Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast

Martel, Anne L.; Froh, Michael S.; Brock, Kristy K.; Plewes, Don B.; Barber, D C

doi:10.1088/0031-9155/52/13/010

Cited by 43 publications

(35 citation statements)

References 29 publications

(43 reference statements)

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“…Phantom measurements that mimic patient anatomy can be used to assess deformable registration accuracy in specific conditions, 31 , 45 , 48 but are time‐consuming and not well‐suited for routine evaluation, as algorithm settings are usually tailored to image quality, 49 , 50 levels of noise, (51) or partial volume effects, with estimations obtained on phantom studies overestimating accuracy if images of lesser quality are used as input. Furthermore, accuracy depends on parameter selection, which is closely associated with intensity gradients of the underlying image, (45) or are site‐specific.…”

Section: Discussionmentioning

confidence: 99%

A measure to evaluate deformable registration fields in clinical settings

Schreibmann

Pantalone

Waller

et al. 2012

J Applied Clin Med Phys

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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

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Section: Discussionmentioning

confidence: 99%

A measure to evaluate deformable registration fields in clinical settings

Schreibmann

Pantalone

Waller

et al. 2012

J Applied Clin Med Phys

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“…Each bilateral acquisition was obtained in 2 min and 48 s. Slice thickness was 2 to 3 mm. The screening study that produced the data used in this paper has been the subject of many other studies [14][15][16][17][18][19].…”

Section: Screening Studymentioning

confidence: 99%

“…This is performed to compensate for any patient motion that occurs during the examination which can obscure acquired lesion measurements and in extreme cases hide a small lesion completely. For this study, we used a three-dimensional non-rigid registration technique for magnetic resonance breast images that was applied globally to the breast examination [14], in order to help ensure that breast tissues are spatially aligned at each time point.…”

Section: Current Studymentioning

confidence: 99%

Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening

2014

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Cancer screening with magnetic resonance imaging (MRI) is currently recommended for very high risk women. The high variability in the diagnostic accuracy of radiologists analyzing screening MRI examinations of the breast is due, at least in part, to the large amounts of data acquired. This has motivated substantial research towards the development of computer-aided diagnosis (CAD) systems for breast MRI which can assist in the diagnostic process by acting as a second reader of the examinations. This retrospective study was performed on 184 benign and 49 malignant lesions detected in a prospective MRI screening study of high risk women at Sunnybrook Health Sciences Centre. A method for performing semi-automatic lesion segmentation based on a supervised learning formulation was compared with the enhancement threshold based segmentation method in the context of a computer-aided diagnostic system. The results demonstrate that the proposed method can assist in providing increased separation between malignant and radiologically suspicious benign lesions. Separation between malignant and benign lesions based on margin measures improved from a receiver operating characteristic (ROC) curve area of 0.63 to 0.73 when the proposed segmentation method was compared with the enhancement threshold, representing a statistically significant improvement. Separation between malignant and benign lesions based on dynamic measures improved from a ROC curve area of 0.75 to 0.79 when the proposed segmentation method was compared to the enhancement threshold, also representing a statistically significant improvement. The proposed method has potential as a component of a computeraided diagnostic system.

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“…Patient motion can obscure the analysis of suspicious lesions and in extreme cases, can completely hide a small tumor. For this study, we have used a three dimensional non-rigid registration technique for magnetic resonance breast images [21]. Registration was performed to align volumes acquired after contrast agent injection to the pre-contrast volume.…”

Section: Data Preprocessingmentioning

confidence: 99%

A Vector Machine Formulation with Application to the Computer-Aided Diagnosis of Breast Cancer from DCE-MRI Screening Examinations

2013

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This study investigates the use of a proposed vector machine formulation with application to dynamic contrastenhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiveroperating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application.

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Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast

Cited by 43 publications

References 29 publications

A measure to evaluate deformable registration fields in clinical settings

A measure to evaluate deformable registration fields in clinical settings

Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening

A Vector Machine Formulation with Application to the Computer-Aided Diagnosis of Breast Cancer from DCE-MRI Screening Examinations

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