Brain Image & Lesions Registration and 3D Reconstruction in DICOM MRI Images C.P. Loizou1, C. Papacharalambous1, G. Samaras1, E. Kyriakou2, T. Kasparis1, M. Patziaris3, E. Eracleous3, C.S. Pattichis4 1Cyprus University of Technology, Department of Electrical, Computer Engineering and Informatics 2Frederick University, Department of Computer Science, Limassol, Cyprus 3Ayios Therissos Medical Diagnostic Centre, Nicosia, Cyprus 4University of Cyprus, Department of Computer Science

Outline Introduction-Multiple sclerosis (MS), 3D Registration & Reconstruction Objective Materials & Methods Study group, Phantom and MRI Image Acquisition MRI image preprocessing and filtering Lesion Segmentation Registration & Reconstruction 3D Volume Estimation Evaluation Metrics & Statistical Analysis Results Discussion, Concluding Remarks, Future Directions

1 Introduction-MS, 3D Registration, Reconstruction MS: Refers to scars particularly in the white matter of the brain with or without neurological symptoms. [1]-[4] 3D Imaging systems follow up the development of the disease [1]-[4] [1] C.P. Loizou et al., Quantitative texture analysis…, J Neuroarad, 2015; [2] N. Chumchob, et al., ‘A robust affine image..”, Int. J. Numr. Anal. & Model., 2009; [3] C. Kumar, et al., “3D Reconstruction of brain tumor from 2D MRI’s … ”, Int. J. Advanced Research Electron. & Commun. Engin., 2014; [4] M. Arakeri, et al., “An effective and efficient approach to 3D… ”, Int. J. Signal Proces. & Image Proces., 2013.

2 Objective-Motivation Propose/evaluate a brain MRI registration and 3D reconstruction system for the 3D reconstruction of DICOM brain images and lesions from MS subjects. Validation on calibrated 3D MRI models and real DICOM MRI brain images and MS lesions. Motivation: Prevention is better than cure Individuals at risk can be identified

Flow diagram analysis of the proposed registration and 3D reconstruction method in DICOM brain MRI images. [1] C.P. Loizou et al., Quantitative texture analysis…, J Neuroarad, 2015

3.1 Materials & Methods-Study group & Dicom MRI Image Acquisition ACR MRI Phantom (17x17x10 mm rectangle grid, cylinders and other points of interest) T2-weigted MRI DICOM using the Phillips scanner Acquisitions at two different time points (T0 and T1) American College of Radiology (ACR) MRI Phantom [1] 56th slice of the ACR MRI phantom Phillips Achieva MRI scanner 3.0 T [1] American College of Radiology. Phantom Test Guidance, 2005, http://www.acr.org/~/media/ACR/Documents/Accreditation/MRI/LargePhantomGuidance.pdf

3.2 Materials & Methods-MRI image preprocessing, filtering, lesion segmentation Image histogram equalisation prior reconstruction [2] Wiener filtering [3] Manual segmentation of lesions by an MS neurologist and confirmed by another radiologist [2] C. P. Loizou, S. Petroudi, I. Seimenis, M. Pantziaris, C.S. Pattichis, “Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome,” J. Neuroradiol., vol. 42, no. 2, pp. 99-114, 2015. [3] M. Martin-Fernandez, et al., “Sequential anisotropic Wiener filtering applied to 3D MRI data,” Magn. Res. Imag., vol. 25, no. 2, pp. 278-292, 2007.

3.3 Materials & Methods-MRI Registration, 3D Reconstruction and volume estimation Time points T0 and T1 were registered using a non-rigid image registration method [8] Generate a 3D volume using iso-surface rendering [9], [10] MS lesions were also registered and reconstructed similarly Volume estimation [9] [8] J. P. Thirion, “Image matching as a diffusion process: … ,” Medic. Image Anal., vol. 2, no. 3, pp. 243–260, 1998. [9] R. M. Sherekar, A. Pawar, “A MATLAB image processing approach …..”, Americ. J. Mechan. Engin. and Autom., vol. 1, no. 5, pp. 48-53, 2014. [10] C. Koniaris, et al., “Survey of Texture mapping techniques ….”, J. Computer Graphics Techniques, vol. 3, no. 2, pp. 18-60, 2014.

3.4 Materials & Methods-Evaluation Metrics Following evaluation metrics were used to assess the proposed registration and 3D reconstruction methods [1]: True positive rate (TPR), True-negative rate (TNR), False positive rate (FPR), False negative rate (FNR), Accuracy (ACC%), Mean square error (MSE), Correlation coefficient (ρ), CXentroid (C), Perimeter (P) and volume (V). [1] C.P. Loizou et al., Quantitative texture analysis…, J Neuroarad, 2015

4.1 Results – Phantom and MRI Registration

4.2 Results – Statistical Evaluation 1

4.3 Results – Statistical Evaluation 2

5. Discussion No other studies found in the literature for 3D MRI brain registration & reconstruction on DICOM images 3D Slices and 3D lesions accurately reconstructed and inserted to the brain volume Integrated software and may be used to detect, extract and follow-up MS lesion in MS subjects in longitudinal studies, Analyzing the evolution of MS and their impact on surrounding structures. Used for treatment planning, therapeutically monitoring, surgery and pathological brain modelling.

5.1 Conclusion and future directions The evaluation showed that the proposed method may also be reliably used in the registration and 3D reconstruction of brain MRI images. However, further work in a larger number of images, as well multiple observers’ evaluation is needed for further validating the proposed method for the clinical practice.

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