Announcements II: January 15, 2008

Posted 12:58 PM, January 15, 2008, by jaruiz

Special Seminar Series on Radiological Informatics: As part of a special series on radiological informatics, we are offering seminars on Jan. 23rd, 24th, 28th, and 31st. Please watch future announcements for each seminar's title and abstract. In addition to the seminars listed below, there will be a few more talks, which are being scheduled now and will be posted shortly. Please contact Dr. Sandy Napel for more information.

1) Wednesday, January 23rd, at noon; location TBA
Julia Patriarche, PhD
Mayo Clinic

Title:
"Detection of Change in Serial Magnetic Resonance Studies of Brain Tumor Patients"

Abstract:
The comparison of serial magnetic resonance imaging studies is a common task in clinical radiology. It is, however, widely considered not to be very reproducible. There are a variety of reasons for this, including the confounding of disease-related changes with acquisition-related changes and issues related to information presentation. We have constructed a computational system that performs the comparison of serial magnetic resonance imaging studies and presents changes in the form of a color-coded change map, superimposed on the anatomical images. The system additionally formats the output as a quantitative summary. We used this quantitative summary to conduct a study with 88 brain tumor serial comparisons. Our results were suggestive that it may be possible to use the change detector to identify cancer changes months earlier than is possible using manual inspection, alone.

We have recently implemented an integrated system for the change detector, which includes a graphical user interface (GUI). The GUI not only displays the color-coded change map, but also allows the user to turn it on and off. The GUI provides linked cursors, and it additionally provides "flicker" functionality to allow the user to rapidly alternate between the serial acquisitions. We are preparing to deploy the GUI change detector clinically, which will greatly increase the size and variety of possible future research studies and which will allow the direct clinical application of this technology.

The change detector is an example of a layered artificial intelligence (AI) architecture in which each layer builds upon the layer below, with each layer accomplishing progressively more sophisticated analyses. Specifically, the change detector is built on a lesion-finder application. The lesion finder is built on an automated sample point's algorithm. The automated sample point's algorithm is built on a significant region detection algorithm. Each of these algorithms has merit in its own right, and each can be used in a modular fashion in a variety of contexts. As a unified application, they together automatically address a complex clinical task. Early detection of changes may facilitate improved care through more rapid intervention following recurrence. It may also facilitate screening and personalized therapy. We additionally see the change detector as providing a solution to the problem of novel therapy comparison, by providing fully automatic, reproducible, and quantitative measures of change. We envision the change detector as a model of layered artificial intelligence, not only freeing the radiologist from the drudgery of information overload, but providing a model whereby greater information will enable many sophisticated automatic analyses by the computer, with the computer bringing to the attention of the clinician only what is relevant.

Biography:
Julia Patriarche is an informatics fellow in the Radiology Informatics Lab at the Mayo Clinic College of Medicine. She has completed an undergraduate degree in electrical engineering/computer engineering option at Queen's University in Kingston, Canada; a PhD in medical science/medical imaging; and a neurology fellowship at the Mayo Clinic College of Medicine.

2) Thursday, January 24th, at noon; location TBA
Ross Mitchell, PhD
University of Calgary

Title:
"Virtual Biopsies: Non-Invasive Molecular Diagnosis"

Abstract:
Our expanding knowledge of the genetic basis and molecular mechanisms of cancer is beginning to revolutionize the practice of clinical oncology. Increasingly, molecular biomarkers of prognosis and treatment response are being used to classify tumors and direct treatment decisions. Advanced medical imaging platforms such as MRI, PET, and CT provide incredibly detailed images of tumors that reflect their structure, biochemistry, physiology, and perhaps genetics.

Studies by the Imaging Informatics Lab at the University of Calgary, and others, show that information about a tumor's molecular phenotype can be obtained by using novel algorithms and computational tools to more fully analyze tumor images. Such "virtual biopsies," performed by applying these image-processing techniques to routine diagnostic images (e.g. MRI, PET, or CT), could be a rapid and powerful means of assaying important cancer biomarkers. If successfully validated, and proven to have suitable sensitivity and specificity, the use of non-invasive, imaging-based molecular diagnostic tests would offer significant advantages over conventional surgical biopsies. For example, this could be important in the context of large heterogeneous tumors, multiple metastases, surgically inaccessible tumors, and settings where disease progression needs to be monitored frequently over time. Virtual biopsy research lies at the intersection of molecular imaging, medical imaging physics, and biocomputation, and is highly complementary to these areas. This presentation will cover key enabling technologies behind virtual biopsies and discuss some recent successes in this research.

Biography: Dr. Ross Mitchell is an associate professor of the Departments of Radiology and Clinical Neurosciences and an adjunct professor of the Department of Computer Science at the University of Calgary. He is also the founding and chief scientist of Calgary Scientific Incorporated, a Multiple Sclerosis Society of Canada; a Donald Paty Scholar; and an Alberta Heritage Foundation for Medical Research Senior Scholar. Dr. Mitchell has received numerous awards for his research including the Berlex Canada MS Research Award; several Dean's Awards of Excellence from the University of Western Ontario; Best Paper Awards from the Canadian Association of Radiologists and the International Organization for Medical Physics; and two Awards of Merit from the Radiological Society of North America. Dr. Mitchell has a proven research track-record comprising 11 patents, 73 invited presentations, 63 peer-reviewed articles, and 150 published abstracts.

Dr. Mitchell supervises a research team investigating space/frequency analysis, medical image processing, as well as segmentation and visualization technologies. For more information, please see, http://www.ImagingInformatics.ca.

3) Monday, January 28th, at noon; location TBA
Jianming Liang, PhD
Siemens Medical Solutions

4) Thursday, January 31st, at noon; location TBA
Daniel Rubin, MS, MD
Stanford University