Preclinical evaluation and pilot clinical study of [Ga]Ga-NOTA-H006 for non-invasive PET imaging of 5T4 oncofetal antigen.

Purpose: Trophoblast glycoprotein, the so-called 5T4, is an oncofetal antigen expressed in many different cancers. However, no 5T4-specific radioligand is employed in the clinic for non-invasive diagnosis. Thus, the aim of the current study was to develop a PET radiotracer for imaging 5T4 expression in preclinical and clinical stages.

Development of a Monte Carlo simulation platform for the systematic evaluation of photon-counting detector-based micro-CT.

Purpose: This study aimed to develop a photon-counting detector (PCD) based micro-CT simulation platform for assessing the performance of three different PCD sensor materials: cadmium telluride (CdTe), gallium arsenide (GaAs), and silicon (Si). The evaluation encompasses the components of primary and scatter signals, performance of imaging contrast agents, and detector efficiency.

Methods: Simulations were performed using the Geant4 Monte Carlo toolkit, and a micro-PCD-CT system was meticulously modeled based on realistic geometric parameters.

Patient-specific radiation dose for Chinese pediatric patients undergoing whole-body PET/CT examinations.

To assess potential variations in the absorbed dose between Chinese and Caucasian children exposed toF-FDG PET scan and to investigate the factors contributing to dose differences, this work employed patient-specific phantoms and our compartment model for calculating the patient-specific absorbed dose in Chinese children.Data of 29 Chinese pediatric patients undergoing whole-bodyF-FDG PET/CT studies were retrospectively collected, including PET images for activity distributions and corresponding CT images for organ segmentation and phantom construction. A biokinetic compartment model was implemented to obtain cumulated activities.

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging.

Introduction: In contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors.

Methods: For automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed.

Ultrasound image segmentation of renal tumors based on UNet++ with fusion of multiscale residuals and dual attention.

. Laparoscopic renal unit-preserving resection is a routine and effective means of treating renal tumors. Image segmentation is an essential part before tumor resection.

Guided block matching and 4-D transform domain filter projection denoising method for dynamic PET image reconstruction.

Purpose: Dynamic PET is an essential tool in oncology due to its ability to visualize and quantify radiotracer uptake, which has the potential to improve imaging quality. However, image noise caused by a low photon count in dynamic PET is more significant than in static PET. This study aims to develop a novel denoising method, namely the Guided Block Matching and 4-D Transform Domain Filter (GBM4D) projection, to enhance dynamic PET image reconstruction.

CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors.

Objective: To investigate the feasibility and efficiency of automatic segmentation of contrast-enhanced ultrasound (CEUS) images in renal tumors by convolutional neural network (CNN) based models and their further application in radiomic analysis.

Materials And Methods: From 94 pathologically confirmed renal tumor cases, 3355 CEUS images were extracted and randomly divided into training set (3020 images) and test set (335 images). According to the histological subtypes of renal cell carcinoma, the test set was further split into clear cell renal cell carcinoma (ccRCC) set (225 images), renal angiomyolipoma (AML) set (77 images) and set of other subtypes (33 images).

Patient-specific fetal radiation dosimetry for pregnant patients undergoing abdominal and pelvic CT imaging.

Background: Accurate estimation of fetal radiation dose is crucial for risk-benefit analysis of radiological imaging, while the radiation dosimetry studies based on individual pregnant patient are highly desired.

Purpose: To use Monte Carlo calculations for estimation of fetal radiation dose from abdominal and pelvic computed tomography (CT) examinations for a population of patients with a range of variations in patients' anatomy, abdominal circumference, gestational age (GA), fetal depth (FD), and fetal development.

Methods: Forty-four patient-specific pregnant female models were constructed based on CT imaging data of pregnant patients, with gestational ages ranging from 8 to 35 weeks.

Development of a voxel S-value database for patient internal radiation dosimetry.

Purpose: Personalized dosimetry with high accuracy drew great attention in clinical practices. Voxel S-value (VSV) convolution has been proposed to speed up absorbed dose calculations. However, the VSV method is efficient for personalized internal radiation dosimetry only when there are pre-calculated VSVs of the radioisotope.

Automatic organ completion with image stitching for personalized radiation dosimetry in CT examinations.

Purpose: Computed tomography (CT) image-based patient-specific voxel-based dosimetry has difficulties complementing missing tissues for organs located partially inside or completely outside the image volume. Previous studies constructed patient-specific whole-body models by rescaling reference phantoms or extending regional CT images with manually adjusted phantoms. This study proposes a methodology for automatic organ completion of regional CT images for CT dosimetry using a stitching approach.

Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study.

Purpose: The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution.

Tracers progress for positron emission tomography imaging of glial-related disease.

Glial cells play an essential part in the neuron system. They can not only serve as structural blocks in the human brain but also participate in many biological processes. Extensive studies have shown that astrocytes and microglia play an important role in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, as well as glioma, epilepsy, ischemic stroke, and infections.

Construction of a digital fetus library for radiation dosimetry.

Purpose: Accurate estimations of fetal absorbed dose and radiation risks are crucial for radiation protection and important for radiological imaging research owing to the high radiosensitivity of the fetus. Computational anthropomorphic models have been widely used in patient-specific radiation dosimetry calculations. In this work, we aim to build the first digital fetal library for more reliable and accurate radiation dosimetry studies.

Internal dosimetry in F-18 FDG PET examinations based on long-time-measured organ activities using total-body PET/CT: does it make any difference from a short-time measurement?

Purpose: A 2-m axial field-of-view, total-body PET/CT scanner (uEXPLORER) has been recently developed to provide total-body coverage and ultra-high sensitivity, which together, enables opportunities for in vivo time-activity curve (TAC) measurement of all investigated organs simultaneously with high temporal resolution. This study aims at quantifying the cumulated activity and patient dose of 2-[F-18]fluoro-2-deoxy-D-glucose (F-18 FDG ) imaging by using delayed time-activity curves (TACs), measured out to 8-h post-injection, for different organs so that the comparison between quantifying approaches using short-time method (up to 75 min post-injection) or long-time method (up to 8 h post-injection) could be performed.

Methods: Organ TACs of 10 healthy volunteers were collected using total-body PET/CT in 4 periods after the intravenous injection of F-18 FDG.

Multi-scale Monte Carlo simulations of gold nanoparticle-induced DNA damages for kilovoltage X-ray irradiation in a xenograft mouse model using TOPAS-nBio.

Background: Gold nanoparticles (AuNPs) are considered as promising agents to increase the radiosensitivity of tumor cells. However, the biological mechanisms of radiation enhancement effects of AuNPs are still not well understood. We present a multi-scale Monte Carlo simulation framework within TOPAS-nBio to investigate the increase of DNA damage due to the presence of AuNPs in mouse tumor models.

Age-dependent dose calculations for common PET radionuclides and brain radiotracers in nonhuman primate computational models.

Purpose: The combination of nonhuman primates (NHPs) with the state-of-the-art molecular imaging technologies allows for within-subject longitudinal research aiming at gaining new insights into human normal and disease conditions and provides an ideal foundation for future translational studies of new diagnostic tools, medical interventions, and therapies. However, radiation dose estimations for nonhuman primates from molecular imaging probes are lacking and are difficult to perform experimentally. The aim of this work is to construct age-dependent NHP computational model series to estimate the absorbed dose to NHP specimens in common molecular imaging procedures.

Assessment of uncertainties associated with Monte Carlo-based personalized dosimetry in clinical CT examinations.

The clinical value of x-ray computed tomography (CT) has skyrocketed in the last decade while at the same time being the main source of medical exposure to the population. Concerns regarding the potential health hazards associated with the use of ionizing radiation were raised and an appropriate estimation of absorbed dose to patients is highly desired. In this work, we aim to validate our developed Monte Carlo CT simulator using in-phantom dose measurements and further assess the impact of personalized scan-related parameters on dosimetric calculations.

Development of a nonhuman primate computational phantom for radiation dosimetry.

Purpose: The nonhuman primate (NHP) is an important animal model for evaluating the response of the human body to radiation exposure owing to similarities between its organ structure, genome, life span, and metabolism. However, there is a lack of radiation dosimetry estimations for NHPs. The aim of this work is to construct a computational phantom of NHPs and estimate absorbed fractions and specific absorbed fractions for internal radiation dosimetry.

Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks.

Objectives: The conceptus dose during diagnostic imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. The aim of this work is to develop a methodology for automated construction of patient-specific computational phantoms based on actual patient CT images to enable accurate estimation of conceptus dose.

Methods: We developed a 3D deep convolutional network algorithm for automated segmentation of CT images to build realistic computational phantoms.

Construction of patient-specific computational models for organ dose estimation in radiological imaging.

Purpose: Diagnostic imaging procedures require optimization depending on the medical task at hand, the apparatus being used, and patient physical and anatomical characteristics. The assessment of the radiation dose and associated risks plays a key role in safety and quality management for radiation protection purposes. In this work, we aim at developing a methodology for personalized organ-level dose assessment in x-ray computed tomography (CT) imaging.

Patient-Specific Computational Model and Dosimetry Calculations for PET/CT of a Patient Pregnant with Twins.

The radiation dose delivered to pregnant patients during radiologic imaging procedures raises health concerns because the developing embryo and fetus are considered to be highly radiosensitive. To appropriately weigh the diagnostic benefits against the radiation risks, the radiologist needs reasonably accurate and detailed estimates of the fetal dose. Expanding our previously developed series of computational phantoms for pregnant women, we here describe a personalized model for twin pregnancy, based on an actual clinical scan.

Assessment of CT dose to the fetus and pregnant female patient using patient-specific computational models.

Purpose: This work provides detailed estimates of the foetal dose from diagnostic CT imaging of pregnant patients to enable the assessment of the diagnostic benefits considering the associated radiation risks.

Materials And Methods: To produce realistic biological and physical representations of pregnant patients and the embedded foetus, we developed a methodology for construction of patient-specific voxel-based computational phantoms based on existing standardised hybrid computational pregnant female phantoms. We estimated the maternal absorbed dose and foetal organ dose for 30 pregnant patients referred to the emergency unit of Geneva University Hospital for abdominal CT scans.

Effects of body habitus on internal radiation dose calculations using the 5-year-old anthropomorphic male models.

Computational phantoms are commonly used in internal radiation dosimetry to assess the amount and distribution pattern of energy deposited in various parts of the human body from different internal radiation sources. Radiation dose assessments are commonly performed on predetermined reference computational phantoms while the argument for individualized patient-specific radiation dosimetry exists. This study aims to evaluate the influence of body habitus on internal dosimetry and to quantify the uncertainties in dose estimation correlated with the use of fixed reference models.

Computational hybrid anthropometric paediatric phantom library for internal radiation dosimetry.

Hybrid computational phantoms combine voxel-based and simplified equation-based modelling approaches to provide unique advantages and more realism for the construction of anthropomorphic models. In this work, a methodology and C++ code are developed to generate hybrid computational phantoms covering statistical distributions of body morphometry in the paediatric population. The paediatric phantoms of the Virtual Population Series (IT'IS Foundation, Switzerland) were modified to match target anthropometric parameters, including body mass, body length, standing height and sitting height/stature ratio, determined from reference databases of the National Centre for Health Statistics and the National Health and Nutrition Examination Survey.