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Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality.

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Lee, Kang-Lung 
Kessler, Dimitri A 
Dezonie, Simon 
Chishaya, Wellington 
Shepherd, Christopher 


PURPOSE: To evaluate the impact of a commercially available deep learning-based reconstruction (DLR) algorithm with varying combinations of DLR noise reduction settings and imaging parameters on quantitative and qualitative image quality, PI-RADS classification and examination time in prostate T2-weighted (T2WI) and diffusion-weighted (DWI) imaging. METHOD: Forty patients were included. Standard-of-care (SoC) prostate MRI sequences including T2WI and DWI were reconstructed without and with different DLR de-noising levels (low, medium, high). In addition, faster T2WI(Fast) and DWI(Fast) sequences, and a higher resolution T2WI(HR) sequence were evaluated. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) values. Two radiologists performed qualitative analysis, independently evaluating imaging datasets using 5-point scoring scales for image quality and artifacts. PI-RADS category assignment was also performed by the more experienced radiologist. RESULTS: All DLR levels resulted in significantly higher SNR and CNR compared to the DLR(off) acquisitions. DLR allowed the acquisition time to be reduced by 33% for T2WI(Fast) and 49% for DWI(Fast) compared to SoC, without affecting image quality, whilst T2WI(HR) with DLR allowed for a 73% increase in spatial resolution in the phase encode direction compared to SoC. The inter-reader agreement for image quality and artifact scores was substantial for all subjective measurements on T2WI and DWI. The T2WI(Fast) protocol with DLR(medium) and DWI(Fast) with DLR(low) received the highest qualitative quality score. CONCLUSION: DLR can reduce T2WI and DWI acquisition time and increase SNR and CNR without compromising image quality or altering PI-RADS classification.



Deep learning reconstruction, Diffusion-weighted imaging, Image quality, Prostate, T2-weighted imaging, Male, Humans, Prostate, Magnetic Resonance Imaging, Prostatic Neoplasms, Deep Learning, Diffusion Magnetic Resonance Imaging

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Eur J Radiol

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Elsevier BV
Prostate Cancer UK (RIA19-ST2-014)
Acknowledgements This research was supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The authors also acknowledge support from Cancer Research UK (Cambridge Imaging Centre grant number C197/A16465), the Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester, and the Cambridge Experimental Cancer Medicine Centre.