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Rotzinger DC, Qanadli SD, Fahrni G. Imaging the Vulnerable Carotid Plaque with CT: Caveats to Consider. Comment on Wang et al. Identification Markers of Carotid Vulnerable Plaques: An Update. Biomolecules 2022, 12, 1192. Biomolecules 2023; 13:biom13020397. [PMID: 36830766 PMCID: PMC9953174 DOI: 10.3390/biom13020397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/03/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023] Open
Abstract
We read with great interest the review by Wang et al. entitled "Identification Markers of Carotid Vulnerable Plaques: An Update", recently published in Biomolecules [...].
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Affiliation(s)
- David C. Rotzinger
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
- Correspondence: ; Tel.: +41-21-314-44-75
| | - Salah D. Qanadli
- Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
- Riviera-Chablais Hospital, 1847 Rennaz, Switzerland
| | - Guillaume Fahrni
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
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Jiang C, Jin D, Liu Z, Zhang Y, Ni M, Yuan H. Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance. Insights Imaging 2022; 13:182. [DOI: 10.1186/s13244-022-01308-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/24/2022] [Indexed: 11/28/2022] Open
Abstract
Abstract
Objectives
To evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V).
Methods
Carotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups.
Results
The image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000–0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000–0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000–0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05).
Conclusions
Compared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction.
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Michael AE, Boriesosdick J, Schoenbeck D, Lopez-Schmidt I, Kroeger JR, Moenninghoff C, Horstmeier S, Pennig L, Borggrefe J, Niehoff JH. Photon Counting CT Angiography of the Head and Neck: Image Quality Assessment of Polyenergetic and Virtual Monoenergetic Reconstructions. Diagnostics (Basel) 2022; 12:diagnostics12061306. [PMID: 35741116 PMCID: PMC9222087 DOI: 10.3390/diagnostics12061306] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/21/2022] [Accepted: 05/22/2022] [Indexed: 11/16/2022] Open
Abstract
Background: The purpose of the present study was the evaluation of the image quality of polyenergetic and monoenergetic reconstructions (PERs and MERs) of CT angiographies (CTAs) of the head and neck acquired with the novel photon counting CT (PCCT) method in clinical routine. Methods: Thirty-seven patients were enrolled in this retrospective study. Quantitative image parameters of the extracranial, intracranial and cerebral arteries were evaluated for the PER and MER (40–120 keV). Additionally, two radiologists rated the perceived image quality. Results: The mean CTDIvol used in the PCCT was 8.31 ± 1.19 mGy. The highest signal within the vessels was detected in the 40 keV MER, whereas the lowest noise was detected in the 115 keV MER. The most favorable contrast-to-noise-ratio (CNR) and signal-to-noise-ratio (SNR) were detected in the PER and low keV MER. In the qualitative image analysis, the PER was superior to the MER in all rated criteria. For MER, 60–65 keV was rated as best image quality. Conclusion: Overall, PCCT offers excellent image quality for CTAs of the head and neck. At the current state, the PER of the PCCT seems to be the most favorable reconstruction for diagnostic reporting.
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Affiliation(s)
- Arwed Elias Michael
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
- Correspondence: ; Tel.: +49-571-790-4601
| | - Jan Boriesosdick
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
| | - Denise Schoenbeck
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
| | - Ingo Lopez-Schmidt
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
| | - Jan Robert Kroeger
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
| | - Christoph Moenninghoff
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
| | - Sebastian Horstmeier
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
| | - Lenhard Pennig
- Institute of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50923 Cologne, Germany;
| | - Jan Borggrefe
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
| | - Julius Henning Niehoff
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, 32429 Minden, Germany; (J.B.); (D.S.); (I.L.-S.); (J.R.K.); (C.M.); (S.H.); (J.B.); (J.H.N.)
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