fewer artifacts at boundaries and a stable error. In terms of the contrast-to-noise ratio enhancement, the TNV approach obtained an average percentage improvement of 34% in simulation, 38% in the experimental phantom, and 89% in two in vivo breast tissue samples compared to TVSLD, showing potential to enhance visual clarity and depiction of attenuation images.

Temperature Simulation of an Ablation Needle for the Prediction of Tissue Necrosis during Liver Ablation

Background/Objectives: Microwave ablation (MWA) is the leading therapy method for treating patients with liver cancer. MWA simulation is used to further improve the therapy and to help develop new devices. Methods: A water-cooled ablation needle was reconstructed. MWA simulations of a polyacrylamide phantom were carried out and compared with a representative clinical example (tumor diameter: 8.75 mm). The Arrhenius damage model and a critical temperature approach of 60 °C were applied to assess the necrosis zones. Finally, the simulation results were compared to the corresponding MR measurements. Results: Most of the heating in the simulation took place at a distance of 5 mm along the transverse axis and 20 mm along the longitudinal axis above the needle tip. The calculated Dice scores for the Arrhenius model were 0.77/0.53 for the phantom/clinical case. For the critical temperature approach, Dice scores of 0.60/0.66 for the phantom/clinical case were achieved. Conclusions: The comparison between simulated and measured temperature increases showed an excellent agreement. However, differences in the predicted necrosis volume might be caused by omitting consideration of the heat sink effect, especially in the clinical case. Nevertheless, this workflow enables short MWA simulation times (approximately 3 min) and demonstrates a step towards possible integration into daily clinical use.

Assessment of dual time point protocols to produce parametric Ki images in FDG PET/CT: A virtual clinical study.

This simulation study investigated the feasibility of generating Patlak Ki images using a dual time point (DTP-Ki) scan protocol involving two 3-min/bed routine static PET scans and, subsequently, assessed DTP-Ki performance for an optimal DTP scan time frame combination, against conventional Patlak Ki estimated from complete 0-93min dynamic PET data. Six realistic heterogeneous tumors of different characteristic spatiotemporal [18F]FDG uptake distributions for three noise levels commonly found in clinical studies and 20 noise realizations (N=360 samples) were produced by analytic simulations of the XCAT phantom. Subsequently, DTP-Ki images were generated by performing standard linear indirect Patlak analysis with t* -min (Patlakt* = 12) using a scaled population-based input function (sPBIF) model on 66 combinations of early and late 3-min/bed static whole-body PET reconstructed images. All DTP-Ki images were evaluated against respective DTP-Ki images estimated with Patlakt* = 12 and 0-93min individual input functions (iIFs) and against gold standard Ki images estimated with Patlakt* = 12, 0-93min iIFs and tissue time activity curves from all reconstructed WB passes 12-93min post injection. The optimal combination of early and late frames, in terms of attaining the highest correlation between DTP-Ki with sPBIF and gold standard Ki was also determined from a set of 66 different combinations of 2-min early and late frames. Moreover, the performance of DTP-Ki with sPBIF was compared against that of the retention index (RI) in terms of their correlation to the gold standard Ki. Finally, the feasibility and practicality of DTP protocol in the clinic were assessed through the analysis of nine patients. High correlations(>0.9) were observed between DTP-Ki values from sPBIF and those from iIFs for all evaluated DTP protocols while the mean AUC difference between sPBIF and iIFs was less than 10%. The percentage difference of mean values between DTP-Ki from sPBIF and from iIFs was less than 1%. DTP Ki from sPBIF exhibited significantly higher correlation with gold standard Ki, in contrast to RI, across all 66 DTP protocols (p<0.05 using the two-tailed t-test by Williams) with the highest correlation attained for the 50-53-min early + 90-93-min late scan time frames (optimal DTP protocol). Feasibility of generating Patlak Ki [18F] FDG images from an early and a late post injection 3-min/bed routine static scan using a population-based input function model was demonstrated and an optimal DTP scan protocol was determined. The results indicated high correlations between DTP-Ki and gold-standard Ki images that are significantly larger than those between RI and gold-standard Ki.

Rapid wideband characterization of viscoelastic material properties by Bessel function-based time harmonic ultrasound elastography (B-THE)

Elastography is an emerging diagnostic technique that uses conventional imaging modalities such as sonography or magnetic resonance imaging to quantify tissue stiffness. However, different elastography methods provide different stiffness values, which require calibration using well-characterized phantoms or tissue samples. A comprehensive, fast, and cost-effective elastography technique for phantoms or tissue samples is still lacking.Therefore, we propose ultrasound Bessel-fit-based time harmonic elastography (B-THE) as a novel tool to provide rapid feedback on stiffness-related shear wave speed (SWS) and viscosity-related wave penetration rate (PR) over a wide range of harmonic vibration frequencies. The method relies on external induction and B-mode capture of cylindrical shear waves that satisfy the Bessel wave equation for efficient fit-based parameter recovery. B-THE was demonstrated in polyacrylamide phantoms in the frequency range of 20–200 Hz and was cross-validated by magnetic resonance elastography (MRE) using clinical 3-T MRI and compact 0.5-T tabletop MRI scanners. Frequency-independent material parameters were derived from rheological models and validated by numerical simulations.B-THE quantified frequency-resolved SWS and PR 13 to 176 times faster than more expensive clinical MRE and tabletop MRE and have a good accuracy (relative deviation to reference: 6 %, 10 % and 4 % respectively). Simulations of liver-mimicking material phantoms showed that a simultaneous fit of SWS and PR based on the fractional Maxwell rheological model outperformed a fit on PR solely.B-THE provides a comprehensive and fast elastography technique for the quantitative characterization of the viscoelastic behavior of soft tissue mimicking materials.

Features of the Formation of the Practical Skill of Conducting Basic Cardiopulmonary Resuscitation in Undergraduate Students of Medical Universities

The conducted single-center prospective study of the formation of practical skills for performing basic cardiopulmonary resuscitation demonstrated high results in eduction using phantom simulation techniques. At the same time, the terms and volumes of reduction of the performance skill in the absence of periodic repeated training are determined.

Implantation of CRT pacemaker during pregnancy: estimated fetal radiation dose.

The benefits of a cardiac resynchronization therapy (CRT) implantation are known in severe heart failure and its implantation may also be considered during pregnancy to ensure safe pregnancy and delivery for both the mother and the fetus. This study on a patient case aimed to estimate the absorbed dose (AD) to the fetus during the CRT implantation of 21st week of the pregnant woman. AD measurement was done using anthropomorphic phantom, radiophotoluminescence dosemeters and Monte Carlo simulation utilizing kerma area product (KAP) of the procedures. The measured AD for a phantom's uterus and heart was 0.116 and 14.7mGy with total KAP of 27.9 Gycm2. The estimated actual AD to the fetus and the mother's heart was 0.004 and 1.2mGy, with total KAP of 1.5 Gycm2. The dose to the fetus can be minimized with the optimized procedure.

Design and proof of concept of a double-panel TOF-PET system

ObjectivePositron Emission Tomography (PET) is a well-known imaging technology for the diagnosis, treatment, and monitoring of several diseases. Most PET scanners use a Ring-Shaped Detector Configuration (RSDC), which helps obtain homogeneous image quality but are restricted to an invariable Field-of-View (FOV), scarce spatial resolution, and low sensitivity. Alternatively, few PET systems use Open Detector Configurations (ODC) to permit an accessible FOV adaptable to different target sizes, thus optimizing sensitivity. Yet, to compensate the lack of angular coverage in ODC-PET, developing a detector with high-timing performance is mandatory to enable Time-of-Flight (TOF) techniques during reconstruction.The main goal of this work is to provide a proof of concept PET scanner appropriate for constructing the new generation of ODC-PET suitable for biopsy guidance and clinical intervention during acquisition. The designed detector has to be compact and robust, and its requirements in terms of performance are spatial and time resolutions < 2 mm and < 200 ps, respectively.MethodsThe present work includes a simulation study of an ODC-PET based on 2-panels with variable distance. The image quality (IQ) and Derenzo phantoms have been simulated and evaluated. The phantom simulations have also been performed using a ring-shaped PET for comparison purposes of the ODC approach with conventional systems. Then, an experimental evaluation of a prototype detector that has been designed following the simulation results is presented. This study focused on tuning the ASIC parameters and evaluating the scintillator surface treatment (ESR and TiO2), and configuration that yields the best Coincidence Time Resolution (CTR). Moreover, the scalability of the prototype to a module of 64 × 64mm2 and its preliminary evaluation regarding pixel identification are provided.ResultsThe simulation results reported sensitivity (%) values at the center of the FOV of 1.96, 1.63, and 1.18 for panel distances of 200, 250, and 300 mm, respectively. The IQ reconstructed image reported good uniformity (87%) and optimal CRC values, and the Derenzo phantom reconstruction suggests a system resolution of 1.6–2 mm.The experimental results demonstrate that using TiO2 coating yielded better detector performance than ESR. Acquired data was filtered by applying an energy window of ± 30% at the photopeak level. After filtering, best CTR of 230 ± 2 ps was achieved for an 8 × 8 LYSO pixel block with 2 × 2 × 12mm3 each. The detector performance remained constant after scaling-up the prototype to a module of 64 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 64mm2, and the flood map demonstrates the module’s capabilities to distinguish the small pixels; thus, a spatial resolution < 2 mm (pixel size) is achieved.ConclusionsThe simulated results of this biplanar scanner show high performance in terms of image quality and sensitivity. These results are comparable to state-of-the-art PET technology and, demonstrate that including TOF information minimizes the image artifacts due to the lack of angular projections. The experimental results concluded that using TiO2 coating provide the best performance. The results suggest that this scanner may be suitable for organ study, breast, prostate, or cardiac applications, with good uniformity and CRC.

Abdominal MR Multitasking for radiotherapy treatment planning: A motion-resolved and multicontrast 3D imaging approach.

Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion-resolved imaging are desired for accurate delineation of the target and various organs-at-risk and patient-tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)-based technique to produce volumetric, motion-resolved, multicontrast images for abdominal radiotherapy treatment planning. The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization-prepared (saturation recovery and T2 preparations) spoiled gradient-echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors. In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast-weighting selection for better visualization. Initial clinical testing with interobserver analysis demonstrated acceptable target delineation quality (Dice coefficient = 0.85 ± 0.05, Hausdorff distance = 3.3 ± 0.72 mm). The developed MT-based, abdomen-dedicated technique is capable of providing motion-resolved, multicontrast volumetric images in a single scan, which may facilitate abdominal radiotherapy treatment planning.

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application

Background and Objective:Ultrasound information entropy imaging is an emerging quantitative ultrasound technique for characterizing local tissue scatterer concentrations and arrangements. However, the commonly used ultrasound Shannon entropy imaging based on histogram-derived discrete probability estimation suffers from the drawbacks of histogram settings dependence and unknown estimator performance. In this paper, we introduced the information-theoretic cumulative residual entropy (CRE) defined in a continuous distribution of cumulative distribution functions as a new entropy measure of ultrasound backscatter envelope uncertainty or complexity, and proposed ultrasound CRE imaging for tissue characterization. Methods:We theoretically analyzed the CRE for Rayleigh and Nakagami distributions and proposed a normalized CRE for characterizing scatterer distribution patterns. We proposed a method based on an empirical cumulative distribution function estimator and a trapezoidal numerical integration for estimating the normalized CRE from ultrasound backscatter envelope signals. We presented an ultrasound normalized CRE imaging scheme based on the normalized CRE estimator and the parallel computation technique. We also conducted theoretical analysis of the differential entropy which is an extension of the Shannon entropy to a continuous distribution, and introduced a method for ultrasound differential entropy estimation and imaging. Monte-Carlo simulation experiments were performed to evaluate the estimation accuracy of the normalized CRE and differential entropy estimators. Phantom simulation and clinical experiments were conducted to evaluate the performance of the proposed normalized CRE imaging in characterizing scatterer concentrations and hepatic steatosis (n = 204), respectively. Results:The theoretical normalized CRE for the Rayleigh distribution was π/4, corresponding to the case where there were ≥10 randomly distributed scatterers within the resolution cell of an ultrasound transducer. The theoretical normalized CRE for the Nakagami distribution decreased as the Nakagami parameter m increased, corresponding to that the ultrasound backscattered statistics varied from pre-Rayleigh to Rayleigh and to post-Rayleigh distributions. Monte-Carlo simulation experiments showed that the proposed normalized CRE and differential entropy estimators can produce a satisfying estimation accuracy even when the size of the test samples is small. Phantom simulation experiments showed that the proposed normalized CRE and differential entropy imaging can characterize scatterer concentrations. Clinical experiments showed that the proposed ultrasound normalized CRE imaging is capable to quantitatively characterize hepatic steatosis, outperforming ultrasound differential entropy imaging and being comparable to ultrasound Shannon entropy and Nakagami imaging. Conclusion:This study sheds light on the theory and methodology of ultrasound normalized CRE. The proposed ultrasound normalized CRE can serve as a new, flexible quantitative ultrasound envelope statistics parameter. The proposed ultrasound normalized CRE imaging may find applications in quantified characterization of biological tissues. Our code will be made available publicly at https://github.com/zhouzhuhuang.

A bronchoscopic navigation method based on neural radiation fields.

We introduce a novel approach for bronchoscopic navigation that leverages neural radiance fields (NeRF) to passively locate the endoscope solely from bronchoscopic images. This approach aims to overcome the limitations and challenges of current bronchoscopic navigation tools that rely on external infrastructures or require active adjustment of the bronchoscope. To address the challenges, we leverage NeRF for bronchoscopic navigation, enabling passive endoscope localization from bronchoscopic images. We develop a two-stage pipeline: offline training using preoperative data and online passive pose estimation during surgery. To enhance performance, we employ Anderson acceleration and incorporate semantic appearance transfer to deal with the sim-to-real gap between training and inference stages. We assessed the viability of our approach by conducting tests on virtual bronchscopic images and a physical phantom against the SLAM-based methods. The average rotation error in our virtual dataset is about 3.18 and the translation error is around 4.95 mm. On the physical phantom test, the average rotation and translation error are approximately 5.14 and 13.12 mm. Our NeRF-based bronchoscopic navigation method eliminates reliance on external infrastructures and active adjustments, offering promising advancements in bronchoscopic navigation. Experimental validation on simulation and real-world phantom models demonstrates its efficacy in addressing challenges like low texture and challenging lighting conditions.

FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool

Objective. Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently. Approach. FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation. Main results. According to the NEMA IQ phantom simulation, FAST-PET’s simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures. Significance. FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines.

Fun with electricity: A novel ballistics gelatin model with LED tracking for ultrasound needle guidance.

Use of ultrasound (US) for procedural needle guidance can improve success rates, safety, and accuracy. Often, training is performed on task trainers, which can be prohibitively expensive. Determining undesired needle placement is difficult when the needle is poorly visualized with US. Currently available simulation phantoms cannot provide real-time feedback on the location of needle placement. The primary objective was to develop and determine feasibility of a low-cost simulation phantom with an internal circuit and LED light system to determine when a needle contacts internal structures. We also aimed to determine whether its use was associated with increased comfort level. Emergency medicine (EM) residents (PGY-1 to PGY-3) performed in-plane and out-of-plane US needle guidance using homemade phantoms. Comfort levels were assessed by pre- and post intervention survey. Outcomes were measured on Likert scale (minimum = 1, maximum = 5). The primary outcome was change in confidence markers before and after the simulation task. Secondary outcomes were survey results of comparisons of these models to prior training experiences on simulators and humans. All EM residents (30) in our program were invited to participate. Twenty participants enrolled and completed the study. In the primary outcome, median comfort with out-of-plane and in-plane guidance increased after using the model but was more pronounced for out-of-plane guidance. On a posttest survey, residents rated the models overall very similar to prior experience on simulators (median 5/5 [IQR 4.0-5.0]) and moderately similar to humans (median 3/5 [IQR 3.0-4.0]). We created a low-cost ballistic gelatin phantom with an internal electric needle guidance system. Use of the phantom for training was associated with increased learner comfort with the procedure. Learners rated the characteristics of the phantom as similar to higher-cost commercial equipment and humans.

Joint k-ω Space Image Reconstruction and Data Fitting for Chemical Exchange Saturation Transfer Magnetic Resonance Imaging.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a novel MRI technology to image certain compounds at extremely low concentrations. Long acquisition time to measure signals at a set of offset frequencies of the Z-spectra and to repeat measurements to reduce noise pose significant challenges to its applications. This study explores correlations of CEST MR images along the spatial and Z-spectral dimensions to improve MR image quality and robustness of magnetization transfer ratio (MTR) asymmetry estimation via a joint k-ω reconstruction model. The model was formulated as an optimization problem with respect to MR images at all frequencies ω, while incorporating regularizations along the spatial and spectral dimensions. The solution was subject to a self-consistency condition that the Z-spectrum of each pixel follows a multi-peak data fitting model corresponding to different CEST pools. The optimization problem was solved using the alternating direction method of multipliers. The proposed joint reconstruction method was evaluated on a simulated CEST MRI phantom and semi-experimentally on choline and iopamidol phantoms with added Gaussian noise of various levels. Results demonstrated that the joint reconstruction method was more tolerable to noise and reduction in number of offset frequencies by improving signal-to-noise ratio (SNR) of the reconstructed images and reducing uncertainty in MTR asymmetry estimation. In the choline and iopamidol phantom cases with 10.5% noise in the measurement data, our method achieved an averaged SNR of 31.0 dB and 32.2 dB compared to the SNR of 24.7 dB and 24.4 dB in the conventional reconstruction approach. It reduced uncertainty of the MTR asymmetry estimation over all regions of interest by 54.4% and 43.7%, from 1.71 and 2.38 to 0.78 and 1.71, respectively.

Deep learning-enabled high-speed, multi-parameter diffuse optical tomography.

Frequency-domain diffuse optical tomography (FD-DOT) could enhance clinical breast tumor characterization. However, conventional diffuse optical tomography (DOT) image reconstruction algorithms require case-by-case expert tuning and are too computationally intensive to provide feedback during a scan. Deep learning (DL) algorithms front-load computational and tuning costs, enabling high-speed, high-fidelity FD-DOT. We aim to demonstrate a simultaneous reconstruction of three-dimensional absorption and reduced scattering coefficients using DL-FD-DOT, with a view toward real-time imaging with a handheld probe. A DL model was trained to solve the DOT inverse problem using a realistically simulated FD-DOT dataset emulating a handheld probe for human breast imaging and tested using both synthetic and experimental data. Over a test set of 300 simulated tissue phantoms for absorption and scattering reconstructions, the DL-DOT model reduced the root mean square error by and , increased the spatial similarity by and , increased the anomaly contrast accuracy by ( ), and reduced the crosstalk by and , respectively, compared with model-based tomography. The average reconstruction time was reduced from 3.8min to 0.02s for a single reconstruction. The model was successfully verified using two tumor-emulating optical phantoms. There is clinical potential for real-time functional imaging of human breast tissue using DL and FD-DOT.

Position dependence of recovery coefficients in 177Lu-SPECT/CT reconstructions – phantom simulations and measurements

BackgroundAlthough the importance of quantitative SPECT has increased tremendously due to newly developed therapeutic radiopharmaceuticals, there are still no accreditation programs to harmonize SPECT imaging. Work is currently underway to develop an accreditation for quantitative 177Lu SPECT/CT. The aim of this study is to verify whether the positioning of the spheres within the phantom has an influence on the recovery and thus needs to be considered in SPECT harmonization. In addition, the effects of these recovery coefficients on a potential partial volume correction as well as absorbed-dose estimates are investigated.MethodsUsing a low-dose CT of a SPECT/CT acquisition, a computerized version of the NEMA body phantom was created using a semi-automatic threshold-based method. Based on the mass-density map, the detector orbit, and the sphere centers, realistic SPECT acquisitions of all possible 720 sphere configurations of both the PET and the SPECT versions of the NEMA Body Phantom were generated using Monte Carlo simulations. SPECT reconstructions with different numbers of updates were performed without (CASToR) and with resolution modeling (STIR). Recovery coefficients were calculated for all permutations, reconstruction methods, and phantoms, and their dependence on the sphere positioning was investigated. Finally, the simulation-based findings were validated using SPECT/CT acquisitions of six different sphere configurations.ResultsOur analysis shows that sphere positioning has a significant impact on the recovery for both of the reconstruction methods and the phantom type. Although resolution modeling resulted in significantly higher recovery, the relative variation in recovery within the 720 permutations was even larger. When examining the extreme values of the recovery, reconstructions without resolution modeling were influenced primarily by the sphere position, while with resolution modeling the volume of the two adjacent spheres had a larger influence. The SPECT measurements confirmed these observations, and the recovery curves showed good overall agreement with the simulated data.ConclusionOur study shows that sphere positioning has a significant impact on the recovery obtained in NEMA sphere phantom measurements and should therefore be considered in a future SPECT accreditation. Furthermore, the single-measurement method normally performed for PVC should be reconsidered to account for the position dependency.