Abstract #4790

Clinical Viability of AI-enabled 0.5T MRI Scanner to Improve Access

Arjun Narula¹, Uday Patil², Anand SH³, Harikrishnan Raveendran⁴, Shailaja Muniraj⁵, Pallavi Rao⁶, Anurita Menon⁶, Allison Nicole Garza⁷, Santosh Kumar⁷, Gautam Kumar⁷, Srinivas NR⁷, Syam Babu⁷, Ravi Jaiswal⁷, Rajagopalan Sundaresan⁷, Ashok Kumar Reddy⁷, Sajith Rajamani⁷, Rajdeep Das⁷, Nitin Jain⁷, Sudhir Ramanna⁷, Sundar V⁷, Florintina Charlaas⁷, Sudhanya Chatterjee⁷, Rohan Patil⁷, Megha Goel⁷, Dattesh Shanbhag⁷, Vikas Kumar Anand⁷, Abhishek Galagali⁷, Sathish KV⁷, Preetham Shankpal⁷, Harsh Kumar Agarwal⁷, Suresh Emmanuel Joel⁷, and Ramesh Venkatesan⁷

¹Narula Diagnostics, Rohtak, India, ²Manipal Hospitals, Bangalore, India, ³Jivaa Diagnostics, Tumkur, India, ⁴Core Clinico PET Imaging, Thane, India, ⁵Prima Diagnostics, Bangalore, India, ⁶Image Core Lab, Bangalore, India, ⁷GE Healthcare, Bangalore, India

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Low-Field MRI, Accessible MRIWe validate the clinical viability of a 0.5T scanner to reduce cost and improve access to quality MRI using AI based IQ enhancement to compensate for IQ reduction due to lower field and other lower hardware specifications. We obtained data from 65 patients from the re-ramped 0.5T and a commercially available 1.5T MRI system for brain and cervical spine. Radiologists compared image quality between the two and rated the image on the ability to perform diagnosis. We observed that more than 90% of the images were rated to be above diagnostic levels and AI reconstruction significantly improved the image quality.

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