A simple but tough-to-beat baseline for fMRI time-series classification.

Current neuroimaging studies frequently use complex machine learning models to classify human fMRI data, distinguishing healthy and disordered brains, often to validate new methods or enhance prediction accuracy. Yet, where prediction accuracy is a concern, our results suggest that precision in prediction does not always require such sophistication. When a classifier as simple as logistic regression is applied to feature-engineered fMRI data, it can match or even outperform more sophisticated recent models.

Standardization of scan protocols for RT CT simulator from different vendors using quantitative image quality technique.

Objective: To investigate the feasibility of standardizing RT simulation CT scanner protocols between vendors using target-based image quality (IQ) metrics.

Method And Materials: A systematic assessment process in phantom was developed to standardize clinical scan protocols for scanners from different vendors following these steps: (a) images were acquired by varying CTDI and using an iterative reconstruction (IR) method (IR: iDose and model-based iterative reconstruction [IMR] of CT-Philips Big Bore scanner, SAFIRE of CT-Siemens biograph PETCT scanner), (b) CT exams were classified into body and brain protocols, (c) the rescaled noise power spectrum (NPS) was calculated, (d) quantified the IQ change due to varied CTDI and IR, and (e) matched the IR strength level. IQ metrics included noise and texture from NPS, contrast, and contrast-to-noise ratio (CNR), low contrast detectability (d').

Determinants of radiation exposure during mobile cone-beam CT-guided robotic-assisted bronchoscopy.

Background And Objective: Robotic-assisted bronchoscopy (RAB) is an emerging modality to sample pulmonary lesions. Cone-beam computed tomography (CBCT) can be incorporated into RAB. We investigated the magnitude and predictors of patient and staff radiation exposure during mobile CBCT-guided shape-sensing RAB.

An adult and pediatric size-based contrast administration reduction phantom study for single and dual-energy CT through preservation of contrast-to-noise ratio.

Background: Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams.

Purpose: The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study.

Materials And Methods: In SECT study, phantoms with effective diameters of 9.

An automated technique for global noise level measurement in CT image with a conjunction of image gradient.

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta.

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.

The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples.

GLACIER: GLASS-BOX TRANSFORMER FOR INTERPRETABLE DYNAMIC NEUROIMAGING.

Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features.

Artificial Intelligence in CT and MR Imaging for Oncological Applications.

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples.

Patient Radiation Doses in IR Procedures: The American College of Radiology Dose Index Registry-Fluoroscopy Pilot.

Purpose: To update normative data on fluoroscopy dose indices in the United States for the first time since the Radiation Doses in Interventional Radiology study in the late 1990s.

Materials And Methods: The Dose Index Registry-Fluoroscopy pilot study collected data from March 2018 through December 2019, with 50 fluoroscopes from 10 sites submitting data. Primary radiation dose indices including fluoroscopy time (FT), cumulative air kerma (K), and kerma area product (P) were collected for interventional radiology fluoroscopically guided interventional (FGI) procedures.

Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI.

Brain network interactions are commonly assessed via functional (network) connectivity, captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity can represent static and dynamic relations, but often these are modeled using a fixed choice for the data window Alternatively, deep learning models may flexibly learn various representations from the same data based on the model architecture and the training task. However, the representations produced by deep learning models are often difficult to interpret and require additional posthoc methods, e.

Patient Radiation Doses in Interventional Radiology Procedures: Comparison of Fluoroscopy Dose Indices between the American College of Radiology Dose Index Registry-Fluoroscopy Pilot and the Radiation Doses in Interventional Radiology Study.

Purpose: To compare radiation dose index distributions for fluoroscopically guided interventions in interventional radiology from the American College of Radiology (ACR) Fluoroscopy Dose Index Registry (DIR-Fluoro) pilot to those from the Radiation Doses in Interventional Radiology (RAD-IR) study.

Materials And Methods: Individual and grouped ACR Common identification numbers (procedure types) from the DIR-Fluoro pilot were matched to procedure types in the RAD-IR study. Fifteen comparisons were made.

Interpreting models interpreting brain dynamics.

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics.

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans.

We map single energy CT (SECT) scans to synthetic dual-energy CT (synth-DECT) material density iodine (MDI) scans using deep learning (DL) and demonstrate their value for liver segmentation. A 2D pix2pix (P2P) network was trained on 100 abdominal DECT scans to infer synth-DECT MDI scans from SECT scans. The source and target domain were paired with DECT monochromatic 70 keV and MDI scans.

Associations, microbiology and outcomes of pre-training peritoneal dialysis-related peritonitis.

Background: Pre-training peritonitis (PTP), defined as peritonitis that occurred after catheter insertion and before peritoneal dialysis (PD) training, is increasingly recognized as a risk factor for adverse patient outcomes, yet poorly understood with limited studies conducted to date. This study was conducted to identify the associations, microbiologic profiles and outcomes of PTP compared to post-training peritonitis.

Methods: This single-centre, case-control study involved patients with kidney failure who had PD as their first kidney replacement therapy and had experienced an episode of PD peritonitis between 1 January 2005 and 31 December 2015.

AAPM Task Group Report 272: Comprehensive acceptance testing and evaluation of fluoroscopy imaging systems.

Modern fluoroscopes used for image guidance have become quite complex. Adding to this complexity are the many regulatory and accreditation requirements that must be fulfilled during acceptance testing of a new unit. Further, some of these acceptance tests have pass/fail criteria, whereas others do not, making acceptance testing a subjective and time-consuming task.

Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems.

Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety.

Quality control of radiomic features using 3D-printed CT phantoms.

: The lack of standardization in quantitative radiomic measures of tumors seen on computed tomography (CT) scans is generally recognized as an unresolved issue. To develop reliable clinical applications, radiomics must be robust across different CT scan modes, protocols, software, and systems. We demonstrate how custom-designed phantoms, imprinted with human-derived patterns, can provide a straightforward approach to validating longitudinally stable radiomic signature values in a clinical setting.

Comparison of Multimaterial Decomposition Fat Fraction with DECT and Proton Density Fat Fraction with IDEAL IQ MRI for Quantification of Liver Steatosis in a Population Exposed to Chemotherapy.

Introduction: Oncologic patients who develop chemotherapy-associated liver injury (CALI) secondary to chemotherapy treatment tend to have worse outcomes. Biopsy remains the gold standard for the diagnosis of hepatic steatosis. The purpose of this article is to compare 2 alternatives: Proton-Density-Fat-Fraction (PDFF) MRI and MultiMaterial-Decomposition (MMD) DECT.

Reproducibility of radiomic features using network analysis and its application in Wasserstein -means clustering.

The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. We propose a regularized partial correlation network to identify reliable and reproducible radiomic features. This approach was tested on two radiomic feature sets generated using two different reconstruction methods on computed tomography (CT) scans from a cohort of 47 lung cancer patients.

Initial evaluation of dual-energy computed tomography as an imaging biomarker for hepatic metastases from neuroendocrine tumor of the gastrointestinal tract.

Background: To evaluate quantitative iodine parameters from the arterial phase dual-energy computed tomography (DECT) scans as an imaging biomarker for tumor grade (TG), mitotic index (MI), and Ki-67 proliferation index of hepatic metastases from neuroendocrine tumors (NETs) of the gastrointestinal (GI) tract. Imaging biomarkers have the potential to provide relevant clinical information about pathologic processes beyond lesion morphology. NETs are a group of rare, heterogeneous neoplasms classified by World Health Organization (WHO) TG, which is derived from MI and Ki-67 proliferation index.

A Novel Splice-Site Variant in Causes a Phenotype Synonymous with Åland Island Eye Disease and Incomplete Congenital Stationary Night Blindness.

Background: -related disorders encompass progressive and non-progressive disorders, including Åland island eye disease and incomplete congenital stationary night blindness. These two X-linked disorders are characterized by nystagmus, color vision defect, myopia, and electroretinography (ERG) abnormalities. Ocular hypopigmentation and iris transillumination are reported only in patients with Åland island eye disease.

The American College of Radiology Fluoroscopy Dose Index Registry Pilot: Technical Considerations and Dosimetric Performance of the Interventional Fluoroscopes.

Purpose: To characterize the accuracy and consistency of fluoroscope dose index reporting and report rates of occupational radiation safety hardware availability and use, trainee participation in procedures, and optional hardware availability at pilot sites for the American College of Radiology (ACR) Fluoroscopy Dose Index Registry (DIR).

Materials And Methods: Nine institutions participated in the registry pilot, providing fluoroscopic technical and clinical practice data from 38 angiographic C-arm-type fluoroscopes. These data included measurements of the procedure table and mattress transmission factors and accuracy measurements of the reference-point air kerma (K) and air kerma-area product (P).