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?

Date of Award

Summer 2023

Document Type

Dissertation

Degree Name

Doctor of Healthcare Administration (DHA)

Committee Chair

Jesse Florang

Committee Member

Scott Mcdoniel

Committee Member

David Meckstroth

Abstract

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.

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