Real-time scatter estimation for medical CT using the deep scatter estimation

• Verfasst von: Maier, Joscha [VerfasserIn]
• Verfasst von: Eulig, Elias [VerfasserIn]
• Verfasst von: Vöth, Tim [VerfasserIn]
• Verfasst von: Sawall, Stefan [VerfasserIn]
• Verfasst von: Kachelrieß, Marc [VerfasserIn]
• Titel: Real-time scatter estimation for medical CT using the deep scatter estimation
• Titelzusatz: Method and robustness analysis with respect to different anatomies, dose levels, tube voltages, and data truncation
• Verf.angabe: Joscha Maier, Elias Eulig, Tim Vöth, Michael Knaup, Jan Kuntz, Stefan Sawall, Marc Kachelrieß
• Jahr: 2019
• Jahr des Originals: 2018
• Umfang: 12 S.
• Fussnoten: published 26 November 2018 ; Gesehen am 31.07.2019
• Titel Quelle: Enthalten in: Medical physics
• Ort Quelle: Hoboken, NJ : Wiley, 1974
• Jahr Quelle: 2019
• Band/Heft Quelle: 46(2019), 1, Seite 238-249
• ISSN Quelle: 2473-4209
• ISSN Quelle: 1522-8541
• DOI: doi:10.1002/mp.13274
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1670315630

Abstract

Purpose X-ray scattering leads to CT images with a reduced contrast, inaccurate CT values as well as streak and cupping artifacts. Therefore, scatter correction is crucial to maintain the diagnostic value of CT and CBCT examinations. However, existing approaches are not able to combine both high accuracy and high computational performance. Therefore, we propose the deep scatter estimation (DSE): a deep convolutional neural network to derive highly accurate scatter estimates in real time. Methods Gold standard scatter estimation approaches rely on dedicated Monte Carlo (MC) photon transport codes. However, being computationally expensive, MC methods cannot be used routinely. To enable real-time scatter correction with similar accuracy, DSE uses a deep convolutional neural network that is trained to predict MC scatter estimates based on the acquired projection data. Here, the potential of DSE is demonstrated using simulations of CBCT head, thorax, and abdomen scans as well as measurements at an experimental table-top CBCT. Two conventional computationally efficient scatter estimation approaches were implemented as reference: a kernel-based scatter estimation (KSE) and the hybrid scatter estimation (HSE). Results The simulation study demonstrates that DSE generalizes well to varying tube voltages, varying noise levels as well as varying anatomical regions as long as they are appropriately represented within the training data. In any case the deviation of the scatter estimates from the ground truth MC scatter distribution is less than 1.8% while it is between 6.2% and 293.3% for HSE and between 11.2% and 20.5% for KSE. To evaluate the performance for real data, measurements of an anthropomorphic head phantom were performed. Errors were quantified by a comparison to a slit scan reconstruction. Here, the deviation is 278 HU (no correction), 123 HU (KSE), 65 HU (HSE), and 6 HU (DSE), respectively. Conclusions The DSE clearly outperforms conventional scatter estimation approaches in terms of accuracy. DSE is nearly as accurate as Monte Carlo simulations but is superior in terms of speed (≈10 ms/projection) by orders of magnitude.