Ultra-low-dose hepatic computed tomography with a novel real-time deep learning-based noise reduction algorithm: a prospective cross-sectional analysis of image quality and lesion detection.

Background: Contrast-enhanced computed tomography (CT) is essential for tumor assessment, but the detection of low-contrast liver lesions remains challenging. Reducing the radiation dose increases image noise, compromising image quality and diagnostic accuracy. Iterative reconstruction (IR) algorithms can reduce noise; however, they can also alter image texture and limit lesion detection.

Patient-tailored versus fixed post-trigger delay for optimized scan timing: a comparison of image quality and diagnostic confidence in run-off CT angiography.

Objectives: This study aimed to validate bolus tracking using a patient-tailored post-trigger delay (PTD) in run-off computed tomography angiography (CTA) and to compare the resulting image quality and diagnostic confidence with those obtained using a fixed PTD.

Materials And Methods: Participants were prospectively assigned to either fixed (10 s) or patient-tailored cohorts. We measured attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for each vascular segment.

Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) and other subtypes of primary liver cancer (PLC) have overlapping clinical manifestations and radiological characteristics. The objective of this study was to evaluate the efficacy of deep learning (DL) radiomics analysis, performed using computed tomography (CT) and magnetic resonance imaging (MRI), in diagnosing iCCA within PLC. 178 pathologically confirmed PLC patients (training cohort: test cohort = 124: 54) who underwent both CT and MRI examinations was enrolled.

Optimized contrast enhancement and homogeneity in aortic CT angiography: bolus tracking with personalized post-trigger delay.

Background: The bolus tracking technique has been used for decades, yet still faces the challenging task of determining the optimal scanning time for individuals. Our study aimed to assess the feasibility of a novel bolus tracking method with a personalized post-trigger delay (PTD) to optimize scanning time and achieve optimized enhancement and contrast homogeneity in aortic computed tomography angiography (CTA).

Methods: Participants undergoing aortic CTA with bolus tracking were prospectively assigned to two different groups: Group A with a fixed 6-second PTD and Group B with a personalized PTD.

A paradigm shift in oncology imaging: a prospective cross-sectional study to assess low-dose deep learning image reconstruction versus standard-dose iterative reconstruction for comprehensive lesion detection in dual-energy computed tomography.

Background: Low-kiloelectron volt (keV) virtual monochromatic images (VMIs) from low-dose (LD) dual-energy computed tomography (DECT) can enhance lesion contrast but suffer from high image noise. Recently, a deep learning image reconstruction (DLIR) algorithm has been developed and shown significant potential in suppressing image noise and improving image quality. To date, the capacity of LD low-keV thoracic-abdominal-pelvic DECT with DLIR to detect various types of tumor lesions have not been assessed.

Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography.

To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pairing basic information, 210 patients were enrolled in this study: Group A, 0.

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study.

Background: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC.

Methods: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases.

Image quality improvement in head and neck CT angiography: Individualized post-trigger delay versus fixed delay.

Purpose: To compare the contrast media opacification of head and neck CT angiography (CTA) between conventional fixed trigger delay and individualized post-trigger delay (PTD).

Methods: In this prospective study (April-October 2022), 196 consecutive participants were randomly divided into two groups to perform head and neck CTA in bolus tracking with either an individualized PTD (Group A) or a fixed 4-second PTD (Group B). All CT and contrast media protocol parameters were consistent between the two groups.

Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.

Objective: This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs).

Methods: A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m), 100-kVp group (BMI 24-28.

Survival prediction of hepatocellular carcinoma by measuring the extracellular volume fraction with single-phase contrast-enhanced dual-energy CT imaging.

Purpose: This study aimed to investigate the value of quantified extracellular volume fraction (fECV) derived from dual-energy CT (DECT) for predicting the survival outcomes of patients with hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE).

Materials And Methods: A total of 63 patients with HCC who underwent DECT before treatment were retrospectively included. Virtual monochromatic images (VMI) (70 keV) and iodine density images (IDI) during the equilibrium phase (EP) were generated.

Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy.

Objectives: To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR).

Methods: In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area.

Patient-specific post-trigger delay in coronary CT angiography: A prospective study comparing with fixed delay.

Objectives: To validate the peak enhancement timing of a patient-specific post-trigger delay (PTD) in Coronary CT angiography (CCTA) and compare its image quality against a fixed PTD.

Methods: In this prospective study, 204 consecutive participants were randomly divided into two groups to perform CCTA in bolus tracking with either a fixed 5-second PTD (Group A) or a patient-specific PTD (Group B). Test bolus was also performed in Group B to determine the reference peak enhancement timing.

Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

Objectives: To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR).

Methods: A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results.

CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma.

Purpose: The study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively.

Materials And Methods: From February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time.

CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.

Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019.

Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.

Objective: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm.

Methods: A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H).

TCGA-TCIA-Based CT Radiomics Study for Noninvasively Predicting Epstein-Barr Virus Status in Gastric Cancer.

The purpose of this study was to investigate the value of TCGA-TCIA (The Cancer Genome Atlas and The Cancer Imaging Archive)-based CT radiomics for noninvasive prediction of Epstein-Barr virus (EBV) status in gastric cancer (GC). A total of 133 patients with pathologically confirmed GC (94 in the training cohort and 39 in the validation cohort) who were identified from the TCGA-TCIA public data repository and two hospitals were retrospectively enrolled in the study. Two-dimensional and 3D radiomics features were extracted to construct corresponding radiomics signatures.

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

Background: The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown.

Purpose: To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions.

Material And Methods: A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited.

The Performance of Chest CT in Evaluating the Clinical Severity of COVID-19 Pneumonia: Identifying Critical Cases Based on CT Characteristics.

Objectives: The aim of this study was to assess the clinical severity of COVID-19 pneumonia using qualitative and/or quantitative chest computed tomography (CT) indicators and identify the CT characteristics of critical cases.

Materials And Methods: Fifty-one patients with COVID-19 pneumonia including ordinary cases (group A, n = 12), severe cases (group B, n = 15), and critical cases (group C, n = 24) were retrospectively enrolled. The qualitative and quantitative indicators from chest CT were recorded and compared using Fisher exact test, one-way analysis of variance, Kruskal-Wallis H test, and receiver operating characteristic analysis.

[Preoperative assessment value of spectral CT quantitative parameters in lymph node metastasis of gastric cancer].

Objective: To investigate the preoperative assessment value of spectral CT quantitative parameters in lymph node metastasis of gastric cancer.

Methods: From December 2013 to June 2015, clinical and image data of 86 patients with gastric cancer confirmed by gastroscope pathology undergoing preoperative enhanced CT were prospectively collected. Enhanced CT included nonenhanced CT of conventional 120 kVp mode, arterial phase (AP) and venous phase (VP) with GSI mode on Discover GSI CT scanner.