Photon-Counting CT Scan Phantom Study: Stability of Radiomics Features

<b>Background/Objectives</b>: To evaluate and optimize the reconstruction parameters of images acquired with a photon-counting CT scanner to achieve a stable radiomics signal. <b>Methods</b>: Radiomics is a quantitative imaging biomarker correlated to survival in oncology patients. Implementing radiomics in clinical routine remains challenging due to the feature's instability. Photon-counting CT scans use innovative technology directly converting photons into electrical signals resulting in higher-resolution images with reduced artifacts. This study used two organic phantoms: a natural wet sponge and a dry sausage. UHR images were acquired using a NAEOTOM Alpha photon-counting CT scan (Siemens) with a 0.4 mm slice thickness and 0.3 × 0.3 mm pixel size. Tube current and voltage were fixed at 112 mA and 120 KvP. A total of 24 reconstruction parameter sets were obtained by combining different values of kernel (Br), quantitative iterative reconstruction (QIR), spectral reconstruction (keV), and matrix size. Ten successive acquisitions were obtained on both phantoms. In total, 93 radiomic features were extracted on an ROI using the default parameters of Pyradiomic 3.0.1. Each feature's stability was evaluated using the coefficient of variation (CV) within each parameter set. <b>Results</b>: Of the 24 reconstruction parameter sets, 5 were selected based on best image quality by seven radiologists and three radiology technologists. Radiomics features were considered stable on a set when CV was less than 15%. Feature stability was impacted by reconstruction parameters and the phantom used. The most stable combination included 90 and 65 stable features of the 93 tested on the sausage and sponge respectively. It was configured with Br36, QIR 4, 60 keV, and a 1024 × 1024 matrix size. <b>Conclusions</b>: Images obtained on photon-counting CT scans offer promising radiomic feature stability with optimal parameter configurations that could be applied in a clinical setting.

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Publication Details

Journal
Diagnostics
Published
2025-03-07
DOI
https://doi.org/10.3390/diagnostics15060649
Citations
3
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
4.08
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article

Photon-Counting CT Scan Phantom Study: Stability of Radiomics Features

Serge Muller, Corinne Balleyguier, Nathalie Lassau, Rémy Barbé et al.
3 citations
Diagnostics
Radiomics and Machine Learning in Medical Imaging
4.08
article

Photon-Counting CT Scan Phantom Study: Stability of Radiomics Features

Serge Muller, Corinne Balleyguier, Nathalie Lassau, Rémy Barbé, A. Moussier, Salma Moalla, Gabriel Garcia, A. M. Dupont, Lama Dawi, Thibaut Pierre, Cédric Croisille, François Bidault, Jakob Dupont, Kodjodenis Amouzouga, Cyril Nallet, Benoit Vielliard
article en
3 citations

Abstract

<b>Background/Objectives</b>: To evaluate and optimize the reconstruction parameters of images acquired with a photon-counting CT scanner to achieve a stable radiomics signal. <b>Methods</b>: Radiomics is a quantitative imaging biomarker correlated to survival in oncology patients. Implementing radiomics in clinical routine remains challenging due to the feature's instability. Photon-counting CT scans use innovative technology directly converting photons into electrical signals resulting in higher-resolution images with reduced artifacts. This study used two organic phantoms: a natural wet sponge and a dry sausage. UHR images were acquired using a NAEOTOM Alpha photon-counting CT scan (Siemens) with a 0.4 mm slice thickness and 0.3 × 0.3 mm pixel size. Tube current and voltage were fixed at 112 mA and 120 KvP. A total of 24 reconstruction parameter sets were obtained by combining different values of kernel (Br), quantitative iterative reconstruction (QIR), spectral reconstruction (keV), and matrix size. Ten successive acquisitions were obtained on both phantoms. In total, 93 radiomic features were extracted on an ROI using the default parameters of Pyradiomic 3.0.1. Each feature's stability was evaluated using the coefficient of variation (CV) within each parameter set. <b>Results</b>: Of the 24 reconstruction parameter sets, 5 were selected based on best image quality by seven radiologists and three radiology technologists. Radiomics features were considered stable on a set when CV was less than 15%. Feature stability was impacted by reconstruction parameters and the phantom used. The most stable combination included 90 and 65 stable features of the 93 tested on the sausage and sponge respectively. It was configured with Br36, QIR 4, 60 keV, and a 1024 × 1024 matrix size. <b>Conclusions</b>: Images obtained on photon-counting CT scans offer promising radiomic feature stability with optimal parameter configurations that could be applied in a clinical setting.

DiagnosticsVol. 15(6)
Siemens (Germany) (DE), Inserm (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Institut Gustave Roussy (FR), Laboratoire d’Imagerie Biomédicale (FR), Siemens Healthineers (Germany) (DE)
Openalex Percentile: Top 7%
Radiomics and Machine Learning in Medical Imaging
4.08
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