PURPOSE: To demonstrate the efficacy of an automated three-dimensional (3D) template matching-based algorithm in detecting brain metastases on conventional MR scans and the potential of our algorithm to be developed into a computer-aided detection tool that will allow radiologists to maintain a high level of detection sensitivity while reducing image reading time. MATERIALS AND METHODS: Spherical tumor appearance models were created to match the expected geometry of brain metastases while accounting for partial volume effects and offsets due to the cut of MRI sampling planes. A 3D normalized cross-correlation coefficient was calculated between the brain volume and spherical templates of varying radii using a fast frequency domain algorithm to identify likely positions of brain metastases. RESULTS: Algorithm parameters were optimized on training datasets, and then data were collected on 22 patient datasets containing 79 total brain metastases producing a sensitivity of 89.9% with a false positive rate of 0.22 per image slice when restricted to the brain mass. CONCLUSION: Study results demonstrate that the 3D template matching-based method can be an effective, fast, and accurate approach that could serve as a useful tool for assisting radiologists in providing earlier and more definitive diagnoses of metastases within the brain. (c) 2009 Wiley-Liss, Inc.
PURPOSE: To demonstrate the efficacy of an automated three-dimensional (3D) template matching-based algorithm in detecting brain metastases on conventional MR scans and the potential of our algorithm to be developed into a computer-aided detection tool that will allow radiologists to maintain a high level of detection sensitivity while reducing image reading time. MATERIALS AND METHODS: Spherical tumor appearance models were created to match the expected geometry of brain metastases while accounting for partial volume effects and offsets due to the cut of MRI sampling planes. A 3D normalized cross-correlation coefficient was calculated between the brain volume and spherical templates of varying radii using a fast frequency domain algorithm to identify likely positions of brain metastases. RESULTS: Algorithm parameters were optimized on training datasets, and then data were collected on 22 patient datasets containing 79 total brain metastases producing a sensitivity of 89.9% with a false positive rate of 0.22 per image slice when restricted to the brain mass. CONCLUSION: Study results demonstrate that the 3D template matching-based method can be an effective, fast, and accurate approach that could serve as a useful tool for assisting radiologists in providing earlier and more definitive diagnoses of metastases within the brain. (c) 2009 Wiley-Liss, Inc.
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Authors: M Zhou; J Scott; B Chaudhury; L Hall; D Goldgof; K W Yeom; M Iv; Y Ou; J Kalpathy-Cramer; S Napel; R Gillies; O Gevaert; R Gatenby Journal: AJNR Am J Neuroradiol Date: 2017-10-05 Impact factor: 3.825
Authors: Jeffrey D Rudie; Andreas M Rauschecker; R Nick Bryan; Christos Davatzikos; Suyash Mohan Journal: Radiology Date: 2019-01-22 Impact factor: 11.105
Authors: Jeffrey D Rudie; David A Weiss; John B Colby; Andreas M Rauschecker; Benjamin Laguna; Steve Braunstein; Leo P Sugrue; Christopher P Hess; Javier E Villanueva-Meyer Journal: Radiol Artif Intell Date: 2021-03-10