Abstract
Inter-frame patient motion introduces spatial misalignment and degrades parametric imaging in whole-body dynamic positron emission tomography (PET). Most current deep learning inter-frame motion correction works consider only the image registration problem, ignoring tracer kinetics. We propose an inter-frame Motion Correction framework with Patlak regularization (MCP-Net) to directly optimize the Patlak fitting error and further improve model performance. The MCP-Net contains three modules: a motion estimation module consisting of a multiple-frame 3-D U-Net with a convolutional long short-term memory layer combined at the bottleneck; an image warping module that performs spatial transformation; and an analytical Patlak module that estimates Patlak fitting with the motion-corrected frames and the individual input function. A Patlak loss penalization term using mean squared percentage fitting error is introduced to the loss function in addition to image similarity measurement and displacement gradient loss. Following motion correction, the parametric images were generated by standard Patlak analysis. Compared with both traditional and deep learning benchmarks, our network further corrected the residual spatial mismatch in the dynamic frames, improved the spatial alignment of Patlak \(K_i\)/\(V_b\) images, and reduced normalized fitting error. With the utilization of tracer dynamics and enhanced network performance, MCP-Net has the potential for further improving the quantitative accuracy of dynamic PET. Our code is released at https://github.com/gxq1998/MCP-Net.
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
Similar content being viewed by others
References
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)
Bhushan, M., Schnabel, J.A., Risser, L., Heinrich, M.P., Brady, J.M., Jenkinson, M.: Motion correction and parameter estimation in dceMRI sequences: application to colorectal cancer. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6891, pp. 476–483. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23623-5_60
Carson, R.E.: Tracer kinetic modeling in PET. In: Bailey, D.L., Townsend, D.W., Valk, P.E., Maisey, M.N. (eds.) Positron Emission Tomography. Springer, London (2005). https://doi.org/10.1007/1-84628-007-9_6
Chen, K., Reiman, E., Lawson, M., Feng, D., Huang, S.C.: Decay correction methods in dynamic pet studies. IEEE Trans. Nucl. Sci. 42(6), 2173–2179 (1995)
Cheng, X.: Improving reconstruction of dynamic PET imaging by utilizing temporal coherence and pharmacokinetics. Ph.D. thesis, Technische Universität München (2015)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Dimitrakopoulou-Strauss, A., Pan, L., Sachpekidis, C.: Kinetic modeling and parametric imaging with dynamic pet for oncological applications: general considerations, current clinical applications, and future perspectives. Eur. J. Nucl. Med. Mol. Imaging 48(1), 21–39 (2021)
Fahrni, G., Karakatsanis, N.A., Di Domenicantonio, G., Garibotto, V., Zaidi, H.: Does whole-body patlak 18 f-fdg pet imaging improve lesion detectability in clinical oncology? Eur. Radiol. 29(9), 4812–4821 (2019)
Guo, X., et al.: Inter-pass motion correction for whole-body dynamic parametric pet imaging. In: 2021 Society of Nuclear Medicine and Molecular Imaging Annual Meeting (SNMMI 2021), pp. 1421. SNMMI, Soc Nuclear Med (2021)
Guo, X., Zhou, B., Pigg, D., Spottiswoode, B., Casey, M.E., Liu, C., Dvornek, N.C.: Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network. Med. Image Anal. 80, 102524 (2022). https://doi.org/10.1016/j.media.2022.102524
Jiao, J., Searle, G.E., Tziortzi, A.C., Salinas, C.A., Gunn, R.N., Schnabel, J.A.: Spatio-temporal pharmacokinetic model based registration of 4d pet neuroimaging data. Neuroimage 84, 225–235 (2014)
Joshi, A., et al.: Unified framework for development, deployment and robust testing of neuroimaging algorithms. Neuroinformatics 9(1), 69–84 (2011)
Li, M., Wang, C., Zhang, H., Yang, G.: Mv-ran: multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis. Comput. Biol. Med. 120, 103728 (2020)
Lu, Y., et al.: Data-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic pet. Phys. Med. Biol. 64(6), 065002 (2019)
Mojica, M., Ebrahimi, M.: Motion correction in dynamic contrast-enhanced magnetic resonance images using pharmacokinetic modeling. In: Medical Imaging 2021: Image Processing, vol. 11596, p. 115962S. International Society for Optics and Photonics (2021)
Naganawa, M., et al.: Assessment of population-based input functions for Patlak imaging of whole body dynamic 18 f-fdg pet. EJNMMI Phys. 7(1), 1–15 (2020)
Panin, V., Smith, A., Hu, J., Kehren, F., Casey, M.: Continuous bed motion on clinical scanner: design, data correction, and reconstruction. Phys. Med. Biol. 59(20), 6153 (2014)
Patlak, C.S., Blasberg, R.G., Fenstermacher, J.D.: Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J. Cerebral Blood Flow Metabolism 3(1), 1–7 (1983)
Shi, L., et al.: Automatic inter-frame patient motion correction for dynamic cardiac pet using deep learning. IEEE Trans. Med. Imaging 40, 3293–3304 (2021)
Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional lstm network: A machine learning approach for precipitation nowcasting. arXiv preprint arXiv:1506.04214 (2015)
Vaquero, J.J., Kinahan, P.: Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu. Rev. Biomed. Eng. 17, 385–414 (2015)
Wang, G., Rahmim, A., Gunn, R.N.: Pet parametric imaging: past, present, and future. IEEE Trans. Radiat. Plasma Med. Sci. 4(6), 663–675 (2020)
Zhao, S., Lau, T., Luo, J., Eric, I., Chang, C., Xu, Y.: Unsupervised 3d end-to-end medical image registration with volume Tweening network. IEEE J. Biomed. Health Inform. 24(5), 1394–1404 (2019)
Zhou, B., Tsai, Y.J., Chen, X., Duncan, J.S., Liu, C.: MDPET: a unified motion correction and denoising adversarial network for low-dose gated pet. IEEE Trans. Med. Imaging 40, 3154–3164 (2021)
Acknowledgements
This work is supported by National Institutes of Health (NIH) through grant R01 CA224140.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Guo, X., Zhou, B., Chen, X., Liu, C., Dvornek, N.C. (2022). MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-16440-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16439-2
Online ISBN: 978-3-031-16440-8
eBook Packages: Computer ScienceComputer Science (R0)