Hemodynamic Relevance Evaluation of Coronary Artery Anomaly During Stress Using FFR/IVUS in an Artificial Twin.

Anomalous aortic origin of coronary artery can lead to ischemia. Due to the limitations of invasive catheterization dobutamine stress testing, an alternative noninvasive approach is desired. A 65-year-old woman with atypical chest pain was referred for coronary computed tomography angiography.

Deep Radiogenomics Sequencing for Breast Tumor Gene-Phenotype Decoding Using Dynamic Contrast Magnetic Resonance Imaging.

Purpose: We aim to perform radiogenomic profiling of breast cancer tumors using dynamic contrast magnetic resonance imaging (MRI) for the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) genes.

Methods: The dataset used in the current study consists of imaging data of 922 biopsy-confirmed invasive breast cancer patients with ER, PR, and HER2 gene mutation status. Breast MR images, including a T1-weighted pre-contrast sequence and three post-contrast sequences, were enrolled for analysis.

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.

Background: Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non-invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches.

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18 F-FDG PET Scans.

Introduction: We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18 F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence-driven 18 F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets.

Patients And Methods: Two experienced nuclear medicine physicians separately evaluated the quality of 174 18 F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale.

Prognostic Implications of Clinical and Imaging Diagnostic Criteria for Myocarditis.

Background: The European Society of Cardiology (ESC), the American College of Cardiology, the American Heart Association, and expert consensus documents provide different diagnostic criteria for myocarditis. Their overlap and prognostic value have never been compared.

Objectives: This study aims to assess and compare the predictive value of ESC criteria for clinically suspected myocarditis, updated Lake-Louise criteria (LLC), American Heart Association criteria for probable acute myocarditis (pAM), and expert consensus criteria for acute myocarditis (AM) and complicated myocarditis (CM).

Sex- specific differences in suspected myocarditis presentations and outcomes.

Background: Signs and symptoms of myocarditis may vary among men and women.

Objectives: This study aimed to analyze sex-specific differences in the presentation and outcomes of patients with suspected myocarditis.

Methods: Patients meeting clinical ESC criteria for suspected myocarditis were included from two tertiary centers between 2002 and 2021.

Risk Stratification in Nonischemic Dilated Cardiomyopathy Using CMR Imaging: A Systematic Review and Meta-Analysis.

Importance: Accurate risk stratification of nonischemic dilated cardiomyopathy (NIDCM) remains challenging.

Objective: To evaluate the association of cardiac magnetic resonance (CMR) imaging-derived measurements with clinical outcomes in NIDCM.

Data Sources: MEDLINE, Embase, Cochrane Library, and Web of Science Core Collection databases were systematically searched for articles from January 2005 to April 2023.

Organomics: A Concept Reflecting the Importance of PET/CT Healthy Organ Radiomics in Non-Small Cell Lung Cancer Prognosis Prediction Using Machine Learning.

Purpose: Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions.

Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs).

We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes.

Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies.

Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment of several different types of cancers. However, the current approaches to RPTs often follow a somewhat inflexible "one size fits all" paradigm, where patients are administered the same amount of radioactivity per cycle regardless of their individual characteristics and features. This approach fails to consider inter-patient variations in radiopharmacokinetics, radiation biology, and immunological factors, which can significantly impact treatment outcomes.

Impact of tafamidis on myocardial function and CMR tissue characteristics in transthyretin amyloid cardiomyopathy.

Aims: Tafamidis improves clinical outcomes in transthyretin amyloid cardiomyopathy (ATTR-CM), yet how tafamidis affects cardiac structure and function remains poorly described. This study prospectively analysed the effect of tafamidis on 12-month longitudinal changes in cardiac structure and function by cardiac magnetic resonance (CMR) compared with the natural course of disease in an untreated historic control cohort.

Methods And Results: ATTR-CM patients underwent CMR at tafamidis initiation and at 12 months.

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

Background: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research.

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods.

Purpose: This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC).

Methods: We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC.

Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study.

This study investigated the impact of ComBat harmonization on the reproducibility of radiomic features extracted from magnetic resonance images (MRI) acquired on different scanners, using various data acquisition parameters and multiple image pre-processing techniques using a dedicated MRI phantom. Four scanners were used to acquire an MRI of a nonanatomic phantom as part of the TCIA RIDER database. In fast spin-echo inversion recovery (IR) sequences, several inversion durations were employed, including 50, 100, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 ms.

Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.

Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training.

Prognostic value of visual and quantitative CMR regional myocardial function in patients with suspected myocarditis.

According to updated Lake-Louise Criteria, impaired regional myocardial function serves as a supportive criterion in diagnosing myocarditis. This study aimed to assess visual regional wall motional abnormalities (RWMA) and novel quantitative regional longitudinal peak strain (RLS) for risk stratification in the clinical setting of myocarditis. In patients undergoing CMR and meeting clinical criteria for suspected myocarditis global longitudinal strain (GLS), late gadolinium enhancement (LGE), RWMA and RLS were assessed in the anterior, septal, inferior, and lateral regions and correlated to the occurrence of major adverse cardiac events (MACE), including heart failure hospitalization, sustained ventricular tachycardia, recurrent myocarditis, and all-cause death.

Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet+.

Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures.

The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters.

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions.

Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [Lu]Lu-DOTATATE radiopharmaceutical therapy.

Purpose: Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks.