#61 - Rajpaul Attariwala, M.D., Ph.D.: Cancer screening with full-body MRI scans and a seminar on the field of radiology

Jul 8, 2019

Raj Attariwala, a radiologist and engineer renowned for developing advanced whole-body MRI scanners, shares groundbreaking insights into cancer detection. They discuss the unparalleled resolution of his imaging technology and its radiation-free nature. Attariwala highlights the importance of sensitivity and specificity in screening, while also addressing risks like false positives. The conversation delves into various imaging techniques, comparing their benefits and limitations, ultimately showcasing how innovation in medical imaging can empower patient health decisions.

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ANECDOTE

Initial Introduction

Raj Attariwala's technology was initially brought to Peter Attia's attention through a mutual acquaintance.
This acquaintance suggested Attia investigate Attariwala's unique MRI scanner in Vancouver.

INSIGHT

Technology in Radiology

Radiology attracts individuals with an interest in technology due to its reliance on advanced imaging tools.
Combining anatomical imaging from radiology with functional imaging from nuclear medicine creates a more comprehensive understanding.

INSIGHT

Anatomical vs. Functional Imaging

Anatomical images offer sharp, clear visuals of structures, enabling detailed analysis of specific areas.
Functional imaging, like PET scans, highlights activity or processes, revealing how structures function or change.

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In this episode, radiologist/engineer, Raj Attariwala, explains how he was able to apply his engineering background to create a unique MRI scanner that is capable of constructing whole-body images with a resolution that is unmatched in the industry. Peter and Raj discuss the implications of such a robust, radiation-free imaging tool on the early detection of cancer. They dive deep into cancer screening and define terms such as sensitivity and specificity that are necessary to really understand this complex space. They then describe the biggest risks involved in this type of screening (false positives) and how Raj’s unique technology and process might drive down this risk substantially. But before that, they discuss all the common imaging technology from X-ray, to CT scan, to PET scans, to ultrasound, to MRI, and more. They touch on the history of each, how they work, the usefulness and limitations of each of them, as well as the varying risks involved such as radiation exposure. If you are interested in cancer screening and/or you’ve ever wondered how any radiology tool works, this episode is for you.

We discuss:

Raj’s road from engineering to radiology [7:45];
How X-ray works, the risk of radiation exposure, and the varying amounts of radiation associated with the different imaging technologies [18:00];
Computed tomography scans (CT scans): The history of CT, how it works, and why we use contrast [27:45];
Ultrasound: Benefits and limitations, and a special use for the heart [40:45];
Detecting breast cancer with mammography: When is works, when you need more testing, and defining ‘sensitivity’ and ‘specificity’ [51:15];
Magnetic resonance imaging (MRI): How it works, defining terms, and looking at the most common types of MRI [1:03:45];
Brain aneurysms: Using MRI to find them and save lives [1:23:45];
Raj’s unique MRI technology [1:30:00];
The risk of false positives in cancer detection, and how Raj’s MRI can reduce the number of false positives (i.e., increase specificity) [1:43:40];
The unique software Raj created to pair with his MRI machine [1:51:15];
Comparing the radiation exposure of a whole-body PET-CT to Raj’s equipment (DWIBS-MRI) [1:53:40];
How diffusion-weighted magnetic resonance imaging (DW-MRI) has revolutionized cancer screening [1:55:15];
Why a DW-MRI is still not a perfect test [1:59:00];
The potential for advancing MRI technology: Where does Raj think it could improve in the next 5-10 years? [2:03:00];
Are there any commercially available scanners that can match the resolution of Raj’s images? [2:06:00];
Machine learning: When and where might machine learning/AI impact the field of radiology? [2:08:40]; and
More.

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