Validating a simulation tool for use with patient data for evaluation of advanced CT reconstruction

Daniel W. Shin; Ilmar Hein; Naruomi Akino; Kirsten L. Boedeker

doi:10.1117/12.3006840

1 April 2024 Validating a simulation tool for use with patient data for evaluation of advanced CT reconstruction

Daniel W. Shin, Ilmar Hein, Naruomi Akino, Kirsten L. Boedeker

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252D (2024) https://doi.org/10.1117/12.3006840
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

As Deep Learning Reconstruction (DLR) begins to dominate computed tomography (CT) reconstruction, performance evaluation via conventional phantoms with uniform backgrounds and specific sizes may benefit from augmentation with simulated controlled test objects inserted into anatomical backgrounds. The purpose of this study is to validate a simulation tool with physics-based image quality metrics both in phantom and in patient data. An analytic forward projection tool, based on detector and source geometry with beam spectra was designed to match the specifications of Canon Medical’s Aquilion ONE Prism. The Catphan^TM 500 and two water phantoms, 24cm and 32cm in diameter, were scanned with Aquilion ONE Prism at various mA levels and reconstructed with FBP. Corresponding simulated images were generated. The CT number, noise power spectrum (NPS) and modulation transfer function (MTF) were evaluated and compared between the simulated images and actual images. Simulated projection data of a Catphan^TM sensitometry cylinder was also combined with a patient sinogram and reconstructed with a variety of kernels. The MTF of three different contrast rods were measured and compared with the MTF measured from Catphan^TM. The CT numbers were equivalent between the simulated data and the image acquired from the actual CT system. The MTF measured from the simulated data of both phantom and patient image matched with the MTF from Catphan^TM. The noise properties of the simulated data also aligned with the NPS of the 24cm and 32cm water phantom image. The simulation tool was able to generate images with image quality equivalent to the images scanned and reconstructed from the actual CT system. With this validation study, the simulation will be utilized to further evaluate the performance of deep learning reconstructions (DLRs).

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Daniel W. Shin, Ilmar Hein, Naruomi Akino, and Kirsten L. Boedeker "Validating a simulation tool for use with patient data for evaluation of advanced CT reconstruction", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252D (1 April 2024); https://doi.org/10.1117/12.3006840

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