Measurement Accuracy and Repeatability of RECIST-Defined Pulmonary Lesions and Lymph Nodes in Ultra-Low-Dose CT Based on Deep Learning Image Reconstruction

Abstract

Background: Deep learning image reconstruction (DLIR) improves image quality. We aimed to compare the measured diameter of pulmonary lesions and lymph nodes between DLIR-based ultra-low-dose CT (ULDCT) and contrast-enhanced CT. Methods: The consecutive adult patients with noncontrast chest ULDCT (0.07–0.14 mSv) and contrast-enhanced CT (2.38 mSv) were prospectively enrolled. Patients with poor image quality and body mass index ≥ 30 kg/m2 were excluded. The diameter of pulmonary target lesions and lymph nodes defined by Response Evaluation Criteria in Solid Tumors (RECIST) was measured. The measurement variability between ULDCT and enhanced CT was evaluated by Bland-Altman analysis. Results: The 141 enrolled patients (62 ± 12 years) had 89 RECIST-defined measurable pulmonary target lesions (including 30 malignant lesions, mainly adenocarcinomas) and 45 measurable mediastinal lymph nodes (12 malignant). The measurement variation of pulmonary lesions between high-strength DLIR (DLIR-H) images of ULDCT and contrast-enhanced CT was 2.2% (95% CI: 1.7% to 2.6%) and the variation of lymph nodes was 1.4% (1.0% to 1.9%). Conclusions: The measured diameters of pulmonary lesions and lymph nodes in DLIR-H images of ULDCT are highly close to those of contrast-enhanced CT. DLIR-based ULDCT may facilitate evaluating target lesions with greatly reduced radiation exposure in tumor evaluation and lung cancer screening.

Implementation of Individualized Low-Dose Computed Tomography-Guided Hook Wire Localization of Pulmonary Nodules: Feasibility and Safety in the Clinical Setting

“…In the current study, the hook wire was successfully placed with all lung nodules removed through VATS in 151 patients, and none of the procedures needed conversion to thoracotomy. Pneumothorax and hemorrhage are the most common complications associated with hook wire localization [3], along with other factors, such as the number of needle insertions and the duration of the procedure [29]. This study found no statistically significant differences between groups in terms of needle insertions and total puncture time, which is consistent with earlier research [1,9].…”

“…Maintaining image quality and lesion identification performance is crucial in LDCT, as a decreasing radiation dose leads to a rise in image noise. In fact, a previous study explored the feasibility of LDCT lung scanning procedures that decreased radiation exposure while preserving nodule detection performance with an average ED of 1.5 mSv [29]. In the present study, no significant differences in detection efficacy were observed between groups, which means that LDCT (for BMI > 21 kg/m 2 ) and uLDCT (for BMI ≤ 21 kg/m 2 ) were capable of detecting pulmonary nodules with a similar sensitivity to SDCT.…”

Implementation of AI image reconstruction in CT—how is it validated and what dose reductions can be achieved

“…In a liver metastases detection study, Lyu et al 85 demonstrated non-inferiority down to 50% for all size lesions, but down to 70% reduction for metastasis >1 cm. Zhao et al 86 compared RECIST measurement accuracy for DLR between low dose CT (0.07 mSv) and normal dose CT (2.38 mSv) with pulmonary lesion measurements agreeing to better than 2.2% and lymph node measurements agreeing to better than 1.4%. In a high-contrast detection study, Qu et al 87 compared arterial stenosis measurements made for lower dose extremity CTA and found them to be equally accurate with better image quality and sharper vessel wall visualization than clinical dose SBIR CT. Noda et al 88 evaluated low dose (2.3 mGy) abdominal CT for conspicuity of pancreatic ductal adenocarcinoma in 28 prospective patients; DLR ( i.e.…”

A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results

“…DLIR uses high dose and high quality FBP images as ground truth and significantly suppresses image noises and streak artifacts on various phantom studies and clinical applications [ 10 – 13 ]. Many studies demonstrate that DLIR notably improves image quality while maintaining or even improving diagnostic accuracy in conventional CT scanning using a single tube voltage [ 14 – 16 ]. The DLIR algorithm has recently been extended to DECT imaging mode [ 17 ].…”