Fully automated measurement of noise, signal-to-noise ratio, and contrast-to-noise ratio on chest CT images: feasibility and efficiency.

Background: Rapid and accurate measurement of computed tomography (CT) image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) is a clinical challenge.

Purpose: To explore the feasibility of intelligent measurement of chest CT image noise, SNR, and CNR.

Material And Methods: A total of 300 chest CT scans were included in the study, which was divided into research dataset, internal test dataset, and external test dataset.

Automated measurement of lumbar pedicle screw parameters using deep learning algorithm on preoperative CT scans.

Purpose: This study aims to devise and assess an automated measurement framework for lumbar pedicle screw parameters leveraging preoperative computed tomography (CT) scans and a deep learning algorithm.

Methods: A deep learning model was constructed employing a dataset comprising 1410 axial preoperative CT images of lumbar pedicles sourced from 282 patients. The model was trained to predict several screw parameters, including the axial angle and width of pedicles, the length of pedicle screw paths, and the interpedicular distance.

Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms.

Objective: To construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT.

Methods: A total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction.

Longitudinal Study on Pre- and Post-Operation CT Imaging for Predicting Pedicle Screw Loosening in Patients with Lumbar Degenerative Disease.

Purpose: We conducted a longitudinal study to examine the predictive role of risk factors in the occurrence of pedicle screw loosening, assessed through pre- and post-operative computed tomography (CT) scans.

Methods: A total of 103 patients with degenerative lumbar disease who had undergone L4/5 pedicle screw fixation (involving 412 screws) were included in this study. They were subsequently categorized into two groups-the "loosening group" and the "non-loosening group".

Multi-instance learning based lung nodule system for assessment of CT quality after small-field-of-view reconstruction.

Small-field-of-view reconstruction CT images (sFOV-CT) increase the pixel density across airway structures and reduce partial volume effects. Multi-instance learning (MIL) is proposed as a weakly supervised machine learning method, which can automatically assess the image quality. The aim of this study was to evaluate the disparities between conventional CT (c-CT) and sFOV-CT images using a lung nodule system based on MIL and assessments from radiologists.

A multicenter clinical AI system study for detection and diagnosis of focal liver lesions.

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.

Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes.

Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias.

Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue.

Background: Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT.

Methods: The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets.

Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study.

Objective: To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).

Methods: A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set ( = 116), an internal validation set ( = 30), and an external validation set ( = 80). Patients who experienced progression of WMH were identified from subsequent MRI results.

Predicting progression of white matter hyperintensity using coronary artery calcium score based on coronary CT angiography-feasibility and accuracy.

Objective: Coronary artery disease (CAD) usually coexists with subclinical cerebrovascular diseases given the systematic nature of atherosclerosis. In this study, our objective was to predict the progression of white matter hyperintensity (WMH) and find its risk factors in CAD patients using the coronary artery calcium (CAC) score. We also investigated the relationship between the CAC score and the WMH volume in different brain regions.

A novel CT-responsive hydrogel for the construction of an organ simulation phantom for the repeatability and stability study of radiomic features.

Radiomic features have demonstrated reliable outcomes in tumor grading and detecting precancerous lesions in medical imaging analysis. However, the repeatability and stability of these features have faced criticism. In this study, we aim to enhance the repeatability and stability of radiomic features by introducing a novel CT-responsive hydrogel material.

Retrolaminar migration as a complication of intraocular silicone oil injection detected on unenhanced CT.

Aim: To describe the clinical and radiologic features of retrolaminar migration silicone oil (SiO) and observe the dynamic position of ventricular oil accumulation in supine and prone.

Methods: For this retrospective study, 29 patients who had a history of SiO injection treatment and underwent unenhanced head computed tomography (CT) were included from January 2019 to October 2022. The patients were divided into migration-positive and negative groups.

Is coronary artery calcium an independent risk factor for white matter hyperintensity?

Background: Cardiovascular diseases have been considered the primary cause of disability and death worldwide. Coronary artery calcium (CAC) is an important indicator of the severity of coronary atherosclerosis. This study is aimed to investigate the relationship between CAC and white matter hyperintensity (WMH) in the context of diagnostic utility.

Coronary computed tomography angiography imaging features combined with computed tomography-fractional flow reserve, pericoronary fat attenuation index, and radiomics for the prediction of myocardial ischemia.

Background: This study aimed to predict myocardial ischemia (MIS) by constructing models with imaging features, CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics based on coronary computed tomography angiography (CCTA).

Methods And Results: This study included 96 patients who underwent CCTA and single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI). According to SPECT-MPI results, there were 72 vessels with MIS in corresponding supply area and 105 vessels with no-MIS.

Enhancing the Selectivity of Leucine Aminopeptidase Near-Infrared Fluorescent Probes for Assisting in Surgical Tumor Resection.

Selective fluorescence imaging of analytes is a challenge for monitoring diseases as homologues interfere with the imaging agents. Leucine aminopeptidase (LAP), a kind of protease, is related to tumor pathogenesis. The known LAP fluorescent probes based on leucine recognition have limited selectivity.

Quercetin alleviates subarachnoid hemorrhage-induced early brain injury via inhibiting ferroptosis in the rat model.

Early brain injury (EBI) refers to a series of pathophysiological brain lesions that occur within 72 hr after subarachnoid hemorrhage (SAH), which is an extremely crucial factor in the poor prognosis of patients. In EBI, ferroptosis has been proven to cause neuronal death. Quercetin (QCT) is effective in deactivating reactive oxygen species (ROS), inhibiting lipid peroxidation, and even chelating iron, but its role in SAH remains unclear.

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

Background: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion-neutral-extension cervical lateral radiographs and to evaluate its applicability for the flexion-extension (F/E), flexion-neutral (F/N), and neutral-extension (N/E) motion analysis.

A novel computed tomography radiomic nomogram for early evaluation of small airway dysfunction development.

The common respiratory abnormality, small airway dysfunction (fSAD), is easily neglected. Its prognostic factors, prevalence, and risk factors are unclear. This study aimed to explore the early detection of fSAD using radiomic analysis of computed tomography (CT) images to predict fSAD progress.

CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation.

Retinal vessels play an important role in judging many eye-related diseases, so accurate segmentation of retinal vessels has become the key to auxiliary diagnosis. In this paper, we present a Cascaded Residual Attention U-Net (CRAUNet) that can be regarded as a set of U-Nets, that allows coarse-to-fine representations. In the CRAUNet, we introduce a DropBlock regularization similar to the frequently-used dropout, which greatly reduces the overfitting problem.

Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.

Objectives: Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs.

Methods: This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs.

Development of Deep Learning-based Automatic Scan Range Setting Model for Lung Cancer Screening Low-dose CT Imaging.

Rationale And Objectives: To develop an automatic setting of a deep learning-based system for detecting low-dose computed tomography (CT) lung cancer screening scan range and compare its efficiency with the radiographer's performance.

Materials And Methods: This retrospective study was performed using 1984 lung cancer screening low-dose CT scans obtained between November 2019 and May 2020. Among 1984 CT scans, 600 CT scans were considered suitable for an observational study to explore the relationship between the scout landmarks and the actual lung boundaries.

Engineering a Ratiometric Photoacoustic Probe with a Hepatocyte-Specific Targeting Ability for Liver Injury Imaging.

In situ imaging of biological indicators is imperative for pathological research by utilizing an activatable photoacoustic (PA) probe. However, precise imaging in actual applications is hampered by the inevitable poor accumulation and low sensitivity. Herein, an amphiphilic molecular probe () was rationally constructed as proof of concept for in situ imaging of drug-induced liver injury, which consists of a hydrophilic target unit and a superoxide anion radical (O)-sensitive small-molecule PA moiety.

Highly Selective Fluorescent Probe Design for Visualizing Hepatic Hydrogen Sulfide in the Pathological Progression of Nonalcoholic Fatty Liver.

Hydrogen sulfide (HS), emerging as an important gaseous signal, has attracted more and more attention for its key role in chronic fatty liver diseases. However, lacking tools for HS-specific in situ detection, the changes of endogenous hepatic HS levels in the pathological progression of chronic liver diseases are still unclear. To this end, we adopted a strategy of combining molecular probe design and nanofunctionalization to develop a highly selective near-infrared (NIR) fluorescent probe, which allows in vivo real-time monitoring of hepatic HS levels in the process of nonalcoholic fatty liver disease (NAFLD).

Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion.

Background: Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO).

Methods: A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study.

A novel method for determining the X-ray beam angulation of the supraspinatus outlet view.

Background: Rapid and accurate quantification of the supraspinatus outlet view (SOV) is a clinical challenge.

Purpose: To quantify the X-ray beam angle of the SOV using the horizontal angle of the subscapular spine line (SSSL) and to further verify the feasibility of this method.

Material And Methods: A total of 119 patients who underwent shoulder computed tomography (CT) examination were enrolled in the retrospective study.