Dr Jaesok Yu, lead author of this study, sitting at his desk in the Department of Robotics Engineering at DGIST
Ultrasound super-resolution imaging has the potential to become a powerful diagnostic tool to observe the finer vascular structures of the kidneys and other organs.
The kidneys play a vital role in the body by continuously cleaning up or purifying the blood in circulation. But, progressive damage to them over time can cause chronic kidney disease (CKD), wherein a kidney irreversibly fails. One of the ways in which CKD develops is “rarefaction,” or the gradual deterioration of small blood vessels and capillaries (collectively called microvascular structures), which is triggered when an acute kidney injury (AKI) occurs. However, imaging techniques commonly used in medical research are not of high enough resolution to be able to clearly observe this process. As a consequence, understanding of this disease progression mechanism has been limited.
In recent years, however, a game-changing ultrasonography technique, called ultrasound-super resolution (USR) imaging, has emerged, which has the potential to revolutionize diagnostics in the medical field. Recognizing this potential, Dr Jaesok Yu and his research team from Daegu Gyeongbuk Institute of Science and Technology, Korea, in collaboration with teams from the University of Pittsburgh and University of Pittsburgh Medical Center, USA, applied USR imaging to observing the evolution of microvascular rarefaction following AKI. Their study is published in Kidney International.
Dr Yu and team induced AKI in mice by temporarily restricting blood flow to one of their kidneys using standard surgical practices for animal models. They then imaged both healthy and injured kidneys through USR after 21 and 42 days, observing for expected changes in various objective parameters of CKD progression: kidney size, blood volume, and microvascular density and tortuosity.
Their images were remarkably sharp depictions of CKD progression. Dr Yu explains: “USR imaging provides unmatched resolution compared with conventional ultrasound approaches. This is achieved through a combination of image processing techniques, faster frame rates, and the use of non-toxic contrast agents. Moreover, it is cheaper and safer than other imaging technologies, including magnetic resonance imaging and computed tomography scans.”
Their success not only enables a better understanding of this disease mechanism, but also reaffirms the potential of USR imaging as a powerful diagnostic tool. Excited about the results, Dr Yu comments: “This is the first preclinical trial to successfully quantify microvascular rarefactions and monitor the progression of acute kidney injury to chronic kidney disease in small animal models. But this is only the beginning. Exploring the unprecedented resolution offered by USR imaging will allow us to further understand the underlying mechanisms of many other diseases.”
Dr Yu envisions a future in which USR imaging has been adopted for use with the standard ultrasound probe seen in hospitals, enabling superior assessment of kidney disease severity. Perhaps this future is not far away!
Reference
|
Authors: |
Qiyang Chen1,2,†, Jaesok Yu1,2,†, Brittney M. Rush3, Sean D. Stocker3, Roderick J. Tan3,*, Kang Kim1,2,4,5,6,* |
|
Title of original paper: |
Ultrasound super-resolution imaging provides a noninvasive assessment of renal microvasculature changes during mouse acute kidney injury. |
|
Journal: |
Kidney International |
|
DOI: |
10.1016/j.kint.2020.02.011 |
|
Affiliations: |
1Department of Bioengineering, University of Pittsburgh School of Engineering 2Center for Ultrasound Molecular Imaging and Therapeutics, Department of Medicine & Heart and Vascular Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center (UPMC) 3Renal-Electrolyte Division, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center (UPMC) 4Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine 5McGowan Institute of Regenerative Medicine, University of Pittsburgh and University of Pittsburgh Medical Center (UPMC) 6Department of Mechanical Engineering and Materials Science, University of Pittsburgh School of Engineering |
†These authors contributed equally to this work
*Corresponding author’s email: [email protected] (Roderick J. Tan), [email protected] (Kang Kim)
Jaesok Yu is currently associated with Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST).
About Daegu Gyeongbuk Institute of Science and Technology (DGIST)
Daegu Gyeongbuk Institute of Science and Technology (DGIST) is a well-known and respected research institute located in Daegu, Republic of Korea. Established in 2004 by the Korean Government, the main aim of DGIST is to promote national science and technology, as well as to boost the local economy.
With a vision of “Changing the world through convergence", DGIST has undertaken a wide range of research in various fields of science and technology. DGIST has embraced a multidisciplinary approach to research and undertaken intensive studies in some of today's most vital fields. DGIST also has state-of-the-art-infrastructure to enable cutting-edge research in materials science, robotics, cognitive sciences, and communication engineering.
Website: https://www.dgist.ac.kr/en/html/sub01/010204.html
About the author
Dr Jaesok Yu is an Assistant Professor in Department of Robotics Engineering, DGIST. He received BS and MS degrees in electronics engineering from Sogang University (South Korea) and a PhD degree in bioengineering from the University of Pittsburgh (USA). He worked at Georgia Institute of Technology (USA) as a postdoctoral fellow after graduation. He has been focusing on translating technologies from the benchtop to the real world, that is, the clinic. His recent research interests include super-resolution imaging, real-time 3D imaging, photoacoustic imaging, multi-modal ultrasound imaging and therapeutic techniques, and deep-learning-powered image processing and diagnosis.
