Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms
© 2021. The Author(s)..
OBJECTIVES: To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).
METHODS: Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests.
RESULTS: For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.
CONCLUSIONS: With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.
KEY POINTS: • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
|---|---|
| Enthalten in: |
European radiology - 31(2021), 8 vom: 10. Aug., Seite 5498-5506 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Oostveen, Luuk J [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 13.07.2021 Date Revised 18.09.2021 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1007/s00330-020-07668-x |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM322510805 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM322510805 | ||
| 003 | DE-627 | ||
| 005 | 20231225182207.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00330-020-07668-x |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1074.xml |
| 035 | |a (DE-627)NLM322510805 | ||
| 035 | |a (NLM)33693996 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Oostveen, Luuk J |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 13.07.2021 | ||
| 500 | |a Date Revised 18.09.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021. The Author(s). | ||
| 520 | |a OBJECTIVES: To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT) | ||
| 520 | |a METHODS: Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests | ||
| 520 | |a RESULTS: For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively | ||
| 520 | |a CONCLUSIONS: With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times | ||
| 520 | |a KEY POINTS: • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Brain | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Tomography, X-ray computed | |
| 700 | 1 | |a Meijer, Frederick J A |e verfasserin |4 aut | |
| 700 | 1 | |a de Lange, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Smit, Ewoud J |e verfasserin |4 aut | |
| 700 | 1 | |a Pegge, Sjoert A |e verfasserin |4 aut | |
| 700 | 1 | |a Steens, Stefan C A |e verfasserin |4 aut | |
| 700 | 1 | |a van Amerongen, Martin J |e verfasserin |4 aut | |
| 700 | 1 | |a Prokop, Mathias |e verfasserin |4 aut | |
| 700 | 1 | |a Sechopoulos, Ioannis |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t European radiology |d 1991 |g 31(2021), 8 vom: 10. Aug., Seite 5498-5506 |w (DE-627)NLM087691310 |x 1432-1084 |7 nnns |
| 773 | 1 | 8 | |g volume:31 |g year:2021 |g number:8 |g day:10 |g month:08 |g pages:5498-5506 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s00330-020-07668-x |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 31 |j 2021 |e 8 |b 10 |c 08 |h 5498-5506 | ||