Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mathur, A., et al.: Nonalcoholic fatty pancreas disease. Hpb9(4), 312–318 (2007). https://doi.org/10.1080/13651820701504157
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). http://www.gastrores.org/index.php/Gastrores/article/view/731
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). http://link.springer.com/10.1007/s00261-018-1701-2
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). https://doi.org/10.1002/jmri.23741
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). https://onlinelibrary.wiley.com/doi/10.1002/ca.20543
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). https://doi.org/10.1007/s00125-016-3984-6
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). https://doi.org/10.1002/jmri.25428
Kühn, J.P., et al.: Pancreatic steatosis demonstrated at mr imaging in the general population: clinical relevance. Radiology 276(1), 129–136 (2015). http://pubs.rsna.org/doi/10.1148/radiol.15140446
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). https://doi.org/10.1038/s41467-020-15948-9, www.nature.com/articles/s41467-020-15948-9
Wilman, H.R., et al.: Characterisation of liver fat in the UK Biobank cohort. PLoS ONE 12(2), 1–14 (2017). http://dx.doi.org/10.1371/journal.pone.0172921
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). https://dx.plos.org/10.1371/journal.pone.0204175
Tarroni, G., et al.: Large-scale quality control of cardiac imaging in population studies: application to UK Biobank. Sci. Rep. 10(1), 2408 (2020). http://www.nature.com/articles/s41598-020-58212-2
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). https://ieeexplore.ieee.org/document/9098650/
Liu, Y., et al.: Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learning, pp. 1–66 (2020)
Ferrante, E., Paragios, N.: Slice-to-volume medical image registration: a survey. Med. Image Anal. 39, 101–123 (2017). https://linkinghub.elsevier.com/retrieve/pii/S1361841517300701
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)
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). https://doi.org/10.1007/978-3-030-52791-4_11
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). https://doi.org/10.1007/978-3-642-40811-3_24
Nadarajah, C., et al.: Association of pancreatic fat content with type II diabetes mellitus. Clin. Radiol. 75(1), 51–56 (2020). https://doi.org/10.1016/j.crad.2019.05.027
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). https://www.birpublications.org/doi/10.1259/bjr.20180267
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bagur, A.T., Aljabar, P., Arya, Z., McGonigle, J., Brady, S.M., Bulte, D. (2021). Slice-to-Volume Registration Enables Automated Pancreas MRI Quantification in UK Biobank. In: Papież, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science(), vol 12722. Springer, Cham. https://doi.org/10.1007/978-3-030-80432-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-80432-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80431-2
Online ISBN: 978-3-030-80432-9
eBook Packages: Computer ScienceComputer Science (R0)