Rethinking timing residuals: advancing PET detectors with explicit TOF corrections

Frontiers in Physics, vol. 13

Abstract

PET is a functional imaging method that can visualize metabolic processes and relies on the coincidence detection of emitted annihilation quanta. From the signals recorded by coincident detectors, TOF information can be derived, usually represented as the difference in detection timestamps. Incorporating the TOF information into the reconstruction can enhance the image’s SNR. Typically, PET detectors are assessed based on the coincidence time resolution (CTR) they can achieve. However, the detection process is affected by factors that degrade the timing performance of PET detectors. Research on timing calibrations develops and evaluates concepts aimed at mitigating these degradations to restore the unaffected timing information. While many calibration methods rely on analytical approaches, machine learning techniques have recently gained interest due to their flexibility. We developed a residual physics-based calibration approach, which combines prior domain knowledge with the flexibility and power of machine learning models. This concept revolves around an initial analytical calibration step addressing first-order skews. In the subsequent step, any deviation from a defined expectation is regarded as a residual effect, which we leverage to train machine learning models to eliminate higher-order skews. The main advantage of this idea is that the experimenter can guide the learning process through the definition of the timing residuals. In earlier studies, we developed models that directly predicted the expected time difference, which offered corrections only implicitly (implicit correction models). In this study, we introduce a new definition for timing residuals, enabling us to train models that directly predict correction values (explicit correction models). We demonstrate that the explicit correction approach allows for a massive simplification of the data acquisition procedure, offers exceptionally high linearity, and provides corrections able to improve the timing performance from (371 ± 6) ps to (281 ± 5) ps for coincidences from 430 keV to 590 keV. Furthermore, the novel definition makes it possible to exponentially reduce the models in size, making it suitable for applications with high data throughput, such as PET scanners. All experiments are performed with two detector stacks comprised of 4 × 4 LYSO:Ce,Ca crystals (each 3.8 mm × 3.8 mm x 20 mm), which are coupled to 4 × 4 Broadcom NUV-MT SiPMs and digitized with the TOFPET2 ASIC.

Authors 7

1. Stephan Naunheim corresponding Aachen Institute of Imaging & Computer Vision Institute for Experimental Molecular Imaging Department of Physics of Molecular Imaging Systems

   RWTH Aachen University

   Affiliation as printed

   Department of Physics of Molecular Imaging Systems (PMI), Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany

   Institute of Imaging and Computer Vision (LfB), RWTH Aachen University, Aachen, Germany

2. L. Lopes de Paiva Aachen Institute of Imaging & Computer Vision Institute for Experimental Molecular Imaging Department of Physics of Molecular Imaging Systems

   RWTH Aachen University

   Affiliation as printed

   Department of Physics of Molecular Imaging Systems (PMI), Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany

   Institute of Imaging and Computer Vision (LfB), RWTH Aachen University, Aachen, Germany

3. Vanessa Nadig Aachen Institute for Experimental Molecular Imaging Department of Physics of Molecular Imaging Systems

   RWTH Aachen University

   Affiliation as printed

   Department of Physics of Molecular Imaging Systems (PMI), Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany

4. Yannick Kuhl Aachen Institute of Imaging & Computer Vision Institute for Experimental Molecular Imaging Department of Physics of Molecular Imaging Systems

   RWTH Aachen University

   Affiliation as printed

   Department of Physics of Molecular Imaging Systems (PMI), Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany

   Institute of Imaging and Computer Vision (LfB), RWTH Aachen University, Aachen, Germany

5. S. Gundacker Aachen Institute for Experimental Molecular Imaging Department of Physics of Molecular Imaging Systems

   RWTH Aachen University · Austrian Academy of Sciences · Institute of High Energy Physics

   Affiliation as printed

   Department of Physics of Molecular Imaging Systems (PMI), Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany

   Institute of High Energy Physics (HEPHY), Austrian Academy of Sciences, Vienna, Austria

6. Florian Mueller Aachen Institute for Experimental Molecular Imaging Department of Physics of Molecular Imaging Systems

   RWTH Aachen University

   Affiliation as printed

   Department of Physics of Molecular Imaging Systems (PMI), Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany

7. Volkmar Schulz corresponding Aachen Institute of Imaging & Computer Vision Physics Institute III B

   RWTH Aachen University · Fraunhofer Institute for Digital Medicine

   Affiliation as printed

   Fraunhofer Institute for Digital Medicine MEVIS, Aachen, Germany

   Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany

   Institute of Imaging and Computer Vision (LfB), RWTH Aachen University, Aachen, Germany

   Physics Institute III B, RWTH Aachen University, Aachen, Germany