Progress in CT and Advances in Image Reconstruction
2022; Radiological Society of North America; Volume: 303; Issue: 2 Linguagem: Inglês
10.1148/radiol.212974
ISSN1527-1315
Autores Tópico(s)Medical Imaging Techniques and Applications
ResumoHomeRadiologyVol. 303, No. 2 PreviousNext Reviews and CommentaryFree AccessEditorialProgress in CT and Advances in Image ReconstructionValentin Sinitsyn Valentin Sinitsyn Author AffiliationsFrom the Department of Radiology, University Hospital of Lomonosov, Moscow State University, Lomonosovsky Prospect 27/10, Moscow, Russia 119991.Address correspondence to the author (e-mail: [email protected]).Valentin Sinitsyn Published Online:Feb 1 2022https://doi.org/10.1148/radiol.212974MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Sartoretti et al in this issue.Valentin Sinitsyn, MD, PhD, is the head of the Radiology Department and chair of radiology at the University Hospital of Moscow Lomonosov State University (Russia). He is a pioneer of MRI and CT in Russia. His research interests are cardiovascular imaging, abdominal radiology, contrast media, computer applications, artificial intelligence, and education. He contributed to the implementation of international standards of teaching and training in radiology in Russia; he is the elected president of the Russian Society of Radiology, past president of the European Society of Cardiac Radiology, and president of the European Congress of Radiology in 2014.Download as PowerPointOpen in Image Viewer We have witnessed several major technical leaps in CT technique since its triumphant inception. Spiral and multisection CT, dual-source and wide-detector scanners, and dual-energy CT are probably the most remarkable technologic advances in CT. There were also several remarkable innovations in image reconstruction. Probably the most well known is iterative reconstruction (IR) (1). All major vendors of CT equipment have developed several generations of IR. The broad implementation of IR algorithms significantly improved the quality of CT images compared with the traditional filtered back-projection technique. The emergence of low-dose CT has become standard in modern CT scanners. Better signal-to-noise ratios achieved with IR with preserved contrast-to-noise ratios and good spatial resolution allow for scanning in patients with less radiation exposure but without loss of image quality or diagnostic information.Nevertheless, researchers have been considering further radical improvements in CT technology.Photon-counting detector (PCD) CT is the next big step in this important imaging modality. PCD CT counts the number of incoming photons and measures the energy of individual photons, contrary to modern CT scanners that use energy-integrating detectors (2). PCD CT systems have several advantages. The most important ones are better spatial resolution, higher contrast-to-noise ratio, and better differentiation of tissues and lesions (spectral imaging). PCD CT was a research imaging tool for many years, and its visibility in scientific papers was modest. But in September 2021, the U.S. Food and Drug Administration approved the first photon-counting CT scanner from Siemens, and other major manufacturers of CT systems are ready to present their versions of this promising technology. However, PCD CT requires some specific approaches to image reconstruction and processing.In this issue of Radiology, Sartoretti et al (3) present an IR algorithm for PCD CT image reconstruction. They evaluated the algorithm in both experimental and clinical settings.It is not surprising that IR for conventional CT is not optimal for PCD CT. Such algorithms are optimized for the precise pattern of noise and signal response in our current conventional CT from different manufacturers. For PCD CT, one IR algorithm is called quantum IR (QIR; Siemens Healthcare). Sartoretti et al describe the influence of QIR on PCD CT image quality in phantom (a water-filled cylinder) and abdominal CT examinations performed in the venous phase.Imaging was performed with a vendor-specific model of PCD CT (Naeotom Alpha; Siemens) by using QIR technology with four strength levels (QIR 1–4). The dual-source PCD CT had a tube rotation time of 0.5 second and 144 detector rows with collimation of 0.4 mm. It is worth noting that PCD CT was performed in a multi-energy mode, and images were reconstructed at 60 keV together with polychromatic images above the lowest energy threshold at 20 keV (labeled as T3D).Four expert radiologists analyzed the influence of different levels of QIR on such indexes as noise power spectrum and global noise index, contrast-to-noise ratio, and CT attenuation. Two five-point Likert scales were used: one for subjective grading of image quality and the other for measuring diagnostic confidence and artifacts. The study demonstrated a consistent increase in image quality (lower noise, better contrast-to-noise ratio, and better visual assessment by radiologists) in higher classes of QIR (QIR 3–4) compared with image reconstructions without QIR. QIR had no negative influence on CT attenuation values and image texture. All four radiologists rated QIR-4 reconstructions as the best for studied image quality parameters and lesion conspicuity.Sartoretti et al convincingly demonstrated that the QIR algorithm is an essential tool for the optimal use of the PCD CT system in experimental and clinical settings. The study was not targeted to analyze the clinical performance of PDC CT and QIR for different types of disease. Fifty patients included in the study had a wide spectrum of pathologic diseases, and examinations were performed in the single phase of contrast enhancement. For example, Figure 5 in the article shows liver CT examples in patients with multiple liver metastases with different qualities according to QIR classes. But it is not clear how these differences in image quality could influence the diagnosis or grading of the disease.In conclusion, future studies will show the actual diagnostic value of PCD CT in combination with this IR technology. Evaluation of reconstruction algorithms dedicated to will be essential in helping us maximize the potential of this technology.Disclosures of Conflicts of Interest: V.S. Payment for lectures from GE Healthcare, Siemens, Philips, Bayer; member of Advisory Board of Botkin AI; president of Russian Society of Radiology.References1. 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 Scholar2. Stiller W. Basics of iterative reconstruction methods in computed tomography: A vendor-independent overview. Eur J Radiol 2018;109(147):154. Google Scholar3. Sartoretti T, Landsmann A, Nakhostin D, et al. Quantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image Quality. Radiology 2022;303(2):339–348. Link, Google ScholarArticle HistoryReceived: Nov 22 2021Revision requested: Dec 2 2021Revision received: Dec 6 2021Accepted: Dec 8 2021Published online: Feb 01 2022Published in print: May 2022 FiguresReferencesRelatedDetailsAccompanying This ArticleQuantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image QualityFeb 1 2022RadiologyRecommended Articles Quantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image QualityRadiology2022Volume: 303Issue: 2pp. 339-348Virtual Noncontrast Abdominal Imaging with Photon-counting Detector CTRadiology2022Volume: 305Issue: 1pp. 107-115Contrast Medium Reduction for CTA with Photon-counting CT: A New Opportunity or More of the Same?Radiology: Cardiothoracic Imaging2023Volume: 5Issue: 1State of the Art in Abdominal CT: The Limits of Iterative Reconstruction AlgorithmsRadiology2019Volume: 293Issue: 3pp. 491-503Dealing with Uncertainty in CT ImagesRadiology2016Volume: 279Issue: 1pp. 5-10See More RSNA Education Exhibits CT Noise Reduction Methods to Facilitate Lower Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction and New Kids on the BlockDigital Posters2019Characteristics Of Deep Learning Reconstruction: Application In Clinical PracticeDigital Posters2021Advanced CT Techniques In the Evaluation of Hepatocellular Carcinoma: Roles of Ultra-high-resolution CT, Dual-energy CT, Contrast Enhancement Boost Technique, and RadiomicsDigital Posters2022 RSNA Case Collection LI-RADS 5RSNA Case Collection2021LI-RADS 5RSNA Case Collection2022Metastatic cholangiocarcinomaRSNA Case Collection2021 Vol. 303, No. 2 Metrics Altmetric Score PDF download
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