Differences in Dynamic Susceptibility Contrast MR Perfusion Maps Generated by Different Methods Implemented in Commercial Software

Orsingher, L.; Piccinini, Silvia; Crisi, Girolamo

doi:10.1097/rct.0000000000000115

Cited by 15 publications

(19 citation statements)

References 29 publications

(27 reference statements)

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“…Specifically, use of a preload of contrast agent, and a flip angle less than 90° is proving to be one of the most accurate approaches, further confirmed by two recent studies [19, 41], both incorporating sophisticated simulations of DSC-MRI data representative of brain tumor. Use of a preload might also be an important reason why greater consistency across post-processing methods was found in this study compared to previous studies (eg [32]). Collecting DSC-MRI data after preload was shown to decrease the dependence of tumor rCBV on the chosen method of analysis [20].…”

Section: Discussionmentioning

confidence: 58%

“…For example, in one study [32] two commercial software packages (NordicICE (NordicNeuroLab, Bergen, Norway)) and GE Brainstat (GE Healthcare, Milwaukee, WI)) were compared. Like the present study, one dataset of 24 patients with de novo glioblastoma was used and ROIs of tumor and reference brain were pre-determined.…”

Section: Discussionmentioning

confidence: 99%

“…In the present study, the majority of algorithms involved the integration of the concentration-time course and the application of BSW leakage correction [24], which in a sub-analysis also showed better reliability. In addition, 5 of 10 algorithms relied on the determination of the AIF [32]. Using AIF to compute nRCBV resulted in coefficients of variation (CV) of 15%, but only 2% when AIF was not used.…”

Section: Discussionmentioning

confidence: 99%

“…Gamma-variate fitting was also often used by 5 of 10 analysis methods in the previous SW comparison study [32]. Several studies report a lower SNR [35] as well as greater inaccuracy when gamma-variate fitting is used for brain tumor DSC-MRI data especially in the presence of contrast agent leakage [19, 20].…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

Schmainda¹,

Prah²,

Rand

et al. 2018

AJNR Am J Neuroradiol

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Background and Purpose Standard assessment criteria for brain tumors that only includes anatomic imaging, continues to be insufficient. While numerous studies have demonstrated the value of DSC-MRI perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MRI metrics across sites and platforms. This study addresses this limitation with a comparison of multi-site/multi-platform analyses of a shared brain tumor patient DSC-MRI dataset. Methods DSC-MRI data was collected, after a preload and during a bolus injection of gadolinium contrast agent using a GRE-EPI sequence (TE/TR=30/1200ms; flip angle=72°). Forty-nine low-grade (LG:n=13) and high-grade (HG:n=36) glioma datasets were uploaded to the cancer imaging archive (TCIA). Datasets included a predetermined AIF (arterial input function), enhancing tumor ROIs and ROIs necessary to create normalized relative cerebral blood volume (nRCBV) and cerebral blood flow (nCBF) maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement between sites was assessed with Linn’s-Concordance-Correlation-Coefficient (LCCC). Distinction of LG from HG tumor was evaluated with the Wilcoxon-rank-sum test followed by ROC analysis to identify the optimal threshold(s) based on sensitivity(SN) and specificity(SP). Results For nRCBV and nCBF 93% and 94% of entries showed good or excellent cross-site agreement (0.8≤LCCC≤1.0). All metrics could distinguish LG from HG tumor. Optimum thresholds were determined for pooled data (nRCBV=1.4, SN:SP=90%:77%; nCBF=1.58, SN:SP=86%:77%). Conclusion Using DSC-MRI data, obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing LG from HG tumor.

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Section: Discussionmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

Schmainda¹,

Prah²,

Rand

et al. 2018

AJNR Am J Neuroradiol

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“…Similarly, rCBV, as measured on IBNeuro, showed stronger correlation with image-guided microvessel quantification and higher accuracy in predicting tumor recurrence from pseudoprogression/radiation necrosis, based on validation by surgical histologic tumor quantification. Orsingher et al (26) also found significant differences in rCBV values when comparing General Electric and NordicICE software packages. The findings of both these studies coincide with the findings of the current study.…”

Section: Discussionmentioning

confidence: 94%

Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation

et al. 2016

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Relative cerebral blood volume (rCBV) is a magnetic resonance imaging biomarker that is used to differentiate progression from pseudoprogression in patients with glioblastoma multiforme, the most common primary brain tumor. However, calculated rCBV depends considerably on the software used. Automating all steps required for rCBV calculation is important, as user interaction can lead to increased variability and possible inaccuracies in clinical decision-making. Here, we present an automated tool for computing rCBV from dynamic susceptibility contrast-magnetic resonance imaging that includes leakage correction. The entrance and exit bolus time points are automatically calculated using wavelet-based detection. The proposed tool is compared with 3 Food and Drug Administration-approved software packages, 1 automatic and 2 requiring user interaction, on a data set of 43 patients. We also evaluate manual and automated white matter (WM) selection for normalization of the cerebral blood volume maps. Our system showed good agreement with 2 of the 3 software packages. The intraclass correlation coefficient for all comparisons between the same software operated by different people was >0.880, except for FuncTool when operated by user 1 versus user 2. Little variability in agreement between software tools was observed when using different WM selection techniques. Our algorithm for automatic rCBV calculation with leakage correction and automated WM selection agrees well with 2 out of the 3 FDA-approved software packages.

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A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation

et al. 2021

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Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived.Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model. Methods: Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation.Results: Accuracy of leakage correction was able to be verified using the brainlike phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error. Conclusions: DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the A DYNAMIC SUSCEPTIBILITY CONTRAST MRI DIGITAL REFERENCE OBJECT FOR TESTING SOFTWARE WITH LEAKAGE CORRECTION: EFFECT OF BACKGROUND SIMULATION

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Differences in Dynamic Susceptibility Contrast MR Perfusion Maps Generated by Different Methods Implemented in Commercial Software

Cited by 15 publications

References 29 publications

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation

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