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• Special Focus •
Study on the value of deep reconstruction technique in improving the image quality of magnetic resonance rectal cancer
HU Sijie  FAN Wenwen  TENG Ze  LIU Kan  TONG Xiaowan  JIANG Yueluan  LIU Peng  LANG Yu  NICKEL MarcelDominik  ZHANG Hongmei 

Cite this article as: HU S J, FAN W W, TENG Z, et al. Study on the value of deep reconstruction technique in improving the image quality of magnetic resonance rectal cancer[J]. Chin J Magn Reson Imaging, 2024, 15(10): 30-35. DOI:10.12015/issn.1674-8034.2024.10.006.


[Abstract] Objective To evaluate the value of deep learning reconstruction (DL Recon) technique in improving the image quality of rectal MRI turbo spin-echo (TSE) sequences.Materials and Methods Sixty new cases of rectal cancer diagnosed by pathology in the Chinese Academy of Medical Sciences from September 2023 to January 2024 were studied retrospectively. Each patient was subjected to a conventional TSE sequence and DL-TSE sequence, and the scanning time was recorded. Two imaging doctors had subjective evaluation for the two groups (conventional TSE, DL-TSE). The "five-point method" was used to score lesion contour clarity, the image artifacts, the clarity of the lesion and the reliability of the diagnosis, and the statistical description of the results was performed using the quartile interval M (Q25, Q75). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) between the DL-TSE and the conventional TSE images were computed by two imaging technicians. Paired sample t test was used for statistical analysis of the data conforming to normal distribution, and paired sample non-parametric test (Wilcoxon symbolic significance test) was used for statistical analysis of the data not conforming to normal distribution, and the results were statistically described by the quartile interval M (Q25, Q75).Results Sixty cases of rectal carcinoma aged 35-69 (53±10) years old were enrolled. The subjective evaluation results of conventional TSE sequences and DL-TSE sequences: The focal contour clarity, image artifacts, focal structure clarity and subjective score of diagnostic confidence of DL-TSE sequence were better than those of traditional TSE sequence, and the differences were statistically significant (P<0.001). Objective evaluation results of traditional TSE sequence and DL-TSE sequence images: The SNR of DL-TSE and conventional TSE sequences were 24.26 (15.95, 42.79) and 11.84 (7.63, 18.88). The CNR of DL-TSE and conventional TSE sequences were 10.75 (7.19, 15.63) and 5.47 (3.72, 8.86), the difference was statistically significant (Z=-14.271, P<0.001). The SNR and the CNR of the DL-TSE were obviously higher than those of conventional TSE sequences.Conclusions DL-TSE sequence uses the original K-space data DL Recon reconstruction algorithm to improve the SNR and CNR of sequence images of rectal cancer patients, and can shorten the scanning time by 36.6%, while ensuring the image quality and lesion detectability.
[Keywords] rectal cancer；signal-to-noise ratio；contrast-to-noise ratio；deep learning reconstruction；magnetic resonance imaging

HU Sijie1   FAN Wenwen1   TENG Ze1   LIU Kan1   TONG Xiaowan1   JIANG Yueluan2   LIU Peng1   LANG Yu1   NICKEL MarcelDominik3   ZHANG Hongmei1*  

1 Department of Diagnostic Radiology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

2 Beijing Branch, Siemens Healthineers Ltd., Beijing 100176, China

3 Siemens Healthineers AG, Erlangen, Germany

Corresponding author: ZHANG H M, E-mail: 13581968865@163.com

Conflicts of interest   None.

Received  2024-03-29
Accepted  2024-08-02
DOI: 10.12015/issn.1674-8034.2024.10.006
Cite this article as: HU S J, FAN W W, TENG Z, et al. Study on the value of deep reconstruction technique in improving the image quality of magnetic resonance rectal cancer[J]. Chin J Magn Reson Imaging, 2024, 15(10): 30-35. DOI:10.12015/issn.1674-8034.2024.10.006.

[1]
SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer statistics, 2022[J]. CA Cancer J Clin, 2022, 72(1): 7-33. DOI: 10.3322/caac.21708.
[2]
SIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023[J]. CA Cancer J Clin, 2023, 73(1): 17-48. DOI: 10.3322/caac.21763.
[3]
SIEGEL R L, GIAQUINTO A N, JEMAL A. Cancer statistics, 2024[J]. CA Cancer J Clin, 2024, 74(1): 12-49. DOI: 10.3322/caac.21820.
[4]
WANG J S, WEI J Y, PENG M. Interpretation and enlightenment of 2023 American cancer statistics report and the latest global cancer statistics[J]. J Pract Oncol, 2023, 38(6): 523-527. DOI: 10.13267/j.cnki.syzlzz.2023.083.
[5]
YANG J F, CHEN Y, SONG W. Application value of MRI in staging evaluation of patients with middle and low rectal cancer before transanal total mesorectal excision[J]. Zhejiang J Trauma Surg, 2023, 28(12): 2386-2389. DOI: 10.3969/j.issn.1009-7147.2023.12.057.
[6]
OGAWA M, URANO M, TAKAISHI T, et al. T-staging of rectal cancer: utility of single-shot turbo spin-echo diffusion-weighted imaging with T2-weighted images and fusion images[J/OL]. PLoS One, 2021, 16(4): e0249433 [2024-04-21]. https://pubmed.ncbi.nlm.nih.gov/33882087. DOI: 10.1371/journal.pone.0249433.
[7]
WEI J J, JI L J. Clinical value of magnetic resonance imaging (MRI) and CT in evaluating the preoperative T stage of rectal cancer[J]. Chin J CT MRI, 2017, 15(12): 113-116, 139. DOI: 10.3969/j.issn.1672-5131.2017.12.035.
[8]
LV B H, CHENG X J, XIE Y Z, et al. Predictive value of lesion morphology in rectal cancer based on MRI before surgery[J/OL]. BMC Gastroenterol, 2023, 23(1): 318 [2024-04-21]. https://pubmed.ncbi.nlm.nih.gov/37726671/. DOI: 10.1186/s12876-023-02910-4.
[9]
CHEN C Y, ZHAO Q Q, XU Y K. MRI diagnosis of extramural vascular invasion for assessing prognosis of rectal cancer patients after neoadjuvant therapy and radical resection[J]. Chin J Med Imag Technol, 2020, 36(12): 1848-1852. DOI: 10.13929/j.issn.1003-3289.2020.12.019.
[10]
GÜRSES B, BÖGE M, ALTıNMAKAS E, et al. Multiparametric MRI in rectal cancer[J]. Diagn Interv Radiol, 2019, 25(3): 175-182. DOI: 10.5152/dir.2019.18189.
[11]
FERNANDES M C, GOLLUB M J, BROWN G. The importance of MRI for rectal cancer evaluation[J/OL]. Surg Oncol, 2022, 43: 101739 [2024-03-18]. https://pubmed.ncbi.nlm.nih.gov/35339339. DOI: 10.1016/j.suronc.2022.101739.
[12]
VAN DER POL C B, SHABANA W M, MCINNES M D, et al. High-resolution T2-weighted (T2W) oblique plane turbo spin-echo (TSE) MRI for rectal adenocarcinoma staging[J]. Clin Imaging, 2015, 39(4): 627-631. DOI: 10.1016/j.clinimag.2015.01.014.
[13]
HAN F, XU Y M. Understanding the common types of image artifacts in magnetic resonance examination and their solutions[J]. China Med Device Inf, 2019, 25(20): 22-23. DOI: 10.3969/j.issn.1006-6586.2019.20.011.
[14]
FRITZ J, GUGGENBERGER R, DEL GRANDE F. Rapid musculoskeletal MRI in 2021: clinical application of advanced accelerated techniques[J]. AJR Am J Roentgenol, 2021, 216(3): 718-733. DOI: 10.2214/AJR.20.22902.
[15]
ZHU C G. Clinical application of 654-2 (anisodamine) plus manual pushing and pressing in the treatment of primary intussusception[J]. China Pract Med, 2012, 7(30): 152. DOI: 10.3969/j.issn.1673-7555.2012.30.117.
[16]
ZHANG Y, ZOU J Y, WAN F, et al. Update on the sources, pharmacokinetics, pharmacological action, and clinical application of anisodamine[J/OL]. Biomedecine Pharmacother, 2023, 161: 114522 [2023-03-14]. https://pubmed.ncbi.nlm.nih.gov/37002581. DOI: 10.1016/j.biopha.2023.114522.
[17]
LUSTIG M, PAULY J M. SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space[J]. Magn Reson Med, 2010, 64(2): 457-471. DOI: 10.1002/mrm.22428.
[18]
HAMMERNIK K, KLATZER T, KOBLER E, et al. Learning a variational network for reconstruction of accelerated MRI data[J]. Magn Reson Med, 2018, 79(6): 3055-3071. DOI: 10.1002/mrm.26977.
[19]
WANG J, FENG Y H. Research on medical image detection method based on deep learning technology[J]. Electron Des Eng, 2021, 29(20): 169-173. DOI: 10.14022/j.issn1674-6236.2021.20.036.
[20]
LIU B. Research on medical image segmentation and application based on deep learning[D].Yantai: Yantai University, 2023.
[21]
YIN B C. Research on medical image-aided diagnosis technology based on deep learning[D].Hefei: University of Science and Technology of China, 2022.
[22]
HAHN S, YI J, LEE H J, et al. Image quality and diagnostic performance of accelerated shoulder MRI with deep learning-based reconstruction[J]. AJR Am J Roentgenol, 2022, 218(3): 506-516. DOI: 10.2214/AJR.21.26577.
[23]
KIDOH M, SHINODA K, KITAJIMA M, et al. Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers[J]. Magn Reson Med Sci, 2020, 19(3): 195-206. DOI: 10.2463/mrms.mp.2019-0018.
[24]
XU X, PENG W L, ZHANG J G, et al. The application value of artificial intelligence-based filtering and interpolated image reconstruction algorithm in abdominal magnetic resonance image denoising[J]. J Sichuan Univ Med Sci, 2021, 52(2): 293-299. DOI: 10.12182/20210360104.
[25]
JOHNSON P M, LIN D J, ZBONTAR J, et al. Deep learning reconstruction enables prospectively accelerated clinical knee MRI[J/OL]. Radiology, 2023, 307(2): e220425 [2023-03-14]. https://pubmed.ncbi.nlm.nih.gov/36648347/. DOI: 10.1148/radiol.220425.
[26]
XIE Y X, TAO H Y, LI X W, et al. Prospective comparison of standard and deep learning-reconstructed turbo spin-echo MRI of the shoulder[J/OL]. Radiology, 2024, 310(1): e231405 [2024-01-09]. https://pubmed.ncbi.nlm.nih.gov/38193842. DOI: 10.1148/radiol.231405.
[27]
KHAN M K H, GUO W J, LIU J, et al. Machine learning and deep learning for brain tumor MRI image segmentation[J]. Exp Biol Med, 2023, 248(21): 1974-1992. DOI: 10.1177/15353702231214259.
[28]
OTTENS T, BARBIERI S, ORTON M R, et al. Deep learning DCE-MRI parameter estimation: application in pancreatic cancer[J/OL]. Med Image Anal, 2022, 80: 102512 [2024-03-01]. https://pubmed.ncbi.nlm.nih.gov/35709559/. DOI: 10.1016/j.media.2022.102512.
[29]
ZHAO X, BAI J W, GUO Q, et al. Clinical applications of deep learning in breast MRI[J/OL]. Biochim Biophys Acta Rev Cancer, 2023, 1878(2): 188864 [2023-09-03]. https://pubmed.ncbi.nlm.nih.gov/36822377/. DOI: 10.1016/j.bbcan.2023.188864.
[30]
SLANETZ P J. The potential of deep learning to revolutionize current breast MRI practice[J/OL]. Radiology, 2023, 306(3): e222527 [2023-11-15]. https://pubs.rsna.org/doi/10.1148/radiol.222527. DOI: 10.1148/radiol.222527.
[31]
BURGER B, BERNATHOVA M, SEEBÖCK P, et al. Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study[J/OL]. Eur Radiol Exp, 2023, 7(1): 32 [2024-03-01]. https://pubmed.ncbi.nlm.nih.gov/37280478/. DOI: 10.1186/s41747-023-00343-y.
[32]
HIGAKI T, NAKAMURA Y, TATSUGAMI F, et al. Improvement of image quality at CT and MRI using deep learning[J]. Jpn J Radiol, 2019, 37(1): 73-80. DOI: 10.1007/s11604-018-0796-2.
[33]
ZHOU N, HUA L C, LIU J, et al. Application of deep learning reconstruction in MRI reconstruction[J]. China Med Devices, 2023, 38(12): 165-169. DOI: 10.3969/j.issn.1674-1633.2023.12.029.
[34]
MATSUMOTO S, TSUBOYAMA T, ONISHI H, et al. Ultra-high-resolution T2-weighted PROPELLER MRI of the rectum with deep learning reconstruction: assessment of image quality and diagnostic performance[J]. Invest Radiol, 2024, 59(7): 479-488. DOI: 10.1097/RLI.0000000000001047.
[35]
KANIEWSKA M, DEININGER-CZERMAK E, GETZMANN J M, et al. Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time[J]. Eur Radiol, 2023, 33(3): 1513-1525. DOI: 10.1007/s00330-022-09151-1.
[36]
HALLINAN J T P D. Deep learning for spine MRI: reducing time not quality[J/OL]. Radiology, 2023, 306(3): e222410 [2023-11-01]. https://pubmed.ncbi.nlm.nih.gov/36318033. DOI: 10.1148/radiol.222410.
[37]
PAPP D, CASTILLO T J M, WIELOPOLSKI P A, et al. Deep learning for improving ZTE MRI images in free breathing[J]. Magn Reson Imaging, 2023, 98: 97-104. DOI: 10.1016/j.mri.2023.01.019.
[38]
ZHANG C, SCHWARTZ M, KÜSTNER T, et al. Multiparametric functional MRI of the kidney: current state and future trends with deep learning approaches[J]. Rofo, 2022, 194(9): 983-992. DOI: 10.1055/a-1775-8633.

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