Photon-counting CT: Scouting for Quantitative Imaging Biomarkers
2020; Radiological Society of North America; Volume: 298; Issue: 1 Linguagem: Inglês
10.1148/radiol.2020203896
ISSN1527-1315
Autores Tópico(s)Medical Imaging Techniques and Applications
ResumoHomeRadiologyVol. 298, No. 1 PreviousNext Reviews and CommentaryFree AccessEditorialPhoton-counting CT: Scouting for Quantitative Imaging BiomarkersAmir Pourmorteza Amir Pourmorteza Author AffiliationsFrom the Department of Radiology and Imaging Sciences, Emory University, 1364 Clifton Rd NE, Atlanta, GA 30322.Address correspondence to the author (e-mail: [email protected]).Amir Pourmorteza Published Online:Nov 3 2020https://doi.org/10.1148/radiol.2020203896MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Nowak et al in this issue.Dr Pourmorteza is an assistant professor of radiology and biomedical engineering at the Emory University and Georgia Institute of Technology. He is an internationally known pioneer in the field of photon-counting CT. He was a staff scientist at the NIH Clinical Center, where he conducted numerous first-in-human experiments on photon-counting CT prototypes. His research includes bench-to-bedside translation of CT technologies and investigating their impact on clinical workflow.Download as PowerPointOpen in Image Viewer Localizer radiographs or scout scans are the first step of any CT image acquisition protocol to ensure correct positioning of the patient and accurate scanning range and parameter selection. Although general features, such as dental health, existence of metallic implants, and airway patency, may be assessed, the general function of the localizer image is limited to scanning planning. As the radiation dose of CT has decreased with new advances in detector technology and reconstruction algorithms, the scout scans may comprise a substantial portion of the total radiation dose of a CT examination (1). This is especially true in screening tasks such as coronary artery calcium scoring and low-dose lung cancer screening, where scout views easily result in 5% of the total radiation. There is research ongoing to reduce the radiation dose of scout scans, for example, by using tin-filtered x-ray beams (2). Another approach is to extract more diagnostic information from the scout scans.In this issue of Radiology, Nowak et al (3) report on the potential value of scout scans for opportunistic screening of bone mineral density (BMD) using conventional and novel CT detectors with a prototype CT scanner. Substantial costs are associated with the acute treatment of osteoporosis-related fractures—specifically in the hip, femur, and spine (4). Current clinical standards for BMD measurement are dual-energy x-ray absorptiometry (DXA) and quantitative CT, which are typically used to confirm osteoporosis, rather than to screen for it. The possibility of a screening modality “free” of extra radiation and cost is therefore very intriguing. Nowak and colleagues illustrate the possibility of acquiring accurate and precise quantitative metrics of BMD using conventional and photon-counting detector (PCD) scout scans in a series of phantom and ex vivo animal studies.The concept of using scout scans for BMD measurement has been previously explored in conventional energy-integrating detectors (EIDs) (5). The novel PCDs, however, have advantages that could make them a more suitable choice for measuring opportunistic quantitative imaging biomarkers such as BMD. To understand these advantages, we must take a closer look at x-ray photon detection.Conventional CT detectors operate by transforming the incident x-ray photons into light photons by using scintillating crystals. The higher the energy of the incident x-ray photon, the more light photons are generated. The light photons are detected by light sensors, such as thin-film transistors, over a short time interval (on the order of milliseconds). The output signal of the EID is proportional to the combined energy of the x-ray photons and their counts. For instance, in an ideal EID detector, five x-ray photons with an energy level of 70 keV each would produce the same output signal as seven x-ray photons with an energy level of 50 keV each, that is, 350 arbitrary units (au). Because the light sensors are analog electronic circuits, the actual output is affected by electronic noise and might read as 348.7 or 351.2 au. This is the x-ray detection concept used in all commercial CT scanners.PCDs transform incident x-ray photons directly into electrical pulses. When an x-ray photon hits the PCD semiconductor in a very high voltage field, it creates an electron charge cloud. This cloud is detected by anodes attached to the bottom of the semiconductor. Ideally, each incident photon creates one electric pulse, the height of which is proportional to the energy of the photon. Therefore, PCDs have two output signals: photon energy and photon count. The pulse height is an analog signal and is therefore susceptible to electronic noise. The photon counts are digital and are not affected (ie, there can be 350 or 351 pulses but never 350.5 pulses). Unlike in EIDs, where the contribution of each x-ray photon to the output signal is proportional to their energies, PCDs give equal weight to all detected photons. Lower-energy photons contribute more to the contrast of tissues in the body. Therefore, at identical settings, PCD CT images show more contrast compared with EID CT scans.To count all detected photons in a high-flux clinical scanning protocol, such as CT angiography, the PCDs must be equipped with very fast pulse counters that can detect pulses in intervals in the order of nanoseconds. Otherwise, the pulses would “pile up,” and the output count would be smaller than the actual count. Development of fast application-specific integrated circuits to detect and count the PCD pulses is an active area of research and has led to the translation of PCDs from detecting background radiation in the Large Hadron Collider to diagnostic radiology. One way to reduce pulse pile-up is to make the detector elements smaller than those of a typical EID system. Ultra-high-spatial-resolution PCD detectors with effective pixels one-half to one-quarter of the size of EID pixels have been developed and tested in human studies (6). Last, the energy signal output of the PCDs makes spectral CT imaging possible; most PCD CT prototypes have four to eight energy bin outputs. In summary, PCDs offer better contrast, lower noise, higher spatial resolution, and more spectral information compared with EIDs.The true potential of PCDs is in that they can provide all of these advantages simultaneously, in an “always on” fashion, without requiring any specific input from the operator before scanning. For example, it is possible to acquire low-noise spectral PCD images at a 0.25-mm resolution (7).CT manufacturers have developed whole-body PCD-CT scanner prototypes that have made it possible to explore these advantages and to gain more insight into the clinical performance of PCD CT systems (8). The study by Nowak et al (3) is a good example of these new explorations. It highlights the “always on” advantage of PCD, which allows for dual-energy scout scanning of the patient without additional cost. In the BMD calibration phantom, PCD showed lower error ranges in terms of measurement error of BMD (−1.6% to 1.6%) compared with DXA (−5% to −1.8%) and EID (−2.3% to −1.7%). In the animal phantom, DXA was used as a reference, and both CT systems showed low error (EID, −0.6% to 0.1%; PCD, −0.1% to 0.6). Specifically, in the lumbar section, PCD outperformed EID with errors as low as 0.3% compared with −3%. It should be noted that because of limitations of the prototype scanner, these scans were not dose-matched.Like any exploratory study, the study by Nowak et al (3) had limitations. The study was performed with phantoms and ex vivo animal samples; large clinical studies are warranted to further investigate the feasibility of PCDs to calculate BMD in vivo. To conform with the as low as reasonably achievable, or ALARA, radiation dose principle, the lower bound of radiation at which accurate BMD can be acquired should be explored. This is especially important because the spine, hip, and femur must be in the field of view to perform a full BMD analysis. Furthermore, calibration and segmentation algorithms of DXA have been refined throughout the past decades. Although the initial results of the study by Nowak and colleagues are promising, as mentioned by the authors, PCD-specific calibration and segmentation algorithms must be developed. Last, taking advantage of more than two energy bins and ultra-high-resolution capabilities of PCD may further improve the accuracy and precision of the results.PCDs have many nonlinear behaviors that are being addressed during the current technical development phase (eg, charge sharing) (9). However, their major limitation is the lack of wide availability of the technology to radiology researchers. The sizeable amount of data generated by these scanners, which includes larger matrix size ultra-high-resolution images and spectrally derived images, will require improvements in data storage, interpretation, and radiology workflow management. The current rise in automated image processing, machine learning algorithms, and intelligent decision-making systems are promising solutions that need to be explored in parallel to the technical advancements in CT hardware. This study has helped us contemplate other quantitative imaging biomarkers that may be automatically calculated from the next-generation PCD CT examinations.Disclosure of Conflicts of Interest: A.P. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is a consultant for Cardiowise; has grants/grants pending with Siemens Healthcare and Canon Medical Research; holds stock/stock options in Cardiowise. Other relationships: disclosed no relevant relationships.References1. Bohrer E, Schäfer S, Mäder U, Noël PB, Krombach GA, Fiebich M. Optimizing radiation exposure for CT localizer radiographs. Z Med Phys 2017;27(2):145–158. Crossref, Medline, Google Scholar2. Saltybaeva N, Krauss A, Alkadhi H. Technical Note: Radiation dose reduction from computed tomography localizer radiographs using a tin spectral shaping filter. Med Phys 2019;46(2):544–549. Crossref, Medline, Google Scholar3. Nowak T, Eberhard M, Schmidt B, et al. Bone mineral density quantification from localizer radiographs: accuracy and precision of energy-integrating detector CT and photon-counting detector CT. Radiology 2021;298:147–152. Link, Google Scholar4. Weycker D, Li X, Barron R, Bornheimer R, Chandler D. Hospitalizations for osteoporosis-related fractures: Economic costs and clinical outcomes. Bone Rep 2016;5:186–191. Crossref, Medline, Google Scholar5. Laugerette A, Schwaiger BJ, Brown K, et al. DXA-equivalent quantification of bone mineral density using dual-layer spectral CT scout scans. Eur Radiol 2019;29(9):4624–4634. Crossref, Medline, Google Scholar6. Pourmorteza A, Symons R, Henning A, Ulzheimer S, Bluemke DA. Dose Efficiency of Quarter-Millimeter Photon-Counting Computed Tomography: First-in-Human Results. Invest Radiol 2018;53(6):365–372. Crossref, Medline, Google Scholar7. Symons R, De Bruecker Y, Roosen J, et al. Quarter-millimeter spectral coronary stent imaging with photon-counting CT: Initial experience. J Cardiovasc Comput Tomogr 2018;12(6):509–515. Crossref, Medline, Google Scholar8. Willemink MJ, Persson M, Pourmorteza A, Pelc NJ, Fleischmann D. Photon-counting CT: Technical Principles and Clinical Prospects. Radiology 2018;289(2):293–312. Link, Google Scholar9. Taguchi K. Multi-energy inter-pixel coincidence counters for charge sharing correction and compensation in photon counting detectors. Med Phys 2020;47(5):2085–2098. Crossref, Medline, Google ScholarArticle HistoryReceived: Sept 30 2020Revision requested: Oct 9 2020Revision received: Oct 13 2020Accepted: Oct 14 2020Published online: Nov 03 2020Published in print: Jan 2021 FiguresReferencesRelatedDetailsCited ByDual Source Photon-Counting Computed Tomography—Part II: Clinical Overview of Neurovascular ApplicationsFilippoCademartiri, AntonellaMeloni, LauraPistoia, GiuliaDegiorgi, AlbertoClemente, CarmeloDe Gori, VincenzoPositano, SimonaCeli, SergioBerti, MicheleEmdin, DanielePanetta, LucaMenichetti, BrunaPunzo, CarloCavaliere, EduardoBossone, LucaSaba, RiccardoCau, Ludovico LaGrutta, EricaMaffei2023 | Journal of Clinical Medicine, Vol. 12, No. 11Dual-Source Photon-Counting Computed Tomography—Part I: Clinical Overview of Cardiac CT and Coronary CT Angiography ApplicationsFilippoCademartiri, AntonellaMeloni, LauraPistoia, GiuliaDegiorgi, AlbertoClemente, Carmelo DeGori, VincenzoPositano, SimonaCeli, SergioBerti, MicheleEmdin, DanielePanetta, LucaMenichetti, BrunaPunzo, CarloCavaliere, EduardoBossone, LucaSaba, RiccardoCau, Ludovico LaGrutta, EricaMaffei2023 | Journal of Clinical Medicine, Vol. 12, No. 11Cardiovascular Imaging in ChinaChun XiangTang, ZhenZhou, Jia YinZhang, LeiXu, BinLv, LongJiang Zhang2022 | Journal of Thoracic Imaging, Vol. 37, No. 6Computed tomography with a full FOV photon-counting detector in a clinical setting, the first experienceJiříFerda, TomášVendiš, ThomasFlohr, BernhardSchmidt, AndréHenning, StefanUlzheimer, LadislavPecen, EvaFerdová, JanBaxa, HynekMírka2021 | European Journal of Radiology, Vol. 137Accompanying This ArticleBone Mineral Density Quantification from Localizer Radiographs: Accuracy and Precision of Energy-integrating Detector CT and Photon-counting Detector CTNov 3 2020RadiologyRecommended Articles Bone Mineral Density Quantification from Localizer Radiographs: Accuracy and Precision of Energy-integrating Detector CT and Photon-counting Detector CTRadiology2020Volume: 298Issue: 1pp. 147-152Cost-effectiveness of Virtual Bone Strength Testing in Osteoporosis Screening Programs for Postmenopausal Women in the United StatesRadiology2017Volume: 285Issue: 2pp. 506-517Prediction of Hip Failure Load: In Vitro Study of 80 Femurs Using Three Imaging Methods and Finite Element Models—The European Fracture Study (EFFECT)Radiology2016Volume: 280Issue: 3pp. 837-847Effect of Localizer Radiography Projection on Organ Dose at Chest CT with Automatic Tube Current ModulationRadiology2016Volume: 282Issue: 3pp. 842-8493-T MR Imaging of Proximal Femur Microarchitecture in Subjects with and without Fragility Fracture and Nonosteoporotic Proximal Femur Bone Mineral DensityRadiology2018Volume: 287Issue: 2pp. 608-619See More RSNA Education Exhibits Easy Introduction Of The Photon Counting Detector CT (PCD-CT) For RadiologistsDigital Posters2021Demystifying the CT Dose Sheet and Image HeadersDigital Posters2020Clinical Photon Counting Abdominopelvic CT: A Crash Course!Digital Posters2022 RSNA Case Collection Atypical insufficiency fracture RSNA Case Collection2020Vertebral TuberculosisRSNA Case Collection2021Fibrous dysplasia RSNA Case Collection2020 Vol. 298, No. 1 Metrics Altmetric Score PDF download
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