Metastatic Lung Lesion Changes in Follow-up Chest CT: The Advantage of Deep Learning Simultaneous Analysis of Prior and Current Scans With SimU-Net.

Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.

Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.

Computed tomography referral guidelines adherence in Europe: insights from a seven-country audit.

Background: Ensuring appropriate computed tomography (CT) utilization optimizes patient care while minimizing radiation exposure. Decision support tools show promise for standardizing appropriateness.

Objectives: In the current study, we aimed to assess CT appropriateness rates using the European Society of Radiology (ESR) iGuide criteria across seven European countries.

Insights from a web-based questionnaire: examining diagnostic procedures prior to magnetic resonance imaging.

The appropriate use of diagnostic imaging, particularly MRI, is a critical concern in modern healthcare. This paper examines the current state of MRI utilization in Israel, drawing on a recent study by Kaim et al. that surveyed 557 Israeli adults who underwent MRI in the public health system.

Dual-energy CT in acute cholecystitis- features predicting culture-positive bile and outcome.

Purpose: Low mono-energetic CT has been shown to improve visualization of acute abdominal inflammatory processes. We aimed to determine its utility in patients with acute cholecystitis and potential added value in clinical decision making.

Methods: Sixty-seven consecutive patients with radiological signs of cholecystitis on contrast-enhanced dual-layer CT imaging were retrospectively identified over a four-year period (2/17-8/21).

Evaluation of the Pulmonary Arteries on CTPA With Dual Energy CT: Objective Analysis and Subjective Preferences in a Multireader Study.

Purpose: To perform qualitative and quantitative evaluation of low-monoenergetic images (50 KeV) compared with conventional images (120 kVp) in pulmonary embolism (PE) studies and to determine the extent and clinical relevance of these differences as well as radiologists' preferences.

Materials And Methods: One hundred fifty CT examinations for PE detection conducted on a single-source dual-energy CT were retrospectively evaluated. Attenuation, contrast-to-noise-ratio, and signal-to-noise-ratio were obtained in a total of 8 individual pulmonary arteries on each exam-including both central (450/1200=37.

Optimal CT windowing on low-monoenergetic images using a simplex algorithm-based approach for abdominal inflammatory processes.

Background: OBJECTIVES: To determine optimal window settings for conspicuity of abdominal inflammatory processes on 50 keV low-monoenergetic images derived from dual-energy spectral CT (DECT).

Methods: A retrospective study of 30 patients with clinically proven pancreatitis (15/30) or pyelonephritis (15/30) with inflammatory lesions visible on DECT scans were selected to serve as reference populations. 50 keV low-monoenergetic images in the portal venous phase were iteratively evaluated by 6 abdominal radiologists in twenty-one different windows (7-350HU center; 120-580HU width), selected using a simplex optimization algorithm.

MC3DU-Net: a multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI.

Purpose: Radiological detection and follow-up of pancreatic cysts in multisequence MRI studies are required to assess the likelihood of their malignancy and to determine their treatment. The evaluation requires expertise and has not been automated. This paper presents MC3DU-Net, a novel multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI studies consisting of coronal MRCP and axial TSE MRI sequences.

Graph-based automatic detection and classification of lesion changes in pairs of CT studies for oncology follow-up.

Purpose: Radiological follow-up of oncology patients requires the quantitative analysis of lesion changes in longitudinal imaging studies, which is time-consuming, requires expertise, and is subject to variability. This paper presents a comprehensive graph-based method for the automatic detection and classification of lesion changes in current and prior CT scans.

Methods: The inputs are the current and prior CT scans and their organ and lesion segmentations.

Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1.

Objectives: To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and with aided simultaneous deep learning-based volumetric lesion changes analysis.

Methods: A total of 86 abdominal CT studies from 43 patients (prior and current scans) of abdominal CT scans of patients with 1041 liver metastases (mean = 12.

Real-time influence of intracellular acidification and Na /H exchanger inhibition on in-cell pyruvate metabolism in the perfused mouse heart: A P-NMR and hyperpolarized C-NMR study.

Disruption of acid-base balance is linked to various diseases and conditions. In the heart, intracellular acidification is associated with heart failure, maladaptive cardiac hypertrophy, and myocardial ischemia. Previously, we have reported that the ratio of the in-cell lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) activities is correlated with cardiac pH.

The different clonal origins of metachronous and synchronous metastases.

Background: Metastases are the leading cause of mortality in cancer patients. Linear and parallel are the two prominent models of metastatic progression. Metastases can be detected synchronously along with the primary tumor or metachronously, following treatment of localized disease.

Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT.

Objectives: To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans.

Methods: The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists.

Investigating Cardiac Metabolism in the Isolated Perfused Mouse Heart with Hyperpolarized [1-13C]Pyruvate and 13C/31P NMR Spectroscopy.

Metabolism is the basis of important processes in cellular life. Characterizing how metabolic networks function in living tissues provides crucial information for understanding the mechanism of diseases and designing treatments. In this work, we describe procedures and methodologies for studying in-cell metabolic activity in a retrogradely perfused mouse heart in real-time.

Does clinical decision support system promote expert consensus for appropriate imaging referrals? Chest-abdominal-pelvis CT as a case study.

Objectives: We assessed the appropriateness of chest-abdominal-pelvis (CAP) CT scan use in the Emergency Department (ED), based on expert physicians and the ESR iGuide, a clinical decision support system (CDSS).

Methods: A retrospective cross-study was conducted. We included 100 cases of CAP-CT scans ordered at the ED.

Is it time for redefining oligometastatic disease? Analysis of lung metastases CT in ten tumor types.

Background: Oligometastatic disease (OD) is usually defined arbitrarily as a condition in which there are ≤ 5 metastases. Given limited disease, it is expected that patients with OD should have better prognosis compared to other metastatic patients and that they can potentially benefit from metastasis-directed therapy (MDT). In this study, we attempted to redefine OD based upon objective evidence that fulfill these assumptions.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients.

Quantitative assessment of renal obstruction in multi-phase CTU using automatic 3D segmentation of the renal parenchyma and renal pelvis: A proof of concept.

Purpose: Quantitative evaluation of renal obstruction is crucial for preventing renal atrophy. This study presents a novel method for diagnosing renal obstruction by automatically extracting objective indicators from routine multi-phase CT Urography (CTU).

Material And Methods: The study included multi-phase CTU examinations of 6 hydronephrotic kidneys and 24 non-hydronephrotic kidneys (23,164 slices).

Justification of CT practices across Europe: results of a survey of national competent authorities and radiology societies.

Objectives: Published literature on justification of computed tomography (CT) examinations in Europe is sparse but demonstrates consistent sub-optimal application. As part of the EU initiated CT justification project, this work set out to capture CT justification practices across Europe.

Methods: An electronic questionnaire consisting of mostly closed multiple-choice questions was distributed to national competent authorities and to presidents of European radiology societies in EU member states as well as Iceland, Norway, Switzerland, and the UK (n = 31).

Liver lesion changes analysis in longitudinal CECT scans by simultaneous deep learning voxel classification with SimU-Net.

The identification and quantification of liver lesions changes in longitudinal contrast enhanced CT (CECT) scans is required to evaluate disease status and to determine treatment efficacy in support of clinical decision-making. This paper describes a fully automatic end-to-end pipeline for liver lesion changes analysis in consecutive (prior and current) abdominal CECT scans of oncology patients. The three key novelties are: (1) SimU-Net, a simultaneous multi-channel 3D R2U-Net model trained on pairs of registered scans of each patient that identifies the liver lesions and their changes based on the lesion and healthy tissue appearance differences; (2) a model-based bipartite graph lesions matching method for the analysis of lesion changes at the lesion level; (3) a method for longitudinal analysis of one or more of consecutive scans of a patient based on SimU-Net that handles major liver deformations and incorporates lesion segmentations from previous analysis.

Feasibility and Accuracy of a Novel Hands-Free Robotic System for Percutaneous Needle Insertion and Steering.

Purpose: To assess the performance and accuracy of CT-guided needle insertion for clinical biopsies using a novel, hands-free robotic system that balances accuracy with the duration of the procedure and radiation dose.

Materials And Methods: A prospective, multi-center study was conducted on 60 clinically indicated biopsies of abdominal lesions at two centers (Center 1, n=26; Center 2, n=34). CT datasets were obtained for planning and controlled placement of 17g and 18g needles using a patient-mounted, CT-guided robotic system with 5 degrees of freedom.

Patterns of metastases progression- The linear parallel ratio.

Background: Linear and parallel are the two leading models of metastatic progression. In this study we propose a simple way to differentiate between them. While the linear model predicts accumulation of genetic and epigenetic alterations within the primary tumor by founder cells before spreading as waves of metastases, the parallel model suggests preclinical distribution of less advanced disseminated tumor cells with independent selection and expansion at the ectopic sites.