InvitedTalkLeoJoskowicz2018

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Invited Talk by Prof. Leo Joskowicz

Leo Joskowicz

Prof. Leo Joskowicz
Director, CASMIP Lab: Computer Aided Surgery and Medical Image Processing Laboratory
School of Computer Science and Engineering
The Hebrew University of Jerusalem, ISRAEL

  • Date: Monday, June 25, 2018
  • Time: 14:00 - 15:00
  • Location: MI 03.13.010, Boltzmannstr. 3


Sparse repeat CT scanning: tracking and automatic change detection

Repeat CT scanning consists of acquiring multiple CT scans of the same patient at various times. It is frequently used in many clinical situations, e.g., to assess disease progression, to evaluate response to treatment and to track the patient and the needle during interventional procedures. The main image analysis goal, which is to identify the changes between the baseline and the follow up scans, is time-consuming, error prone, and requires radiological expertise. Moreover, since each scan adds cumulative radiation that may be harmful to the patient, it is highly desirable to optimize the radiation dose of each scan. However, lower doses reduce image quality and thereby difficult even more the image interpretation task.

We have developed a new computational paradigm for on-line radiation dose optimization and automatic change detection in repeat CT scanning. The key principle of our approach is to perform sparse repeat scanning to significantly reduce the radiation dose and to obtain the missing information from the baseline scan without image quality loss. Our approach is unique in that it formulates the problem as sparse sinogram comparison problem in 3D Radon space instead of an image reconstruction problem in image space. We will describe novel methods for registration of the baseline and the repeat scan, for the automatic identification of regions where the changes have occurred, and for image-less needle tracking in interventional radiology. Our experimental results show that these tasks can be accomplished with a dose reduction of about x10.

Joint work with Guy Medan, Naomi Shamul and Zeev Adelman, PhS? students.

Leo Joskowicz is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel. He is the founder and director of the Computer-Aided Surgery and Medical Image Processing Laboratory (CASMIP Lab). Prof. Joskowicz is a Fellow of the IEEE, ASME, and MICCAI (Medical Image Processing and Computer Aided Intervention) Societies. He is the recipient of the 2010 Maurice E. Muller Award for Excellence in Computer Assisted Surgery by the International Society of Computer Aided Orthopaedic Surgery and the 2007 Kaye Innovation Award. He has published over 250 technical works including conference and journal papers, book chapters, and editorials. He is a member of the Board of Directors of the MICCAI and has served on numerous related program committees. He is on the Editorial Boards of six journals, including Medical Image Analysis, Int. J. of Computer Aided Surgery, Computer Aided Surgery, and Nature Scientific Reports. He is the Co-Chair of the MICCAI 2020 conference.

WebEventForm
Title: Invited Talk by Prof. Leo Joskowicz
Date: 25 June 2018
Location: MI 03.13.010
Abstract: Repeat CT scanning consists of acquiring multiple CT scans of the same patient at various times. It is frequently used in many clinical situations, e.g., to assess disease progression, to evaluate response to treatment and to track the patient and the needle during interventional procedures. The main image analysis goal, which is to identify the changes between the baseline and the follow up scans, is time-consuming, error prone, and requires radiological expertise. Moreover, since each scan adds cumulative radiation that may be harmful to the patient, it is highly desirable to optimize the radiation dose of each scan. However, lower doses reduce image quality and thereby difficult even more the image interpretation task. We have developed a new computational paradigm for on-line radiation dose optimization and automatic change detection in repeat CT scanning. The key principle of our approach is to perform sparse repeat scanning to significantly reduce the radiation dose and to obtain the missing information from the baseline scan without image quality loss. Our approach is unique in that it formulates the problem as sparse sinogram comparison problem in 3D Radon space instead of an image reconstruction problem in image space. We will describe novel methods for registration of the baseline and the repeat scan, for the automatic identification of regions where the changes have occurred, and for image-less needle tracking in interventional radiology. Our experimental results show that these tasks can be accomplished with a dose reduction of about x10.
Imageurl: joskowicz-photo-2016.jpg
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