Reduction of the cardiac pulsation artifact and improvement of lesion conspicuity in flow‐compensated diffusion images in the liver—A quantitative evaluation of postprocessing algorithms

Purpose To enhance image quality of flow‐compensated diffusion‐weighted liver MRI data by increasing the lesion conspicuity and reducing the cardiac pulsation artifact using postprocessing algorithms. Methods Diffusion‐weighted image data of 40 patients with liver lesions had been acquired at 1.5 T....

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Vydané v:Magnetic resonance in medicine Ročník 89; číslo 1; s. 423 - 439
Hlavní autori: Führes, Tobit, Saake, Marc, Lorenz, Jennifer, Seuss, Hannes, Stemmer, Alto, Benkert, Thomas, Uder, Michael, Laun, Frederik Bernd
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Wiley Subscription Services, Inc 01.01.2023
Predmet:
Algorithms
cardiac pulsation artifact
Conspicuity
Darkness
Diffusion
Diffusion Magnetic Resonance Imaging - methods
flow‐compensation
Heart
Humans
Image acquisition
Image enhancement
Image quality
Lesions
Liver
Liver - diagnostic imaging
Medical imaging
Noise reduction
Outliers (statistics)
postprocessing
Pulsation
Total quality
algorithms
cardiac pulsation artifact
diffusion
postprocessing
flow-compensation
liver
ISSN:0740-3194, 1522-2594, 1522-2594
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Shrnutí:Purpose To enhance image quality of flow‐compensated diffusion‐weighted liver MRI data by increasing the lesion conspicuity and reducing the cardiac pulsation artifact using postprocessing algorithms. Methods Diffusion‐weighted image data of 40 patients with liver lesions had been acquired at 1.5 T. These data were postprocessed with 5 different algorithms (weighted averaging, p‐mean, percentile, outlier exclusion, and exception set). Four image properties of the postprocessed data were evaluated for optimizing the algorithm parameters. These properties were the lesion to tissue contrast‐to‐noise ratio (CNR), the reduction of the cardiac pulsation artifact, the data consistency, and the vessel darkness. They were combined into a total quality score (Qtotal,$$ {Q}_{\mathrm{total}}, $$ set to 1 for the trace‐weighted reference image), which was used to rate the image quality objectively. Results The weighted averaging algorithm performed best according to the total quality score (Qtotal=1.111±0.067$$ {Q}_{\mathrm{total}}=1.111\pm 0.067 $$). The further ranking was outlier exclusion algorithm (Qtotal=1.086±0.061$$ {Q}_{\mathrm{total}}=1.086\pm 0.061 $$), p‐mean algorithm (Qtotal=1.045±0.049$$ {Q}_{\mathrm{total}}=1.045\pm 0.049 $$), percentile algorithm (Qtotal=1.012±0.049$$ {Q}_{\mathrm{total}}=1.012\pm 0.049 $$), and exception set algorithm (Qtotal=0.957±0.027$$ {Q}_{\mathrm{total}}=0.957\pm 0.027 $$). All optimized algorithms except for the exception set algorithm corrected the pulsation artifact and increased the lesion CNR. Changes in Qtotal$$ {Q}_{\mathrm{total}} $$ were significant for all optimized algorithms except for the percentile algorithm. Liver ADC was significantly reduced (except for the exception set algorithm), particularly in the left lobe. Conclusion Postprocessing algorithms should be used for flow‐compensated liver DWI. The proposed weighted averaging algorithm seems to be suited best to increase the image quality of artifact‐corrupted flow‐compensated diffusion‐weighted liver data.
Bibliografia:Funding information
Deutsche Forschungsgemeinschaft, Grant/Award Numbers: 446875476 and 430650228
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SourceType-Scholarly Journals-1
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.29427