Third version of vendor-specific model-based iterativereconstruction (Veo 3.0): evaluation of CT image quality in the abdomen using new noise reduction presets and varied slice optimization

Telesmanich, Morgan E.; Jensen, Corey T.; Enriquez, Jose Luis; Wagner‐Bartak, Nicolaus A.; Liu, Xinming; Le, Ott; Wei, Wei; Chandler, Adrian; Tamm, Eric P.

doi:10.1259/bjr.20170188

Cited by 15 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results also strongly supported the fact that DLIR could significantly reduce image noise in low‐dose chest CT scans, similar to the study of Kim et al 18 . In addition, we also tried to answer the following question: Does DLIR lose image detail as the strength of the algorithm increases as commonly seen in conventional IR algorithms? 19,20 We found that with DLIR‐M, the display and clarity of small branches of the pulmonary vascular and the small branches of the bronchi under the lung window were not negatively impacted, even though image noise was still significantly reduced. However, DLIR‐H images showed slight blurring at the edges of the previous structures (Figure 2).…”

Section: Discussionsupporting

confidence: 86%

Image quality improvement in low‐dose chest CT with deep learning image reconstruction

Tian

Li³

et al. 2022

J Applied Clin Med Phys

View full text Add to dashboard Cite

Objectives To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low‐dose chest CT in comparison with 40% adaptive statistical iterative reconstruction‐Veo (ASiR‐V40%) algorithm. Methods This retrospective study included 86 patients who underwent low‐dose CT for lung cancer screening. Images were reconstructed with ASiR‐V40% and DLIR at low (DLIR‐L), medium (DLIR‐M), and high (DLIR‐H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures. Results The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR‐V40%, the DLIR‐L, DLIR‐M, and DLIR‐H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p < 0.001). ASiR‐V40%, DLIR‐L, and DLIR‐M had equivalent score in visualizing small structures (all p > 0.05), whereas DLIR‐H had slightly lower score. Conclusions Compared with ASiR‐V40%, DLIR significantly reduces image noise in low‐dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.

show abstract

Section: Discussionsupporting

confidence: 86%

Image quality improvement in low‐dose chest CT with deep learning image reconstruction

Tian

Li³

et al. 2022

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…Malignant lesions according to the reference standard that were either not identified by reviewers or that scored 2 or lower on the malignancy scale were considered false-negative results (11). (15,16). RD FBP has been previously shown to have inferior diagnostic performance and thus was not chosen for evaluation in this study (27).…”

Section: Implications For Patient Carementioning

confidence: 99%

Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT

Jensen

Wagner‐Bartak

et al. 2019

Radiology

Self Cite

View full text Add to dashboard Cite

To evaluate colorectal cancer hepatic metastasis detection and characterization between reduced radiation dose (RD) and standard dose (SD) contrast material-enhanced CT of the abdomen and to qualitatively compare between filtered back projection (FBP) and iterative reconstruction algorithms. Materials and Methods:In this prospective study (from May 2017 through November 2017), 52 adults with biopsy-proven colorectal cancer and suspected hepatic metastases at baseline CT underwent two portal venous phase CT scans: SD and RD in the same breath hold. Three radiologists, blinded to examination details, performed detection and characterization of 2-15-mm lesions on the SD FBP and RD adaptive statistical iterative reconstruction (ASIR)-V 60% series images. Readers assessed overall image quality and lesions between SD FBP and seven different iterative reconstructions. Two nonblinded consensus reviewers established the reference standard using the picture archiving and communication system lesion marks of each reader, multiple comparison examinations, and clinical data.Results: RD CT resulted in a mean dose reduction of 54% compared with SD. Of the 260 lesions (233 metastatic, 27 benign), 212 (82%; 95% confidence interval [CI]: 76%, 86%) were detected with RD CT, whereas 252 (97%; 95% CI: 94%, 99%) were detected with SD (P , .001); per-lesion sensitivity was 79% (95% CI: 74%, 84%) and 94% (95% CI: 90%, 96%) (P , .001), respectively. Mean qualitative scores ranked SD images as higher quality than RD series images, and ASIR-V ranked higher than ASIR and Veo 3.0. Conclusion:CT evaluation of colorectal liver metastases is compromised with modest radiation dose reduction, and the use of iterative reconstructions could not maintain observer performance.

show abstract

“…It measures how much error there is between the raw data actually obtained and the virtual raw data projected from the image, and minimizes this difference during the image reconstruction. [ 20 , 24 , 29 , 30 ] In the process, photon starvation seems to be corrected, resulting in a reduction of low signal artifacts which appear as fine streak artifacts in CT images. [ 4 , 29 , 30 ] The ability of MBIR to reduce fine streak artifacts has been reported previously.…”

Section: Discussionmentioning

confidence: 99%

“…[ 20 , 24 , 29 , 30 ] In the process, photon starvation seems to be corrected, resulting in a reduction of low signal artifacts which appear as fine streak artifacts in CT images. [ 4 , 29 , 30 ] The ability of MBIR to reduce fine streak artifacts has been reported previously. Katsura et al showed that MBIR significantly reduced fine streak artifacts in the cervicothoracic region, as compared to adaptive iterative reconstruction (GE healthcare) and FBP.…”

Section: Discussionmentioning

confidence: 99%

Metal artifacts reduction in computed tomography: A phantom study to compare the effectiveness of metal artifact reduction algorithm, model-based iterative reconstruction, and virtual monochromatic imaging

Ishikawa

Suzuki²,

Harashima³

et al. 2020

Medicine

View full text Add to dashboard Cite

The purpose of this study was to compare the effectiveness of a metal artifact reduction algorithm (MAR), model-based iterative reconstruction (MBIR), and virtual monochromatic imaging (VMI) for reducing metal artifacts in CT imaging. A phantom study was performed for quantitatively evaluating the dark bands and fine streak artifacts generated by unilateral hip prostheses. Images were obtained by conventional scanning at 120 kilovolt peak, and reconstructed using filtered back projection, MAR, and MBIR. Furthermore, virtual monochromatic images (VMIs) at 70 kilo-electron volts (keV) and 140 keV with/without use of MAR were obtained by dual-energy CT. The extents and mean CT values of the dark bands and the differences in the standard deviations and location parameters of the fine streak artifacts evaluated by the Gumbel method in the images obtained by each of the methods were statistically compared by analyses of variance. Significant reduction of the extent of the dark bands was observed in the images reconstructed using MAR than in those not reconstructed using MAR (all, P < .01). Images obtained by VMI at 70 keV and 140 keV with use of MAR showed significantly increased mean CT values of the dark bands as compared to those obtained by reconstructions without use of MAR (all, <.01). Significant reduction of the difference in the standard deviations used to evaluate fine streak artifacts was observed in each of the image sets obtained with VMI at 140 keV with/without MAR and conventional CT with MBIR as compared to the images obtained using other methods (all, P < .05), except between VMI at 140 keV without MAR and conventional CT with MAR. The location parameter to evaluate fine streak artifacts was significantly reduced in CT images obtained using MBIR and in images obtained by VMI at 140 keV with/without MAR as compared to those obtained using other reconstruction methods (all, P < .01). In our present study, MAR appeared to be the most effective reconstruction method for reducing dark bands in CT images, and MBIR and VMI at 140 keV appeared to the most effective for reducing streak artifacts.

show abstract

Third version of vendor-specific model-based iterativereconstruction (Veo 3.0): evaluation of CT image quality in the abdomen using new noise reduction presets and varied slice optimization

Cited by 15 publications

References 24 publications

Image quality improvement in low‐dose chest CT with deep learning image reconstruction

Image quality improvement in low‐dose chest CT with deep learning image reconstruction

Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT

Metal artifacts reduction in computed tomography: A phantom study to compare the effectiveness of metal artifact reduction algorithm, model-based iterative reconstruction, and virtual monochromatic imaging

Contact Info

Product

Resources

About