Saunders manual of critical careby james a. kruse, mitchell p. fink, and richard w. carlson. philadelphia, pa: elsevier science, 2002, 880 pages, $79.95 (softcover)

Gill, Meredith J.

doi:10.1016/s1069-6563(03)00327-0

Academic Emergency Medicine

2003

DOI: 10.1016/s1069-6563(03)00327-0

|View full text |Cite

Saunders manual of critical careby james a. kruse, mitchell p. fink, and richard w. carlson. philadelphia, pa: elsevier science, 2002, 880 pages, $79.95 (softcover)

Meredith J. Gill¹

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the setting of an abdominal MRI, prolonged scanning time leads to more susceptibility to motion artifacts. 2,4,5 Diagnostic confidence of MRI studies is contingent upon image quality, which is related to patient compliance and breath-hold capacity, particularly in multimorbid or pediatric patient populations. 6 Hence, shortening examination time might directly contribute to enhancing the quality of abdominal MRI studies.…”

mentioning

confidence: 99%

Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging

et al. 2021

View full text Add to dashboard Cite

Objectives: The aim of this study was to investigate the impact of a deep learningbased superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold examination (VIBE SR ) on image quality in comparison with standard VIBE images (VIBE SD ). Methods: Between May and August 2020, a total of 46 patients with various abdominal pathologies underwent contrast-enhanced upper abdominal VIBE magnetic resonance imaging (MRI) at 1.5 T. After data acquisition, the precontrast and postcontrast T1-weighted VIBE raw data were processed by a deep learning-based prototype algorithm for deblurring and denoising the images as well as for enhancing their sharpness (VIBE SR ). In a randomized and blinded manner, 2 radiologists independently analyzed the image data sets using the unprocessed images VIBE SD as a standard reference. Outcome measures were as follows: overall image quality, anatomic clarity of organ borders, sharpness of vessels, artifacts, noise, and diagnostic confidence. All ratings were performed on an ordinal 4-point Likert scale. If the MRI examination encompassed a hepatic lesion, the maximum diameter of the largest hepatic lesion was quantified, and lesion sharpness and conspicuity were evaluated on an ordinal 4-point Likert scale. In addition, a post hoc regression analysis for lesion evaluation was computed. Finally, interrater/intrarater agreement was analyzed. Results: The overall image quality, anatomic clarity of organ borders, and sharpness of vessels in both precontrast and postcontrast images were rated significantly higher in VIBE SR than in VIBE SD (P < 0.001). Similarly, diagnostic confidence was higher in VIBE SR than in VIBE SD (P < 0.001). Furthermore, VIBE SR images were rated to have significantly less noise and fewer artifacts in comparison with VIBE SD (P < 0.001). The interreader agreement was substantial with a Cohen κ of 0.72 for the precontrast analysis and a κ of 0.74 for the postcontrast analysis. A total of 28 hepatic lesions were analyzed. For both readers, lesion sharpness and conspicuity were rated significantly better in VIBE SR than in VIBE SD in both the precontrast and postcontrast data sets (P < 0.01), which was consistent with the post hoc regression analysis (for every 1-point increase in sharpness/conspicuity, the odds ratio revealed a positive relation with VIBE SR of 13-fold to 17-fold in comparison with VIBE SD ; P < 0.001). In terms of lesion size, there was no significant difference between the precontrast VIBE SD and VIBE SR or between the postcontrast VIBE SD and VIBE SR for both readers. Similarly, there was an excellent interreader agreement regarding lesion size (intraclass correlation coefficient, >0.9). Conclusions: The data-driven superresolution reconstruction (VIBE SR ) is clinically feasible for precontrast and postcontrast upper abdominal VIBE MRI, providing improved image quality, diagnostic confidence, and lesion conspicuity compared with standard VIBE SD images.

show abstract

mentioning

confidence: 99%

Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging

et al. 2021

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Saunders manual of critical careby james a. kruse, mitchell p. fink, and richard w. carlson. philadelphia, pa: elsevier science, 2002, 880 pages, $79.95 (softcover)

Cited by 1 publication

References 0 publications

Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging

Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging

Contact Info

Product

Resources

About