A finely segmented semi‐monolithic detector tailored for high‐resolution PET.
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| Title: | A finely segmented semi‐monolithic detector tailored for high‐resolution PET. |
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| Authors: | Kuhl, Yannick1 (AUTHOR) yannick.kuhl@pmi.rwth-aachen.de, Mueller, Florian1 (AUTHOR), Naunheim, Stephan1 (AUTHOR), Bovelett, Matthias1 (AUTHOR), Lambertus, Janko1 (AUTHOR), Schug, David1,2 (AUTHOR), Weissler, Bjoern1,2 (AUTHOR), Gegenmantel, Eike2 (AUTHOR), Gebhardt, Pierre1 (AUTHOR), Schulz, Volkmar1,2,3 (AUTHOR) volkmar.schulz@pmi.rwth-aachen.de |
| Source: | Medical Physics. May2024, Vol. 51 Issue 5, p3421-3436. 16p. |
| Subjects: | Scintillators, Positron emission tomography, Detectors, Image converters, Spatial resolution |
| Abstract: | Background: Preclinical research and organ‐dedicated applications use and require high (spatial‐)resolution positron emission tomography (PET) detectors to visualize small structures (early) and understand biological processes at a finer level of detail. Researchers seeking to improve detector and image spatial resolution have explored various detector designs. Current commercial high‐resolution systems often employ finely pixelated or monolithic scintillators, each with its limitations. Purpose: We present a semi‐monolithic detector, tailored for high‐resolution PET applications with a spatial resolution in the range of 1 mm or better, merging concepts of monolithic and pixelated crystals. The detector features LYSO slabs measuring (24 × 10 × 1) mm3, coupled to a 12 × 12 readout channel photosensor with 4 mm pitch. The slabs are grouped in two arrays of 44 slabs each to achieve a higher optical photon density despite the fine segmentation. Methods: We employ a fan beam collimator for fast calibration to train machine‐learning‐based positioning models for all three dimensions, including slab identification and depth‐of‐interaction (DOI), utilizing gradient tree boosting (GTB). The data for all dimensions was acquired in less than 2 h. Energy calculation was based on a position‐dependent energy calibration. Using an analytical timing calibration, time skews were corrected for coincidence timing resolution (CTR) estimation. Results: Leveraging machine‐learning‐based calibration in all three dimensions, we achieved high detector spatial resolution: down to 1.18 mm full width at half maximum (FWHM) detector spatial resolution and 0.75 mm mean absolute error (MAE) in the planar‐monolithic direction, and 2.14 mm FWHM and 1.03 mm MAE for DOI at an energy window of (435–585) keV. Correct slab interaction identification in planar‐segmented direction exceeded 80%, alongside an energy resolution of 12.7% and a CTR of 450 ps FWHM. Conclusions: The introduced finely segmented, high‐resolution slab detector demonstrates appealing performance characteristics suitable for high‐resolution PET applications. The current benchtop‐based detector calibration routine allows these detectors to be used in PET systems. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Background: Preclinical research and organ‐dedicated applications use and require high (spatial‐)resolution positron emission tomography (PET) detectors to visualize small structures (early) and understand biological processes at a finer level of detail. Researchers seeking to improve detector and image spatial resolution have explored various detector designs. Current commercial high‐resolution systems often employ finely pixelated or monolithic scintillators, each with its limitations. Purpose: We present a semi‐monolithic detector, tailored for high‐resolution PET applications with a spatial resolution in the range of 1 mm or better, merging concepts of monolithic and pixelated crystals. The detector features LYSO slabs measuring (24 × 10 × 1) mm3, coupled to a 12 × 12 readout channel photosensor with 4 mm pitch. The slabs are grouped in two arrays of 44 slabs each to achieve a higher optical photon density despite the fine segmentation. Methods: We employ a fan beam collimator for fast calibration to train machine‐learning‐based positioning models for all three dimensions, including slab identification and depth‐of‐interaction (DOI), utilizing gradient tree boosting (GTB). The data for all dimensions was acquired in less than 2 h. Energy calculation was based on a position‐dependent energy calibration. Using an analytical timing calibration, time skews were corrected for coincidence timing resolution (CTR) estimation. Results: Leveraging machine‐learning‐based calibration in all three dimensions, we achieved high detector spatial resolution: down to 1.18 mm full width at half maximum (FWHM) detector spatial resolution and 0.75 mm mean absolute error (MAE) in the planar‐monolithic direction, and 2.14 mm FWHM and 1.03 mm MAE for DOI at an energy window of (435–585) keV. Correct slab interaction identification in planar‐segmented direction exceeded 80%, alongside an energy resolution of 12.7% and a CTR of 450 ps FWHM. Conclusions: The introduced finely segmented, high‐resolution slab detector demonstrates appealing performance characteristics suitable for high‐resolution PET applications. The current benchtop‐based detector calibration routine allows these detectors to be used in PET systems. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00942405 |
| DOI: | 10.1002/mp.16928 |
