Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.

Purpose: To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).

Methods: This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100 keV).

The Environmental Impact of Iodinated Contrast Media: Strategies for Optimized Use and Recycling.

Iodinated contrast media (ICM) is an integral and ubiquitous component of modern diagnostic imaging. Although most radiology practices are familiar with ICM administration and physiological excretion, they may be less aware of how much ICM is wasted on a per exam basis. Furthermore, radiologists may not recognize the environmental fate of discarded ICM waste.

Standardizing technical parameters and terms for abdominopelvic photon-counting CT: laying the groundwork for innovation and evidence sharing.

Photon-counting detector CT (PCD-CT) is a new technology that has multiple diagnostic benefits including increased spatial resolution, iodine signal, and radiation dose efficiency, as well as multi-energy imaging capability, but which also has unique challenges in abdominal imaging. The purpose of this work is to summarize key features, technical parameters, and terms, which are common amongst current abdominopelvic PCD-CT systems and to propose standardized terminology (where none exists). In addition, user-selectable protocol parameters are highlighted to facilitate both scientific evaluation and early clinical adoption.

CT Navigation for Percutaneous Needle Placement: How I Do It.

CT navigation (CTN) has recently been developed to combine many of the advantages of conventional CT and CT-fluoroscopic guidance for needle placement. CTN systems display real-time needle position superimposed on a CT dataset. This is accomplished by placing electromagnetic (EM) or optical transmitters/sensors on the patient and needle, combined with fiducials placed within the scan field to superimpose a known needle location onto a CT dataset.

Artificial Intelligence-Aided Selection of Needle Pathways: Proof-of-Concept in Percutaneous Lung Biopsies.

Purpose: To evaluate the concordance between lung biopsy puncture pathways determined by artificial intelligence (AI) and those determined by expert physicians.

Materials And Methods: An AI algorithm was created to choose optimal lung biopsy pathways based on segmented thoracic anatomy and emphysema in volumetric lung computed tomography (CT) scans combined with rules derived from the medical literature. The algorithm was validated using pathways generated from CT scans of randomly selected patients (n = 48) who had received percutaneous lung biopsies and had noncontrast CT scans of 1.

Consumable Material Waste and Workflow Efficiency Comparison Between Multi-use Syringeless and Single-use Syringe-Based Injectors in Computed Tomography.

Rationale And Objectives: Syringeless power injectors obviate the need for reloading iodinated contrast media (ICM) and plastic consumable pistons between exams. This study evaluates the potential time and material waste (ICM, plastic, saline, and total) saved using a multi-use syringeless injector (MUSI) compared to a single-use syringe-based injector (SUSI).

Materials And Methods: Two observers recorded technologist time spent using a SUSI and a MUSI over three clinical workdays.

CT Number Accuracy and Association With Object Size: A Phantom Study Comparing Energy-Integrating Detector CT and Deep Silicon Photon-Counting Detector CT.

Variable beam hardening based on patient size causes variation in CT numbers for energy-integrating detector (EID) CT. Photon-counting detector (PCD) CT more accurately determines effective beam energy, potentially improving CT number reliability. The purpose of the present study was to compare EID CT and deep silicon PCD CT in terms of both the effect of changes in object size on CT number and the overall accuracy of CT numbers.

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.

Objective: Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only.

Diagnostic performance of the "drooping" sign in CT diagnosis of exophytic renal angiomyolipoma.

Objective: To evaluate the prevalence of angular interface and the "drooping" sign in exophytic renal angiomyolipomas (AMLs) and the diagnostic performance in differentiating exophytic lipid-poor AMLs from other solid renal masses.

Methods: This IRB-approved, two-center study included 185 patients with 188 exophytic solid renal masses < 4 cm with histopathology and pre-operative CT within 30 days of surgical resection or biopsy. Images were reviewed for the presence of angular interface and the "drooping" sign qualitatively by three readers blinded to the final diagnosis, with majority rules applied.

Detectability of Small Low-Attenuation Lesions With Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study.

Iterative reconstruction (IR) techniques are susceptible to contrast-dependent spatial resolution, limiting overall radiation dose reduction potential. Deep learning image reconstruction (DLIR) may mitigate this limitation. The purpose of our study was to evaluate low-contrast detectability performance and radiation-saving potential of a DLIR algorithm in comparison with filtered back projection (FBP) and IR using a human multireader noninferiority study design and task-based observer modeling.

A Method for Reducing Variability Across Dual-Energy CT Manufacturers in Quantification of Low Iodine Content Levels.

Clinical use of the dual-energy CT (DECT) iodine quantification technique is hindered by between-platform (i.e., across different manufacturers) variability in iodine concentration (IC) values, particularly at low iodine levels.

Quantitative dual-energy CT techniques in the abdomen.

Advances in dual-energy CT (DECT) technology and spectral techniques are catalyzing the widespread implementation of this technology across multiple radiology subspecialties. The inclusion of energy- and material-specific datasets has ushered overall improvements in CT image contrast and noise as well as artifacts reduction, leading to considerable progress in radiologists' ability to detect and characterize pathologies in the abdomen. The scope of this article is to provide an overview of various quantitative clinical DECT applications in the abdomen and pelvis.

Virtual Unenhanced Dual-Energy CT Images Obtained with a Multimaterial Decomposition Algorithm: Diagnostic Value for Renal Mass and Urinary Stone Evaluation.

Background Virtual unenhanced (VUE) images obtained by using a dual-energy CT (DECT) multimaterial decomposition algorithm hold promise for diagnostic use in the abdomen in lieu of true unenhanced (TUE) images. Purpose To assess VUE images obtained from a DECT multimaterial decomposition algorithm in patients undergoing renal mass and urinary stone evaluation. Materials and Methods In this retrospective Health Insurance Portability and Accountability Act-compliant study, DECT was performed in patients undergoing evaluation for renal mass or urinary stone.

Use of Dual-Energy CT for Quantification of Essential Trace Metals (Iron, Copper, and Zinc): Proof of Concept.

Altered concentrations of essential trace metals have been associated with the development of abdominal tumors. We developed a method to quantify trace metals (iron, copper, and zinc) using monochromatic data from commercially available dual-energy CT (DECT) implementations. Our data provide a foundation for the use of DECT for noninvasive quantification of essential trace metals.

CT Detectability of Small Low-Contrast Hypoattenuating Focal Lesions: Iterative Reconstructions versus Filtered Back Projection.

Purpose To investigate performance in detectability of small (≤1 cm) low-contrast hypoattenuating focal lesions by using filtered back projection (FBP) and iterative reconstruction (IR) algorithms from two major CT vendors across a range of 11 radiation exposures. Materials and Methods A low-contrast detectability phantom consisting of 21 low-contrast hypoattenuating focal objects (seven sizes between 2.4 and 10.

Microsurgical Pontine Descending Tractotomy in Cases of Intractable Trigeminal Neuralgia.

Background: Current treatment strategies in patients with trigeminal neuralgia (TN) include trials of medical therapy and surgical intervention, when necessary. In some patients, pain is not adequately managed with these existing strategies.

Objective: To present a novel technique, ventral pontine trigeminal tractotomy via retrosigmoid craniectomy, as an adjunct treatment in TN when there is no significant neurovascular compression.

An angiogenic inhibitor, cyclic RGDfV, attenuates MPTP-induced dopamine neuron toxicity.

We previously demonstrated that several dopamine (DA) neurotoxins produced punctate areas of FITC-labeled albumin (FITC-LA) leakage in the substantia nigra and striatum suggesting blood brain barrier (BBB) dysfunction. Further, this leakage was co-localized with αvβ3 integrin up-regulation, a marker for angiogenesis. This suggested that the FITC-LA leakage might have been a result of angiogenesis.