Evaluation of novel AI-based extended field-of-view CT reconstructions.

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Abstract

Modern CT scanners have an extended-field-of-view (eFoV) reconstructing images up to the bore size, which is relevant for patients with higher BMI or non-isocentric positioning due to fixation devices. However, the accuracy of the image reconstruction in eFoV is not well known since truncated data is used. This study introduces a new deep learning based algorithm for extended field of view reconstruction and evaluates the accuracy of the eFoV reconstruction focusing on aspects relevant for radiotherapy.
A life-size 3D printed thorax phantom, based on a patient CT for which eFoV was necessary, was manufactured and used as reference. The phantom has holes allowing the placement of tissue mimicking inserts used to evaluate the Hounsfield Unit (HU) accuracy. CT images of the phantom were acquired using different configurations aiming to evaluate geometric and HU accuracy in the eFoV. Image reconstruction was performed using a state-of-the-art reconstruction algorithm (HDFoV), commercially available, and the novel deep learning based approach (HDeepFoV). Five patient cases were selected to evaluate the performance of both algorithms on patient data. There is no ground truth for patients so the reconstructions were qualitatively evaluated by 5 physicians and 5 medical physicists.
The phantom geometry reconstructed with HDFoV showed boundary deviations from 1.0 cm up to 2.5 cm depending on the volume of the phantom outside the regular scan field of view. HDeepFoV showed a superior performance regardless of the volume of the phantom within eFOV with a maximum boundary deviation below 1.0 cm. The maximum HU (absolute) difference for soft issue inserts is below 79 HU and 41 HU for HDFoV and HDeepFoV, respectively. HDeepFoV has a maximum deviation of -18 HU for an inhaled lung insert whilst HDFoV reached a 229 HU difference. The qualitative evaluation of patient cases shows that the novel deep learning approach produces images that look more realistic and have fewer artefacts.
To be able to reconstruct images outside the sFOV of the CT scanner there is no alternative than to use some kind of extrapolated data. In our study, we proposed and investigated a new deep learning based algorithm and compared it to a commercial solution for eFoV reconstruction. The deep learning based algorithm showed superior performance in quantitative evaluations based on phantom data and in qualitative assessments of patient data.
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