Slice-to-Volume Registration Enables Automated Pancreas MRI Quantification in UK Biobank

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)


Multiparametric MRI of the pancreas can potentially benefit from the fusion of multiple acquisitions. However, its small, irregular structure often results in poor organ alignment between acquisitions, potentially leading to inaccurate quantification. Recent studies using UK Biobank data have proposed using pancreas segmentation from a 3D volumetric scan to extract a region of interest in 2D quantitative maps. A limitation of these studies is that potential misalignment between the volumetric and single-slice scans has not been considered. In this paper, we report a slice-to-volume registration (SVR) method with multimodal similarity that aligns the UK Biobank pancreatic 3D acquisitions with the 2D acquisitions, leading to more accurate downstream quantification of an individual’s pancreas T1. We validate the SVR method on a challenging UK Biobank subset of N = 50, using both direct and indirect performance metrics.


Pancreas Multiparametric Magnetic resonance imaging Volume T1 Slice-to-volume registration UK biobank 


  1. 1.
    Mathur, A., et al.: Nonalcoholic fatty pancreas disease. Hpb9(4), 312–318 (2007).
  2. 2.
    Tariq, H., Nayudu, S., Akella, S., Glandt, M., Chilimuri, S.: Non-alcoholic fatty pancreatic disease: a review of literature. Gastroenterol. Res. 9(6), 87–91 (2016).
  3. 3.
    Mojtahed, A., et al.: Reference range of liver corrected T1 values in a population at low risk for fatty liver disease–a UK Biobank sub-study, with an appendix of interesting cases. Abdom. Radiol. 44(1), 72–84 (2019).
  4. 4.
    Reeder, S.B., Hu, H.H., Sirlin, C.B.: Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J. Magn. Reson. Imaging 36(5), 1011–1014 (2012).
  5. 5.
    Saisho, Y., et al.: Pancreas volumes in humans from birth to age one hundred taking into account sex, obesity, and presence of type-2 diabetes. Clin. Anat. 20(8), 933–942 (2007).
  6. 6.
    Al-Mrabeh, A., Hollingsworth, K.G., Steven, S., Taylor, R.: Morphology of the pancreas in type 2 diabetes: effect of weight loss with or without normalisation of insulin secretory capacity. Diabetologia 59(8), 1753–1759 (2016). Scholar
  7. 7.
    Tirkes, T., Lin, C., Fogel, E.L., Sherman, S.S., Wang, Q., Sandrasegaran, K.: T 1 mapping for diagnosis of mild chronic pancreatitis. J. Magn. Reson. Imaging 45(4), 1171–1176 (2017).
  8. 8.
    Kühn, J.P., et al.: Pancreatic steatosis demonstrated at mr imaging in the general population: clinical relevance. Radiology 276(1), 129–136 (2015).
  9. 9.
    Littlejohns, T.J., et al.: The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11(1), 2624 (2020).,
  10. 10.
    Wilman, H.R., et al.: Characterisation of liver fat in the UK Biobank cohort. PLoS ONE 12(2), 1–14 (2017).
  11. 11.
    Hutton, C., Gyngell, M.L., Milanesi, M., Bagur, A., Brady, M.: Validation of a standardized MRI method for liver fat and T2* quantification. PLOS ONE 13(9), e0204175 (2018).
  12. 12.
    Tarroni, G., et al.: Large-scale quality control of cardiac imaging in population studies: application to UK Biobank. Sci. Rep. 10(1), 2408 (2020).
  13. 13.
    Basty, N., Liu, Y., Cule, M., Thomas, E.L., Bell, J.D., Whitcher, B.: Automated measurement of pancreatic fat and iron concentration using multi-echo and T1-Weighted MRI data. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), vol. 2020-April, pp. 345–348. IEEE (2020).
  14. 14.
    Liu, Y., et al.: Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learning, pp. 1–66 (2020)Google Scholar
  15. 15.
    Ferrante, E., Paragios, N.: Slice-to-volume medical image registration: a survey. Med. Image Anal. 39, 101–123 (2017).
  16. 16.
    Hou, B., et al.: Predicting slice-to-volume transformation in presence of arbitrary subject motion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10434. LNCS, pp. 296–304 (2017)Google Scholar
  17. 17.
    Bagur, A.T., Ridgway, G., McGonigle, J., Brady, S.M., Bulte, D.: Pancreas segmentation-derived biomarkers: volume and shape metrics in the UK Biobank imaging study. In: Papież, B.W., Namburete, A.I.L., Yaqub, M., Noble, J.A. (eds.) MIUA 2020. CCIS, vol. 1248, pp. 131–142. Springer, Cham (2020). Scholar
  18. 18.
    Heinrich, M.P., Jenkinson, M., Papież, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187–194. Springer, Heidelberg (2013). Scholar
  19. 19.
    Nadarajah, C., et al.: Association of pancreatic fat content with type II diabetes mellitus. Clin. Radiol. 75(1), 51–56 (2020).
  20. 20.
    Sakai, N.S., Taylor, S.A., Chouhan, M.D.: Obesity, metabolic disease and the pancreas-Quantitative imaging of pancreatic fat. Br. J. Radiol. 91(1089), 20180267 (2018).
  21. 21.
    Bagur, A.T., Ridgway, G., Brady, M., Bulte, D.: (Abstract accepted for presentation) Automated pancreas parts segmentation by groupwise registration and minimal annotation enables regional assessment of disease. In: ISMRM Annual Meeting (2021)Google Scholar

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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Perspectum Ltd.OxfordUK
  3. 3.Department of OncologyUniversity of OxfordOxfordUK

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