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Deep learning–based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time

  • Gastrointestinal
  • Published:
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Abstract

Objectives

To evaluate the image quality of the 3D hybrid profile order technique and deep-learning-based reconstruction (DLR) for 3D magnetic resonance cholangiopancreatography (MRCP) within a single breath-hold (BH) at 3 T magnetic resonance imaging (MRI).

Methods

This retrospective study included 32 patients with biliary and pancreatic disorders. BH images were reconstructed with and without DLR. The signal-to-noise ratio (SNR), contrast, contrast-to-noise ratio (CNR) between the common bile duct (CBD) and periductal tissues, and full width at half maximum (FWHM) of CBD on 3D-MRCP were evaluated quantitatively. Two radiologists scored image noise, contrast, artifacts, blur, and overall image quality of the three image types using a 4-point scale. Quantitative and qualitative scores were compared using the Friedman test and post hoc Nemenyi test.

Results

The SNR and CNR were not significantly different when under respiratory gating- and BH-MRCP without DLR. However, they were significantly higher under BH with DLR than under respiratory gating (SNR, p = 0.013; CNR, p = 0.027). The contrast and FWHM of MRCP under BH with and without DLR were lower than those under respiratory gating (contrast, p < 0.001; FWHM, p = 0.015). Qualitative scores for noise, blur, and overall image quality were higher under BH with DLR than those under respiratory gating (blur, p = 0.003; overall, p = 0.008).

Conclusions

The combination of the 3D hybrid profile order technique and DLR is useful for MRCP within a single BH and does not lead to the deterioration of image quality and space resolution at 3 T MRI.

Clinical relevance statement

Considering its advantages, this sequence might become the standard protocol for MRCP in clinical practice, at least at 3.0 T.

Key Points

• The 3D hybrid profile order can achieve MRCP within a single breath-hold without a decrease in spatial resolution.

• The DLR significantly improved the CNR and SNR of BH-MRCP.

• The 3D hybrid profile order technique with DLR reduces the deterioration of image quality in MRCP within a single breath-hold.

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Data availability

The datasets generated or analyzed during the study are available from the corresponding author upon reasonable request.

Change history

  • 30 June 2023

    A minor typographical error in the article title has been corrected.

Abbreviations

BH:

Breath-hold

CNR:

Contrast-to-noise ratio

DLR:

Deep-learning-based reconstruction

FWHM:

Full-width at half maximum

MRCP:

Magnetic resonance cholangiopancreatography

ROI:

Regions of interest

SI:

Signal intensity

SNR:

Signal-to-noise ratio

TSE:

Turbo spin-echo

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Acknowledgements

We thank Ms. Tae Hamakawa from the Department of Diagnostic Radiology, Kumamoto University, Japan, for her help with the measuring in the quantitative analysis.

Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan

    Kaori Shiraishi, Takeshi Nakaura, Hiroyuki Uetani, Yasunori Nagayama, Masafumi Kidoh, Naoki Kobayashi & Toshinori Hirai

  2. Department of Radiology, Kumamoto University Hospital, Kumamoto, Japan, Honjo 1-1-1, Kumamoto, Japan

    Kosuke Morita

  3. Canon Medical Systems Corporation, 70-1, Yanagi-Cho, Saiwai-Ku, Kawasaki-Shi, Kanagawa, 212-0015, Japan

    Yuichi Yamahita

  4. Department of Gastroenterology and Hepatology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan

    Yasuhito Tanaka

  5. Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan

    Hideo Baba

Authors
  1. Kaori Shiraishi
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  2. Takeshi Nakaura
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Corresponding author

Correspondence to Takeshi Nakaura.

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Guarantor

The scientific guarantor of this publication is Toshinori Hirai.

Conflict of interest

Takeshi Nakaura has received research support from Nemoto Kyorindo Co., Ltd. Toshinori Hirai has received research support from Canon Medical Systems. The department of diagnostic imaging analysis, to which Dr. Kidoh belongs, is an endowed chair supported by Philips Healthcare. The Nemoto Kyorindo Co., Ltd., Philips Healthcare, and Canon Medical Systems had no control over the interpretation, writing, or publication of this work.

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No complex statistical methods were necessary for this paper.

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Informed consent was waived by Institutional Review Board.

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• Retrospective

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Shiraishi, K., Nakaura, T., Uetani, H. et al. Deep learning–based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time. Eur Radiol 33, 7585–7594 (2023). https://doi.org/10.1007/s00330-023-09703-z

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