End-to-end reproducible AI pipelines in radiology using the cloud.

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging.

Cloud-based large-scale curation of medical imaging data using AI segmentation.

Rapid advances in medical imaging Artificial Intelligence (AI) offer unprecedented opportunities for automatic analysis and extraction of data from large imaging collections. Computational demands of such modern AI tools may be difficult to satisfy with the capabilities available on premises. Cloud computing offers the promise of economical access and extreme scalability.

Enrichment of lung cancer computed tomography collections with AI-derived annotations.

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and can be used to automatically annotate large datasets.

National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence.

The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts large and diverse publicly available cancer image data collections.

Deep Learning Based Parameterization of Diffeomorphic Image Registration for Cardiac Image Segmentation.

Cardiac segmentation from magnetic resonance imaging (MRI) is one of the essential tasks in analyzing the anatomy and function of the heart for the assessment and diagnosis of cardiac diseases. However, cardiac MRI generates hundreds of images per scan, and manual annotation of them is challenging and time-consuming, and therefore processing these images automatically is of interest. This study proposes a novel end-to-end supervised cardiac MRI segmentation framework based on a diffeomorphic deformable registration that can segment cardiac chambers from 2D and 3D images or volumes.

Benefit of stereoscopic volume rendering for the identification of pediatric pulmonary vein stenosis from CT angiography.

The use of three-dimensional (3D) technologies in medical practice is increasing; however, its use is largely untested. One 3D technology, stereoscopic volume-rendered 3D display, can improve depth perception. Pulmonary vein stenosis (PVS) is a rare cardiovascular pathology, often diagnosed by computed tomography (CT), where volume rendering may be useful.

Promoting the use of the PI-QUAL score for prostate MRI quality: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship.

Objectives: The Prostate Imaging Quality (PI-QUAL) score is a new metric to evaluate the diagnostic quality of multiparametric magnetic resonance imaging (MRI) of the prostate. This study assesses the impact of an intervention, namely a prostate MRI quality training lecture, on the participant's ability to apply PI-QUAL.

Methods: Sixteen participants (radiologists, urologists, physicists, and computer scientists) of varying experience in reviewing diagnostic prostate MRI all assessed the image quality of ten examinations from different vendors and machines.

Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19.

There is a crucial need for quick testing and diagnosis of patients during the COVID-19 pandemic. Lung ultrasound is an imaging modality that is cost-effective, widely accessible, and can be used to diagnose acute respiratory distress syndrome in patients with COVID-19. It can be used to find important characteristics in the images, including A-lines, B-lines, consolidation, and pleural effusion, which all inform the clinician in monitoring and diagnosing the disease.

Cardiac MRI-Derived Myocardial Deformation Parameters Correlate with Pulmonary Valve Replacement Indications in Repaired Tetralogy of Fallot.

Right ventricular (RV) volumetric cardiac magnetic resonance (CMR) criteria serve as indicators for pulmonary valve replacement (PVR) in repaired tetralogy of Fallot (rTOF). Myocardial deformation and tricuspid valve displacement parameters may be more sensitive measures of RV dysfunction. This study's aim was to describe rTOF RV deformation and tricuspid displacement patterns using novel CMR semi-automated software and determine associations with standard CMR measures.

A New Semi-automated Algorithm for Volumetric Segmentation of the Left Ventricle in Temporal 3D Echocardiography Sequences.

Purpose: Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is challenging due to the inherent properties of ultrasound imaging, such as the presence of speckle noise and the low signal-to-noise ratio.

Methods: We propose a semi-automated segmentation algorithm for the delineation of the left ventricle in temporal 3D echocardiography sequences.

Validation of a diffeomorphic registration algorithm using true deformation computed from thin plate spline interpolation.

Despite the inter and intraobserver variabilities, manual contours are commonly used as surrogates for ground truth in the validation process for nonrigid medical image registration. In contrast, this study proposes the use of thin plate spline interpolation to create a true deformation field. A diffeomorphic registration method was compared to the true deformation field along with three other algorithms and was evaluated on simulated cardiac motion deformation over 10 subjects from the Automated Cardiac Diagnosis Challenge (ACDC) dataset.

Validation of Diffeomorphic Registration on Cine Cardiac MR via Direct Frame-to-Frame Comparison with HARP Tracking on Tagged MR.

Cardiac magnetic resonance (MR) tissue tagging offers an excellent solution for tracking deformation and is considered the reference standard for the quantification of strain. However, due to the requirements for a dedicated acquisition sequence and post-processing software, tagged MR acquisitions are performed much less frequently in routine clinical practice than the anatomical cine MR sequence. Using tagged MR as the reference standard, this study proposes an approach to evaluate a diffeomorphic image registration algorithm applied on cine MR images to compute the cardiac deformation.

A Novel 4D Semi-Automated Algorithm for Volumetric Segmentation in Echocardiography.

Segmentation of the left ventricle (LV) in temporal 3D echocardiography sequences poses a challenge. However, it is an essential component in generating quantitative clinical measurements for the diagnosis and treatment of various cardiac diseases. Identifying the endocardial borders of the left ventricle can be difficult due to the inherent properties of ultrasound.

Serum levels of soluble urokinase plasminogen activator receptor (suPAR) as a marker of tuberculosis treatment efficacy.

Background: Upon diagnosis, Pulmonary Tuberculosis patients are treated for TB for a period of 6-9 months. At present, there exists very little indication of the efficacy of the particular treatment. A few previous studies have shown that soluble urokinase plasminogen activator receptor (SuPAR) may be used as treatment efficacy marker.

Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury.

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects (n = 9) compared with age-matched control subjects (n = 15).

An exploratory analysis linking neuropsychological testing to quantification of tractography using High Definition Fiber Tracking (HDFT) in military TBI.

To realize the potential value of tractography in traumatic brain injury (TBI), we must identify metrics that provide meaningful information about functional outcomes. The current study explores quantitative metrics describing the spatial properties of tractography from advanced diffusion imaging (High Definition Fiber Tracking, HDFT). In a small number of right-handed males from military TBI (N = 7) and civilian control (N = 6) samples, both tract homologue symmetry and tract spread (proportion of brain mask voxels contacted) differed for several tracts among civilian controls and extreme groups in the TBI sample (high scorers and low scorers) for verbal recall, serial reaction time, processing speed index, and trail-making.

Quantifying white matter structural integrity with high-definition fiber tracking in traumatic brain injury.

There is an urgent, unmet demand for definitive biological diagnosis of traumatic brain injury (TBI) to pinpoint the location and extent of damage. We have developed High-Definition Fiber Tracking, a 3 T magnetic resonance imaging-based diffusion spectrum imaging and tractography analysis protocol, to quantify axonal injury in military and civilian TBI patients. A novel analytical methodology quantified white matter integrity in patients with TBI and healthy controls.

Consumption patterns and levels among households with HIV positive members and economic impoverishment due to medical spending in Pune city, India.

HIV infection poses a serious threat to the economy of a household. Out of pocket (OOP) health spending can be prohibitive and can drag households below poverty level. Based on the data collected from a cross-sectional survey of 401 households with HIV+ members in Pune city, India, this paper examines the consumption levels and patterns among households, and comments on the economic impoverishment resulting from OOP medical spending.

Regulatory network analysis acceleration with reconfigurable hardware.

In medical research it is of great importance to be able to quickly obtain answers to inquiries about system response to different stimuli. Modeling the dynamics of biological regulatory networks is a promising approach to achieve this goal, but existing modeling approaches suffer from complexity issues and become inefficient with large networks. In order to improve the efficiency, we propose the implementation of models of regulatory networks in hardware, which allows for highly parallel simulation of these networks.

Azetidin-2-ones, synthon for biologically important compounds.

Azetidin-2-one, a four-membered cyclic lactam (beta-lactam) skeleton has been recognised as a useful building block for the synthesis of a large number of organic molecules by exploiting the strain energy associated with it, in addition to its use in the synthesis of a variety of beta-lactam antibiotics. Efforts have been made in exploring such new aspects of beta-lactam chemistry using enantiomerically pure beta-lactams as versatile intermediates for the synthesis of aromatic beta-amino acids and their derivatives, peptides, polyamines, polyamino alcohols, amino sugars and polyamino ethers. The development of methodologies based on beta-lactam nucleus is now referred as 'the beta-lactam synthon methods'.