Development of the normalization method for the first large field-of-view plastic-based PET Modular scanner

TL;DR

This paper presents a new normalization method for the J-PET Modular scanner, a large plastic-based PET device, which corrects sensitivity variations and reduces image artefacts, improving image uniformity and quality.

Contribution

The paper introduces a component-based normalization approach for large FOV plastic PET scanners, validated through Monte Carlo simulations and image quality metrics.

Findings

Normalization reduces image artefacts in uniform cylinder reconstructions.

Geometrical corrections decrease non-uniformity to 5.5-8.5%.

Method is computationally efficient and suitable for long axial FOV scanners.

Abstract

In positron emission tomography acquisition (PET), sensitivity along a line of response can vary due to crystal geometrical arrangements in the scanner and/or detector inefficiencies, leading to severe artefacts in the reconstructed image. To mitigate these effects, data must be corrected by a set of normalization coefficients applied to each line of response. The J-PET Modular scanner is a PET device made of 50 cm long plastic strips arranged axially, currently in operation at the Jagiellonian University in Krak\'ow (Poland). We have implemented a normalization method for the large field-of-view plastic-based J-PET Modular scanner using the component-based approach. We estimated the geometric normalization factors for the J-PET Modular scanner using Monte Carlo simulations. We also analysed the effects of variations in detection efficiency. A dedicated cylindrical phantom was simulated to investigate the impact of various factors on image quality. The image quality was quantified in terms of radial and axial uniformity metrics, and the standard deviation to mean intensity ratio, determined for a set of image slices. Without normalization, reconstructions of a uniform cylinder exhibit artefacts. These artefacts were satisfactorily compensated using the normalization factors. Applying geometrical corrections lowers the non-uniformity of the image expressed as a standard deviation-to-mean ratio to a range between 5.5 % to 8.5 %. Computationally, the technique is straightforward to parallelize, making it time-efficient. Preliminary estimates suggest that the method is appropriate for use with long axial field-of-view scanners, such as the total-body J-PET, currently under development at the Jagiellonian University.