<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec id="j_raon-2022-0015_s_006">
<h3>Background</h3>
<p>PET/CT imaging is widely used in oncology and provides both metabolic and anatomic information. Because of the relatively poor spatial resolution of PET, the detection of small lesions is limited. The low spatial resolution introduces the partial-volume effect (PVE) which negatively affects images both qualitatively and quantitatively. The aim of the study was to investigate the effect of small-voxel (2 mm in-line pixel size) <italic><sub>vs.</sub></italic> standard-voxel (4 mm in-line pixel size) reconstruction on lesion detection and image quality in a range of activity ratios.</p></sec>
<sec id="j_raon-2022-0015_s_007">
<h3>Materials and methods</h3>
<p>The National Electrical Manufacturers Association (NEMA) body phantom and the Micro Hollow-Sphere phantom spheres were filled with a solution of [<sup>18</sup>F]fluorodeoxyglucose ([<sup>18</sup>F]FDG) in sphere-to-background ratios of 2:1, 3:1, 4:1 and 8:1. In all images reconstructed with 2 mm and 4 mm in-line pixel size the visual lesion delineation, contrast recovery coefficient (CRC) and contrast-to-noise ratio (CNR) were evaluated.</p></sec>
<sec id="j_raon-2022-0015_s_008">
<h3>Results</h3>
<p>For smaller (≤ 13 mm) phantom spheres, significantly higher CRC and CNR using small-voxel reconstructions were found, also improving visual lesion delineation. CRC did not differ significantly for larger (≥ 17 mm) spheres using 2 mm and 4 mm in-line pixel size, but CNR was significantly lower; however, lower CNR did not affect visual lesion delineation.</p></sec>
<sec id="j_raon-2022-0015_s_009">
<h3>Conclusions</h3>
<p>Small-voxel reconstruction consistently improves precise small lesion delineation, lesion contrast and image quality.</p></sec>
</abstract>

Background
PET/CT imaging is widely used in oncology and provides both metabolic and anatomic information. Because of the relatively poor spatial resolution of PET, the detection of small lesions is limited. The low spatial resolution introduces the partial-volume effect (PVE) which negatively affects images both qualitatively and quantitatively. The aim of the study was to investigate the effect of small-voxel (2 mm in-line pixel size) vs. standard-voxel (4 mm in-line pixel size) reconstruction on lesion detection and image quality in a range of activity ratios.

Materials and methods
The National Electrical Manufacturers Association (NEMA) body phantom and the Micro Hollow-Sphere phantom spheres were filled with a solution of [18F]fluorodeoxyglucose ([18F]FDG) in sphere-to-background ratios of 2:1, 3:1, 4:1 and 8:1. In all images reconstructed with 2 mm and 4 mm in-line pixel size the visual lesion delineation, contrast recovery coefficient (CRC) and contrast-to-noise ratio (CNR) were evaluated.

Results
For smaller (≤ 13 mm) phantom spheres, significantly higher CRC and CNR using small-voxel reconstructions were found, also improving visual lesion delineation. CRC did not differ significantly for larger (≥ 17 mm) spheres using 2 mm and 4 mm in-line pixel size, but CNR was significantly lower; however, lower CNR did not affect visual lesion delineation.

Conclusions
Small-voxel reconstruction consistently improves precise small lesion delineation, lesion contrast and image quality.

Image reconstruction using small-voxel size improves small lesion detection for positron emission tomography

Radiology and Oncology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Background
PET/CT imaging is widely used in oncology and provides both metabolic and anatomic information. Because of the relatively poor spatial resolution of...

Image reconstruction using small-voxel size improves small lesion detection for positron emission tomography

Article Category: Research Article

Online veröffentlicht: 13. Apr. 2022

Seitenbereich: 142 - 149

Eingereicht: 01. Feb. 2022

Akzeptiert: 16. Feb. 2022

DOI: https://doi.org/10.2478/raon-2022-0015

© 2022 Sebastijan Rep, Petra Tomse, Luka Jensterle, Leon Jarabek, Katja Zaletel, Luka Lezaic, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.