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UID:11@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230830T110000
DTEND;TZID=Asia/Kolkata:20230830T120000
DTSTAMP:20231102T051101Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-30th-august-recon-all-cl
 inical-cortical-analysis-of-heterogeneous-clinical-brain-mri-scans/
SUMMARY:{Seminar} @ CDS: #102 : 30th August: “Recon-all-clinical Cortical
  analysis of heterogeneous clinical brain MRI scans”
DESCRIPTION:Department of Computational and Data Sciences\nDepartment Semin
 ar\n\nSPEAKER : Karthik Gopinath is a Postdoctoral Research Fellow at Athi
 noula A.\n\nTITLE : “Recon-all-clinical Cortical analysis of heterogeneo
 us clinical brain MRI scans“\n\nDate &amp\; Time : August 30\, 2023\, 11
 :00 AM\n\nVenue : # 102\, CDS Seminar Hall\n\n============================
 ===============================================================\nAbstract\
 nSurface analysis of the cortex is ubiquitous in human neuroimaging with M
 RI\, e.g.\, for cortical registration\, parcellation\, or thickness estima
 tion. The convoluted cortical geometry requires isotropic scans (e.g.\, 1m
 m MPRAGEs) and good gray-white matter contrast for 3D reconstruction. This
  precludes the analysis of most brain MRI scans ac- quired for clinical pu
 rposes. Analyzing such scans would enable neuroimaging studies with sample
  sizes that cannot be achieved with current research datasets\, particular
 ly for underrepresented populations and rare diseases. Here we present the
  first method for cortical reconstruction\, registration\, parcellation\, 
 and thickness estimation for clinical brain MRI scans of any resolution an
 d pulse sequence. The methods have a learning component and a classical op
 timization module. The former uses domain randomization to train a CNN tha
 t predicts an implicit representation of the white matter and pial surface
 s (a signed distance function) at 1mm isotropic resolution\, independently
  of the pulse sequence and resolution of the input. The latter uses geomet
 ry processing to place the surfaces while accurately satisfying topologica
 l and geometric constraints\, thus enabling subsequent parcellation and th
 ickness estimation with existing methods. We present results on 5mm axial 
 FLAIR scans from ADNI and on a highly heterogeneous clinical dataset with 
 5\,000 scans.\nBiography\nKarthik Gopinath is a Postdoctoral Research Fell
 ow at Athinoula A. Martinos Center for Biomedical Imaging\, Massachusetts 
 General Hospital\, and Harvard Medical School. His work focuses on Brain s
 urface analysis of clinically acquired MR images. Previously he completed 
 his Ph.D. from ETS Montreal\, where his work focused on Geometric learning
  for brain surface analysis\, for which he received the Governor-general a
 cademic gold medal and Best Thesis award. He completed his MS by Research 
 from IIIT-Hyderabad. You can find more information about his work on his w
 ebpage: https://sites.google.com/site/karthikharitz/\n\nHost Faculty: Dr. 
 Vaanathi Sundaresan\n=====================================================
 =============================\nALL ARE WELCOME
CATEGORIES:Events,Talks
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