Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy†

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Abstract

Purpose

The growing use of magnetic resonance imaging (MRI) as a substitute for computed tomography-based treatment planning requires the development of effective algorithms to generate electron density maps for treatment planning and patient setup verification. The purpose of this work was to develop a method to synthesize computerized tomography (CT) for MR-only radiotherapy of head and neck cancer patients.

Methods

The algorithm is based on registration of multiple patient datasets containing both MRI and CT images (a “multiatlas” algorithm). Twelve matched pairs of good quality CT and MRI scans (those without apparent motion and blurring artifacts) were selected from a pool of head and neck cancer patients to form the atlas. All atlas MRI scans were preprocessed to reduce scanner- and patient-induced intensity inhomogeneities and to standardize their intensity histograms. Atlas CT and MRIs were coregistered using a novel bone-to-air replacement technique applied to the CT scans that improves the similarity between CTs and MRIs and facilitates the registration process. For each new patient, all atlas MRIs are deformed initially onto the new patients’ MRI. We introduce a generalized registration error (GRE) metric that automatically measures the goodness of local registration between MRI pairs. The final synthetic CT value at each point is a nonlinear GRE-weighted average of the atlas CTs. For evaluation, the leave-one-out technique was used for synthetic CT generation and the mean absolute error (MAE) between the original and synthetic CT was computed over the entire CT image. The impact of our proposed CT-MR registration scheme on the accuracy of the final synthetic CT was also studied. The original treatment plans were also recomputed on the new synthetic CTs and dose-volume histogram metrics were compared. In addition, the two-dimensional (2D) gamma analysis at 1%/1 mm and 2%/2 mm dose difference/distance to agreement was also performed to study the dose distribution at the isocenter.

Results

MAE error (± standard deviation) between the original and the synthetic CTs was 64 ± 10, 113 ± 12, and 130 ± 28 Hounsfield Unit (HU) for the entire image, air, and bone regions respectively. Our results showed that our proposed bone-suppression based CT-MR fusion and GRE-weighted strategy could lower the overall MAE error between the original and synthetic CTs by ˜69% and ˜34% respectively. Dose recalculation comparison showed highly consistent results between plans based on the synthetic vs. the original CTs. The 2D gamma analysis revealed the pass rate of 95.44 ± 2.5 and 99.36 ± 0.71 for 1%/1 mm and 2%/2 mm criteria respectively. Due to local registration weighting, the method is robust with respect to MRI imaging artifacts.

Conclusion

We developed a novel image analysis technique to synthesize CT for head and neck anatomy. Novel methods were introduced to accurately register atlas CTs and MRIs as well as to weight the final electron density maps using local registration goodness estimates. The resulting accuracy is clinically acceptable, at least for these atlas patients.

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