Program Information
A Novel 3D Registration Method for Multiparametric Radiological Images
A Akhbardeh1 , VS Parekth2 , MA Jacobs3*, (1) The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, (2) Department of Computer Science,The Johns Hopkins University, Baltimore, MD, (3) The Russell H. Morgan Department of Radiology and Radiological Sciences and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Sparks, MD
Presentations
TU-CD-BRA-1 (Tuesday, July 14, 2015) 10:15 AM - 12:15 PM Room: Ballroom A
Purpose: Multiparametric and multimodality radiological imaging methods, such as, magnetic resonance imaging(MRI), computed tomography(CT), and positron emission tomography(PET), provide multiple types of tissue contrast and anatomical information for clinical diagnosis. However, these radiological modalities are acquired using very different technical parameters, e.g.,field of view(FOV), matrix size, and scan planes, which, can lead to challenges in registering the different data sets. Therefore, we developed a hybrid registration method based on 3D wavelet transformation and 3D interpolations that performs 3D resampling and rotation of the target radiological images without loss of information
Methods: T1-weighted, T2-weighted, diffusion-weighted-imaging(DWI), dynamic-contrast-enhanced(DCE) MRI and PET/CT were used in the registration algorithm from breast and prostate data at 3T MRI and multimodality(PET/CT) cases. The hybrid registration scheme consists of several steps to reslice and match each modality using a combination of 3D wavelets, interpolations, and affine registration steps. First, orthogonal reslicing is performed to equalize FOV, matrix sizes and the number of slices using wavelet transformation. Second, angular resampling of the target data is performed to match the reference data. Finally, using optimized angles from resampling, 3D registration is performed using similarity transformation(scaling and translation) between the reference and resliced target volume is performed. After registration, the mean-square-error(MSE) and Dice Similarity(DS) between the reference and registered target volumes were calculated.
Results: The 3D registration method registered synthetic and clinical data with significant improvement(p<0.05) of overlap between anatomical structures. After transforming and deforming the synthetic data, the MSE and Dice similarity were 0.12 and 0.99. The average improvement of the MSE in breast was 62%(0.27 to 0.10) and prostate was 63%(0.13 to 0.04;p<0.05). The Dice similarity was in breast 8%(0.91 to 0.99) and for prostate was 89%(0.01 to 0.90;p<0.05)
Conclusion:Our 3D wavelet hybrid registration approach registered diverse breast and prostate data of different radiological images(MR/PET/CT) with a high accuracy.
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