Quantitative Imaging Biomarker DCE - MRI

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Bernard Whitehead
5 years ago
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1 Quantitative Imaging Biomarker DCE - MRI

2 DCE MRI: What is it about? DCE MRI: quantitative analysis of dynamic T1 contrast enhanced images Use cases: Clinical trial related UC1: pharmacodynamic investigations (e.g. K trans ) in early phase clin trials UC2: biological effect assessment as predictive biomarker UC3: heterogeneity of disease/response Clinical routine use (future) UC4: diagnostic decision making UC5: therapeutic progress assessment in a clinical environment

3 General business environment Push from users for parallel imaging (diagnostic imaging); Current radiological practice is not quantitative Manufacturer have different implementation of basic technical solutions that result in a huge variety of parameters (e.g. echo time); Manufacturers have nothing to compare to Economic challenge to manufacturers in clinical trials vs clinical routine;

4 Acquisition Protocol Specification Differences across scanners, platforms, field strengths, site practices, infusion protocols. Wide variety of imaging sequences. No standardization on T1 map acquisition. DIfferent vendors use different system gain settings which complicate image comparison. Challenge: MEDIUM Need to agree on standards on all aspects of 1 protocol for image acquisition. Need to optimize pulse sequence parameters, and lock down clinical protocols. Incorporate robust methods for motion correction that are common to all manufacturers. Recommendations: select 1 acquisition protocol including T1 mapping based on the dynamic sequence ; each vendor defines machine specific settings to support the protocol (step #1 of QIBA DCE-MRI team) Motion correction is longer term issue.

5 Standardized acquisition protocol representation Fairly standard routine 3D Fast spoiled gradient echo sequences exist on all platforms. There is less standardization in T1 measuring techniques. Challenge: LOW Once acquisition protocols are defined and imaging parameters are optimized, presenting these protocols in a standard format will be relatively straight forward. Recommendation: deal with this issue later on based on the experiences with the first standardized protocol

6 Acquisition Phantom / Calibration Systematic phantom calibration experiments do not exist for the recommended imaging protocols, especially in a multi-vendor environment. Challenge: HIGH Challenge to phantom constructors regarding inflow etc. functions, phantoms will serve as a useful benchmark to test how robustly the acquisition protocols are performing. Several phantoms to be developed, validated and distributed. Show case at e.g. RSNA Recommendation: Priority 1: phantoms should focus on signal intensity measurement =f(contrast agent concentration), Priority 2: T1 and proton density estimation, geometric distortion due to magnetic field changes; inflow not in focus for first activities

7 Biomarker SW Quantification / validation A plethora of software and quantification metrics exist. Many different semi-quantitative and quantitative metrics are commonly calculated and reported. Even for a widely accepted parameter, K trans for example, many different models exist and they each have limitations and assumptions. Challenge: HIGH Picking the right algorithms for analysis is very important and challenging. It is complicated due to the non-standardization of acquisition protocols, as well as the fact that different body applications have very different acquisition scenarios. Robustness of the algorithm, speed of computation, ease of use are vital. Accurate image registration prior to analysis and some form of automated segmentation and analysis of disease after analysis are key challenges. No real ground truth without simulation no gold standard. Recommendation: Simulated and real data are needed for reference purposes. It need to be used to assess algorithm implementation and development. Demonstration of comparability of algorithm implementations and development. Mainly task for research/academic community.

8 Collection/ Analysis SW portability API Because no common platforms for analysis exist, there is no existing definition of a common API or software portability. DICOM is progressing well on the definition of an API for imaging application. Scope of API will have to be evaluated and conforming imaging platforms developed. Challenge: LOW MEDIUM No industry experience exists with DICOM Plug-in API. Constraints on UIs, software environment, performance impact and support makes this a nontrivial task. Analysis software needs to be multi-vendor compliant using DICOM standards. All necessary information needed from images should be available from standard DICOM header fields and should not be contained in proprietary fields. Common hooks for data loading/importing, and results exporting need to be designed. Recommendation: No immediate need - future topic.

9 Reference Population Clinical Data No organized collection of reference data currently available for specific application areas. Challenge: MEDIUM Large volume of clinical data can be obtained from multi-center trials that can serve as use-cases for evaluation of software tools and acquisition robustness. It must be ensured that the data is of high quality and adheres to the requirements of the analysis software. Various groups of datasets for each body application area and targeted types of cancer will have to formed. See validation for challenges - no easy way to build the data base; work with DICOM WG 18 regarding necessary meta data as part of the DICOM header plus SR data Recommendation: Ensure that appropriate DICOM SR is used to make a complete record of input and results obtained (this DICOM SR is likely to support other biomarkers e.g. lesion segmentation, ROI definition). Start awareness campaign to ensure acquisition standardization becomes widely used in order to enable future population data based build up.

10 Clinical Trials Structured Results None Challenge: MEDIUM Simply presenting pharmacokinetic maps for the entire imaging field of view will not be sufficient. It will be necessary to implement methods for automatically segmenting the suspicious areas, quantifying them, sizing them, characterizing their heterogeneity, and monitoring changes during response to therapy. These results needs to presented and saved in a structured and standardized format. A specific DICOM SR Template will need to be specified and when validated, standardized. audit trail, deidentification; work with DICOM WG 18 Recommendations: Initially it is sufficient to use DICOM SR to make a complete record of input and results obtained.

11 Clinical Trial Submissions None Challenge: HIGH Compilation of results, all protocols and processes, documentation etc will have to completed using welldocumented processes. Consult with pharma on this topic. Recommendation: If the previously mentioned recommendations are fullfiled solution is available.

12 RSNA 2009 Akquisition protocol Show case: make image acquisition across different vendors more comparable by protocol specification Next steps: protocol definition right after RSNA inform vendors on upcoming activity prior to RSNA vendor and clinical site call for participation (at RSNA) vendor translation into machine sequence phantom imaging clinical imaging at the interested sites analysis and publication/demonstration at RSNA 2009

DCE MRI Team Activity. Gudrun Zahlmann, Edward Jackson, Sandeep Gupta

DCE MRI Team Activity. Gudrun Zahlmann, Edward Jackson, Sandeep Gupta DCE MRI Team Activity Gudrun Zahlmann, Edward Jackson, Sandeep Gupta QIBA Groundwork DCE-MRI Technical Characteristics and Standards Groundwork ( precursor questions ) Diagnostic Accuracy and Reproducibility