Introduction¶
Motivation¶
Dynamic contrast-enhanced (DCE) and dynamic-susceptibility contrast MRI (DSC-MRI) have emerged as the two primary contrast-agent based MRI modalities for imaging tissue perfusion. Perfusion quantities derived from DCE-MRI and DSC-MRI have enormous potential as quantitative imaging biomarkers (QIBs) in applications such as oncology, neurology, cardiology, liver, renal, and musculoskeletal disorders [1], however translation of these QIBs to the clinic is hindered by lack of standardization in both acquisition and analysis procedures. Despite the best efforts of the research and clinical communities, there is substantial variability in DCE/DSC MRI QIBs between studies, with data analysis being a major contributor to this variation [2], [3].
DCE and DSC-MRI data is typically analyzed using a sequence of computational processes which define an image analysis pipeline. The specific steps included within these pipelines, and the software used, can be highly variable from study to study and between research groups. Clinically-oriented studies often rely on commercial software. Several studies have found substantial differences and inconsistencies between these tools [4], [5], [6], [7] the details of which are rarely fully documented by the vendor. When commercial solutions are not available, or do not have the desired functionality, in-house software is commonly developed, which can result in very different implementations of the same basic analysis pipeline, varying in factors such as the coding language used (Python, Matlab, C++), curve fitting algorithm and tuning parameters, output data type and units, and the overall choice and number of analysis steps. There have been many efforts to set standards in medical imaging including initiatives to improve data standards such as the Digital Imaging and Communications in Medicine (DICOM) standard [8] and the Brain Imaging Data Structure (BIDS) [9] [10], and there are a number of more specific initiatives focused around metrology standards for QIBs [11] and standardized methods and results reporting for neuroimaging (COBIDAS [12], NIDMTerms [13]). Guidance on the acquisition of perfusion MRI for multi-centre studies has been established for many years through the RSNA Quantitative Imaging Biomarkers Initiative (QIBA), which have published a number of recommended protocols focussed on acquisition and analysis of DCE\DSC data. Similar efforts have been made for arterial spin-labeling MRI (ASL-MRI) in brain [14] and kidney [15], [16] However, very little guidance is given on reporting of methods and results, or recommended terminology for quantities, models, or processes. To address the lack of standards for perfusion MRI, particularly in the domain of image analysis and processing, the Open Source Initiative for Perfusion Imaging (OSIPI) [17] was established in 2020 following a call-to-action survey of the Perfusion Study group of the International Society for Magnetic Resonance in Medicine (ISMRM). OSIPI’s mission is to promote the sharing of perfusion imaging software, improve the reproducibility of perfusion imaging research, and speed up the translation of perfusion software into tools for discovery science, drug development, and clinical practice.
OSIPI Taskforce 4.2 [18] was established to develop standards for terminology and reporting of DCE/DSC analyses. This lexicon describes the main deliverable from the first 2-year cycle of OSIPI Task Force 4.2: the development of a consensus-based DCE/DSC lexicon of standard quantities, models and processes (OSIPI-CAPLEX) and reporting guidelines.
Citing OSIPI CAPLEX¶
Original publication¶
References¶
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