Digital Twinning of Dynamic Anthropomorphic Population for Assessment of Data Driven Respiratory Gating Methods in SPECT

Reymann M, Massanes F, Kuwert T, Vija H, Maier A (2022)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2022

Publisher: IEEE

City/Town: Milano

Conference Proceedings Title: IEEE Nuclear Science Symposium and Medical Imaging Conference

Event location: Milano IT

Abstract

Data Driven Gating (DDG) can be used to improve quantification in Myocardial Perfusion Imaging (MPI), given a reliable estimate of the respiratory motion. Current algorithms for extracting a respiratory surrogate have shown large variances on patient data, but it remains unclear what causes this reduced accuracy for most patients. We propose a method for creating large sets of digital twins that can be used to assess the performance of DDG algorithms, and give an example application of our method. We simulate a set of XCAT phantoms enabled with respiratory motion recorded from an Anzai belt and perform Monte Carlo Simulation (MCS) using SIMIND for each time frame. By performing high-count simulations of different regions of the phantoms separately, we are able to create different uptake ratios and count levels of the phantoms. Using this data set we assess the effect of a static-high uptake region in the projection data, represented by the gall bladder, and show the influence on the Laplacian Eigenmaps (LE) and Center of Light (COL) DDG method. The presented method enables the assessment of many factors on DDG and we are planning on performing further experiments.

Authors with CRIS profile

Maximilian Reymann Lehrstuhl für Informatik 5 (Mustererkennung) Torsten Kuwert Lehrstuhl für Klinische Nuklearmedizin Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Reymann, M., Massanes, F., Kuwert, T., Vija, H., & Maier, A. (2022). Digital Twinning of Dynamic Anthropomorphic Population for Assessment of Data Driven Respiratory Gating Methods in SPECT. In IEEE Nuclear Science Symposium and Medical Imaging Conference. Milano, IT: Milano: IEEE.

MLA:

Reymann, Maximilian, et al. "Digital Twinning of Dynamic Anthropomorphic Population for Assessment of Data Driven Respiratory Gating Methods in SPECT." Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference, Milano Milano: IEEE, 2022.

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