Pulmonary nodule visualization and evaluation of AI-based detection at various ultra-low-dose levels using photon-counting detector CT.

Background: Radiation dose should be as low as reasonably achievable. With the invention of photon-counting detector computed tomography (PCD-CT), the radiation dose may be considerably reduced.

Purpose: To evaluate the potential of PCD-CT for dose reduction in pulmonary nodule visualization for human readers as well as for computer-aided detection (CAD) studies.

Assessment of interstitial lung disease in a systemic sclerosis patient cohort using photon-counting detector CT with ultra-high resolution and a 1024-pixel image matrix.

Objective: This study assessed the potential of ultra-high resolution (UHR) and a 1024-matrix in photon-counting-detector CT (PCD-CT) for evaluating interstitial lung disease (ILD) in systemic sclerosis (SSc) patients.

Methods: Sixty-six SSc patients who underwent ILD-CT screening on a first-generation PCD-CT were retrospectively included. Scans were performed in UHR mode at 100 kVp with two different matrix sizes (512×512 and 1024x1024) and reconstructed at slice thicknesses of 1.

Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake.

In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome.

Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network.

Background: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS.

Methods: 884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified.

Multi-Energy Low-Kiloelectron Volt versus Single-Energy Low-Kilovolt Images for Endoleak Detection at CT Angiography of the Aorta.

Purpose To compare image quality, diagnostic performance, and conspicuity between single-energy and multi-energy images for endoleak detection at CT angiography (CTA) after endovascular aortic repair (EVAR). Materials and Methods In this single-center prospective randomized controlled trial, individuals undergoing CTA after EVAR between August 2020 and May 2022 were allocated to imaging using either low-kilovolt single-energy images (SEI; 80 kV, group A) or low-kiloelectron volt virtual monoenergetic images (VMI) at 40 and 50 keV from multi-energy CT (80/Sn150 kV, group B). Scan protocols were dose matched (volume CT dose index: mean, 4.

Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient's Age and Breast Quadrant.

Objectives: Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age.

Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI.

Objectives: Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification.

Methods: For the training and validation of attention U-Net models, data coming from a single 3.

Transurethral injection of autologous muscle precursor cells for treatment of female stress urinary incontinence: a prospective phase I clinical trial.

Introduction And Hypothesis: The purpose was to investigate the safety and feasibility of transurethral injections of autologous muscle precursor cells (MPCs) into the external urinary sphincter (EUS) to treat stress urinary incontinence (SUI) in female patients.

Methods: Prospective and randomised phase I clinical trial. Standardised 1-h pad test, International Consultation on Incontinence Questionnaire-Urinary Incontinence Short Form (ICIQ-UI-SF), urodynamic study, and MRI of the pelvis were performed at baseline and 6 months after treatment.

Diagnostic value of T- and T-weighted 3-Tesla MRI for postmortem detection and age stage classification of myocardial infarction.

The aims of this study are to retrospectively evaluate the diagnostic value of T- and T-weighted 3-T magnetic resonance imaging (MRI) for postmortem detection of myocardial infarction (MI) in terms of sensitivity and specificity and to compare the MRI appearance of the infarct area with age stages. Postmortem MRI examinations (n = 88) were retrospectively reviewed for the presence or absence of MI by two raters blinded to the autopsy results. The sensitivity and specificity were calculated using the autopsy results as the gold standard.

Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle.

Objective: In this study, we investigate the feasibility of a deep Convolutional Neural Network (dCNN), trained with mammographic images, to detect and classify microcalcifications (MC) in breast-CT (BCT) images.

Methods: This retrospective single-center study was approved by the local ethics committee. 3518 icons generated from 319 mammograms were classified into three classes: "no MC" (1121), "probably benign MC" (1332), and "suspicious MC" (1065).

Systematic analysis of changes in radiomics features during dynamic breast-MRI: Evaluation of specific biomarkers.

Objectives: In this retrospective, single-center study we investigate the changes of radiomics features during dynamic breast-MRI for healthy tissue compared to benign and malignant lesions.

Methods: 60 patients underwent breast-MRI using a dynamic 3D gradient-echo sequence. Changes of 34 texture features (TF) in 30 benign and 30 malignant lesions were calculated for 5 dynamic datasets and corresponding 4 subtraction datasets.

BI-RADS-Based Classification of Mammographic Soft Tissue Opacities Using a Deep Convolutional Neural Network.

The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convolutional neural network (dCNN). Soft tissue opacities were classified based on their radiological appearance using the ACR BI-RADS atlas. We included 1744 mammograms from 438 patients to create 7242 icons by manual labeling.

Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification.

Background: We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT).

Methods: In this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a-d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria.

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network.

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) "breast tissue", (2) "benign lymph nodes", and (3) "suspicious lymph nodes". Following data preprocessing, a dCNN model was trained and validated with 5385 images.

Potential of Photon-Counting Detector CT for Radiation Dose Reduction for the Assessment of Interstitial Lung Disease in Patients With Systemic Sclerosis.

Objective: The aim of this study was to determine the potential of photon-counting detector computed tomography (PCD-CT) for radiation dose reduction compared with conventional energy-integrated detector CT (EID-CT) in the assessment of interstitial lung disease (ILD) in systemic sclerosis (SSc) patients.

Methods: In this retrospective study, SSc patients receiving a follow-up noncontrast chest examination on a PCD-CT were included between May 2021 and December 2021. Baseline scans were generated on a dual-source EID-CT by selecting the tube current-time product for each of the 2 x-ray tubes to obtain a 100% (D 100 ), a 66% (D 66 ), and a 33% dose image (D 33 ) from the same data set.

Quantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image Quality.

Background An iterative reconstruction (IR) algorithm was introduced for clinical photon-counting detector (PCD) CT. Purpose To investigate the image quality and the optimal strength level of a quantum IR algorithm (QIR; Siemens Healthcare) for virtual monoenergetic images and polychromatic images (T3D) in a phantom and in patients undergoing portal venous abdominal PCD CT. Materials and Methods In this retrospective study, noise power spectrum (NPS) was measured in a water-filled phantom.

Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?

The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed study, 634 examinations of 317 patients were included. After image selection and preparation, 5589 images from 634 different BCT examinations were sorted by a four-level density scale, ranging from A to D, using ACR BI-RADS-like criteria.

[Pulmonary nodules and pneumonia : A diagnostic guideline].

Background: Chest X‑ray is one of the most frequent examinations in radiology and its interpretation is considered part of the basic knowledge of every radiologist.

Objectives: The purpose of this article is to recognize common signs and patterns of pneumonias and pseudonodules in chest X‑rays and to provide a diagnostic guideline for young radiologists.

Materials And Methods: Recent studies and data are analyzed and an overview of the most common signs and patterns in chest X‑ray is provided.