Deformable registration has migrated from a research topic to a widely used clinical tool that can improve radiotherapeutic treatment accuracy by tracking anatomical changes. Although various mathematical formulations have been reported in the literature and implemented in commercial software, we lack a straightforward method to verify a given solution in routine clinical use. We propose a metric using concepts derived from vector analysis that complements the standard evaluation tools to identify unrealistic wrappings in a displacement field. At the heart of the proposed procedure is identification of vortexes in the displacement field that do not correspond to underlying anatomical changes. Vortexes are detected and their intensity quantified using the CURL operator and presented as a vortex map overlaid on the original anatomy for rapid identification of problematic regions. We show application of the proposed metric on clinical scenarios of adaptive radiotherapy and treatment response assessment, where the CURL operator quantitatively detected errors in the displacement field and identified problematic regions that were invisible to classical voxel‐based evaluation methods. Unrealistic warping not visible to standard voxel‐based solution assessment can produce erroneous results when the deformable solution is applied on a secondary dataset, such as dose matrix in adaptive therapy or PET data for treatment response assessment. The proposed metric for evaluating deformable registration provides increased usability and accuracy of detecting unrealistic deformable registration solutions when compared to standard intensity‐based approaches. It is computationally efficient and provides a valuable platform for the clinical acceptance of image‐guided radiotherapy.PACS numbers: 87.57.nj; 87.55.Qr; 87.57.cp
To improve the objectivity of the integration of positron emission tomography (PET), we used the conformality index (CI) to measure the goodness of fit of a given PET iso-SUV (standardized uptake value) level with the GTV defined on PET (GTV PET ) and CT (GTV CT ). Twenty-two datasets involving 20 head and neck cancer patients were identified. GTV PET and GTV CT were delineated manually. An iso-intensity method was developed to automatically segment GTV PET-ISO using (a) SUV and (b) maximum intensity thresholding (%Max), over a range of intensities. For each intensity, GTV PET-ISO was compared to GTV PET using the conformality index CI PET (and, similarly, to GTV CT using CI CT ). Comparing GTV PET to GTV PET-ISO vs comparing GTV CT to GTV PET-ISO, the average peak CI was 0.68 ± 0.09 vs 0.49 ± 0.12 (p<0.001), the optimum iso-SUV was 2.7 ± 0.7 vs 2.9 ± 1.0 (p=0. 253), and the %Max SUV was 21.8% ± 7.6% vs 23.8% ± 8.6% (p=0. 310), respectively. The radiation oncologist's volumes corresponded to a lower iso-SUV (3.02 ± 0.58 vs 4.36 ± 0.77, p < 0.001) and lower %Max SUV (24.1 ± 9.1% vs 34.3 ± 11.2%, p<0.001) than those drawn by the nuclear medicine physician. Though manual editing may still be necessary, PET iso-contouring is one method to improve the objectivity of GTV definition in head and neck cancer patients. Iso-SUV's can also be used to study the differences between PET's role as a nuclear medicine diagnostic test versus a radiation oncology treatment planning tool.
Purpose: Deformable registration is an essential tool in adaptive radiotherapy, as it accounts for anatomical changes during treatment. In recent years, research has focused on proposing different deformable registration algorithms and inter‐comparing their results in academic settings. We contend that finding an efficient method for quality assurance of deformable registration in clinical settings is crucial for a global acceptance of adaptive radiotherapy. This study proposes measures derived from computational fluid dynamics as a simple and efficient tool to quantify a displacement field. Method: Our aim was to develop quantitative metrics of registration quality designed for routine use that are algorithm‐independent, labor‐efficient, and accurately identify errors in a given displacement field. The quality assurance (QA) framework identifies unrealistic anatomical motion through vortexes in the displacement field as detected using the CURL operator and presented as a vortex map overlaid on the original anatomy for a quick identification of problematic regions. Regions of compression/expansion are identified through the determinant of the Jacobian matrix. The warp energy measure is proposed as a global measure of displacement field smoothness. Results: The new evaluation approach was tested on numerous inter and intra patient cases using both single and multi‐modality registration algorithms. The CURL operator quantitatively detected errors in the displacement field and identified problematic regions that were invisible to classical voxel‐based evaluation methods. Warping emerges above 1 indicated unrealistic displacement fields. Conclusions: The proposed QA framework for deformable image registration provides increased usability and accuracy in detecting unrealistic warping over classical registration assessment methods. It is computationally efficient and provides a valuable platform for the clinical acceptance of adaptive therapy in the future.
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