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European Radiology
Published in: European Radiology 10/2022

10-06-2022 | Magnetic Resonance Imaging | Gastrointestinal

Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN

Authors: Takahiro Matsuyama, Yoshiharu Ohno, Kaori Yamamoto, Masato Ikedo, Masao Yui, Minami Furuta, Reina Fujisawa, Satomu Hanamatsu, Hiroyuki Nagata, Takahiro Ueda, Hirotaka Ikeda, Saki Takeda, Akiyoshi Iwase, Takashi Fukuba, Hokuto Akamatsu, Ryota Hanaoka, Ryoichi Kato, Kazuhiro Murayama, Hiroshi Toyama

Published in: European Radiology | Issue 10/2022

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Abstract

Objective

To compare the utility of deep learning reconstruction (DLR) for improving acquisition time, image quality, and intraductal papillary mucinous neoplasm (IPMN) evaluation for 3D MRCP obtained with parallel imaging (PI), multiple k-space data acquisition for each repetition time (TR) technique (Fast 3D mode multiple: Fast 3Dm) and compressed sensing (CS) with PI.

Materials and methods

A total of 32 IPMN patients who had undergone 3D MRCPs obtained with PI, Fast 3Dm, and CS with PI and reconstructed with and without DLR were retrospectively included in this study. Acquisition time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) obtained with all protocols were compared using Tukey’s HSD test. Results of endoscopic ultrasound, ERCP, surgery, or pathological examination were determined as standard reference, and distribution classifications were compared among all 3D MRCP protocols by McNemar’s test.

Results

Acquisition times of Fast 3Dm and CS with PI with and without DLR were significantly shorter than those of PI with and without DLR (p < 0.05). Each MRCP sequence with DLR showed significantly higher SNRs and CNRs than those without DLR (p < 0.05). IPMN distribution accuracy of PI with and without DLR and Fast 3Dm with DLR was significantly higher than that of Fast 3Dm without DLR and CS with PI without DLR (p < 0.05).

Conclusion

DLR is useful for improving image quality and IPMN evaluation capability on 3D MRCP obtained with PI, Fast 3Dm, or CS with PI. Moreover, Fast 3Dm and CS with PI may play as substitution to PI for MRCP in patients with IPMN.

Key Points

• Mean examination times of multiple k-space data acquisitions for each TR and compressed sensing with parallel imaging were significantly shorter than that of parallel imaging (p < 0.0001).
• When comparing image quality of 3D MRCPs with and without deep learning reconstruction, deep learning reconstruction significantly improved signal-to-noise ratio and contrast-to-noise ratio (p < 0.05).
• IPMN distribution accuracies of parallel imaging with and without deep learning reconstruction (with vs. without: 88.0% vs. 88.0%) and multiple k-space data acquisitions for each TR with deep learning reconstruction (86.0%) were significantly higher than those of others (p < 0.05).
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Literature
1.
Basturk O, Hong SM, Wood LD et al (2015) A revised classification system and recommendations from the Baltimore consensus meeting for neoplastic precursor lesions in the pancreas. Am J Surg Pathol. 39(12):1730–1741CrossRef
2.
Tanaka M, Fernández-Del Castillo C et al (2017) Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatology. 17(5):738–753CrossRef
3.
Vege SS, Ziring B, Jain R, Moayyedi P, Clinical Guidelines Committee; American Gastroenterology Association (2015) American gastroenterological association institute guideline on the diagnosis and management of asymptomatic neoplastic pancreatic cysts. Gastroenterology. 148(4):819–822CrossRef
4.
European Study Group on Cystic Tumours of the Pancreas (2018) European evidence-based guidelines on pancreatic cystic neoplasms. Gut. 67(5):789–804CrossRef
5.
Rossi RE, Massironi S (2018) Intraductal papillary mucinous neoplasms of the pancreas: a clinical challenge. Expert Rev Gastroenterol Hepatol. 12(11):1123–1133CrossRef
6.
Deshmane A, Gulani V, Griswold MA, Seiberlich N (2012) Parallel MR imaging. J Magn Reson Imaging. 36(1):55–72CrossRef
7.
Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H (2017) Compressed sensing for body MRI. J Magn Reson Imaging. 45(4):966–987CrossRef
8.
Ueda T, Ohno Y, Yamamoto K et al (2021) Compressed sensing and deep learning reconstruction for women’s pelvic MRI denoising: utility for improving image quality and examination time in routine clinical practice. Eur J Radiol. 134:109430CrossRef
9.
Ikeda H, Ohno Y, Murayama K et al (2021) Compressed sensing and parallel imaging accelerated T2 FSE sequence for head and neck MR imaging: comparison of its utility in routine clinical practice. Eur J Radiol. 135:109501CrossRef
10.
Mugler JP, Menzel MI, Horger W, Kiefer B (2006) Efficient phase-encoding for 3D turbo-spin-echo imaging with very long echo trains. Proc Intl Soc Mag Reson Med 14:2429
11.
Mugler JP 3rd. (2014) Optimized three-dimensional fast-spin-echo MRI. J Magn Reson Imaging. 39(4):745–767CrossRef
12.
Akatsuka J, Yamamoto Y, Sekine T et al (2019) Illuminating clues of cancer buried in prostate MR image: deep learning and expert approaches. Biomolecules. 9(11):673CrossRef
13.
Kidoh M, Shinoda K, Kitajima M et al (2020) Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers. Magn Reson Med Sci. 19(3):195–206CrossRef
14.
Qiu D, Zhang S, Liu Y, Zhu J, Zheng L (2020) Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning. Comput Methods Programs Biomed. 187:105059CrossRef
15.
Yokota Y, Takeda C, Kidoh M et al (2021) Effects of deep learning reconstruction technique in high-resolution non-contrast magnetic resonance coronary angiography at a 3-tesla machine. Can Assoc Radiol J. 72(1):120–127CrossRef
16.
Isogawa K, Ida T, Shiodera T et al (2018) Deep shrinkage convolutional neural network for adaptive noise reduction. IEEE Signal Processing Letters 25(2):224–228CrossRef
17.
Yokoyama K, Nakaura T, Iyama Y et al (2016) Usefulness of 3D hybrid profile order technique with 3T magnetic resonance cholangiography: comparison of image quality and acquisition time. J Magn Reson Imaging. 44(5):1346–1353CrossRef
18.
Chen Z, Sun B, Duan Q et al (2019) Three-dimensional breath-hold MRCP using SPACE pulse sequence at 3 T: comparison with conventional navigator-triggered technique. AJR Am J Roentgenol. 213(6):1247–1252CrossRef
19.
Sahani D, Prasad S, Saini S, Mueller P (2002) Cystic pancreatic neoplasms evaluation by CT and magnetic resonance cholangiopancreatography. Gastrointest Endosc Clin N Am. 12(4):657–672CrossRef
20.
Kawamoto S, Lawler LP, Horton KM, Eng J, Hruban RH, Fishman EK (2006) MDCT of intraductal papillary mucinous neoplasm of the pancreas: evaluation of features predictive of invasive carcinoma. AJR Am J Roentgenol. 186(3):687–695CrossRef
21.
Tanaka M (2015) Current roles of endoscopy in the management of intraductal papillary mucinous neoplasm of the pancreas. Dig Endosc. 27(4):450–457CrossRef
22.
Costa DAPD, Guerra JG, Goldman SM et al (2019) Magnetic resonance cholangiopancreatography (MRCP) versus endosonography-guided fine needle aspiration (EUS-FNA) for diagnosis and follow-up of pancreatic intraductal papillary mucinous neoplasms. Arq Bras Cir Dig 32(4):e1471CrossRef
23.
Svanholm H, Starklint H, Gundersen HJ, Fabricius J, Barlebo H, Olsen S (1989) Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic. APMIS. 197(8):689–698CrossRef
24.
Mandrekar JN (2011) Measures of interrater agreement. J Thorac Oncol. 6(1):6–7CrossRef
25.
Cristobal-Huerta A, Poot DHJ, Vogel MW, Krestin GP, Hernandez-Tamames JA (2019) Compressed sensing 3D-GRASE for faster high-resolution MRI. Magn Reson Med. 82(3):984–999CrossRef
26.
Sartoretti T, Sartoretti E, Wyss M et al (2019) Compressed SENSE accelerated 3D T1w black blood turbo spin echo versus 2D T1w turbo spin echo sequence in pituitary magnetic resonance imaging. Eur J Radiol. 120:108667CrossRef
27.
Hausmann D, Niemann T, Kreul D et al (2019) Free-breathing dynamic contrast-enhanced imaging of the upper abdomen using a cartesian compressed-sensing sequence with hard-gated and motion-state-resolved reconstruction. Invest Radiol. 54(11):728–736CrossRef
28.
Bratke G, Rau R, Weiss K et al (2019) Accelerated MRI of the lumbar spine using compressed sensing: quality and efficiency. J Magn Reson Imaging. 49(7):e164–e175CrossRef
29.
Morita K, Nakaura T, Maruyama N et al (2020) Hybrid of compressed sensing and parallel imaging applied to three-dimensional isotropic T2-weighted turbo spin-echo MR imaging of the lumbar spine. Magn Reson Med Sci. 19(1):48–55CrossRef
Metadata
Title
Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN
Authors
Takahiro Matsuyama
Yoshiharu Ohno
Kaori Yamamoto
Masato Ikedo
Masao Yui
Minami Furuta
Reina Fujisawa
Satomu Hanamatsu
Hiroyuki Nagata
Takahiro Ueda
Hirotaka Ikeda
Saki Takeda
Akiyoshi Iwase
Takashi Fukuba
Hokuto Akamatsu
Ryota Hanaoka
Ryoichi Kato
Kazuhiro Murayama
Hiroshi Toyama
Publication date
10-06-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 10/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08877-2

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