Image-quality characteristics in the longitudinal direction from different image-reconstruction algorithms during single-rotation volume acquisition on head computed tomography: A phantom study
Background A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. Purpose To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and streng...
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| Vydáno v: | Acta Radiologica Open Ročník 12; číslo 4; s. 205846012311689 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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London, England
SAGE Publications
01.04.2023
Sage Publications Ltd |
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| ISSN: | 2058-4601, 2058-4601 |
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| Abstract | Background
A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation.
Purpose
To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms.
Material and methods
Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction (Z-axis).
Results
The MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center.
Conclusion
Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis. |
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| AbstractList | A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation.
To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms.
Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction (
-axis).
The MBIR images showed smaller variation in CT numbers in the
-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS
was lower for strong MBIR than for other algorithms. The high-frequency component at NPS
was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center.
Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the
-axis. A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation.BackgroundA multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation.To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms.PurposeTo investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms.Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction (Z-axis).Material and methodsSingle-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction (Z-axis).The MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center.ResultsThe MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center.Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis.ConclusionModel-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis. Background A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. Purpose To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms. Material and methods Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction (Z-axis). Results The MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center. Conclusion Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis. Background A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. Purpose To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms. Material and methods Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction (Z-axis). Results The MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center. Conclusion Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis. |
| Author | Ayako Zensho Ryo Watanabe Yoshinori Funama Yoshitaka Ohishi |
| Author_xml | – sequence: 1 givenname: Ryo orcidid: 0000-0002-2224-6277 surname: Watanabe fullname: Watanabe, Ryo organization: Department of Medical Radiation Sciences, Faculty of Life Sciences – sequence: 2 givenname: Ayako surname: Zensho fullname: Zensho, Ayako organization: Department of Medical Radiation Sciences, Faculty of Life Sciences – sequence: 3 givenname: Yoshitaka surname: Ohishi fullname: Ohishi, Yoshitaka organization: Department of Medical Radiation Sciences, Faculty of Life Sciences – sequence: 4 givenname: Yoshinori orcidid: 0000-0003-1797-9625 surname: Funama fullname: Funama, Yoshinori organization: Department of Medical Radiation Sciences, Faculty of Life Sciences |
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| Keywords | Head computed tomography reconstruction algorithm image quality single-rotation volume scan longitudinal direction |
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| References | Watanabe, Ino, Yoshikawa 2015; 5 Mori, Machida 2009; 2 Solomon, Lyu, Marin 2020; 47 Brady, Trout, Somasundaram 2021; 298 Urikura, Ichikawa, Hara 2014; 7 Racine, Becce, Viry 2020; 76 Shirasaka, Hiwatashi, Yamashita 2017; 90 San Millán Ruíz, Murphy, Gailloud 2010; 74 Gervaise, Osemont, Lecocq 2012; 22 Ehman, Yu, Manduca 2014; 34 Jeon, Choi, Cheon 2018; 48 Hayashida, Hirai, Nakamura 2016; 34 Mileto, Guimaraes, McCollough 2019; 293 Biswas, Chandran, Roughley 2015; 205 Geyer, Schoepf, Meinel 2015; 276 Greffier, Dabli, Hamard 2022; 12 Richard, Husarik, Yadava 2012; 39 Schneider, Rasband, Eliceiri 2012; 9 Siebert, Diekmann, Masuhr 2012; 33 Samei, Bakalyar, Boedeker 2019; 46 Komlosi, Zhang, Leiva-Salinas 2014; 56 Radon, Chandran, Bhojak 2016; 37 Surabhi, Ashu, Arun 2020 Fletcher, Yu, Fidler 2017; 24 Greffier, Hamard, Pereira 2020; 30 bibr9-20584601231168986 Surabhi V (bibr5-20584601231168986) 2020 bibr6-20584601231168986 bibr14-20584601231168986 bibr24-20584601231168986 bibr19-20584601231168986 bibr1-20584601231168986 bibr11-20584601231168986 bibr21-20584601231168986 bibr22-20584601231168986 bibr18-20584601231168986 bibr8-20584601231168986 bibr25-20584601231168986 bibr12-20584601231168986 bibr2-20584601231168986 bibr7-20584601231168986 Ehman EC (bibr15-20584601231168986) 2014; 34 bibr4-20584601231168986 bibr23-20584601231168986 bibr10-20584601231168986 bibr17-20584601231168986 bibr20-20584601231168986 bibr26-20584601231168986 bibr13-20584601231168986 bibr16-20584601231168986 bibr3-20584601231168986 |
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A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation.
Purpose
To... A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. To investigate... Background A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. Purpose To... A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation.BackgroundA multi... |
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| SubjectTerms | Algorithms Computed tomography Image acquisition Image filters Image processing Image quality Image reconstruction Image resolution Iterative methods Medical imaging Original Research Rotation Scanners Spatial discrimination Spatial resolution Tomography Transfer functions Variation Visual perception |
| Title | Image-quality characteristics in the longitudinal direction from different image-reconstruction algorithms during single-rotation volume acquisition on head computed tomography: A phantom study |
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