Quantitative Analysis of Dynamic Contrast-Enhanced MRI Datasets of the Metacarpophalangeal Joints

Rationale and Objectives

In this article, we propose an alternative approach to voxel-by-voxel analysis, which overcomes problems associated with heuristic methods currently used for dynamic contrast-enhanced MRI (DCE-MRI) data assessment. We aim to allow fully automated extraction of various heuristic parameters via robust preprocessing methods and a new technique for classification of temporal patterns of contrast agent uptake, making full use of all available dynamic frames of the datasets. We also demonstrate that application of efficient preprocessing methods permits more accurate analysis of the dynamic data.

Material and Methods

Ten DCE-MRI datasets enhanced by gadolinium diethylene triamine pentacetic acid were acquired from patients with rheumatoid arthritis using a 1.5-T MRI scanner. Fully automated voxel-by-voxel analysis of DCE-MRI signal intensity curves from 60 temporal slices was performed using a new method. Qualitative evaluation of the degree of inflammation was done via constructing parametric maps and quantitative by computing various heuristics such as maximum rate of enhancement, initial rate of enhancement, and time of onset of enhancement.

Results

Quantitative and qualitative evaluation obtained for 10 DCE-MRI datasets is presented. We demonstrate that preprocessing techniques compensate for patient movement, contribute to data fidelity and therefore permit more robust estimation of various heuristics such as maximum rate of enhancement, initial rate of enhancement, and time of onset of enhancement. Automatically generated parametric maps of these heuristics show favorable characteristics, permitting easier differentiation of structures of interest. These results are free from the subjective input and therefore easily reproducible. Furthermore, the proposed classification scheme provides information on the pattern of contrast uptake previously unavailable.

Conclusion

Our preliminary results demonstrate the potential of the proposed method for providing objective quantitative and qualitative assessment of DCE-MRI in the metacarpophalangeal joints. Further evaluation within a clinical setting is needed to examine the method’s diagnostic utility.

Dynamic contrast-enhanced MR imaging (DCE-MRI) has evolved as an important method for evaluating various diseases of the musculoskeletal system ( ). Temporal changes of signal intensity during and immediately after bolus injection of contrast agent reflect underlying changes in local concentration of the agent. Therefore this technique provides information about tissue vascularity, perfusion, and capillary permeability ( ).

Advances in MRI acquisition have led to increasing use of parametric images designed to display physiological features of tissues in addition to anatomical structure. DCE-MRI is used extensively in a wide range of applications involving different organs and pathologies ( ).

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Materials and methods

Data Acquisition

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Moving-Window–Based Method

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New Method for DCE-MRI Data Analysis

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Registration

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Segmentation

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Classification of Tissue Behavior

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S̄ 4 is not normally observed: here, while a change in the rate of increase is plausible, a significant plateau during increase is not. Experiments reveal that any observation of this model has a very short plateau (≤3 time instants) equally well attributed to noise. We henceforward assume that all SI curves can be modeled by one of the shapes proposed in Figure 4 excluding this case.

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Noise Model Estimation

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Determining Best Model Fit

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Median Filtering

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Computing BB Parameters

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Discussion

The Noise Model

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Table 1

Relative Label Populations Using Different Noise Models

Model N 1 Noise N 2 Noise N 1 ∪ N 2 M 0 1194 (12%) 1152 (12%) 1166 (13%)M 1 51 (2%) 42 (1%) 48 (1%)M 2 5588 (60%) 5584 (60%) 5583 (59%)M 3 2459 (26%) 2514 (27%) 2495 (27%)

Images given in Kubassova et al. ( ).

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T onset

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N total

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Table 2

Proportion of Enhancing Voxels: Moving Window (MW) and New Approaches

Study New MW 1 0.58 (22%) 0.68 2 0.59 (6%) 0.63 3 0.42 (10%) 0.51 4 0.39 (13%) 0.57 5 0.39 (2%) 0.57 6 0.54 (7%) 0.68 7 0.64 (7%) 0.65 8 0.34 (7%) 0.44 8 0.46 (7%) 0.58 9 0.59 (7%) 0.67 10 0.39 (7%) 0.48

Second column includes the percentage of voxels corresponding to locations where we observe incomplete Gd-DTPA absorption (model M 1 ).

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Two- Versus Three-dimensional Registration

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Results

Parametric Maps

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Codings of Gd-DTPA Take-up

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Conclusion

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Acknowledgments

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References

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Quantitative Analysis of Dynamic Contrast-Enhanced MRI Datasets of the Metacarpophalangeal Joints

Rationale and Objectives

Material and Methods

Results

Conclusion

Materials and methods

Data Acquisition

Moving-Window–Based Method

New Method for DCE-MRI Data Analysis

Registration

Segmentation

Classification of Tissue Behavior

Noise Model Estimation

Determining Best Model Fit

Median Filtering

Computing BB Parameters

Discussion

The Noise Model

T onset

N total

Two- Versus Three-dimensional Registration

Results

Parametric Maps

Codings of Gd-DTPA Take-up

Conclusion

Acknowledgments

References

Further Reading

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