Deep-learning denoising minimizes radiation exposure in neck CT beyond the limits of conventional reconstruction.

BACKGROUND: Neck computed tomography (NCT) is essential for diagnosing suspected neck tumors and abscesses, but radiation exposure can be an issue. In conventional reconstruction techniques, limiting radiation dose comes at the cost of diminished diagnostic accuracy. Therefore, this study aimed to evaluate the effects of an AI-based denoising post-processing software solution in low-dose neck computer tomography. MATERIALS AND METHODS: From 01 September 2023 to 01 December 2023, we retrospectively included patients with clinically suspected neck tumors from the same single-source scanner. The scans were reconstructed using Advanced Modeled Iterative Reconstruction (Original) at 100% and simulated 50% and 25% radiation doses. Each dataset was post-processed using a novel denoising software solution (Denoising). Three radiologists with varying experience levels subjectively rated image quality, diagnostic confidence, sharpness, and contrast for all pairwise combinations of radiation dose and reconstruction mode in a randomized, blinded forced-choice setup. Objective image quality was assessed using ROI measurements of mean CT numbers, noise, and a contrast-to-noise ratio (CNR). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. RESULTS: At each radiation dose level, pairwise comparisons showed significantly lower image noise and higher CNR for Denoising than for Original (p < 0.001). In subjective analysis, image quality, diagnostic confidence, sharpness, and contrast were significantly higher for Denoising than for Original at 100 and 50 % (p < 0.001). However, there were no significant differences in the subjective ratings between Original 100 % and Denoising 25 % (p = 0.906). CONCLUSIONS: The investigated denoising algorithm enables diagnostic-quality neck CT images with radiation doses reduced to 25% of conventional levels, significantly minimizing patient exposure.

Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression.

OBJECTIVE: Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-DixonDL). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). METHODS: This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed. RESULTS: Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-DixonDL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-DixonDL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-DixonDL (P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-DixonDL technique (P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-DixonDL. Interreader agreement between VIBE-Dixon and VIBE-DixonDL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXONDL was observed in both the precontrast (P = 0.025) and postcontrast images (P < 0.001). Also, an increase of splenic SNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.34 and P = 0.003, respectively). Similarly, an increase of pancreas CNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.557 and P = 0.026, respectively). CONCLUSIONS: The prospectively accelerated, DL-enhanced VIBE with Dixon fat suppression was clinically feasible. It enabled a 52% reduction in breath-hold time and provided superior image quality, diagnostic confidence, and pancreatic lesion conspicuity. This technique might be especially useful for patients with limited breath-hold capacity.