Effect of Matrix Size on the Image Quality of Ultra-high-resolution CT of the Lung

Hata, Akinori; Yanagawa, Masahiro; Honda, Osamu; Kikuchi, Noriko; Miyata, Tomo; Tsukagoshi, Shinsuke; Uranishi, Ayumi; Tomiyama, Noriyuki

doi:10.1016/j.acra.2017.11.017

Cited by 115 publications

(92 citation statements)

References 9 publications

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“…The UHRCT system used has been previously described [3]. Briefly, this system has three resolution modes: normal resolution (NR), high resolution (HR) and super-high resolution (SHR).…”

Section: Ct Scanner and Acquisitionmentioning

confidence: 99%

“…New detector hardware has resulted in the introduction of wider detectors Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-019-06635-5) contains supplementary material, which is available to authorized users. [1], new electronic designs with lower noise [2] and, more recently, smaller detector elements [3]. The ability to visualise anatomy and pathology in greater detail has the potential to benefit the imaging of the lung, temporal bone, vasculature and stent structure, as well as visualisation of small tumours and structures [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…[1], new electronic designs with lower noise [2] and, more recently, smaller detector elements [3]. The ability to visualise anatomy and pathology in greater detail has the potential to benefit the imaging of the lung, temporal bone, vasculature and stent structure, as well as visualisation of small tumours and structures [3][4][5][6]. Ultra-high spatial resolution capabilities may also lead to a reduction in artefacts such as blooming, as well as an increased ability to quantify features of anatomical and pathological structures.…”

Section: Introductionmentioning

confidence: 99%

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Physical evaluation of an ultra-high-resolution CT scanner

et al. 2020

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Objectives To evaluate the technical performance of an ultra-high-resolution CT (UHRCT) system. Methods The physico-technical capabilities of a novel commercial UHRCT system were assessed and compared with those of a current-generation multi-detector (MDCT) system. The super-high-resolution (SHR) mode of the system uses 0.25 mm (at isocentre) detector elements (dels) in the in-plane and longitudinal directions, while the high-resolution (HR) mode bins two dels in the longitudinal direction. The normal-resolution (NR) mode bins dels 2 × 2, resulting in a del-size equivalent to that of the MDCT system. In general, standard procedures and phantoms were used to perform these assessments.Results The UHRCT MTF (10% MTF 4.1 lp/mm) is twice as high as that of the MDCT (10% MTF 1.9 lp/mm), which is comparable to the MTF in the NR mode (10% MTF 1.7 lp/mm). The width of the slice sensitivity profile in the SHR mode (FWHM 0.45 mm) is about 60% of that of the MDCT (FWHM 0.77 mm). Uniformity and CT numbers are within the expected range. Noise in the high-resolution modes has a higher magnitude and higher frequency components compared with MDCT. Low-contrast visibility is lower for the NR, HR and SHR modes compared with MDCT, but about a 14%, for NR, and 23%, for HR and SHR, dose increase gives the same results. Conclusions HR and SHR mode scanning results in double the spatial resolution, with about a 23% increase in dose required to achieve the same low-contrast detectability. Key Points • Resolution on UHRCT is up to twice as high as for the tested MDCT.• With abdominal settings, UHRCT needs higher dose for the same low-contrast detectability as MDCT, but dose is still below achievable levels as defined by current diagnostic reference levels. • The UHRCT system used in normal-resolution mode yields comparable resolution and noise characteristics as the MDCT system.

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“…The UHRCT system used has been previously described [3]. Briefly, this system has three resolution modes: normal resolution (NR), high resolution (HR) and super-high resolution (SHR).…”

Section: Ct Scanner and Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physical evaluation of an ultra-high-resolution CT scanner

et al. 2020

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“…Recently, ultra-high-resolution CT (U-HRCT) has been developed, characterized by a smaller focal size of the detector elements and the X-ray tube compared with conventional highresolution CT (conventional HRCT), allowing clearer CT images [5]. U-HRCT with 1024 × 1024 matrix size has been reported to improve image quality and diagnostic potential [6], and that a U-HRCT matrix size of 2048 × 2048 leads to larger improvement than sizes 512 × 512 or 1024 × 1024 [7]. However, no studies investigated the influence of FOV size on image quality in U-HRCT.…”

Section: Introductionmentioning

confidence: 99%

“…However, no studies investigated the influence of FOV size on image quality in U-HRCT. The spatial resolution of conventional HRCT has been reported to range from 0.23 to 0.35 mm, and that of U-HRCT to be 0.14 mm [5,[7][8][9]. One pixel of matrix size (512 × 512) corresponds to 0.156 mm (0.078 mm) when the FOV size is 80 mm (40 mm).…”

Section: Introductionmentioning

confidence: 99%

Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT

et al. 2020

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Objectives This study was conducted in order to compare the effect of field of view (FOV) size on image quality between ultrahigh-resolution CT (U-HRCT) and conventional high-resolution CT (HRCT). Methods Eleven cadaveric lungs were scanned with U-HRCT and conventional HRCT and reconstructed with five FOVs (40, 80, 160, 240, and 320 mm). Three radiologists evaluated and scored the images. Three image evaluations were performed, comparing the image quality with the five FOVs with respect to the 160-mm FOV. The first evaluation was performed on conventional HRCT images, and the second evaluation on U-HRCT images. Images were scored on normal structure, abnormal findings, and overall image quality. The third evaluation was a comparison of the images obtained with conventional HRCT and U-HRCT, with scoring performed on overall image quality. Quantitative evaluation of noise was performed by setting ROIs. Results In conventional HRCT, image quality was improved when the FOV was reduced to 160 mm. In U-HRCT, image quality, except for noise, improved when the FOV was reduced to 80 mm. In the third evaluation, overall image quality was improved in U-HRCT over conventional HRCT at all FOVs. Noise of U-HRCT increased with respect to conventional HRCT when the FOV was reduced from 160 to 40 mm. However, at 240-and 320-mm FOVs, the noise of U-HRCT and conventional HRCT showed no differences. Conclusions In conventional HRCT, image quality did not improve when the FOV was reduced below 160 mm. However, in U-HRCT, image quality improved even when the FOV was reduced to 80 mm. Key Points • Reducing the size of the field of view to 160 mm improves diagnostic imaging quality in high-resolution CT.• In ultra-high-resolution CT, improvements in image quality can be obtained by reducing the size of the field of view to 80 mm.• Ultra-high-resolution CT produces images of higher quality compared with conventional HRCT irrespective of the size of the field of view.

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Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study

Shirasaka

Kojima

Funama

et al. 2021

J Applied Clin Med Phys

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In an ultrahigh-resolution CT (U-HRCT), deep learning-based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation doses assuming an abdominal CT protocol.Methods: For the normal-sized abdominal models, a Catphan 600 was scanned by U-HRCT with 100%, 50%, and 25% radiation doses. In all acquisitions, DLR was compared to model-based iterative reconstruction (MBIR), filtered back projection (FBP), and hybrid iterative reconstruction (HIR). For the quantitative assessment, we compared image noise, which was defined as the standard deviation of the CT number, and spatial resolution among all reconstruction algorithms.Results: Deep learning-based reconstruction yielded lower image noise than FBP and HIR at each radiation dose. DLR yielded higher image noise than MBIR at the

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Effect of Matrix Size on the Image Quality of Ultra-high-resolution CT of the Lung

Abstract: In U-HRCT scans, a large matrix size maintained the spatial resolution and improved the image quality and assessment of lung diseases, despite an increase in image noise, when compared to a 512 matrix size.

Cited by 115 publications

References 9 publications

Physical evaluation of an ultra-high-resolution CT scanner

Physical evaluation of an ultra-high-resolution CT scanner

Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT

Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study

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