Abstract
ObjectivesTo compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST).MethodsEffects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient’s abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient ≥ 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient ≥ 0.75).ResultsThe median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses ≥ 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features.ConclusionsAiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features.Key Points• Image quality of CT images reconstructed with filtered back projection and iterative methods is inadequate for the majority of radiomics features due to inconsistent tissue characterization, low discriminative power, or low repeatability.• Deep learning reconstruction enhances image quality for radiomics and more than doubled the feature yield at doses that are typically used in clinical CT imaging.• Image reconstruction algorithms can optimize image quality for more reliable quantification of tissues in CT images.
Highlights
Radiomics uses quantitative features extracted from computed tomography (CT) images to build predictive models for improved diagnosis, prognosis, and therapy of cancer [1, 2]
Images acquired with 0.2 mGy demonstrate the strong impact of noise at low doses with Filtered back projection (FBP) reconstruction and the denoising power of Adaptive Iterative Dose Reduction 3D (AIDR 3D), Forward projected model-based Iterative Reconstruction SoluTion (FIRST), and Advanced intelligent Clear-IQ Engine (AiCE)
We used a patient-mimicking phantom with hepatic metastases to analyze the discriminative power of features, feature stability across different positions in CT images, and feature stability in repeated acquisitions
Summary
Radiomics uses quantitative features extracted from computed tomography (CT) images to build predictive models for improved diagnosis, prognosis, and therapy of cancer [1, 2]. Inadequate robustness towards clinical image quality limits the development and application of radiomics [3]. It is of interest to better understand image quality requirements for radiomics and to identify imaging techniques that increase the yield of reliable features. Introduced deep learning reconstruction was reported to control noise, which is abundant with FBP, and to maintain noise texture, which is a limitation of iterative reconstruction [7, 8]. In light of these improvements, deep learning reconstruction may allow more reliable quantification of tissues for extraction of radiomics features
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