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

Abstract

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.

Robust multishot diffusion‐weighted imaging of the abdomen with region‐based shot rejection

“…The presented rejection method addresses diffusion-related dropout artifacts beginning from the raw signal level, instead of as a post-processing method for shot-combined data. 40,41 We applied respiratory triggering and avoided applications that would require prospective slice planning or additional registration steps. These would be necessary to integrate shot rejection into acquisitions with large bulk motion, such as free-breathing DWI.…”

“…This method does not correct signal variations due to rigid motion, nor does it correct scenarios where parallel imaging in the phase navigator fails. The presented rejection method addresses diffusion‐related dropout artifacts beginning from the raw signal level, instead of as a post‐processing method for shot‐combined data 40,41 . We applied respiratory triggering and avoided applications that would require prospective slice planning or additional registration steps.…”

Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs

“…Several algorithms have been proposed, which are mostly based on outlier rejections or a greater weighting for higher signals during the averaging of repetitions [50][51][52], sometimes combined with deep learning [53,54]. In a quantitative comparison, the choice of algorithm parameters has been shown to be non-trivial, indeed affecting the outcome [20].…”

“…velocity-compensation) has been shown to reduce the signal loss considerably, yet not completely [ 10 ]. Post-processing schemes can ameliorate the problem [ 20 ], but a compensation at the sequence level is more desirable. The compensation of the second gradient moment M 2 (acceleration-compensation) has thus been proposed.…”

“…Previous studies have demonstrated that motion-compensated encodings, which are insensitive to motion at constant velocity and/or constant acceleration [12][13][14][15][16][17], are useful in correcting this signal loss [14,[18][19][20]. Compensation of the first gradient moment M 1 (i.e.…”