A system for detecting and describing pathological changes using dynamic perfusion computer tomography brain maps

This paper presents a novel method of detecting and describing pathological changes that can be visualized on dynamic computer tomography brain maps (perfusion CT). The system was tested on a set of dynamic perfusion computer tomography maps. Each set consisted of two perfusion maps (CBF, CBV and TTP for testing the irregularity detection algorithm) and one CT brain scan (for the registration algorithm) from 8 different patients with suspected strokes. In 36 of the 84 brain maps, abnormal perfusion was diagnosed. The results of the algorithm were compared with the findings of a team of two radiologists. All of the CBF and CBV maps that did not show a diagnosed asymmetry were classified correctly (i.e. no asymmetry was detected). In four of the TTP maps the algorithm found asymmetries, which were not classified as irregularities in the medical diagnosis; 84.5% of the maps were diagnosed correctly (85.7% for the CBF, 85.7% for the CBV and 82.1% for the TTP); 75% of the errors in the CBF maps and 100% of the errors in the CBV and the TTP maps were caused by the excessive detection of asymmetry regions. Errors in the CBFs and the CBVs were eliminated in cases in which the symmetry axis was selected manually. Subsequently, 96.4% of the CBF maps and 100% of the CBV maps were diagnosed correctly.

[1]  Joseph N. Wilson,et al.  Handbook of computer vision algorithms in image algebra , 1996 .

[2]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[3]  V Hachinski,et al.  Identification of Penumbra and Infarct in Acute Ischemic Stroke Using Computed Tomography Perfusion–Derived Blood Flow and Blood Volume Measurements , 2006, Stroke.

[4]  Tong San Koh,et al.  Cerebral perfusion mapping using a robust and efficient method for deconvolution analysis of dynamic contrast-enhanced images , 2006, NeuroImage.

[5]  Daniel Normolle,et al.  The prediction of radiation-induced liver dysfunction using a local dose and regional venous perfusion model. , 2007, Medical physics.

[6]  Max Mignotte,et al.  Detection of abnormal diffuse perfusion in SPECT using a normal brain atlas , 2003, SPIE Medical Imaging.

[7]  Marek R. Ogiela,et al.  Modern Computational Intelligence Methods for the Interpretation of Medical Images , 2008, Studies in Computational Intelligence.

[8]  Christopher Putman,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 , 2001, Stroke.

[9]  Aleksander Mendyk,et al.  Artificial neural networks (ANNs) as a tool of estimating relation between parameters of anti-oxidative ability and nutritional ingredients in the diet of pregnant women , 2006, Bio Algorithms Med Syst..

[10]  Tomasz Hachaj.  An algorithm for detecting lesions in CBF and CBV perfusion maps , 2008, Bio Algorithms Med Syst..

[11]  Haiying Liu,et al.  A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations , 2001, MICCAI.

[12]  Tomasz Hachaj.  The registration and atlas construction of noisy brain computer tomography images based on free form deformation technique , 2008, Bio Algorithms Med Syst..

[13]  W. Yuh,et al.  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. , 2003, Stroke.

[14]  Zbigniew Paszenda,et al.  Silesian University of Technology Faculty of Biomedical Engineering - Wide Offer for Future Biomedical Engineers , 2011, Bio Algorithms Med Syst..

[16]  John L. Semmlow,et al.  Biosignal and biomedical image processing : MATLAB-based applications , 2004 .

[17]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[18]  Marek R. Ogiela,et al.  Possible Uses of Information Technology (IT) in Anaesthesiology, Intensive Care and Emergency Medicine (eHealth). , 2006 .

[19]  Jean-Philippe Thiran,et al.  Prognostic accuracy of cerebral blood flow measurement by perfusion computed tomography, at the time of emergency room admission, in acute stroke patients , 2002, Annals of neurology.

[20]  Fumihiko Ino,et al.  A data distributed parallel algorithm for nonrigid image registration , 2005, Parallel Comput..

[21]  E. Halpern,et al.  Quantitative computed tomography perfusion of prostate cancer: correlation with whole-mount pathology. , 2005, Clinical prostate cancer.

[22]  Monika Piwowar,et al.  Laparoscopic colpopexy technique performed with laparoscopic supracervical hysterectomy, total laparoscopic hysterectomy, and following total abdominal hysterectomy , 2011, Bio Algorithms Med Syst..

[23]  Max Mignotte,et al.  Detection of diffuse abnormal perfusion in SPECT using a normal brain atlas , 2004, NeuroImage.

[24]  Oleg S. Pianykh,et al.  Body tumor CT perfusion protocols: optimization of acquisition scan parameters in a rat tumor model. , 2009, Radiology.

[25]  E. Ruíz,et al.  CT protocol for acute stroke: tips and tricks for general radiologists. , 2008, Radiographics : a review publication of the Radiological Society of North America, Inc.

[26]  M. Lev,et al.  Dynamic contrast-enhanced brain perfusion imaging: technique and clinical applications. , 2000, Seminars in ultrasound, CT, and MR.

[27]  Marek R. Ogiela,et al.  Medical Image Understanding Technology - Artificial Intelligence and Soft-Computing for Image Understanding , 2004, Studies in Fuzziness and Soft Computing.

[28]  Afra M. Wohlschläger,et al.  CT-perfusion imaging of the human brain: Advanced deconvolution analysis using circulant singular value decomposition , 2008, Comput. Medical Imaging Graph..

[29]  Sinan Batman,et al.  Morphological Methods for Biomedical Image Analysis , 2000 .

[30]  Usha Sinha,et al.  Principal component analysis for content-based image retrieval. , 2002, Radiographics : a review publication of the Radiological Society of North America, Inc.

[31]  Reto Meuli,et al.  CT perfusion scanning with deconvolution analysis: pilot study in patients with acute middle cerebral artery stroke. , 2002, Radiology.

[32]  B. Thompson,et al.  Cerebral perfusion CT: technique and clinical applications. , 2004, Radiology.

[33]  Marek R. Ogiela,et al.  Cognitive techniques in medical information systems , 2008, Comput. Biol. Medicine.

[34]  Hirobumi Oikawa,et al.  CT perfusion for acute stroke: Current concepts on technical aspects and clinical applications , 2006 .