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Pre-processing Methods on Three Dimensional T1 Weighted Structural Magnetic Resonance Images

Yıl 2020, Sayı: 19, 227 - 240, 31.08.2020
https://doi.org/10.31590/ejosat.719062

Öz

T1 weighted three-dimensional structural magnetic resonance imaging is an imaging technique that enables high resolution imaging of tissue defects and volumetric losses in the brain due to diseases. With this imaging technique, images can be taken without any external intervention to the patient . Radio frequency technology is at the basis of physical image acquisition. Firstly, a radio frequency wave is sent in which the protons in the hydrogen atoms in the brain will interact. When the radio frequency wave is stopped, protons tend to return to their former state. While returning to their former state, the energy their emitted is collected and induced as a current, then the image is obtained by Fourier transforms. Images can be taken in different sequences upon request. Each sequence has different distinctive features in the clinic with respect to the disease. Magnetic resonance images consist of successive slices. The disease can be observed in any slice, or it can be seen by analyzing several slices in succession. Among the magnetic resonance image sequences, the most used images are in 3D T1-weighted images. Since soft brain tissue can be displayed in high resolution in this sequence, many rigid changes such as volumetric disorders, degeneration, symmetry disruption, tissue disruption, brain shrinkage and enlargement can be clearly observed. The images obtained are analyzed and interpreted by radiologists in hospitals. However, some numerical tools are needed especially in artificial intelligence and classification studies. In order to use these numerical tools, some preprocessing must be done on the images. In this study, axis conversion, image reorientation, normalization, modulation, segmentation, co-registration, noise and bias removal, smoothing, removal of non-brain structures are examined, which are the preprocessing methods of T1-weighted three-dimensional structural magnetic resonance images. How and in which order to use the numerical tools used for pre-processing has been defined and their applications are made on a three-dimensional magnetic resonance image.

Kaynakça

  • Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., & Moriarty, T. (2002). A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE transactions on medical imaging, 21(3), 193-199.
  • Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95-113.
  • Ashburner, J., Barnes, G., Chen, C., Daunizeau, J., Flandin, G., Friston, K., . . . Litvak, V. (2008). SPM8 manual. Functional Imaging Laboratory, Institute of Neurology, 41.
  • Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. Neuroimage, 11(6), 805-821.
  • Ashburner, J., & Friston, K. J. (2005). Unified segmentation. Neuroimage, 26(3), 839-851.
  • Brett, M., Christoff, K., Cusack, R., & Lancaster, J. J. N. (2001). Using the Talairach atlas with the MNI template. 13(6), 85-85.
  • Brett, M., Johnsrude, I. S., & Owen, A. M. (2002). OPINION: The problem of functional localization in the human brain. Nature reviews. Neuroscience, 3(3), 243.
  • Dağdeviren, Z. A. (2012). Hastaların Yapısal MR Görüntülerinin MNI görüntü Uzayına Kayıtlanması. (Yüksek Lisans Tezi), Ege Üniversitesi Fen Bilimleri Enstitüsü, İzmir.
  • Davis, E., & Loh, E. (2011). Methods for Dummies: Coregistration and Spatial Normalization. Retrieved from http://www.fil.ion.ucl.ac.uk/mfd_archive/2011/page1/mfd2011_coregistration.pptx
  • Evans, A. C., Collins, D. L., Mills, S., Brown, E., Kelly, R., & Peters, T. M. (1993). 3D statistical neuroanatomical models from 305 MRI volumes. Paper presented at the Nuclear Science Symposium and Medical Imaging Conference, 1993., 1993 IEEE Conference Record.
  • Evans, A. C., Collins, D. L., & Milner, B. (1992). An MRI-based stereotactic atlas from 250 young normal subjects. Paper presented at the Soc. neurosci. abstr.
  • Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
  • Goto, M., Abe, O., Aoki, S., Hayashi, N., Miyati, T., Takao, H., . . . Mori, H. (2013). Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects. Neuroradiology, 55(7), 869-875.
  • Gudbjartsson, H., & Patz, S. (1995). The Rician distribution of noisy MRI data. Magnetic resonance in medicine, 34(6), 910-914.
  • Gülay, E., & İçer, S. (2020). Evaluation of Lung Size in Patients with Pneumonia and Healthy Individuals. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 304-309.
  • Hemanth, D. J., & Anitha, J. (2012). Image Pre-processing and Feature Extraction Techniques for Magnetic Resonance Brain Image Analysis. In Computer Applications for Communication, Networking, and Digital Contents (pp. 349-356): Springer.
  • Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62(2), 782-790.
  • Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., . . . Hellier, P. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage, 46(3), 786-802.
  • Kurth, F., Gaser, C., & Luders, E. (2015). A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM). Nature Protocols, 10(2), 293-304.
  • Kurth, F., Luders, E., & Gaser, C. (2010). VBM8 toolbox manual. Jena: University of Jena.
  • Lancaster, J. L., & Fox, P. T. (2000). Talairach space as a tool for intersubject standardization in the brain. Paper presented at the Handbook of medical imaging.
  • Liu, Y., Collins, R. T., & Rothfus, W. E. (2001). Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images. IEEE transactions on medical imaging, 20(3), 175-192.
  • Manjón, J. V., Carbonell-Caballero, J., Lull, J. J., García-Martí, G., Martí-Bonmatí, L., & Robles, M. (2008). MRI denoising using non-local means. Medical image analysis, 12(4), 514-523.
  • Manjón, J. V., Coupé, P., Buades, A., Collins, D. L., & Robles, M. (2012). New methods for MRI denoising based on sparseness and self-similarity. Medical image analysis, 16(1), 18-27.
  • Manjón, J. V., Coupé, P., Martí‐Bonmatí, L., Collins, D. L., & Robles, M. (2010). Adaptive non‐local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203.
  • Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience, 19(9), 1498-1507.
  • Mechelli, A., Price, C. J., Friston, K. J., & Ashburner, J. (2005). Voxel-based morphometry of the human brain: methods and applications. Current medical imaging reviews, 1(2), 105-113.
  • Özel, P. (2020). A Decision Support System to Assess the Masses in Breast Tissue using Classification Algorithms. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 114-119.
  • Öziç, M. Ü. (2018). 3B Alzheimer MR Görüntülerinin Sınıflandırılmasında Yeni Yaklaşımlar. (Doktora Tezi), Selçuk Üniversitesi, Fen Bilimleri Enstitüsü.
  • Öziç, M. Ü., & Özşen, S. (2020). Comparison Global Brain Volume Ratios on Alzheimer’s Disease Using 3D T1 Weighted MR Images. Avrupa Bilim ve Teknoloji Dergisi, (18), 599-606.
  • Patil, S., & Udupi, V. (2012). Preprocessing to be considered for MR and CT images containing tumors. IOSR journal of electrical and electronics engineering, 1(4), 54-57.
  • Rajapakse, J. C., Giedd, J. N., & Rapoport, J. L. (1997). Statistical approach to segmentation of single-channel cerebral MR images. IEEE transactions on medical imaging, 16(2), 176-186.
  • Ridgway, G. (2008). Voxel-Based Morphometry with Unified Segmentation Retrieved from http://www.fil.ion.ucl.ac.uk/spm/course/slides08-zurich/Ged_seg_vbm.ppt
  • Ridgway, G. (2010a). Spatial Preprocessing. Retrieved from http://www.fil.ion.ucl.ac.uk/spm/course/slides10-vancouver/01_Spatial_Preprocessing.pdf
  • Ridgway, G. (2010b). SPM Course: Voxel Based Morphometry Practical Retrieved from http://www0.cs.ucl.ac.uk/staff/g.ridgway/zurich/vbm_practical_zurich2010.ppt
  • Rorden, C. (2005). MRIcro. Retrieved from https://people.cas.sc.edu/rorden/mricro/mricro.html
  • Şenvardar, E. K. (2011). Nörogörüntülemede SPM Uygulamaları: İşlevsel MR İmgelerinde Uzaysal Önişleme. Retrieved from http://www.bad.org.tr/usk10/spmonesin.pdf
  • Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain. 3-Dimensional proportional system: an approach to cerebral imaging.
  • Tohka, J., Zijdenbos, A., & Evans, A. (2004). Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage, 23(1), 84-97.
  • Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: improved N3 bias correction. IEEE transactions on medical imaging, 29(6), 1310-1320.
  • UCL. (2020). SPM8. Retrieved from https://www.fil.ion.ucl.ac.uk/spm/software/spm8/
  • Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999). Automated model-based bias field correction of MR images of the brain. IEEE transactions on medical imaging, 18(10), 885-896.
  • Varol, A. B., & İşeri, İ. (2019). Lenf Kanserine İlişkin Patoloji Görüntülerinin Makine Öğrenimi Yöntemleri ile Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 404-410.

Üç Boyutlu T1 Ağırlıklı Manyetik Rezonans Görüntülerinde Ön İşleme Yöntemleri

Yıl 2020, Sayı: 19, 227 - 240, 31.08.2020
https://doi.org/10.31590/ejosat.719062

Öz

T1 ağırlıklı üç boyutlu yapısal manyetik rezonans görüntüleme, hastalıklardan dolayı beyinde meydana gelen doku bozuklukları ve hacimsel kayıpların yüksek çözünürlükte görüntülenmesini sağlayan bir görüntüleme tekniğidir. Bu görüntüleme tekniği ile hastaya dışarıdan herhangi bir müdahale yapılmadan görüntüler alınabilmektedir. Fiziksel olarak görüntü alımının temelinde radyo frekans teknolojisi bulunmaktadır. Öncelikle beyinde bulunan hidrojen atomlarındaki protonların etkileşime gireceği bir radyo frekans dalga gönderilir. Radyo frekans dalgası durdurulduğunda protonlar eski durumlarına geri dönmek eğilimindedir. Eski durumlarına dönerken, yaydıkları enerji bir akım olarak toplanır ve indüklenir, daha sonra görüntü Fourier dönüşümleri ile elde edilir. Görüntüler isteğe göre farklı sekanslarda alınabilir. Her bir sekansın hastalığa göre klinikte farklı ayırt edici özellikleri bulunmaktadır. Manyetik rezonans görüntüleri birbirini takip eden kesitlerden oluşur. Hastalık herhangi bir kesitte gözlemlenebileceği gibi birbirini takip eden birkaç kesitin beraber analiz edilmesi ile de görülebilmektedir. Manyetik rezonans görüntü sekansları içerisinde en çok kullanılan görüntüler üç boyutlu T1 ağırlıklı görüntülerdedir. Bu sekansta yumuşak beyin dokusu yüksek çözünürlükte görüntülenebildiği için hacimsel bozukluklar, dejenerasyon, simetri bozulması, doku bozulması, beyin küçülmesi ve büyümesi gibi birçok katı değişiklikler net bir şekilde izlenebilmektedir. Elde edilen görüntüler hastanelerde radyologlar tarafından analiz edilerek yorumlanmaktadır. Ancak özellikle yapay zeka ve sınıflandırma çalışmalarında birtakım sayısal araçlara ihtiyaç duyulmaktadır. Bu sayısal araçların kullanılabilmesi için görüntüler üzerinde bazı ön işlemelerin yapılması gerekmektedir. Bu çalışmada T1 ağırlıklı üç boyutlu yapısal manyetik rezonans görüntülerinin ön işleme yöntemlerinden olan eksen dönüştürme, görüntü reoryantasyonu, normalizasyon, modülasyon, segmentasyon, birlikte çakıştırma, gürültü ve bias giderme, yumuşatma, beyin dışı yapıların giderilmesi incelenmiştir. Ön işleme için kullanılan sayısal araçların nasıl ve hangi sırada kullanılacağı tanımlanmış ve üç boyutlu bir manyetik rezonans görüntü üzerinde uygulamaları yapılmıştır.

Kaynakça

  • Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., & Moriarty, T. (2002). A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE transactions on medical imaging, 21(3), 193-199.
  • Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95-113.
  • Ashburner, J., Barnes, G., Chen, C., Daunizeau, J., Flandin, G., Friston, K., . . . Litvak, V. (2008). SPM8 manual. Functional Imaging Laboratory, Institute of Neurology, 41.
  • Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. Neuroimage, 11(6), 805-821.
  • Ashburner, J., & Friston, K. J. (2005). Unified segmentation. Neuroimage, 26(3), 839-851.
  • Brett, M., Christoff, K., Cusack, R., & Lancaster, J. J. N. (2001). Using the Talairach atlas with the MNI template. 13(6), 85-85.
  • Brett, M., Johnsrude, I. S., & Owen, A. M. (2002). OPINION: The problem of functional localization in the human brain. Nature reviews. Neuroscience, 3(3), 243.
  • Dağdeviren, Z. A. (2012). Hastaların Yapısal MR Görüntülerinin MNI görüntü Uzayına Kayıtlanması. (Yüksek Lisans Tezi), Ege Üniversitesi Fen Bilimleri Enstitüsü, İzmir.
  • Davis, E., & Loh, E. (2011). Methods for Dummies: Coregistration and Spatial Normalization. Retrieved from http://www.fil.ion.ucl.ac.uk/mfd_archive/2011/page1/mfd2011_coregistration.pptx
  • Evans, A. C., Collins, D. L., Mills, S., Brown, E., Kelly, R., & Peters, T. M. (1993). 3D statistical neuroanatomical models from 305 MRI volumes. Paper presented at the Nuclear Science Symposium and Medical Imaging Conference, 1993., 1993 IEEE Conference Record.
  • Evans, A. C., Collins, D. L., & Milner, B. (1992). An MRI-based stereotactic atlas from 250 young normal subjects. Paper presented at the Soc. neurosci. abstr.
  • Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
  • Goto, M., Abe, O., Aoki, S., Hayashi, N., Miyati, T., Takao, H., . . . Mori, H. (2013). Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects. Neuroradiology, 55(7), 869-875.
  • Gudbjartsson, H., & Patz, S. (1995). The Rician distribution of noisy MRI data. Magnetic resonance in medicine, 34(6), 910-914.
  • Gülay, E., & İçer, S. (2020). Evaluation of Lung Size in Patients with Pneumonia and Healthy Individuals. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 304-309.
  • Hemanth, D. J., & Anitha, J. (2012). Image Pre-processing and Feature Extraction Techniques for Magnetic Resonance Brain Image Analysis. In Computer Applications for Communication, Networking, and Digital Contents (pp. 349-356): Springer.
  • Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62(2), 782-790.
  • Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., . . . Hellier, P. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage, 46(3), 786-802.
  • Kurth, F., Gaser, C., & Luders, E. (2015). A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM). Nature Protocols, 10(2), 293-304.
  • Kurth, F., Luders, E., & Gaser, C. (2010). VBM8 toolbox manual. Jena: University of Jena.
  • Lancaster, J. L., & Fox, P. T. (2000). Talairach space as a tool for intersubject standardization in the brain. Paper presented at the Handbook of medical imaging.
  • Liu, Y., Collins, R. T., & Rothfus, W. E. (2001). Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images. IEEE transactions on medical imaging, 20(3), 175-192.
  • Manjón, J. V., Carbonell-Caballero, J., Lull, J. J., García-Martí, G., Martí-Bonmatí, L., & Robles, M. (2008). MRI denoising using non-local means. Medical image analysis, 12(4), 514-523.
  • Manjón, J. V., Coupé, P., Buades, A., Collins, D. L., & Robles, M. (2012). New methods for MRI denoising based on sparseness and self-similarity. Medical image analysis, 16(1), 18-27.
  • Manjón, J. V., Coupé, P., Martí‐Bonmatí, L., Collins, D. L., & Robles, M. (2010). Adaptive non‐local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203.
  • Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience, 19(9), 1498-1507.
  • Mechelli, A., Price, C. J., Friston, K. J., & Ashburner, J. (2005). Voxel-based morphometry of the human brain: methods and applications. Current medical imaging reviews, 1(2), 105-113.
  • Özel, P. (2020). A Decision Support System to Assess the Masses in Breast Tissue using Classification Algorithms. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 114-119.
  • Öziç, M. Ü. (2018). 3B Alzheimer MR Görüntülerinin Sınıflandırılmasında Yeni Yaklaşımlar. (Doktora Tezi), Selçuk Üniversitesi, Fen Bilimleri Enstitüsü.
  • Öziç, M. Ü., & Özşen, S. (2020). Comparison Global Brain Volume Ratios on Alzheimer’s Disease Using 3D T1 Weighted MR Images. Avrupa Bilim ve Teknoloji Dergisi, (18), 599-606.
  • Patil, S., & Udupi, V. (2012). Preprocessing to be considered for MR and CT images containing tumors. IOSR journal of electrical and electronics engineering, 1(4), 54-57.
  • Rajapakse, J. C., Giedd, J. N., & Rapoport, J. L. (1997). Statistical approach to segmentation of single-channel cerebral MR images. IEEE transactions on medical imaging, 16(2), 176-186.
  • Ridgway, G. (2008). Voxel-Based Morphometry with Unified Segmentation Retrieved from http://www.fil.ion.ucl.ac.uk/spm/course/slides08-zurich/Ged_seg_vbm.ppt
  • Ridgway, G. (2010a). Spatial Preprocessing. Retrieved from http://www.fil.ion.ucl.ac.uk/spm/course/slides10-vancouver/01_Spatial_Preprocessing.pdf
  • Ridgway, G. (2010b). SPM Course: Voxel Based Morphometry Practical Retrieved from http://www0.cs.ucl.ac.uk/staff/g.ridgway/zurich/vbm_practical_zurich2010.ppt
  • Rorden, C. (2005). MRIcro. Retrieved from https://people.cas.sc.edu/rorden/mricro/mricro.html
  • Şenvardar, E. K. (2011). Nörogörüntülemede SPM Uygulamaları: İşlevsel MR İmgelerinde Uzaysal Önişleme. Retrieved from http://www.bad.org.tr/usk10/spmonesin.pdf
  • Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain. 3-Dimensional proportional system: an approach to cerebral imaging.
  • Tohka, J., Zijdenbos, A., & Evans, A. (2004). Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage, 23(1), 84-97.
  • Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: improved N3 bias correction. IEEE transactions on medical imaging, 29(6), 1310-1320.
  • UCL. (2020). SPM8. Retrieved from https://www.fil.ion.ucl.ac.uk/spm/software/spm8/
  • Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999). Automated model-based bias field correction of MR images of the brain. IEEE transactions on medical imaging, 18(10), 885-896.
  • Varol, A. B., & İşeri, İ. (2019). Lenf Kanserine İlişkin Patoloji Görüntülerinin Makine Öğrenimi Yöntemleri ile Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 404-410.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Muhammet Üsame Öziç 0000-0002-3037-2687

Seral Özşen 0000-0001-5332-8665

Yayımlanma Tarihi 31 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Sayı: 19

Kaynak Göster

APA Öziç, M. Ü., & Özşen, S. (2020). Üç Boyutlu T1 Ağırlıklı Manyetik Rezonans Görüntülerinde Ön İşleme Yöntemleri. Avrupa Bilim Ve Teknoloji Dergisi(19), 227-240. https://doi.org/10.31590/ejosat.719062