Education
RANZCR Research Grants in Action An Interview with Associate Professor Frank Galliard
A/Prof Frank Galliard
What is your current role/s? I am currently a consultant neuroradiologist and Director of Research of the Radiology Department Royal Melbourne Hospital, and a clinical associate professor in the Faculty of Medicine, Dentistry, and Health Sciences at the University of Melbourne. Additionally, I am the founder and Editor in Chief of Radiopaedia.org.
What are your research interests? My research interests are varied, primarily investigating how novel computer visualisations can be used to make routine imaging tasks faster and more accurate. I have a small grantsupported lab that has been working on this since 2015, with the bulk of the work so far on multiple sclerosis. My additional research interests are in tumour radiomics and perceptual learning as a way of training radiologists (in collaboration with the University of Melbourne psychology department).
Are there any big unanswered research questions in this area? In recent years, an enormous amount of interest and effort is being expended in artificial intelligence/machine learning applications. Although many of these are fascinating, in many instances they
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Inside News
“In recent years, an enormous amount of interest and effort is being expended in artificial intelligence/ machine learning applications. Although many of these are fascinating, in many instances they provide an AI solution to a problem that doesn’t really exist. In contrast, little innovation in how traditional imaging is shown has occurred since moving from printed film to PACS.” provide an AI solution to a problem that doesn’t really exist. In contrast, little innovation in how traditional imaging is shown has occurred since moving from printed film to PACS. In fact, other than scrolling through stacks and performing multiplanar reformats, we largely present imaging in a way very reminiscent of printed film. We hang studies side by side to compare them and we present them in near identical ways regardless of the specific clinical question. Furthermore, most PACS viewers present imaging in a similar way regardless of context; the view shown
to diagnostic radiologists is very similar to that shown on the ward, or in the outpatient setting. There is no a priori reason to believe that this is optimum, and we believe there is a lot of ‘low hanging fruit’; improvements in how routine imaging is presented can have significant impact on outcomes. We have demonstrated this multiple times within the context of MRI for the follow-up of multiple sclerosis: see www.github.com/mh-cad
In 2019, you received RANZCR Research Grant funding for your project “MRI brain 4D time-lapse viewer.” Can you tell us a bit more about this project? Brain tumours, along with many other conditions, are followed up for extended periods of time in an effort to visualise gradual growth and to assess for alterations’ rate of change. This can be challenging especially as the interval between follow-up studies is variable and patient positioning can subtly vary. Moreover, these features are even harder for a patient to appreciate in the outpatient setting and the ability to fully understand the nature and progression of their condition can play a vital role in empowering them to make decisions. To this end, we have created an application which presents a longitudinal view of all available scans allowing radiologist, clinicians and patients to navigate not only through three spatial dimensions (as is usual) but also through time in a manner reminiscent of timelapse photography. See Figure 1: 4D Viewer Interface Additionally, we are working on implementing an MRI equivalent of ultrasound M mode that we are all familiar with; showing change in signal intensity along a line drawn in a particular location. See Figure 2: M mode
