Matt Gounis, PhD, is a biomedical engineer and tenured Professor of the Department of Radiology, University of Massachusetts Medical School. He co-founded the New England Center for Stroke Research at UMASS in 2006 where the team works to bring new imaging and medical device technology from the bench to the clinic. For 25 years, Matt has performed research on the minimally invasive treatment of cerebrovascular disease with a focus on device technology, pre-clinical disease modeling, and image-guided surgery. Matt is the 2010 recipient of the Y.C. Fung Award from the ASME, the Founding President of the SB3C Foundation, formerly the Chair of the Bioengineering Division of the ASME and the AHA Clinical Bioengineering Committee, Fellow of ASME and currently serves as the Basic Science Associate Editor for the Journal of Neurointerventional Surgery and on the Editorial Board of the journals Stroke, and Neurosurgery. Matt has given over 150 invited lectures around the world, including delivering the distinguished Pierre Lasjuanias Memorial Lecture at the 15th Congress of the World Federation of Interventional and Therapeutic Neuroradiology in Naples, Italy. Matt currently serves at the Chair of the UMMS IACUC, Vice Chair of Research for the Department of Radiology and as the Chair of the Departmental Personnel Action Committee.
On-going research projects:
1. 5R44NS076272-03, NIH (PI: A Krtolica) 8/1/2017-7/31/2020 (NCE)
Reducing brain injury after focal ischemia using a nitric oxide-neutral oxygen carrier.
Role: Subcontract PI
In this project, we test engineered a safe first-in-class oxygen carrier (OMX) that improves brain oxygenation after stroke to preserve the neuronal and glial cell network viability within the oxygen-deprived penumbra tissue, resulting in the amelioration of neurological function.
2. 5R01NS091552-04, NIH (MPIs: AA Bogdanov, MJ Gounis) 9/15/2015-7/31/2020 (NCE)
Unruptured Intracranial Aneurysms: Rupture-Risk Assessment by Non-Invasive Molecular Imaging
Role: MPI
The ultimate goal of this proposal is to develop inroads to identification of aneurysms at risk of rupture and identify through the proposed experiments how specific and sensitive the imaging probes are in tracking active remodeling in aneurysm models.
3. 2R44NS100163-02, NIH (PI: GJ Ughi) 7/15/2018-4/30/2020
Micro-imaging catheters for high-resolution, image-guided, endovascular repair of brain aneurysms
Role: Subcontract Co-I
The goal of this program is to provide a highly effective, high-resolution, imaging tool for the guidance of neuroendovascular procedures for brain aneurysm repair.
4. 2017104, BSF (PIs: N Korin, MJ Gounis) 10/1/2018-9/30/2022
Hemodynamic transport of drug carriers to cerebral aneurysms
Role: Co-PI
Our main goal is to elucidate the role of hemodynamics on particle dynamics at aneurysm sites and study the effect of particulate drug carrier properties on targeting aneurysms.
5. 2R44NS095573-03A1, NIH (PI: Merrill – FocalCool) 9/30/2019-8/31/2021
Rapid and effective localized neurovascular cooling using the Khione insulative catheter.
Role: Subcontract PI
Following a successful Phase 1 study that demonstrated proof-of-concept of rapid, deep focal cooling during temporary middle cerebral artery occlusion in a large animal model; this Phase 2 grant seeks to demonstrate the efficacy of this approach of peri-procedural focal hypothermia to improve functional outcomes in an acute stroke model.
6. Award #563749, Gilbert Family Fdn (PIs: MS Sena-Esteves) 12/1/2018-11/30/2021
Multipronged Approach to Development of NF1 Gene Therapeutics Neurofibromatosis Type I
Role: Co-I
The aims of this project are to develop (1) AAV vectors for NF1 delivery and expression restoration and (2) a zinc finger protein (ZFP) or antisense oligonucleotides (ASOs) to achieve dosage compensation in haploinsufficient (NF1+/-) cells.
7. Bits to Bytes, Massachusetts Life Sciences Center (PI: MJ Gounis) 7/1/2019-06/30/2022
Artificial intelligence in high-resolution neurovascular imaging for improving the treatment of stroke
Role: PI
The aim of the proposed research is the development of machine learning methodologies for an efficient analysis of HF-OCT data. The developments proposed in this grant hold great potential to revolutionize the current treatment and understanding of brain aneurysms.