Dr. Jonas Andersson Profile

Dr. Jonas Andersson

at Umeå Univ

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Publications (2)

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SPIE Journal Paper | 20 November 2024 Open Access

Deep learning estimation of proton stopping power with photon-counting computed tomography: a virtual study

Karin Larsson, Dennis Hein, Ruihan Huang, Daniel Collin, Andrea Scotti, Erik Fredenberg, Jonas Andersson, Mats Persson

JMI, Vol. 11, Issue S1, S12809, (November 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.S1.S12809

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PurposeProton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise. We assessed the potential of photon-counting computed tomography (PCCT) for improving SPR estimation by training a deep neural network on a domain transform from PCCT images to SPR maps.ApproachThe XCAT phantom was used to simulate PCCT images of the head with CatSim, as well as to compute corresponding ground truth SPR maps. The tube current was set to 260 mA, tube voltage to 120 kV, and number of view angles to 4000. The CT images and SPR maps were used as input and labels for training a U-Net.ResultsPrediction of SPR with the network yielded average root mean square errors (RMSE) of 0.26% to 0.41%, which was an improvement on the RMSE for methods based on physical modeling developed for single-energy CT at 0.40% to 1.30% and dual-energy CT at 0.41% to 3.00%, performed on the simulated PCCT data.ConclusionsThese early results show promise for using a combination of PCCT and deep learning for estimating SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.

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Proceedings Article | 1 April 2024 Poster + Paper

Proton-stopping power ratio prediction using photon-counting computed tomography and deep learning

Karin Larsson, Dennis Hein, Ruihan Huang, Daniel Collin, Jonas Andersson, Mats Persson

Proceedings Volume 12925, 129252P (2024) https://doi.org/10.1117/12.3006363

KEYWORDS: Computed tomography, Deep learning, Optical computing, Radiotherapy, Proton therapy, Neural networks, Body composition, Virtual reality, Tumors, Tissues

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Proton radiation therapy has the potential of achieving precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the sharp Bragg peaks of protons. Aligning the high dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors within CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution and filtering of electronic noise. In this study, the potential of photon-counting computed tomography for improving SPR estimation was assessed by training a deep neural network on a domain transform from photon-counting CT images to SPR maps. XCAT phantoms of the head were generated and used to simulate photon-counting CT images with CatSim, as well as to compute corresponding ground truth SPR maps. The CT images and SPR maps were then used as input and labels to a neural network. Prediction of SPR with the network yielded mean root mean square errors (RMSE) of 0.26-0.41 %, which is an improvement on errors reported for methods based on dual energy CT (DECT). These early results show promise for using a combination of photon-counting CT and deep learning for predicting SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.

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