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Final Dissertation Gianni Venturi.pdf (3.75 MB)

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What Are Radiologists' Perceptions in Regard to Image Quality and Increased Utilization Due to Vendor Provided Deep Learning Signal to Noise Ratio and Deep Learning Reconstruction on 3.0T Magnetic Resonance Imagine?

Venturi, Gianni
http://rave.ohiolink.edu/etdc/view?acc_num=frank1690903539000906

Abstract Details

2023, Doctorate of Healthcare Administration, Franklin University, Health Programs.
Deep learning (DL) algorithms are prevalent in radiology as workflow assistants and as modality enhancements. Magnetic resonance imaging (MRI), computerized tomography (CT), diagnostic imaging (DI), ultrasound (US), and positron emission tomography (PET) are modalities that benefit from the DL algorithms and shorter exam times or greater image accuracy. Faster scan time is achieved by the signal to noise ratio (SNR). The distinction is that the technology can enhance images beyond the original resolution from the modality or shorten exam time and rebuild the image quality through SNR algorithms back to approximately the original standard of care (SOC) image. While artificial intelligence signal to noise ratio algorithms (AI-SNR) can enhance an image to a greater accuracy, shorter exam times are a measurable component of a return on investment (ROI) in calculating modality utilization. The algorithm-derived images may have visual variations that are not found on normally acquired original images. The research focused on DL-SNR images on a three-tesla magnetic resonance imaging (3.0T MRI) unit, a high resolution MRI deployment in the industry. The primary research question for this research study is: What are radiologists’ perceptions in regard to image quality and increased utilization due to vendor provided DL-SNR on 3.0T MRI? This will be an exploratory qualitative research study using detailed interviews with fellowship-trained radiologists that are using AI-SNR in 3.0T MRI and shortened exam time protocols. Fifteen interviews were conducted. The interview transcriptions were coded using ATLAS.ti to identify common themes and sub-themes in the radiologists’ perceptions of DL-SNR imaging. This paper assumes the reader has an adequate understanding of deep learning and radiology processes. The interviews included discussions on key elements on image quality, workflow, reimbursement, legal concerns, and radiologist workload. Issues were identified and potential solutions for optimizations were rendered.
David Meckstroth (Committee Chair)
Jesse Florang (Committee Member)
Scott McDoniel (Committee Member)
193 p.
Artificial Intelligence; Computer Science; Medical Imaging; Radiology
DL; SNR; 3.0T; SOC; Radiologists; Deep Learning Reconstruction; DLR; perception; image quality; signal to noise; vendor; reimbursement rate

Recommended Citations

Citations

  • Venturi, G. (2023). What Are Radiologists' Perceptions in Regard to Image Quality and Increased Utilization Due to Vendor Provided Deep Learning Signal to Noise Ratio and Deep Learning Reconstruction on 3.0T Magnetic Resonance Imagine? [Doctoral dissertation, Franklin University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=frank1690903539000906

    APA Style (7th edition)

  • Venturi, Gianni. What Are Radiologists' Perceptions in Regard to Image Quality and Increased Utilization Due to Vendor Provided Deep Learning Signal to Noise Ratio and Deep Learning Reconstruction on 3.0T Magnetic Resonance Imagine? 2023. Franklin University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=frank1690903539000906.

    MLA Style (8th edition)

  • Venturi, Gianni. "What Are Radiologists' Perceptions in Regard to Image Quality and Increased Utilization Due to Vendor Provided Deep Learning Signal to Noise Ratio and Deep Learning Reconstruction on 3.0T Magnetic Resonance Imagine?" Doctoral dissertation, Franklin University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=frank1690903539000906

    Chicago Manual of Style (17th edition)

frank1690903539000906
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© 2023, some rights reserved.Creative Commons License What Are Radiologists' Perceptions in Regard to Image Quality and Increased Utilization Due to Vendor Provided Deep Learning Signal to Noise Ratio and Deep Learning Reconstruction on 3.0T Magnetic Resonance Imagine? by Gianni Venturi is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Franklin University and OhioLINK.