Rapid technological advances in the field of medical imaging have provided huge opportunities to non-invasively obtain biomarkers which have the potential to improve our understanding of the etiology of various diseases. Moreover, these imaging-derived biomarkers may improve current clinical prediction rules of specific diseases, for example cardiovascular or neurodegenerative diseases.
Quantitative imaging is the extraction of quantifiable measures from medical images for the assessment of various phenotypes. Quantitative imaging includes development, standardization, and optimization of anatomical, functional, or molecular imaging protocols, but also data analyses, display methods, and reporting structures.
Quantitative Imaging Biomarkers Alliance (QIBA) is an initiative to advance quantitative imaging and the use of imaging biomarkers. Key aspects include:
- collaborations to identify needs, barriers, and solutions to develop and test consistent, reliable, valid, and achievable quantitative imaging results across imaging platforms, clinical sites, and time.
- accelerating the development and adoption of hardware and software standards needed to achieve accurate and reproducible quantitative results from imaging methods.
- Kessler, LG, et. al., The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Pubmed: click here.
- Raunig, DL, et. al., Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment. Pubmed: click here.
- Obuchowski, NA, et. al., Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons. Pubmed: click here.
- Obuchowski, NA, et. al., Statistical issues in the comparison of quantitative imaging biomarker algorithms using pulmonary nodule volume as an example. Pubmed: click here.
- Huang, EP, et. al., Meta-analysis of the technical performance of an imaging procedure: Guidelines and statistical methodology. Pubmed: click here.
For more information, please visit the QIBA website.
See also the novel ESR Position Paper on Imaging Biobanks in Insights Into Imaging on: http://link.springer.com/article/10.1007/s13244-015-0409-x
For more information on the challenges of biomedical image analysis, please click here.
Grand Challenges in Biomedical Image Analysis
Every year, thousands of papers are published that describe new algorithms to be applied to medical and biomedical images, and various new products appear on the market based on such algorithms. But few papers and products provide a fair and direct comparison of the newly proposed solution with the state-of-the-art. We believe that such comparisons can help the research community and industry to develop better algorithms. We support the organization of these comparative studies and the dissemination of their results. For more information, see: http://grand-challenge.org/