Dual time-point [F]FDG PET imaging for quantification of metabolic uptake rate: Evaluation of a simple, clinically feasible method.

Purpose: We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (K) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy.

Methods: We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.

Strategies for deep learning-based attenuation and scatter correction of brain F-FDG PET images in the image domain.

Background: Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging.

Purpose: In this study, different input settings were considered in the model training to investigate deep learning-based AC in the image space.

Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier.

This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules.

Radiomics predictive modeling from dual-time-point FDG PET K parametric maps: application to chemotherapy response in lymphoma.

Background: To investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [F]FDG PET metabolic uptake rate K parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients.

Methods: We analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP K parametric maps.

Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance.

Purpose: This work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.

Methods: Clinical SPECT-MPI images of 345 patients acquired on a dedicated cardiac SPECT camera in list-mode format were retrospectively employed to predict standard-dose from low-dose images at half-, quarter-, and one-eighth-dose levels.

Short-duration dynamic FDG PET imaging: Optimization and clinical application.

Purpose: We aimed to investigate whether short dynamic PET imaging started at injection, complemented with routine clinical acquisition at 60-min post-injection (static), can achieve reliable kinetic analysis.

Methods: Dynamic and static 18F-2-fluoro-2-deoxy-D-glucose (FDG) PET data were generated using realistic simulations to assess uncertainties due to statistical noise as well as bias. Following image reconstructions, kinetic parameters obtained from a 2-tissue-compartmental model (2TCM) were estimated, making use of the static image, and the time duration of dynamic PET data were incrementally shortened.

A novel approach to classify urinary stones using dual-energy kidney, ureter and bladder (DEKUB) X-ray imaging.

Objectives: Detection of urinary stone composition before treatment can help in its management. The purpose of this work is to study the feasibility of classifying the kidney stone compositions in vivo by dual-energy kidney, ureter, and bladder (DEKUB) X-ray imaging.

Methods: Six urinary stone compositions with nine diameters were simulated in a water phantom, and two 70- and 120-kVp images were acquired by radiography tally of the Monte Carlo code.

Proposition of a practical protocol for obtaining a valid radiology image using radiography tally of MCNPX Monte Carlo Code.

Background And Objective: Over the past years, Monte Carlo codes have widespread use in the radiation research field. Radiography tally of MCNPX Monte Carlo code can be a popular and applicable tool for simulation of radiography images. However, validation is the most important prerequisite before using its results.

Reconstruction/segmentation of attenuation map in TOF-PET based on mixture models.

Attenuation correction is known as a necessary step in positron emission tomography (PET) system to have accurate and quantitative activity images. Emission-based method is known as a promising approach for attenuation map estimation on TOF-PET scanners. The proposed method in this study imposes additional histogram-based information as a mixture model prior on the emission-based approach using maximum a posteriori (MAP) framework to improve its performance and make such a nearly segmented attenuation map.

Calculation of the contrast of the calcification in digital mammography system: Gate validation.

Purpose: Validation of the Gate tool in digital mammography image simulation from the viewpoint of image quality (contrast of calcifications).

Materials And Methods: The polymethyl methacrylate (PMMA) phantom containing aluminum foils in different thicknesses is used for measuring the contrast of calcifications in a real system. In this research, the phantom and mammography system have been simulated by the Gate tool with the maximum possible details.

Semi-automated carotid lumen segmentation in computed tomography angiography images.

Carotid artery stenosis causes narrowing of carotid lumens and may lead to brain infarction. The purpose of this study was to develop a semi-automated method of segmenting vessel walls, surrounding tissues, and more importantly, the carotid artery lumen by contrast computed tomography angiography (CTA) images and to define the severity of stenosis and present a three-dimensional model of the carotid for visual inspection. In vivo contrast CTA images of 14 patients (7 normal subjects and 7 patients undergoing endarterectomy) were analyzed using a multi-step segmentation algorithm.

Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system.

Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate. The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification.

Low-dose 90Y PET/CT imaging optimized for lesion detectability and quantitative accuracy: a phantom study to assess the feasibility of pretherapy imaging to plan the therapeutic dose.

Objective: The overall aim of this work is to optimize the reconstruction parameters for low-dose yttrium-90 (Y) PET/CT imaging, and to determine Y minimum detectable activity, in an endeavor to investigate the feasibility of performing low-dose Y imaging in-vivo to plan the therapeutic dose in radioembolization.

Materials And Methods: This study was carried out using a Siemens Biograph 6 True Point PET/CT scanner. A Jaszczak phantom containing five hot syringes was imaged serially over 15 days.

Compton scatter tomography in TOF-PET.

Scatter coincidences contain hidden information about the activity distribution on the positron emission tomography (PET) imaging system. However, in conventional reconstruction, the scattered data cause the blurring of images and thus are estimated and subtracted from detected coincidences. List mode format provides a new aspect to use time of flight (TOF) and energy information of each coincidence in the reconstruction process.

Spinning slithole collimation for high-sensitivity small animal SPECT: Design and assessment using GATE simulation.

Purpose: While traditional collimations are widely used in preclinical SPECT imaging, they usually suffer from possessing a low system sensitivity leading to noisy images. In this study, we are aiming at introducing a novel collimator, the slithole, offering a superior resolution-sensitivity tradeoff for small animal SPECT.

Methods: The collimator was designed for a molecular SPECT scanner, the HiReSPECT.

Edge-illumination x-ray phase contrast imaging with Pt-based metallic glass masks.

Edge-illumination x-ray phase contrast imaging (EI XPCI) is a non-interferometric phase-sensitive method where two absorption masks are employed. These masks are fabricated through a photolithography process followed by electroplating which is challenging in terms of yield as well as time- and cost-effectiveness. We report on the first implementation of EI XPCI with Pt-based metallic glass masks fabricated by an imprinting method.

DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique.

This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied.

An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies.

Extravascular extracellular space (v ) is a key parameter to characterize the tissue of cerebral tumors. This study introduces an artificial neural network (ANN) as a fast, direct, and accurate estimator of v from a time trace of the longitudinal relaxation rate, ΔR (R  = 1/T ), in DCE-MRI studies. Using the extended Tofts equation, a set of ΔR profiles was simulated in the presence of eight different signal to noise ratios.

Evaluating the Application of Tissue-Specific Dose Kernels Instead of Water Dose Kernels in Internal Dosimetry: A Monte Carlo Study.

Purpose: The aim of this work is to evaluate the application of tissue-specific dose kernels instead of water dose kernels to improve the accuracy of patient-specific dosimetry by taking tissue heterogeneities into consideration.

Materials And Methods: Tissue-specific dose point kernels (DPKs) and dose voxel kernels (DVKs) for yttrium-90 (Y), lutetium-177 (Lu), and phosphorus-32 (P) are calculated using the Monte Carlo (MC) simulation code GATE (version 7). The calculated DPKs for bone, lung, adipose, breast, heart, intestine, kidney, liver, and spleen are compared with those of water.

Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies.

Purpose: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like computation time and general fitting problems. This article proposes an approach based on Artificial Neural Networks (ANNs) for solving these problems.

A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [F]FDG-PET Imaging.

Purpose: Determination of intra-tumor high-uptake area using 2-deoxy-2-[F]fluoro-D-glucose ([F]FDG) positron emission tomography (PET) imaging is an important consideration for dose painting in radiation treatment applications. The aim of our study was to develop a framework towards automated segmentation and labeling of homogeneous vs. heterogeneous tumors in clinical lung [F]FDG-PET with the capability of intra-tumor high-uptake region delineation.

Combined fuzzy logic and random walker algorithm for PET image tumor delineation.

Purpose: The random walk (RW) technique serves as a powerful tool for PET tumor delineation, which typically involves significant noise and/or blurring. One challenging step is hard decision-making in pixel labeling. Fuzzy logic techniques have achieved increasing application in edge detection.

Effects of filtration on right ventricular function by the gated blood pool SPECT.

Objective: Gated blood po ol single photon emission computed tomography (GBPS) offers the possibility of obtaining additional functional information from blood pool studies, including evaluation of left and right ventricular function simultaneously. The calculation of ventricular volumes based on the identification of the endocardial surface would be influenced by the spatial resolution in the reconstructed images. This study was performed to evaluate the effect of different filters on the right ventricular function.