Impact of cell geometry, cellular uptake region, and tumour morphology on Ac and Lu dose distributions in prostate cancer.

Background: Radiopharmaceutical therapy with Ac- and Lu-PSMA has shown promising results for the treatment of prostate cancer. However, the distinct physical properties of alpha and beta radiation elicit varying cellular responses, which could be influenced by factors such as tumour morphology. In this study, we use simulations to examine how cell geometry, region of pharmaceutical uptake within the cell to model different internalization fractions, and the presence of tumour hypoxia and necrosis impact nucleus absorbed doses and dose heterogeneity with Ac and Lu.

Relating Macroscopic PET Radiomics Features to Microscopic Tumor Phenotypes Using a Stochastic Mathematical Model of Cellular Metabolism and Proliferation.

Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images.

Multi-tracer PET correlation analysis reveals disease-specific patterns in Parkinson's disease and asymptomatic LRRK2 pathogenic variant carriers compared to healthy controls.

Several genetic pathogenic variants increase the risk of Parkinson's disease (PD) with pathogenic variants in the leucine-rich repeat kinase 2 (LRRK2) gene being among the most common. A joint pattern analysis based on multi-set canonical correlation analysis (MCCA) was utilized to extract PD and LRRK2 pathogenic variant-specific spatial patterns in relation to healthy controls (HCs) from multi-tracer Positron Emission Tomography (PET) data. Spatial patterns were extracted for individual subject cohorts, as well as for pooled subject cohorts, to explore whether complementary spatial patterns of dopaminergic denervation are different in the asymptomatic and symptomatic stages of PD.

Evaluating Outcome Prediction via Baseline, End-of-Treatment, and Delta Radiomics on PET-CT Images of Primary Mediastinal Large B-Cell Lymphoma.

Objectives: Accurate outcome prediction is important for making informed clinical decisions in cancer treatment. In this study, we assessed the feasibility of using changes in radiomic features over time (Delta radiomics: absolute and relative) following chemotherapy, to predict relapse/progression and time to progression (TTP) of primary mediastinal large B-cell lymphoma (PMBCL) patients.

Material And Methods: Given the lack of standard staging PET scans until 2011, only 31 out of 103 PMBCL patients in our retrospective study had both pre-treatment and end-of-treatment (EoT) scans.

PSMA-Hornet: Fully-automated, multi-target segmentation of healthy organs in PSMA PET/CT images.

Background: Prostate-specific membrane antigen (PSMA) PET imaging represents a valuable source of information reflecting disease stage, response rate, and treatment optimization options, particularly with PSMA radioligand therapy. Quantification of radiopharmaceutical uptake in healthy organs from PSMA images has the potential to minimize toxicity by extrapolation of the radiation dose delivery towards personalization of therapy. However, segmentation and quantification of uptake in organs requires labor-intensive organ delineations that are often not feasible in the clinic nor scalable for large clinical trials.

Spatiotemporal patterns of putaminal dopamine processing in Parkinson's disease: A multi-tracer positron emission tomography study.

Alterations in different aspects of dopamine processing may exhibit different progressive behaviours throughout the course of Parkinson's disease. We used a novel data-driven multivariate approach to quantify and compare spatiotemporal patterns related to different aspects of dopamine processing from cross-sectional Parkinson's subjects obtained with: 1) 69 [C]±dihydrotetrabenazine (DTBZ) scans, most closely related to dopaminergic denervation; 2) 73 [C]d-threo-methylphenidate (MP) scans, marker of dopamine transporter density; 3) 50 6-[F]fluoro-l-DOPA (FD) scans, marker of dopamine synthesis and storage. The anterior-posterior gradient in the putamen was identified as the most salient feature associated with disease progression, however the temporal progression of the spatial gradient was different for the three tracers.

Testing the Ability of Convolutional Neural Networks to Learn Radiomic Features.

Background And Objective: Radiomics and deep learning have emerged as two distinct approaches to medical image analysis. However, their relative expressive power remains largely unknown. Theoretically, hand-crafted radiomic features represent a mere subset of features that neural networks can approximate, thus making deep learning a more powerful approach.

Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer.

PET imaging with targeted novel tracers has been commonly used in the clinical management of prostate cancer. The use of artificial intelligence (AI) in PET imaging is a relatively new approach and in this review article, we will review the current trends and categorize the currently available research into the quantification of tumor burden within the organ, evaluation of metastatic disease, and translational/supplemental research which aims to improve other AI research efforts.

Radiomics in PET Imaging:: A Practical Guide for Newcomers.

Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization.

Design of an anthropomorphic PET phantom with elastic lungs and respiration modeling.

Purpose: Respiratory motion during positron emission tomography (PET) scans can be a major detriment to image quality in oncological imaging. The impact of motion on lesion quantification and detectability can be assessed using phantoms with realistic anatomy representation and motion modeling. In this work, we develop an anthropomorphic phantom for PET imaging that combines anatomic fidelity and a realistic breathing mechanism with deformable lungs.

Dynamic PET image reconstruction utilizing intrinsic data-driven HYPR4D denoising kernel.

Purpose: Reconstructed PET images are typically noisy, especially in dynamic imaging where the acquired data are divided into several short temporal frames. High noise in the reconstructed images translates to poor precision/reproducibility of image features. One important role of "denoising" is therefore to improve the precision of image features.

Detection of transient neurotransmitter response using personalized neural networks.

Measurement of stimulus-induced dopamine release and other types of transient neurotransmitter response (TNR) from dynamic positron emission tomography (PET) images typically suffers from limited detection sensitivity and high false positive (FP) rates. Measurement of TNR of a voxel-level can be particularly problematic due to high image noise. In this work, we perform voxel-level TNR detection using artificial neural networks (ANN) and compare their performance to previously used standard statistical tests.

A Monte Carlo approach for improving transient dopamine release detection sensitivity.

Current methods using a single PET scan to detect voxel-level transient dopamine release-using F-test (significance) and cluster size thresholding-have limited detection sensitivity for clusters of release small in size and/or having low release levels. Specifically, simulations show that voxels with release near the peripheries of such clusters are often rejected-becoming false negatives and ultimately distorting the F-distribution of rejected voxels. We suggest a Monte Carlo method that incorporates these two observations into a cost function, allowing erroneously rejected voxels to be accepted under specified criteria.

Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.

Purpose: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PD patients.

Methods: We systematically probed a range of robust predictor algorithms, aiming to find best combinations of features for significantly improved prediction of motor outcome (MDS-UPDRS-III) in PD.

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from high levels of noise, drastically reducing the precision of parametric image analysis. In this paper, we investigate the use of machine learning and artificial neural networks to denoise dynamic PET images. We train a deep denoising autoencoder (DAE) using noisy and noise-free spatiotemporal image patches, extracted from the simulated images of [C]raclopride, a dopamine D receptor agonist.

Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson's disease induced presynaptic alterations.

Most neurodegenerative diseases are known to affect several aspects of brain function, including neurotransmitter systems, metabolic and functional connectivity. Diseases are generally characterized by common clinical characteristics across subjects, but there are also significant inter-subject variations. It is thus reasonable to expect that in terms of brain function, such clinical behaviors will be related to a general overall multi-system pattern of disease-induced alterations and additional brain system-specific abnormalities; these additional abnormalities would be indicative of a possible unique system response to disease or subject-specific propensity to a specific clinical progression.

Artificial Neural Network-Based Prediction of Outcome in Parkinson's Disease Patients Using DaTscan SPECT Imaging Features.

Purpose: Quantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confidence and improve their potential as a biomarker to monitor the progression of Parkinson's disease (PD). In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques.

Procedures: We designed and trained artificial neural networks (ANNs) to analyze the data from 69 patients within the Parkinson's Progressive Marker Initiative (PPMI) database.

Data-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration.

Spatial patterns of radiotracer binding in positron emission tomography (PET) images may convey information related to the disease topology. However, this information is not captured by the standard PET image analysis that quantifies the mean radiotracer uptake within a region of interest (ROI). On the other hand, spatial analyses that use more advanced radiomic features may be difficult to interpret.