Abstract
This chapter describes basis of brain perfusion computed tomography imaging (CTP) and computer based method for classification and visualization perfusion abnormalities. The solution proposed by author – perfusion abnormality detection measure and description (DMD) system – is consisted of the unified algorithm for detection of asymmetry in CBF and CBV perfusion maps, the image registration algorithm based on adaptation of a free form deformation model and the description / diagnosis algorithm. The DMD system was validated on set of 37 triplets of medical images acquired from 30 different adult patients (man and woman) with suspicious of ischemia / stroke. 77.0% of tested maps were rightly classified and the visible lesions were detected and described identically to radiologist diagnosis. In this chapter the author presents also portable augmented reality interface for visualization of medical data capable to render not only perfusion CT data but also volumetric images in real time that can be run on off – the – shelf computer.
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References
Aksoy, F., Lev, M.: Dynamic Contrast-Enhanced Brain Perfusion Imaging: Technique and Clinical Applications. Semin Ultrasound CT MR 21, 462–477 (2000)
Allchin, D.: Error Types. Perspectives on Science 9, 38–59 (2001)
Bardera, A., Boada, I., Feixas, M.: A Framework to Assist Acute Stroke Diagnosis, Vision, Modeling, and Visualization (VMV 2005), Erlangen (2005)
Bodzioch, S., Ogiela, M.R.: New Approach to Gallbladder Ultrasonic Images Analysis and Lesions Recognition. Computerized Medical Imaging and Graphics 33, 154–170 (2009)
Eastwood, J.D., et al.: CT perfusion scanning with deconvolution analysis: pilot study in patients with acute middle cerebral artery stroke. Radiology 222(1), 227–236 (2002)
Hachaj, T.: An algorithm for detecting lesions in CBF and CBV perfusion maps. Bio-Algorithms and Med-Systems / Collegium Medicum - Jagiellonian University (6) (2007)
Hachaj, T.: Artificial Intelligence Methods for Understanding Dynamic Computer Tomography Perfusion Maps. In: International Conference on Complex, Intelligent and Software Intensive Systems (cisis), pp. 866–871 (2010)
Hachajl, T.: The unified algorithm for detection potential lesions in dynamic perfusion maps CBF, CBV and TTP. Journal of Medical Informatics & Technologies 12 (2008)
Hachaj, T.: The registration and atlas construction of noisy brain computer tomography images based on free form deformation technique. Bio-Algorithms and Med-Systems, Collegium Medicum - Jagiellonian University (6) (2007)
Hachaj, T., Ogiela, M.R.: Automatic detection and lesion description in cerebral blood flow and cerebral blood volume perfusion maps. Journal of Signal Processing Systems for Signal, Image, and Video Technology 61, 317–328 (2010)
Haller, M., Billinghurst, M., Thomas, B.: Emerging Technologies of Augmented Reality: Interfaces and Design. Idea Group Publishing (2006)
Hoeffner, E.G., et al.: Cerebral Perfusion CT: Technique and Clinical Applications. Radiology 231(3), 632–644 (2004)
Koenig, M., Klotz, E., Heuser, L.: Perfusion CT in acute stroke: characterization of cerebral ischemia using parameter images of cerebral blood flow and their therapeutic relevance. Clinical experiences, Electromedica 66, 61–67 (1998)
Kalkofen, D., et al.: Integrated Medical Workflow for Augmented Reality Applications. In: International Workshop on Augmented environments for Medical Imaging and Computer-aided Surgery, AMI-ARCS (2006)
Kato, H., Billinghurst, M.: Marker Tracking and HMD Calibration for a video-based Augmented Reality Conferencing System. In: Proceedings of the 2nd International Workshop on Augmented Reality (IWAR 1999), pp. 85–94 (1999)
Koenig, M., Kraus, M., Theek, C., Klotz, E., Gehlen, W., Heuser, L.: Quantitative assessment of the ischemic brain by means of perfusion-related parameters derived from perfusion CT. Stroke; a Journal of Cerebral Circulation 32(2), 431–437 (2001)
Krüger, J., Westermann, R.: Acceleration Techniques for GPU-based Volume Rendering. IEEE Visualization (2003)
Latchaw, R.E., Yonas, H., Hunter, G.J.: Guidelines and recommendations for perfusion imaging in cerebral ischemia: a scientific statement for healthcare professionals by the writing group on perfusion imaging, from the Council on Cardiovascular Radiology of the American Heart Association. Stroke 34, 1084–1104 (2003)
Lev, M.H., et al.: Utility of Perfusion-Weighted CT Imaging in Acute Middle Cerebral Artery Stroke Treated With Intra-Arterial Thrombolysis: Prediction of Final Infarct Volume and Clinical Outcome. Stroke 32, 2021 (2001)
Lucas, E., Sánchez, E., et al.: CT Protocol for Acute Stroke: Tips and Tricks for General Radiologists. Radiographics 28(6), 1673–1687 (2008)
Marmulla, R., et al.: An augmented reality system for image-guided surgery. Medical Image Analysis 34 (2005)
Muir, K.W., Buchan, A., von Kummer, R., Rother, J., Baron, J.-C.: Imaging of acute stroke. Lancet Neurol. 5, 755–768 (2006)
Nowinski, W.L., et al.: Analysis of Ischemic Stroke MR Images by Means of Brain Atlases of Anatomy and Blood Supply Territories. Acad. Radiol. 13, 1025–1034 (2006)
Nowinski, W.L., et al.: Fast talairach transformation for magnetic resonance neuroimages. Journal of Computer Assisted Tomography 30 (2006)
Przelaskowski, A., Ostrek, G., Sklinda, K.: Ischemic stroke monitor as diagnosis assistant for CT examinations. Elektronika: konstrukcje, technologie, zastosowania 49, 104–114 (2008)
Przelaskowski, A., Ostrek, G., Sklinda, K., Walecki, J., Jóźwiak, R.: Stroke Slicer for CT-based Automatic Detection of Acute Ischemia. Computer Recognition Systems 3, 447–454 (2009)
Przelaskowski, A., Sklinda, K., Ostrek, G., Jóźwiak, R., Walecki, J.: Computer - Aided Diagnosis in Hyper-acute Ischemic Stroke. Progress in Neuroradiology, 69–78 (2009)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Transaction on Medical Imaging 18(8) (1999)
Sasaki, M., et al.: CT perfusion for acute stroke: Current concepts on technical aspects and clinical applications. International Congress Series 1290, 30–36 (2006)
Sasaki, M.: Joint Committee for the Procedure Guidelines for CT/MR Perfusion Imaging (2006), http://mr-proj2.umin.jp/data/guidelineCtpMrp2006-e.pdf
Thirion, J.-P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Medical Image Analysis 2(3), 243–260 (1998)
Thompson, P., Mega, M., Narr, K., Sowell, E., Blanton, R., Toga, A.: Brain image analysis and atlas construction. In: Handbook of Medical Imaging, SPIE. ch. 17, pp. 1066–1119 (2000)
Tietke, M., Riedel, C.: Whole brain perfusion CT imaging and CT angiography with a 64-channel. CT system Medica Mundi 52/1(07), 21–23 (2008)
Wang, H., et al.: Validation of an accelerated ’demons’ algorithm for deformable image registration in radiation therapy. Phys. Med. Biol. (2005)
Warfield, S., et al.: Advanced Nonrigid Registration Algorithms for Image Fusion, Brain Mapping: The Methods,, 2nd edn., pp. 661–690. Academic Press, San Diego (2002)
Wintermark, M., Reichhart, M., Thiran, J.P., et al.: Prognostic accuracy of cerebral blood flow measurement by perfusion computed tomography, at the time of emergency room admission, in acute stroke patients. Ann. Ann. Neurol. 51, 417–432 (2002)
Yang, G., Jiang, T.: Medical Imaging and Augmented Reality. In: Yang, G.-Z., Jiang, T.-Z. (eds.) MIAR 2004. LNCS, vol. 3150. Springer, Heidelberg (2004)
Young, I.T., Vliet, L.J.: Recursive implementation of the Gaussian filter. Signal Processing 44(2), 139–151 (1995)
Zierler, K.L.: Equations for measuring blood flow by external monitoring of radioisotopes. Circ. Res. 16, 309–321 (1965)
NyARTToolkit, CS home page, http://nyatla.jp/nyartoolkit/wiki/index.php?NyARToolkitCS
Brilliance Workspace for CT, MedicaMundi 50/3 (12) pp. 21-23 (2006)
Siemens, A.G.: Clinical Applications. Application Guide. In: Software Version syngo CT 2007, Siemens Medical (2006)
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Hachaj, T. (2012). Pattern Classification Methods for Analysis and Visualization of Brain Perfusion CT Maps. In: Ogiela, M., Jain, L. (eds) Computational Intelligence Paradigms in Advanced Pattern Classification. Studies in Computational Intelligence, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24049-2_8
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DOI: https://doi.org/10.1007/978-3-642-24049-2_8
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