Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions

Matsukiyo, Ryo; Ohno, Yoshiharu; Matsuyama, Takahiro; Nagata, Hiroyuki; Kimata, Hirona; Ito, Yuya; Ogawa, Y.; Murayama, Kazuhiro; Kato, Ryuichi; Toyama, Hiroshi

doi:10.1007/s11604-020-01045-w

Cited by 21 publications

(20 citation statements)

References 34 publications

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“…Only Noda et al reported a lower CT value with DLR in the spleen, but differences were also insignificant [21]. During the literature search, one study (Matsukiyo et al [27]) was excluded because this study assessed the abdominal arteries and no organs. However, hybrid IR and DLR were compared, and this study showed a significant difference in reconstructed CT values of two arteries.…”

Section: Discussionmentioning

confidence: 99%

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

et al. 2021

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Objective To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). Methods PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made. Results Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was − 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was − 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was − 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence. Conclusions There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR. Key Points CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.

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Section: Discussionmentioning

confidence: 99%

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

et al. 2021

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“…The data presented also confirms that an increase in the delivered dose is currently required to achieve the maximal spatial resolution potential of UHRCT. Part of this dose increase requirement might likely be reduced or even suppressed by further improvements in iterative reconstruction algorithms and detector technology such as deep learning-based iterative reconstruction (3,21,22). Meanwhile, the AHARA (as high as reasonably achievable) principle should be advocated to optimize spatial resolution with UHRCT being well suited for evaluating peripheral joints, which have low radiosensitivity (23).…”

Section: Discussionmentioning

confidence: 99%

Ultra-high resolution computed tomography of joints: practical recommendations for acquisition protocol optimization

Teixeira¹,

Villani²,

Idir³

et al. 2021

Quant Imaging Med Surg

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Background: To assess the influence on the spatial resolution of various Ultra-high-resolution computed tomography (CT) parameters and provide practical recommendations for acquisition protocol optimization in musculoskeletal imaging.Methods: All acquisitions were performed with an Ultra-high resolution scanner, and variations of the following parameters were evaluated: field-of-view (150-300 mm), potential (80-140 KVp), current (25-250 mAs), focal spot size (0.4×0.5 to 0.8×1.3 mm 2 ), slice thickness (0.25-0.5 mm), reconstruction matrix (512×512 to 2048×2048), and iso-centering (up to 85 mm off-center). Two different image reconstruction algorithms were evaluated: hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR). CATPHAN 600 phantom images were analyzed to calculate the number of visible line pairs per centimeter (lp/cm). Task transfer function (TTF) curves were calculated to quantitatively evaluated spatial resolution. Cadaveric knee acquisitions were also performed.Results: Under the conditions studied, the factor that most intensely influenced spatial resolution was the matrix size (additional visualization of up to 8 lp/cm). Increasing the matrix from 512×512 to 2048×2048 led to a 28.2% increase in TTF10% values with a high-dose protocol and a 5.6% increase with a low-dose protocol with no change in the number of visually distinguishable line pairs. The second most important factor affecting spatial resolution was the tube output (29.6% TTF10% gain and 5 additional lp/cm visualized), followed by the reconstruction algorithm choice and lateral displacement (both with a 4 lp/cm gain). Decreasing the slice thickness from 0.5 to 0.25 mm, led to an increase of 3 lp/cm (from 17 to 20 lp/cm) and a 17.3% increase in TTF10% values with no change in the "in-plane" spatial resolution.Conclusions: This study provides practical recommendations for spatial resolution optimization using Ultra-high-resolution CT.

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“…During the same periods, the number of detector rows for MDCT has been increased for wider coverage within one rotation as well as reduction in detector collimations. High-definition CTs (HDCTs) with and without a photon counting system have been clinically applied and tested since 2015 [ 11 , 13 – 24 ]. One of these HDCTs, an ultra-high-resolution or super-high-resolution CT (UHR-CT or SHR-CT) which is widely available in routine clinical practice, is tested by many investigators using in vitro and in vivo studies [ 11 , 13 , 14 , 16 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…High-definition CTs (HDCTs) with and without a photon counting system have been clinically applied and tested since 2015 [ 11 , 13 – 24 ]. One of these HDCTs, an ultra-high-resolution or super-high-resolution CT (UHR-CT or SHR-CT) which is widely available in routine clinical practice, is tested by many investigators using in vitro and in vivo studies [ 11 , 13 , 14 , 16 – 20 ]. This CT system has three different scan modes: normal resolution (NR: 0.5 mm × 80 rows/896 channels), high-resolution (HR: 0.5 mm × 80 rows/1792 channels) and super-high-resolution (SHR: 0.25 mm × 160 rows/1792 channels), and improvements in spatial resolutions for UHR-CT have been reported by several investigators [ 11 , 13 , 14 , 16 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…One of these HDCTs, an ultra-high-resolution or super-high-resolution CT (UHR-CT or SHR-CT) which is widely available in routine clinical practice, is tested by many investigators using in vitro and in vivo studies [ 11 , 13 , 14 , 16 – 20 ]. This CT system has three different scan modes: normal resolution (NR: 0.5 mm × 80 rows/896 channels), high-resolution (HR: 0.5 mm × 80 rows/1792 channels) and super-high-resolution (SHR: 0.25 mm × 160 rows/1792 channels), and improvements in spatial resolutions for UHR-CT have been reported by several investigators [ 11 , 13 , 14 , 16 – 20 ]. Moreover, UHR-CT makes it possible to use larger matrix sizes such as 1024 or 2048 for certain CT examinations, and it has been suggested this may be useful for some clinical purposes [ 11 , 13 , 14 , 16 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images

et al. 2023

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Purpose Deep learning reconstruction (DLR) has been introduced by major vendors, tested for CT examinations of a variety of organs, and compared with other reconstruction methods. The purpose of this study was to compare the capabilities of DLR for image quality improvement and lung texture evaluation with those of hybrid-type iterative reconstruction (IR) for standard-, reduced- and ultra-low-dose CTs (SDCT, RDCT and ULDCT) obtained with high-definition CT (HDCT) and reconstructed at 0.25-mm, 0.5-mm and 1-mm section thicknesses with 512 × 512 or 1024 × 1024 matrixes for patients with various pulmonary diseases. Materials and methods Forty age-, gender- and body mass index-matched patients with various pulmonary diseases underwent SDCT (CT dose index volume <CTDIvol>: mean ± standard deviation, 9.0 ± 1.8 mGy), RDCT (CTDIvol: 1.7 ± 0.2 mGy) and ULDCT (CTDIvol: 0.8 ± 0.1 mGy) at a HDCT. All CT data set were then reconstructed with 512 × 512 or 1024 × 1024 matrixes by means of hybrid-type IR and DLR. SNR of lung parenchyma and probabilities of all lung textures were assessed for each CT data set. SNR and detection performance of each lung texture reconstructed with DLR and hybrid-type IR were then compared by means of paired t tests and ROC analyses for all CT data at each section thickness. Results Data for each radiation dose showed DLR attained significantly higher SNR than hybrid-type IR for each of the CT data (p < 0.0001). On assessments of all findings except consolidation and nodules or masses, areas under the curve (AUCs) for ULDCT with hybrid-type IR for each section thickness (0.91 ≤ AUC ≤ 0.97) were significantly smaller than those with DLR (0.97 ≤ AUC ≤ 1, p < 0.05) and the standard protocol (0.98 ≤ AUC ≤ 1, p < 0.05). Conclusion DLR is potentially more effective for image quality improvement and lung texture evaluation than hybrid-type IR on all radiation dose CTs obtained at HDCT and reconstructed with each section thickness with both matrixes for patients with a variety of pulmonary diseases.

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Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions

Cited by 21 publications

References 34 publications

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

Ultra-high resolution computed tomography of joints: practical recommendations for acquisition protocol optimization

Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images

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