Pixelshine enhances low-dose CT quality


A retrospective study in Germany assessed the efficacy of PixelShine (AlgoMedica), an AI tool designed for the post-processing of CT scans. This study involved 74 participants undergoing non-contrast chest and low-dose abdominal CT scans due to suspected SARS-CoV-2 pneumonia and urolithiasis, respectively. The aim was to evaluate the impact of AI-based post-processing on scan quality, applying PixelShine to images initially reconstructed via filtered back-projection (FBP), and performing iterative reconstruction (IR) on abdominal CTs. The intervention notably improved image quality by significantly reducing noise and preserving vital image details. This enhancement was quantitatively supported by an increase in Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), crucial metrics for evaluating image clarity (max. difference in SNR 2.59 ± 2.9 and CNR 15.92 ± 8.9, p < 0.001). In particular, FBP-reconstructed abdominal images post-processed with PixelShine surpassed the quality achieved by IR alone (max. difference in SNR 0.76 ± 0.5, p ≤ 0.001). Subjective evaluations by 3 radiologists supported these findings. Consequently, PixelShine's deep learning-based denoising, comparable to IR, offers significant potential for improving image quality in medical institutions equipped with older scanners lacking IR.

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Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen

Diagnostics, 2024


Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast chest and low-dose abdominal CTs. In this retrospective study, non-contrast chest CTs of patients suspected of COVID-19 pneumonia and low-dose abdominal CTs suspected of urolithiasis were analysed. All images were reconstructed using filtered back-projection (FBP) and were post-processed using an artificial intelligence (AI)-based commercial software (PixelShine (PS)). Additional iterative reconstruction (IR) was performed for abdominal CTs. Objective and subjective image quality were evaluated. AI-based post-processing led to an overall significant noise reduction independent of the protocol (chest or abdomen) while maintaining image information (max. difference in SNR 2.59 ± 2.9 and CNR 15.92 ± 8.9, p < 0.001). Post-processing of FBP-reconstructed abdominal images was even superior to IR alone (max. difference in SNR 0.76 ± 0.5, p ≤ 0.001). Subjective assessments verified these results, partly suggesting benefits, especially in soft-tissue imaging (p < 0.001). All in all, the deep learning-based denoising—which was non-inferior to IR—offers an opportunity for image quality improvement especially in institutions using older scanners without IR availability. Further studies are necessary to evaluate potential effects on dose reduction benefits.