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Resolving inter-crystal scatter in a light-sharing depth-encoding PET detector.
Petersen, Eric; LaBella, Andy; Li, Yixin; Wang, Zipai; Goldan, Amir H.
Affiliation
  • Petersen E; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States of America.
  • LaBella A; Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, United States of America.
  • Li Y; Department of Radiology, Stony Brook University, Stony Brook, NY, United States of America.
  • Wang Z; Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, United States of America.
  • Goldan AH; Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, United States of America.
Phys Med Biol ; 69(3)2024 Feb 02.
Article in En | MEDLINE | ID: mdl-38169459
ABSTRACT
Objective.Inter-crystal scattering (ICS) in light-sharing positron emission tomography (PET) detectors leads to ambiguity in positioning the initial interaction, which significantly degrades the contrast, quantitative accuracy, and spatial resolution of the resulting image. Here, we attempt to resolve the positioning ambiguity of ICS in a light-sharing depth-encoding detector by exploiting the confined, deterministic light-sharing enabled by the segmented light guide unique to Prism-PET.Approach.We first considered a test case of ICS between two adjacent crystals using an analytical and a neural network approach. The analytical approach used a Bayesian estimation framework constructed from a scatter absorption model-the prior-and a detector response model-the likelihood. A simple neural network was generated for the same scenario, to provide mutual validation for the findings. Finally, we generalized the solution to three-dimensional event positioning that handles all events in the photopeak using a convolutional neural network with unique architecture that separately predicts the identity and depth-of-interaction (DOI) of the crystal containing the first interaction.Main results.The analytical Bayesian method generated an estimation error of 20.5 keV in energy and 3.1 mm in DOI. Further analysis showed that the detector response model was sufficiently robust to achieve adequate performance via maximum likelihood estimation (MLE), without prior information. We then found convergent results using a simple neural network. In the generalized solution using a convolutional neural network, we found crystal identification accuracy of 83% and DOI estimation error of 3.0 mm across all events. Applying this positioning algorithm to simulated data, we demonstrated significant improvements in image quality over the baseline, centroid-based positioning approach, attaining 38.9% improvement in intrinsic spatial resolution and enhanced clarity in hot spots of diameters 0.8 to 2.5 mm.Significance.The accuracy of our findings exceeds those of previous reports in the literature. The Prism-PET light guide, mediating confined and deterministic light-sharing, plays a key role in ICS recovery, as its mathematical embodiment-the detector response model-was the essential driver of accuracy in our results.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Tomography, X-Ray Computed / Positron-Emission Tomography Type of study: Prognostic_studies Language: En Journal: Phys Med Biol Year: 2024 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Tomography, X-Ray Computed / Positron-Emission Tomography Type of study: Prognostic_studies Language: En Journal: Phys Med Biol Year: 2024 Document type: Article Affiliation country: United States
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