Data Driven Motion Correction in PET

Data driven motion correction in PET

What is data driven motion correction in PET?

  • Conventional 3D PET can be dimensionally expanded into an inherently 4D technology

    • Motion information can be extracted from raw data to create motion corrected images without requiring any gating equipment

    • Images can be displayed with denoising strategies so similar to non-gated image.

Proof of principle

We have seen a significant amount of research establishing the proof of principle that motion information exists within our raw data and can be extracted via data driven methods. Below is an animated image from our recent publications which showed the data driven gating can work as well as hardware, and produce images with equivalent noise levels:

Example population of FDG PET scans acquired with no gating equipment, with Vendor reconstruction and Kesner’s motion correction reconstruction compared side by side

(Acknowledgment: Data derived from collaborations with National Jewish Health, Denver, and Siemens USA)

References:

Kesner, A.L., Chung JH, Lind KE, et al., “Frequency based gating: An alternative, conformal, approach to 4D PET data utilization”, Med Phys. 2016;43(3):1451. http://www.ncbi.nlm.nih.gov/pubmed/26936729

Kesner, A.L., Chung JH, Lind KE, et al., “Validation of Software Gating: A Practical Technology for Respiratory Motion Correction in PET”, Radiology. 2016:152105. http://www.ncbi.nlm.nih.gov/pubmed/27027335

Data Driven motion correction - a layman's perspective

The technology and opportunities presented by data driven motion correction were nicely presented in medicalphysicsweb publications:

Data Driven motion correction - a imaging researcher's perspective

Data driven motion correction represents a modern solution to an old problem in imaging (patient motion). Using information contained in our data represents a "do more with less" and a "work smarter not harder" framework. General scientific discusions on data driven gating can be found here:

Innovation of record

The evolution of data driven motion correction in PET imaging has been a culmination of a number of hardworking researchers and research groups. This has been a sub-field that has largely been built on creative innovations of many.

Our group has long been interested in developing data driven gating in PET. We are proud of the principles and concepts we have pioneered, and to see that they have inspired and contributed to building the modern field.

  • Kesner et. al. 2007 - first to present concept of using local signal fluctuations, rather than center of mass, to characterize motion (1) (2) (3)

  • Kesner et. al. 2010 - first to collapse data to smaller manageable form to enable fast and/or real time processing for data driven gating (4)

  • Kesner et. al. 2010 - first to present idea of real-time data driven gating processing (4)

  • Kesner et. al. 2013 - first to present data driven gating in small animal PET (5)

  • Kesner et. al. 2013 - first to present concept of fully automated motion correction workflow - data driven gating + motion correction (5) (6)

  • Kesner et. al. 2015 - first to show data driven gating can compare well, and even outperform hardware driven gating in large non-specific population (7)

Of course, the field has been built and evolved with numerous innovations from a global community. Some contributions of note include:

  • Bundschuh et. al. 2007 - Early center of mass data driven gating study (8)

  • He et. al. 2007 – early (somewhat primitive) method for data driven gating (geometric sensitivity) (9)

  • Büther et. al. 2009 - Data driven cardiac gating (10)

  • Schleyer et. al. 2009 - Demonstration of information in sinogram space (11)

  • Thielemans et. al. 2011 - Elegant combination of mathematical algorithm (PCA) and data driven gating (12) for an efficient workflow.

  • Guerra et. al. 2012 - First large multi-center gating study (hardware based) (13)

  • Herraiz et. al. – Small animal PET cardiac gating (14)

  • Büther et. al 2016 - Impact of data driven gating on lesion quantification (15)

  • Sanders et. al. 2016 – data driven gating in SPECT (16)

  • Jaewon et. al 2017 - Data driven gating in PET using singles (17)

  • Feng et. al 2018 - Robust center-of-mass based data driven gating (18)

  • Büther et. al. - Data driven quality characterization of signal (19)

Overall, we have seen a global field present creative innovation and build off each other's work to establish this growing subfield.

References:

    1. Kesner AL, Bundschuh RA, Detorie NC, Dahlbom M, Czernin J, Silverman DHS. Respiratory gated PET derived from raw PET data. Paper presented at: Nuclear Science Symposium Conference Record, 2007. NSS '07. IEEE; Oct. 26 2007-Nov. 3 2007, 2007.

    2. Kesner AL, Bundschuh RA, Detorie NC, et al. Respiratory Gated PET Derived in a Fully Automated Manner From Raw PET Data. Nuclear Science, IEEE Transactions on. 2009;56:677-686.

    3. Kesner AL, Dahlbom M, Czernin J, Silverman DH. Respiratory Gated PET Derived From Raw PET Data. 2007.

    4. Kesner AL, Kuntner C. A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. Medical Physics. 2010;37:5550-5559.

    5. Kesner AL, Abourbeh G, Mishani E, Chisin R, Tshori S, Freedman N. Gating, enhanced gating, and beyond: information utilization strategies for motion management, applied to preclinical PET. EJNMMI Res. 2013;3:29.

    6. Kesner A, Schleyer P, Buther F, Walter M, Schafers K, Koo P. On transcending the impasse of respiratory motion correction applications in routine clinical imaging - a consideration of a fully automated data driven motion control framework. EJNMMI Physics. 2014;1:8.

    7. Kesner AL, Chung JH, Lind KE, et al. Validation of Software Gating: A Practical Technology for Respiratory Motion Correction in PET. Radiology. 2016:152105.

    8. Bundschuh RA, Martínez-Moeller A, Essler M, et al. Postacquisition Detection of Tumor Motion in the Lung and Upper Abdomen Using List-Mode PET Data: A Feasibility Study. Journal of Nuclear Medicine. 2007;48:758-763.

    9. He J, Ackerly T, O'Keefe GJ, Geso M. Respiratory Motion Gating Based on List-Mode Data in 3D PET: A Simulation Study Using the Dynamic NCAT Phantom. Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering: IEEE Computer Society; 2009:3697-3700.

    10. Buther F, Dawood M, Stegger L, et al. List mode-driven cardiac and respiratory gating in PET. J Nucl Med. 2009;50:674-681.

    11. Schleyer PJ, O'Doherty MJ, Barrington SF, Marsden PK. Retrospective data-driven respiratory gating for PET/CT. Phys Med Biol. 2009;54:1935-1950.

    12. Thielemans K, Rathore S, Engbrant F, Razifar P. Device-less gating for PET/CT using PCA. Paper presented at: Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE; 23-29 Oct. 2011, 2011.

    13. Guerra L, De Ponti E, Elisei F, et al. Respiratory gated PET/CT in a European multicentre retrospective study: added diagnostic value in detection and characterization of lung lesions. Eur J Nucl Med Mol Imaging. 2012;39:1381-1390.

    14. Herraiz JL, Herranz E, Cal-González J, et al. Automatic Cardiac Self-Gating of Small-Animal PET Data. Molecular Imaging and Biology. 2016;18:109-116.

    15. Buther F, Vehren T, Schafers KP, Schafers M. Impact of Data-driven Respiratory Gating in Clinical PET. Radiology. 2016:152067.

    16. Sanders JC, Ritt P, Kuwert T, Vija AH, Maier AK. Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps. IEEE Transactions on Medical Imaging. 2016;35:2425-2435.

    17. Jaewon Y, Mehdi K, A. HT, Karen O, Youngho S. Technical Note: Fast respiratory motion estimation using sorted singles without unlist processing: A feasibility study. Medical Physics. 2017;44:1632-1637.

    18. Feng T, Wang J, Sun Y, Zhu W, Dong Y, Li H. Self-Gating: An Adaptive Center-of-mass Approach for Respiratory Gating in PET. Vol PP; 2017.

    19. Buther F, Ernst I, Frohwein LJ, Pouw J, Schafers KP, Stegger L. Data-driven gating in PET: Influence of respiratory signal noise on motion resolution. Med Phys. 2018;45:3205-3213.Challenges in data driven motion correction

Current status of the technology

It has taken a long time - too long, to move data driven motion correction ideas into clinical solutions. However, in the last few years data driven motion correction has begun to gain recognition in the research community as a relevant area of exploration. Vendors are beginning to take interest and develop their own commercial products.

If we consider data driven motion correction in the context of the Diffusion of Innovations theory (pioneered by E Rogers), it is likely that we are at the intersection of innovators and early adopters. Thus, this is an opportune time for researchers interested in this topic to insert themselves into one of these two groups.

Because of the effortless application of data driven motion correction, and "free" information it provides, it is likely that this technology will soon be ubiquitously available across PET technology.

Challenges in data driven motion correction

Looking forward there is an exciting future for data driven motion correction. Traditionally there is a boundary of research and industry at image creation - pre-reconstructed data is often considered preparatory and in a preparatory format. Modern innovations like data driven motion correction will require a new data access architecture to support access to required data. This is an idea that is bigger than motion correction alone.

We have been working on our vision for a modernized research landscape. References for this vision can be found below.

    • Kesner AL, Daou D, Schindler TH, Koo PJ. Carpe Datum: A Consideration of the Barriers and Potential of Data-Driven PET Innovation. Journal of the American College of Radiology. (2015)

    • Kesner AL, Weber WA. Small Data: A Ubiquitous, Yet Untapped, Resource for Low-Cost Imaging Innovation. J Nucl Med. (2017)

    • Kesner A, Laforest R, Otazo R, Jennifer K, Pan T. Medical imaging data in the digital innovation age. Med Phys. (2018) (OPEN ACCESS)

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