Gabor Opposits1, Sándor A Kis2, Lajos Trón2, Ervin Berényi3, Endre Takács4, József G Dobai5, László Bognár5, Bernadett Szűcs6, Miklós Emri2. 1. Department of Nuclear Medicine, Faculty of Medicine, University of Debrecen, Hungary. Electronic address: ogabor@pet.dote.hu. 2. Department of Nuclear Medicine, Faculty of Medicine, University of Debrecen, Hungary. 3. Department of Biomedical Laboratory and Imaging Science, Faculty of Medicine, University of Debrecen, Hungary. 4. Rotating Gamma Ltd., Hungary. 5. Department of Neurosurgery, Medical Center, University of Debrecen, Hungary. 6. ScanoMed Ltd., Hungary.
Abstract
BACKGROUND: Clinical practice often requires simultaneous information obtained by two different imaging modalities. Registration algorithms are commonly used for this purpose. Automated procedures are very helpful in cases when the same kind of registration has to be performed on images of a high number of subjects. Radiotherapists would prefer to use the best automated method to assist therapy planning, however there are not accepted procedures for ranking the different registration algorithms. PURPOSE: We were interested in developing a method to measure the population level performance of CT-MRI registration algorithms by a parameter of values in the [0,1] interval. MATERIALS AND METHODS: Pairs of CT and MRI images were collected from 1051 subjects. Results of an automated registration were corrected manually until a radiologist and a neurosurgeon expert both accepted the result as good. This way 1051 registered MRI images were produced by the same pair of experts to be used as gold standards for the evaluation of the performance of other registration algorithms. Pearson correlation coefficient, mutual information, normalized mutual information, Kullback-Leibler divergence, L1 norm and square L2 norm (dis)similarity measures were tested for sensitivity to indicate the extent of (dis)similarity of a pair of individual mismatched images. RESULTS: The square Hellinger distance proved suitable to grade the performance of registration algorithms at population level providing the developers with a valuable tool to rank algorithms. CONCLUSIONS: The developed procedure provides an objective method to find the registration algorithm performing the best on the population level out of newly constructed or available preselected ones.
BACKGROUND: Clinical practice often requires simultaneous information obtained by two different imaging modalities. Registration algorithms are commonly used for this purpose. Automated procedures are very helpful in cases when the same kind of registration has to be performed on images of a high number of subjects. Radiotherapists would prefer to use the best automated method to assist therapy planning, however there are not accepted procedures for ranking the different registration algorithms. PURPOSE: We were interested in developing a method to measure the population level performance of CT-MRI registration algorithms by a parameter of values in the [0,1] interval. MATERIALS AND METHODS: Pairs of CT and MRI images were collected from 1051 subjects. Results of an automated registration were corrected manually until a radiologist and a neurosurgeon expert both accepted the result as good. This way 1051 registered MRI images were produced by the same pair of experts to be used as gold standards for the evaluation of the performance of other registration algorithms. Pearson correlation coefficient, mutual information, normalized mutual information, Kullback-Leibler divergence, L1 norm and square L2 norm (dis)similarity measures were tested for sensitivity to indicate the extent of (dis)similarity of a pair of individual mismatched images. RESULTS: The square Hellinger distance proved suitable to grade the performance of registration algorithms at population level providing the developers with a valuable tool to rank algorithms. CONCLUSIONS: The developed procedure provides an objective method to find the registration algorithm performing the best on the population level out of newly constructed or available preselected ones.
Authors: Gábor Opposits; Marianna Nagy; Zoltán Barta; Csaba Aranyi; Dániel Szabó; Attila Makai; Imre Varga; László Galuska; Lajos Trón; László Balkay; Miklós Emri Journal: EJNMMI Res Date: 2021-07-26 Impact factor: 3.138