Skip to main content
Erschienen in: European Radiology 11/2022

21.05.2022 | Computed Tomography

The impact of deep learning reconstruction on image quality and coronary CT angiography-derived fractional flow reserve values

verfasst von: Cheng Xu, Min Xu, Jing Yan, Yan-Yu Li, Yan Yi, Yu-Bo Guo, Ming Wang, Yu-Mei Li, Zheng-Yu Jin, Yi-Ning Wang

Erschienen in: European Radiology | Ausgabe 11/2022

Einloggen, um Zugang zu erhalten

Abstract

Objectives

To explore the impact of deep learning reconstruction (DLR) on image quality and machine learning-based coronary CT angiography (CTA)-derived fractional flow reserve (CT-FFRML) values.

Methods

Thirty-three consecutive patients with known or suspected coronary artery disease who underwent coronary CTA and subsequent invasive coronary angiography were enrolled. DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), model-based iterative reconstruction (MBIR) Cardiac, and MBIR Cardiac sharp for objective image qualities of coronary CTA. Invasive fractional flow reserve (FFR) and quantitative flow ratio (QFR) were used as the reference standards. The diagnostic performances of different reconstruction approach-based CT-FFRML were calculated.

Results

A total of 182 lesions in 33 patients were enrolled for analysis. The image quality of DLR was superior to the others. There were no significant differences in the CT-FFRML values among these five approaches (all p > 0.05). Of the 182 lesions, 17 had invasive FFR results, and 70 had QFR results. Using FFR as a reference, MBIR Cardiac, MBIR Cardiac sharp, and DLR achieved equal diagnostic performance, slightly higher than the other reconstruction approaches (MBIR Cardiac, MBIR Cardiac sharp, and DLR: AUC = 0.82, FBP and AIDR: AUC = 0.78, all p > 0.05). Using QFR as a reference, the AUCs of FBP, SBIR, MBIR Cardiac, MBIR Cardiac sharp, and DLR were 0.83, 0.81, 0.86, 0.84, and 0.83, respectively (all p > 0.05).

Conclusions

Our study showed that the DLR algorithm improved image quality, but there were no significant differences in the CT-FFRML values and diagnostic performance among different reconstruction approaches.

Key Points

Deep learning-based image reconstruction (DLR) improves the image quality of coronary CTA.
CT-FFRML values and diagnostic performance of DLR revealed no significant differences compared to other reconstruction approaches.
Literatur
1.
Zurück zum Zitat Tonino PA, Fearon WF, De Bruyne B et al (2010) Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol 55:2816–2821CrossRef Tonino PA, Fearon WF, De Bruyne B et al (2010) Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol 55:2816–2821CrossRef
2.
Zurück zum Zitat Tonino PA, De Bruyne B, Pijls NH et al (2009) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med 360:213–224CrossRef Tonino PA, De Bruyne B, Pijls NH et al (2009) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med 360:213–224CrossRef
3.
Zurück zum Zitat Montalescot G, Sechtem U, Achenbach S et al (2013) 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J 34:2949–3003CrossRef Montalescot G, Sechtem U, Achenbach S et al (2013) 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J 34:2949–3003CrossRef
4.
Zurück zum Zitat Xu B, Tu S, Qiao S et al (2017) Diagnostic accuracy of angiography-based quantitative flow ratio measurements for online assessment of coronary stenosis. J Am Coll Cardiol 70:3077–3087CrossRef Xu B, Tu S, Qiao S et al (2017) Diagnostic accuracy of angiography-based quantitative flow ratio measurements for online assessment of coronary stenosis. J Am Coll Cardiol 70:3077–3087CrossRef
5.
Zurück zum Zitat Westra J, Andersen BK, Campo G et al (2018) Diagnostic performance of in-procedure angiography-derived quantitative flow reserve compared to pressure-derived fractional flow reserve: The FAVOR II Europe-Japan Study. J Am Heart Assoc 7:e009603CrossRef Westra J, Andersen BK, Campo G et al (2018) Diagnostic performance of in-procedure angiography-derived quantitative flow reserve compared to pressure-derived fractional flow reserve: The FAVOR II Europe-Japan Study. J Am Heart Assoc 7:e009603CrossRef
6.
Zurück zum Zitat De Maria GL, Garcia-Garcia HM, Scarsini R et al (2020) Novel indices of coronary physiology: do we need alternatives to fractional flow reserve? Circ Cardiovasc Interv 13:e008487CrossRef De Maria GL, Garcia-Garcia HM, Scarsini R et al (2020) Novel indices of coronary physiology: do we need alternatives to fractional flow reserve? Circ Cardiovasc Interv 13:e008487CrossRef
7.
Zurück zum Zitat Hwang D, Choi KH, Lee JM et al (2019) Diagnostic agreement of quantitative flow ratio with fractional flow reserve and instantaneous wave-free ratio. J Am Heart Assoc 8:e011605CrossRef Hwang D, Choi KH, Lee JM et al (2019) Diagnostic agreement of quantitative flow ratio with fractional flow reserve and instantaneous wave-free ratio. J Am Heart Assoc 8:e011605CrossRef
8.
Zurück zum Zitat Collet C, Onuma Y, Sonck J et al (2018) Diagnostic performance of angiography-derived fractional flow reserve: a systematic review and Bayesian meta-analysis. Eur Heart J 39:3314–3321CrossRef Collet C, Onuma Y, Sonck J et al (2018) Diagnostic performance of angiography-derived fractional flow reserve: a systematic review and Bayesian meta-analysis. Eur Heart J 39:3314–3321CrossRef
9.
Zurück zum Zitat Tesche C, De Cecco CN, Albrecht MH et al (2017) Coronary CT angiography-derived fractional flow reserve. Radiology 285:17–33CrossRef Tesche C, De Cecco CN, Albrecht MH et al (2017) Coronary CT angiography-derived fractional flow reserve. Radiology 285:17–33CrossRef
10.
Zurück zum Zitat Coenen A, Kim YH, Kruk M et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE Consortium. Circ Cardiovasc Imaging 11:e007217CrossRef Coenen A, Kim YH, Kruk M et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE Consortium. Circ Cardiovasc Imaging 11:e007217CrossRef
11.
Zurück zum Zitat Renker M, Nance JW Jr, Schoepf UJ et al (2011) Evaluation of heavily calcified vessels with coronary CT angiography: comparison of iterative and filtered back projection image reconstruction. Radiology 260:390–399CrossRef Renker M, Nance JW Jr, Schoepf UJ et al (2011) Evaluation of heavily calcified vessels with coronary CT angiography: comparison of iterative and filtered back projection image reconstruction. Radiology 260:390–399CrossRef
12.
Zurück zum Zitat Yin WH, Lu B, Li N et al (2013) Iterative reconstruction to preserve image quality and diagnostic accuracy at reduced radiation dose in coronary CT angiography: an intraindividual comparison. JACC Cardiovasc Imaging 6:1239–1249CrossRef Yin WH, Lu B, Li N et al (2013) Iterative reconstruction to preserve image quality and diagnostic accuracy at reduced radiation dose in coronary CT angiography: an intraindividual comparison. JACC Cardiovasc Imaging 6:1239–1249CrossRef
13.
Zurück zum Zitat Mastrodicasa D, Albrecht MH, Schoepf UJ et al (2019) Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFR(ML)): impact of iterative and filtered back projection reconstruction techniques. J Cardiovasc Comput Tomogr 13:331–335CrossRef Mastrodicasa D, Albrecht MH, Schoepf UJ et al (2019) Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFR(ML)): impact of iterative and filtered back projection reconstruction techniques. J Cardiovasc Comput Tomogr 13:331–335CrossRef
14.
Zurück zum Zitat Li S, Chen C, Qin L et al (2020) The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFR(ML)) values. Int J Cardiovasc Imaging 36:1177–1185CrossRef Li S, Chen C, Qin L et al (2020) The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFR(ML)) values. Int J Cardiovasc Imaging 36:1177–1185CrossRef
15.
Zurück zum Zitat Tatsugami F, Higaki T, Nakamura Y et al (2019) Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 29:5322–5329CrossRef Tatsugami F, Higaki T, Nakamura Y et al (2019) Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 29:5322–5329CrossRef
16.
Zurück zum Zitat Shirota G, Maeda E, Namiki Y et al (2017) Pediatric 320-row cardiac computed tomography using electrocardiogram-gated model-based full iterative reconstruction. Pediatr Radiol 47:1463–1470CrossRef Shirota G, Maeda E, Namiki Y et al (2017) Pediatric 320-row cardiac computed tomography using electrocardiogram-gated model-based full iterative reconstruction. Pediatr Radiol 47:1463–1470CrossRef
17.
Zurück zum Zitat Fuchs A, Kühl JT, Chen MY et al (2018) Subtraction CT angiography improves evaluation of significant coronary artery disease in patients with severe calcifications or stents-the C-Sub 320 multicenter trial. Eur Radiol 28:4077–4085CrossRef Fuchs A, Kühl JT, Chen MY et al (2018) Subtraction CT angiography improves evaluation of significant coronary artery disease in patients with severe calcifications or stents-the C-Sub 320 multicenter trial. Eur Radiol 28:4077–4085CrossRef
18.
Zurück zum Zitat Tatsugami F, Higaki T, Sakane H et al (2017) Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Acad Radiol 24:975–981CrossRef Tatsugami F, Higaki T, Sakane H et al (2017) Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Acad Radiol 24:975–981CrossRef
19.
Zurück zum Zitat Guo W, Tripathi P, Yang S, Qian J, Rai B, Zeng M (2019) Modified subtraction coronary CT angiography with a two-breathhold technique: image quality and diagnostic accuracy in patients with coronary calcifications. Korean J Radiol 20:1146–1155CrossRef Guo W, Tripathi P, Yang S, Qian J, Rai B, Zeng M (2019) Modified subtraction coronary CT angiography with a two-breathhold technique: image quality and diagnostic accuracy in patients with coronary calcifications. Korean J Radiol 20:1146–1155CrossRef
20.
Zurück zum Zitat Funama Y, Utsunomiya D, Hirata K et al (2017) Improved estimation of coronary plaque and luminal attenuation using a vendor-specific model-based iterative reconstruction algorithm in contrast-enhanced CT coronary angiography. Acad Radiol 24:1070–1078CrossRef Funama Y, Utsunomiya D, Hirata K et al (2017) Improved estimation of coronary plaque and luminal attenuation using a vendor-specific model-based iterative reconstruction algorithm in contrast-enhanced CT coronary angiography. Acad Radiol 24:1070–1078CrossRef
21.
Zurück zum Zitat Tesche C, De Cecco CN, Baumann S et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288:64–72CrossRef Tesche C, De Cecco CN, Baumann S et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288:64–72CrossRef
22.
Zurück zum Zitat Tu S, Westra J, Yang J et al (2016) Diagnostic accuracy of fast computational approaches to derive fractional flow reserve from diagnostic coronary angiography: The International Multicenter FAVOR Pilot Study. JACC Cardiovasc Interv 9:2024–2035CrossRef Tu S, Westra J, Yang J et al (2016) Diagnostic accuracy of fast computational approaches to derive fractional flow reserve from diagnostic coronary angiography: The International Multicenter FAVOR Pilot Study. JACC Cardiovasc Interv 9:2024–2035CrossRef
23.
Zurück zum Zitat Westra J, Tu S, Winther S et al (2018) Evaluation of coronary artery stenosis by quantitative flow ratio during invasive coronary angiography: The WIFI II Study (Wire-Free Functional Imaging II). Circ Cardiovasc Imaging 11:e007107CrossRef Westra J, Tu S, Winther S et al (2018) Evaluation of coronary artery stenosis by quantitative flow ratio during invasive coronary angiography: The WIFI II Study (Wire-Free Functional Imaging II). Circ Cardiovasc Imaging 11:e007107CrossRef
24.
Zurück zum Zitat Chang Y, Chen L, Westra J et al (2020) Reproducibility of quantitative flow ratio: an inter-core laboratory variability study. Cardiol J 27:230–237CrossRef Chang Y, Chen L, Westra J et al (2020) Reproducibility of quantitative flow ratio: an inter-core laboratory variability study. Cardiol J 27:230–237CrossRef
25.
Zurück zum Zitat Benz DC, Benetos G, Rampidis G et al (2020) Validation of deep-learning image reconstruction for coronary computed tomography angiography: impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr 14:444–451CrossRef Benz DC, Benetos G, Rampidis G et al (2020) Validation of deep-learning image reconstruction for coronary computed tomography angiography: impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr 14:444–451CrossRef
26.
Zurück zum Zitat Hirata K, Utsunomiya D, Kidoh M et al (2018) Tradeoff between noise reduction and inartificial visualization in a model-based iterative reconstruction algorithm on coronary computed tomography angiography. Medicine (Baltimore) 97:e10810CrossRef Hirata K, Utsunomiya D, Kidoh M et al (2018) Tradeoff between noise reduction and inartificial visualization in a model-based iterative reconstruction algorithm on coronary computed tomography angiography. Medicine (Baltimore) 97:e10810CrossRef
28.
Zurück zum Zitat Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121:42–52CrossRef Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121:42–52CrossRef
29.
Zurück zum Zitat Leipsic J, Yang TH, Thompson A et al (2014) CT angiography (CTA) and diagnostic performance of noninvasive fractional flow reserve: results from the Determination of Fractional Flow Reserve by Anatomic CTA (DeFACTO) study. AJR Am J Roentgenol 202:989–994CrossRef Leipsic J, Yang TH, Thompson A et al (2014) CT angiography (CTA) and diagnostic performance of noninvasive fractional flow reserve: results from the Determination of Fractional Flow Reserve by Anatomic CTA (DeFACTO) study. AJR Am J Roentgenol 202:989–994CrossRef
Metadaten
Titel
The impact of deep learning reconstruction on image quality and coronary CT angiography-derived fractional flow reserve values
verfasst von
Cheng Xu
Min Xu
Jing Yan
Yan-Yu Li
Yan Yi
Yu-Bo Guo
Ming Wang
Yu-Mei Li
Zheng-Yu Jin
Yi-Ning Wang
Publikationsdatum
21.05.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 11/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08796-2

Weitere Artikel der Ausgabe 11/2022

European Radiology 11/2022 Zur Ausgabe

Screening-Mammografie offenbart erhöhtes Herz-Kreislauf-Risiko

26.04.2024 Mammografie Nachrichten

Routinemäßige Mammografien helfen, Brustkrebs frühzeitig zu erkennen. Anhand der Röntgenuntersuchung lassen sich aber auch kardiovaskuläre Risikopatientinnen identifizieren. Als zuverlässiger Anhaltspunkt gilt die Verkalkung der Brustarterien.

S3-Leitlinie zu Pankreaskrebs aktualisiert

23.04.2024 Pankreaskarzinom Nachrichten

Die Empfehlungen zur Therapie des Pankreaskarzinoms wurden um zwei Off-Label-Anwendungen erweitert. Und auch im Bereich der Früherkennung gibt es Aktualisierungen.

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

18.04.2024 Pädiatrische Notfallmedizin Nachrichten

Im Choosing-Wisely-Programm, das für die deutsche Initiative „Klug entscheiden“ Pate gestanden hat, sind erstmals Empfehlungen zum Umgang mit Notfällen von Kindern erschienen. Fünf Dinge gilt es demnach zu vermeiden.

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

13.04.2024 Klinik aktuell Kongressbericht

Die Teilnehmer eines Forums beim DGIM-Kongress waren sich einig: Fehler in der Medizin sind häufig in ungeeigneten Prozessen und mangelnder Kommunikation begründet. Gespräche mit Patienten und im Team können helfen.

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.