Dedicated prostate DOI-TOF-PET based on the ProVision detection concept.
Authors
Affiliations (3)
Affiliations (3)
- Institute of Medical Engineering, Universität zu Lübeck, Ratzeburger Allee 160, Building 64, Lübeck, 23562, GERMANY.
- Picotech SAS, Technopark, St Genis Pouilly, 01630, FRANCE.
- Institute of Medical Engineering, University of Lübeck, Ratzeburger Allee 160, Building 64, Lübeck, 23562, GERMANY.
Abstract
The ProVision scanner is a dedicated prostate PET system with limited angular coverage; it employs a new detector technology that provides high spatial resolution as well as information about depth-of-interaction (DOI) and time-of-flight (TOF). The goal of this work is to develop a flexible image reconstruction framework and study the image performance of the current ProVision scanners.
Approach: Experimental datasets, including point-like sources, an image quality phantom, and a pelvic phantom, were acquired using the ProVision scanner to investigate the impact of oblique lines of response introduced via a multi-offset scanning protocol. This approach aims to mitigate data truncation artifacts and further characterise the current imaging performance of the system. For image reconstruction, we applied the list-mode Maximum Likelihood Expectation Maximisation algorithm incorporating TOF information. The system matrix and sensitivity models account for both detector attenuation and position uncertainty.
Main Results: The scanner provides good spatial resolution on the coronal plane; however, elongations caused by the limited angular coverage distort the reconstructed images. The availability of TOF and DOI information, as well as the addition of a multi-offset scanning protocol, could not fully compensate for these distortions.
Significance: The ProVision scanner concept, with innovative detector technology, shows promising outcomes for fast and inexpensive PET without CT. Despite current limitations due to limited angular coverage, which leads to image distortions, ongoing advancements, such as improved timing resolution, regularisation techniques, and artificial intelligence, are expected to significantly reduce these artifacts and enhance image quality.