Preliminary protocol for measuring the reproducibility and accuracy of flow values on digital PET/CT systems in [O]HO myocardial perfusion imaging using a flow phantom.

Background: Several factors may decrease the accuracy of quantitative PET myocardial perfusion imaging (MPI). It is therefore essential to ensure that myocardial blood flow (MBF) values are reproducible and accurate, and to design systematic protocols to achieve this. Until now, no systematic phantom protocols have been available to assess the technical factors affecting measurement accuracy and reproducibility in MPI.

Explainable deep-learning-based ischemia detection using hybrid O-15 HO perfusion positron emission tomography and computed tomography imaging with clinical data.

Background: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 HO perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings.

Methods: A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image-and data-based classifier considering 35 clinical, CTA, and PET variables.

Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data.

Background: Cardiac positron emission tomography (PET) can visualize and quantify the molecular and physiological pathways of cardiac function. However, cardiac and respiratory motion can introduce blurring that reduces PET image quality and quantitative accuracy. Dual cardiac- and respiratory-gated PET reconstruction can mitigate motion artifacts but increases noise as only a subset of data are used for each time frame of the cardiac cycle.

Evaluation metrics and statistical tests for machine learning.

Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval.

Imaging of Myocardial αβ Integrin Expression for Evaluation of Myocardial Injury After Acute Myocardial Infarction.

[Ga]Ga-NODAGA-Arg-Gly-Asp (RGD) is a PET tracer targeting αβ integrin, which is upregulated during angiogenesis soon after acute myocardial infarction (AMI). We prospectively evaluated determinants of myocardial uptake of [Ga]Ga-NODAGA-RGD and its associations with left ventricular (LV) function in patients after AMI. Myocardial blood flow and [Ga]Ga-NODAGA-RGD uptake (60 min after injection) were evaluated by PET in 31 patients 7.

Quantitative Perfusion Imaging with Total-Body PET.

Recently, PET systems with a long axial field of view have become the current state of the art. Total-body PET scanners enable unique possibilities for scientific research and clinical diagnostics, but this new technology also raises numerous challenges. A key advantage of total-body imaging is that having all the organs in the field of view allows studying biologic interaction of all organs simultaneously.

Incremental prognostic value of downstream positron emission tomography perfusion imaging after coronary computed tomography angiography: a study using machine learning.

Aims: To evaluate the incremental value of positron emission tomography (PET) myocardial perfusion imaging (MPI) over coronary computed tomography angiography (CCTA) in predicting short- and long-term outcome using machine learning (ML) approaches.

Methods And Results: A total of 2411 patients with clinically suspected coronary artery disease (CAD) underwent CCTA, out of whom 891 patients were admitted to downstream PET MPI for haemodynamic evaluation of obstructive coronary stenosis. Two sets of Extreme Gradient Boosting (XGBoost) ML models were trained, one with all the clinical and imaging variables (including PET) and the other with only clinical and CCTA-based variables.

Hybridizing machine learning in survival analysis of cardiac PET/CT imaging.

Background: Machine Learning (ML) allows integration of the numerous variables delivered by cardiac PET/CT, while traditional survival analysis can provide explainable prognostic estimates from a restricted number of input variables. We implemented a hybrid ML-and-survival analysis of multimodal PET/CT data to identify patients who developed myocardial infarction (MI) or death in long-term follow up.

Methods: Data from 739 intermediate risk patients who underwent coronary CT and selectively stress O-water-PET perfusion were analyzed for the occurrence of MI and all-cause mortality.

Carimas: An Extensive Medical Imaging Data Processing Tool for Research.

Carimas is a multi-purpose medical imaging data processing tool, which can be used to visualize, analyze, and model different medical images in research. Originally, it was developed only for positron emission tomography data in 2009, but the use of this software has extended to many other tomography imaging modalities, such as computed tomography and magnetic resonance imaging. Carimas is especially well-suited for analysis of three- and four-dimensional image data and creating polar maps in modeling of cardiac perfusion.

The performance of machine learning approaches for attenuation correction of PET in neuroimaging: A meta-analysis.

Purpose: This systematic review provides a consensus on the clinical feasibility of machine learning (ML) methods for brain PET attenuation correction (AC). Performance of ML-AC were compared to clinical standards.

Methods: Two hundred and eighty studies were identified through electronic searches of brain PET studies published between January 1, 2008, and August 1, 2022.

A retrospective evaluation of Bayesian-penalized likelihood reconstruction for [O]HO myocardial perfusion imaging.

Background: New Block-Sequential-Regularized-Expectation-Maximization (BSREM) image reconstruction technique has been introduced for clinical use mainly for oncologic use. Accurate and quantitative image reconstruction is essential in myocardial perfusion imaging with positron emission tomography (PET) as it utilizes absolute quantitation of myocardial blood flow (MBF). The aim of the study was to evaluate BSREM reconstruction for quantitation in patients with suspected coronary artery disease (CAD).

Evaluation of three methods for delineation and attenuation estimation of the sinus region in MR-based attenuation correction for brain PET-MR imaging.

Background: Attenuation correction is crucial in quantitative positron emission tomography-magnetic resonance (PET-MRI) imaging. We evaluated three methods to improve the segmentation and modelling of the attenuation coefficients in the nasal sinus region. Two methods (cuboid and template method) included a MRI-CT conversion model for assigning the attenuation coefficients in the nasal sinus region, whereas one used fixed attenuation coefficient assignment (bulk method).

Classification of ischemia from myocardial polar maps in O-HO cardiac perfusion imaging using a convolutional neural network.

We implemented a two-dimensional convolutional neural network (CNN) for classification of polar maps extracted from Carimas (Turku PET Centre, Finland) software used for myocardial perfusion analysis. 138 polar maps from O-HO stress perfusion study in JPEG format from patients classified as ischemic or non-ischemic based on finding obstructive coronary artery disease (CAD) on invasive coronary artery angiography were used. The CNN was evaluated against the clinical interpretation.

Comparison of: (2,4)-4-[F]Fluoroglutamine, [C]Methionine, and 2-Deoxy-2-[F]Fluoro--Glucose and Two Small-Animal PET/CT Systems Imaging Rat Gliomas.

Purpose: The three positron emission tomography (PET) imaging compounds: (2,4)-4-[F]Fluoroglutamine ([F]FGln), -[methyl-C]Methionine ([C]Met), and 2-deoxy-2-[F]fluoro--glucose ([F]FDG) were investigated to contrast their ability to image orthotopic BT4C gliomas in BDIX rats. Two separate small animal imaging systems were compared for their tumor detection potential. Dynamic acquisition of [F]FGln was evaluated with multiple pharmacokinetic models for future quantitative comparison.

Episodic memory and cortical amyloid pathology: PET study in cognitively discordant twin pairs.

We studied the association between episodic memory and cortical fibrillar β-amyloid pathology within twin pairs. Using telephone-administered cognitive screening of 1415 twin pairs in a population-based older Finnish Twin Cohort study, we identified 45 (mean [SD] age 72.9 [4.

Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification.

Background: Dual-gating reduces respiratory and cardiac motion effects but increases noise. With motion correction, motion is minimized and image quality preserved. We applied motion correction to create end-diastolic respiratory motion corrected images from dual-gated images.

A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography.

We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation technique. The presented method can be used to detect respiratory cycles and estimate their lengths with state-of-the-art accuracy when compared to other IMU-based methods, and is the first based on commercial MEMS devices, which can estimate quantitatively both the magnitude and the phase of respiratory motion from the abdomen and chest regions.

Assessment of a digital and an analog PET/CT system for accurate myocardial perfusion imaging with a flow phantom.

Unlabelled: In Myocardial Perfusion Imaging (MPI) with Positron Emission Tomography/Computed Tomography (PET/CT) systems, accurate quantification is essential. We assessed flow quantification accuracy over various injected activities using a flow phantom.

Methods: The study was performed on the digital 4-ring Discovery MI (DMI-20) and analog Discovery 690 (D690) PET/CT systems, using 325-1257 MBq of [O]HO.

Evaluation of image quality with four positron emitters and three preclinical PET/CT systems.

Background: We investigated the image quality of C, Ga, F and Zr, which have different positron fractions, physical half-lifes and positron ranges. Three small animal positron emission tomography/computed tomography (PET/CT) systems were used in the evaluation, including the Siemens Inveon, RAYCAN X5 and Molecubes β-cube. The evaluation was performed on a single scanner level using the national electrical manufacturers association (NEMA) image quality phantom and analysis protocol.

Estimation of optimal number of gates in dual gated F-FDG cardiac PET.

Gating of positron emission tomography images has been shown to reduce the motion effects, especially when imaging small targets, such as coronary plaques. However, the selection of optimal number of gates for gating remains a challenge. Selecting too high number of gates results in a loss of signal-to-noise ratio, while too low number of gates does remove only part of the motion.

Change in abdominal, but not femoral subcutaneous fat CT-radiodensity is associated with improved metabolic profile after bariatric surgery.

Background And Aims: Computed tomography (CT)-derived adipose tissue radiodensity represents a potential noninvasive surrogate marker for lipid deposition and obesity-related metabolic disease risk. We studied the effects of bariatric surgery on CT-derived adipose radiodensities in abdominal and femoral areas and their relationships to circulating metabolites in morbidly obese patients.

Methods And Results: We examined 23 morbidly obese women who underwent CT imaging before and 6 months after bariatric surgery.

Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination.

Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF). However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited.