ILYANA ROSENBERG: <\/strong>My name is Ilyana Rosenberg, principal product manager in Health Futures, where our mission is to empower every person on the planet to live a healthier future. <\/p>\n\n\n\nToday, I will explain how our open-source Tyger remote signal processing framework is transforming MRI imaging by accelerating reconstruction, decoupling programming from the instrument for rapid prototyping, and making MRIs more accessible across the globe. <\/p>\n\n\n\n
I want to acknowledge the team behind this technology, who continue to push boundaries with AI in medical imaging, led by Senior Director of Health Futures Michael Hansen.<\/p>\n\n\n\n
Let me start with a simple reality for three individuals who sit in the imaging workflow.<\/p>\n\n\n\n
The first: the developer.<\/p>\n\n\n\n
Developers build applications that can be used to advance MRI scans, including image reconstruction and denoising techniques that aid in motion correction. Today, code is developed in isolation from the scanner, and prototype testing can take weeks if not longer, as the code is needed to be first deployed onto the scanner.<\/p>\n\n\n\n
Second: the patient.<\/p>\n\n\n\n
A typical MRI exam can take 20 to 60 minutes with complex exams even longer, causing not only patient discomfort but also long wait times to get an MRI.<\/p>\n\n\n\n
And third: the radiologist.<\/p>\n\n\n\n
There is an increasing shortage of radiologists, and burnout is common. Radiologists review on average 20 to 40 exams per day, each with many individual scans. Review on complex cases can take even longer. <\/p>\n\n\n\n
Tyger, developed in Microsoft Research, alleviates these challenges. Tyger is an open-source and cloud-based remote signal processing framework that enables online MRI reconstructions. It enables rapid prototyping of algorithms in minutes rather than weeks due to flexibility of where the code can be run. It improves patient experience by accelerating reconstruction and thus reducing scan time. And it optimizes image interpretation due to the ease of leveraging advanced denoising and other modern algorithms during the MRI scan. <\/p>\n\n\n\n
Here\u2019s how. <\/p>\n\n\n\n
As the MRI scan is being completed, images are streamed to the cloud real time. Data from the scanner is divided into blocks and uploaded as buffers to Azure Blob Storage, enabling reliable, high-throughput, and asynchronous data transfer between the scanner and the cloud. Think of buffers as a sequence of files that can be tagged for easy retrieval and organization. <\/p>\n\n\n\n
Next, Kubernetes orchestrates the compute jobs by launching containers that read from the buffer, processing the data and writing results back to the output buffers. Tyger acts as an abstraction layer over Kubernetes for tasks such as job scheduling, resource allocation, and job execution per predefined specifications. The resulting output buffers are stored as files on Azure Blob Storage. Output buffers are flexible and can hold any data type produced by the job. For an image reconstruction job, the reconstructed images are now sent back to the scanner to be viewed on the MRI console for interpretation.<\/p>\n\n\n\n
We are partnering with Siemens Healthineers to scale and integrate the Tyger technology into Siemens Healthineers MRI scanners through the Open Recon prototype framework. Tyger is currently available as a work-in-progress package for customers to evaluate. <\/p>\n\n\n\n
One common application of Tyger is for accelerated and scalable image reconstruction. Scale opportunity is huge. On average, there are 100,000 MRI scans completed per hour in the United States. Tyger alone can do 250,000 reconstructions per hour. Way more than what is needed across the entire US for that same time frame. <\/p>\n\n\n\n
Connecting these powerful cloud-based computing resources that allow scale to the scanner also enables MRIs to be usable and more accessible across the globe. By shifting the complex MRI image reconstruction from local scanners to the cloud, Tyger also makes items such as advanced image correction and enhancement feasible even in low-resource environments. <\/p>\n\n\n\n
In addition to scale, Tyger is transforming MRI development by removing the bottleneck of scanner dependency. Instead of waiting weeks to deploy code, developers can prototype reconstruction and denoising algorithms in minutes using Tyger\u2019s cloud-based compute.<\/p>\n\n\n\n
This flexibility\u2014running the same APIs on a laptop, local cluster, or Azure\u2014means faster innovation and quicker implementation of advanced techniques like motion correction and AI-driven denoising. <\/p>\n\n\n\n
One such application is the advanced denoising model for MRI images that was recently developed and published by the Diagnostic Imaging Team in Microsoft Health Futures.<\/p>\n\n\n\n
On the left of this image, you see a late Gd enhancement cardiac MRI. It shows significant noise where the heart is not even visible, something that is unfortunately common, especially in low-resource imaging scenarios. On the right side is the same scan after applying our denoising model using Tyger. The heart is now visible for cardiologists to review.<\/p>\n\n\n\n
This is just one example of how Tyger can run an application on an MRI scan without needing to first deploy the code onto the scanner itself. Tyger is designed as a general framework for scalable, remote signal processing and is not limited to MRIs; it can be applied to any instrument or modality where data needs to be transmitted and processed remotely. This includes but is not limited to CTs, ultrasounds, emergency response with drones, and even transportation infrastructure. The only requirements are the ability to read and write files and build a Docker container that runs on Linux.<\/p>\n\n\n\n
Thank you for joining the Microsoft Research Forum. Please check out these links to learn more about Tyger and our Health Futures MRI image denoising model.<\/p>\n\n\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\tShow more\t\t\t<\/button>\n\t\t<\/div>\n\t<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"The Tyger framework enables faster, more accessible medical imaging by streaming raw data to the cloud for accelerated reconstruction\u2014reducing patient wait times and discomfort\u2014while empowering researchers to rapidly test and deploy new algorithms.<\/p>\n","protected":false},"featured_media":1157854,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":null,"footnotes":""},"research-area":[13556,13553],"msr-video-type":[268311],"msr-locale":[268875],"msr-post-option":[269148,269142],"msr-session-type":[256174],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[270184],"msr-research-theme":[270111],"class_list":["post-1157590","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-video-type-microsoft-research-forum","msr-locale-en_us","msr-post-option-approved-for-river","msr-post-option-include-in-river"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/TT-ShkskzIA","msr_secondary_video_url":"","msr_video_file":"http:\/\/0","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/1157590","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":4,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/1157590\/revisions"}],"predecessor-version":[{"id":1158064,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/1157590\/revisions\/1158064"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/1157854"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1157590"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1157590"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=1157590"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1157590"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1157590"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=1157590"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1157590"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=1157590"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=1157590"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=1157590"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}