Vendors provide easy tools to perform quantitative imaging but the question is how reliable and repeatable this quantification is.
Many clinical examples can already be listed where quantitative imaging is used in clinical practice. Such as carotid stenosis, coronary artery stenosis, calcium scoring, pulmonary nodules, renal donor evaluation, liver and tumor volumetry, brain perfusion, emphysema quantification.
Each of these show good results in literature and could be applied in clinical practice.
However, the question is whether the numbers we get out of the software are usefull and what is reality. Different vendors provide different results in the same patient and even within one software system measurements are influenced by postprocessing choices but also by decisions during the acquisiiton.
One of the things that can be done to get proper quantification we should provide reference datasets.
QIBA is ran by a group of stakeholders to improve quantitative imaging. They define profiles to get precise, repeatable measurements. QIBA has setup a imaging data warehouse (QIDW) including standard datasets that can be used to validate quantitative imaging algorithm.
The QIDW is free, open source, modular software based on MIDAS.
In conclusion tagging of the image data in radiology is essential to allow computers to work with the information. The quantification is part of this tagging. Developments like AIM are trying to cover this and allow export in XML or DICOM SR, however current PACSs and EMRs do not yet support these kind of measurement. When the storage and data mining of all the information available in the images becomes possible it will provide the key information to get to personalized diagnosis and treatment.
