Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

• Published in: European radiology Vol. 33; no. 10; pp. 7056 - 7065
• Main Authors: Schwartz, Fides R., Clark, Darin P., Rigiroli, Francesca, Kalisz, Kevin, Wildman-Tobriner, Benjamin, Thomas, Sarah, Wilson, Joshua, Badea, Cristian T., Marin, Daniele
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2023; Springer Nature B.V
• Subjects: Adult; Aged; Algorithms; Comfort; Computed Tomography; Diagnosis; Diagnostic Radiology; Diagnostic systems; Female; Humans; Image quality; Imaging; Internal Medicine; Interventional Radiology; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Noise reduction; Obesity - complications; Obesity - diagnostic imaging; Phantoms, Imaging; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Retrospective Studies; Signal-To-Noise Ratio; Statistical analysis; Tomography, X-Ray Computed - methods; Ultrasound; Medical image processing; Obesity; Tomography, X-ray computed; Image quality enhancement; Multidetector computed tomography
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-09644-7

Objectives Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging. Methods Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m 2 , range: 35–62 kg/m 2 ) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019–12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)–100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t -tests and mixed-effects linear modeling. Results Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p  < 0.001), and 8.9 ± 2.9 (test; p  < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67% (original, comparison, test)). The noise received a mean score of 54, 84, and 66 ( p  < 0.05); image quality 59, 61, and 65; and the diagnostic comfort 63, 68, and 68, respectively. Quality and comfort scores were not statistically significantly different between algorithms. Conclusions The test algorithm produces quantitatively higher image quality than current standard and existing denoising algorithms in obese patients imaged with DECT and readers show a preference for it. Clinical relevance statement Accurate diagnosis on CT imaging of obese patients is challenging and denoising algorithms can increase the diagnostic comfort and quantitative image quality. This could lead to better clinical reads. Key Points • Improving image quality in DECT imaging of obese patients is important for accurate and confident clinical reads, which may be aided by novel denoising algorithms using image domain data . • Accurate diagnosis on CT imaging of obese patients is especially challenging and denoising algorithms can increase quantitative and qualitative image quality . • Image domain algorithms can generalize well and can be implemented at other institutions .