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Schaart DR. Physics and technology of time-of-flight PET detectors. Phys Med Biol 2021; 66. [PMID: 33711831 DOI: 10.1088/1361-6560/abee56] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 03/12/2021] [Indexed: 01/04/2023]
Abstract
The imaging performance of clinical positron emission tomography (PET) systems has evolved impressively during the last ∼15 years. A main driver of these improvements has been the introduction of time-of-flight (TOF) detectors with high spatial resolution and detection efficiency, initially based on photomultiplier tubes, later silicon photomultipliers. This review aims to offer insight into the challenges encountered, solutions developed, and lessons learned during this period. Detectors based on fast, bright, inorganic scintillators form the scope of this work, as these are used in essentially all clinical TOF-PET systems today. The improvement of the coincidence resolving time (CRT) requires the optimization of the entire detection chain and a sound understanding of the physics involved facilitates this effort greatly. Therefore, the theory of scintillation detector timing is reviewed first. Once the fundamentals have been set forth, the principal detector components are discussed: the scintillator and the photosensor. The parameters that influence the CRT are examined and the history, state-of-the-art, and ongoing developments are reviewed. Finally, the interplay between these components and the optimization of the overall detector design are considered. Based on the knowledge gained to date, it appears feasible to improve the CRT from the values of 200-400 ps achieved by current state-of-the-art TOF-PET systems to about 100 ps or less, even though this may require the implementation of advanced methods such as time resolution recovery. At the same time, it appears unlikely that a system-level CRT in the order of ∼10 ps can be reached with conventional scintillation detectors. Such a CRT could eliminate the need for conventional tomographic image reconstruction and a search for new approaches to timestamp annihilation photons with ultra-high precision is therefore warranted. While the focus of this review is on timing performance, it attempts to approach the topic from a clinically driven perspective, i.e. bearing in mind that the ultimate goal is to optimize the value of PET in research and (personalized) medicine.
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Affiliation(s)
- Dennis R Schaart
- Delft University of Technology, Radiation Science & Technology dept., section Medical Physics & Technology, Mekelweg 15, 2629 JB Delft, The Netherlands
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Du J, Schmall JP, Judenhofer MS, Di K, Yang Y, Cherry SR. A Time-Walk Correction Method for PET Detectors Based on Leading Edge Discriminators. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2017; 1:385-390. [PMID: 29276798 PMCID: PMC5739333 DOI: 10.1109/trpms.2017.2726534] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The leading edge timing pick-off technique is the simplest timing extraction method for PET detectors. Due to the inherent time-walk of the leading edge technique, corrections should be made to improve timing resolution, especially for time-of-flight PET. Time-walk correction can be done by utilizing the relationship between the threshold crossing time and the event energy on an event by event basis. In this paper, a time-walk correction method is proposed and evaluated using timing information from two identical detectors both using leading edge discriminators. This differs from other techniques that use an external dedicated reference detector, such as a fast PMT-based detector using constant fraction techniques to pick-off timing information. In our proposed method, one detector was used as reference detector to correct the time-walk of the other detector. Time-walk in the reference detector was minimized by using events within a small energy window (508.5 - 513.5 keV). To validate this method, a coincidence detector pair was assembled using two SensL MicroFB SiPMs and two 2.5 mm × 2.5 mm × 20 mm polished LYSO crystals. Coincidence timing resolutions using different time pick-off techniques were obtained at a bias voltage of 27.5 V and a fixed temperature of 20 °C. The coincidence timing resolution without time-walk correction were 389.0 ± 12.0 ps (425 -650 keV energy window) and 670.2 ± 16.2 ps (250-750 keV energy window). The timing resolution with time-walk correction improved to 367.3 ± 0.5 ps (425 - 650 keV) and 413.7 ± 0.9 ps (250 - 750 keV). For comparison, timing resolutions were 442.8 ± 12.8 ps (425 - 650 keV) and 476.0 ± 13.0 ps (250 - 750 keV) using constant fraction techniques, and 367.3 ± 0.4 ps (425 - 650 keV) and 413.4 ± 0.9 ps (250 - 750 keV) using a reference detector based on the constant fraction technique. These results show that the proposed leading edge based time-walk correction method works well. Timing resolution obtained using this method was equivalent to that obtained using a reference detector and was better than that obtained using constant fraction discriminators.
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Affiliation(s)
- Junwei Du
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | | | - Martin S Judenhofer
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | - Kun Di
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | - Yongfeng Yang
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | - Simon R Cherry
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
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Chang CM, Grant AM, Lee BJ, Kim E, Hong K, Levin CS. Performance characterization of compressed sensing positron emission tomography detectors and data acquisition system. Phys Med Biol 2015; 60:6407-21. [PMID: 26237671 DOI: 10.1088/0031-9155/60/16/6407] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In the field of information theory, compressed sensing (CS) had been developed to recover signals at a lower sampling rate than suggested by the Nyquist-Shannon theorem, provided the signals have a sparse representation with respect to some base. CS has recently emerged as a method to multiplex PET detector readouts thanks to the sparse nature of 511 keV photon interactions in a typical PET study. We have shown in our previous numerical studies that, at the same multiplexing ratio, CS achieves higher signal-to-noise ratio (SNR) compared to Anger and cross-strip multiplexing. In addition, unlike Anger logic, multiplexing by CS preserves the capability to resolve multi-hit events, in which multiple pixels are triggered within the resolving time of the detector. In this work, we characterized the time, energy and intrinsic spatial resolution of two CS detectors and a data acquisition system we have developed for a PET insert system for simultaneous PET/MRI. The CS detector comprises a 2 x 4 mosaic of 4 x 4 arrays of 3.2 x 3.2 x 20 mm(3) lutetium-yttrium orthosilicate crystals coupled one-to-one to eight 4 x 4 silicon photomultiplier arrays. The total number of 128 pixels is multiplexed down to 16 readout channels by CS. The energy, coincidence time and intrinsic spatial resolution achieved by two CS detectors were 15.4±0.1% FWHM at 511 keV, 4.5 ns FWHM and 2.3 mm FWHM, respectively. A series of experiments were conducted to measure the sources of time jitter that limit the time resolution of the current system, which provides guidance for potential system design improvements. These findings demonstrate the feasibility of compressed sensing as a promising multiplexing method for PET detectors.
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Affiliation(s)
- Chen-Ming Chang
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA. Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA 94305, USA
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Chahkandi Nejad H, Khayat O, Razjouyan J. Software development of an intelligent Spirography test system for neurological disorder detection and quantification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015. [DOI: 10.3233/ifs-141496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Hadi Chahkandi Nejad
- Electrical Engineering Department, Birjand Branch, Islamic Azad University, Birjand, Iran
| | - Omid Khayat
- Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Javad Razjouyan
- Engineering Department, Garmsar Branch, Islamic Azad University, Garmsar, Iran
- College of Medicine, University of Arizona, Tucson, AZ, USA
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Carpenter KLH, Czosnyka M, Jalloh I, Newcombe VFJ, Helmy A, Shannon RJ, Budohoski KP, Kolias AG, Kirkpatrick PJ, Carpenter TA, Menon DK, Hutchinson PJ. Systemic, local, and imaging biomarkers of brain injury: more needed, and better use of those already established? Front Neurol 2015; 6:26. [PMID: 25741315 PMCID: PMC4332345 DOI: 10.3389/fneur.2015.00026] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 01/30/2015] [Indexed: 02/02/2023] Open
Abstract
Much progress has been made over the past two decades in the treatment of severe acute brain injury, including traumatic brain injury and subarachnoid hemorrhage, resulting in a higher proportion of patients surviving with better outcomes. This has arisen from a combination of factors. These include improvements in procedures at the scene (pre-hospital) and in the hospital emergency department, advances in neuromonitoring in the intensive care unit, both continuously at the bedside and intermittently in scans, evolution and refinement of protocol-driven therapy for better management of patients, and advances in surgical procedures and rehabilitation. Nevertheless, many patients still experience varying degrees of long-term disabilities post-injury with consequent demands on carers and resources, and there is room for improvement. Biomarkers are a key aspect of neuromonitoring. A broad definition of a biomarker is any observable feature that can be used to inform on the state of the patient, e.g., a molecular species, a feature on a scan, or a monitoring characteristic, e.g., cerebrovascular pressure reactivity index. Biomarkers are usually quantitative measures, which can be utilized in diagnosis and monitoring of response to treatment. They are thus crucial to the development of therapies and may be utilized as surrogate endpoints in Phase II clinical trials. To date, there is no specific drug treatment for acute brain injury, and many seemingly promising agents emerging from pre-clinical animal models have failed in clinical trials. Large Phase III studies of clinical outcomes are costly, consuming time and resources. It is therefore important that adequate Phase II clinical studies with informative surrogate endpoints are performed employing appropriate biomarkers. In this article, we review some of the available systemic, local, and imaging biomarkers and technologies relevant in acute brain injury patients, and highlight gaps in the current state of knowledge.
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Affiliation(s)
- Keri L. H. Carpenter
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK,*Correspondence: Keri L. H. Carpenter, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Box 167, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK e-mail:
| | - Marek Czosnyka
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ibrahim Jalloh
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Virginia F. J. Newcombe
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK,Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Adel Helmy
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Richard J. Shannon
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Karol P. Budohoski
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Angelos G. Kolias
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter J. Kirkpatrick
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Thomas Adrian Carpenter
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K. Menon
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK,Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Peter J. Hutchinson
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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