A Novel mCAD for pediatric metabolic brain diseases incorporating DW imaging and MR spectroscopy

With the increase in the number of identified rare diseases and the intricacy involved in diagnosis, as exemplified by metabolic brain diseases, the need for computerized diagnostic systems is inevitable. We propose a pilot computer-assisted medical decision support system mCAD which tries to identify and further categorize these diseases, utilizing the information available from magnetic resonance spectroscopy MRS and diffusion-weighted imaging DWI. In this study, we have utilized wavelets, fuzzy relational classifiers and a collection of signal/image processing routines to extract and to classify disease features. The combined MRS+ DWI system achieved a sensitivity Se and positive predictivity PP of 65.00% and 72.22%, respectively, in detecting seven categories of metabolic brain diseases. The combined MRS+ DWI system exhibits a 10% and 3.47% increase in Se and PP, respectively, in comparison to the system using only DWI information. It also increases the Se and PP of the system using only the MRS information by 15% and 22.22%, respectively.

[1]  Richard L. Robertson,et al.  Diffusion-weighted imaging of the brain in infants and children , 2007, Pediatric Radiology.

[2]  Javad Alirezaie,et al.  Wavelets and fuzzy relational classifiers: A novel spectroscopy analysis system for pediatric metabolic brain diseases , 2010, Fuzzy Sets Syst..

[3]  Rangaraj M. Rangayyan,et al.  Feature identification in the time-frequency plane by using the Hough-Radon transform , 2001, Pattern Recognit..

[4]  Mehmet Rahmi Canal,et al.  Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals , 2010, Journal of Medical Systems.

[5]  Jonathan M. Garibaldi,et al.  Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making , 2007, IEEE Transactions on Fuzzy Systems.

[6]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[7]  Pierre Alusta,et al.  Improving proton MR spectroscopy of brain tissue for noninvasive diagnostics , 2010, Journal of magnetic resonance imaging : JMRI.

[8]  Abdel-Badeeh M. Salem,et al.  Hybrid intelligent techniques for MRI brain images classification , 2010, Digit. Signal Process..

[9]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients , 2005, Journal of Neuroscience Methods.

[10]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[11]  N. Ayache,et al.  Combined Diffusion Imaging and MR Spectroscopy in the Diagnosis of Human Prion Diseases , 2010, American Journal of Neuroradiology.

[12]  Derek A. Linkens,et al.  Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms: Part I. Classification of depth of anaesthesia and development of a patient model , 2005, Artif. Intell. Medicine.

[13]  Michael J. Martinez,et al.  Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template , 2007, Human brain mapping.

[14]  Gleb Beliakov,et al.  Appropriate choice of aggregation operators in fuzzy decision support systems , 2001, IEEE Trans. Fuzzy Syst..

[15]  S. Van Huffel,et al.  MRS signal quantitation: a review of time- and frequency-domain methods. , 2008, Journal of magnetic resonance.

[16]  Richard Wagner.  An overview of computer-assisted diagnostic software , 2005, Metabolic Brain Disease.

[17]  J. Valk,et al.  Magnetic Resonance of Myelination and Myelin Disorders , 1989 .

[18]  Ujjwal Maulik,et al.  A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification , 2005, Fuzzy Sets Syst..

[19]  Ludmil Mikhailov,et al.  An interpretable fuzzy rule-based classification methodology for medical diagnosis , 2009, Artif. Intell. Medicine.

[20]  Ludmila I. Kuncheva,et al.  Fuzzy Classifier Design , 2000, Studies in Fuzziness and Soft Computing.

[21]  J. Alirezaie,et al.  PCA-SGA implementation in classification and disease specific feature extraction of the brain MRS signals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook , 2002 .

[23]  Johannes Bernarding,et al.  Quantitation of simulated short echo time 1H human brain spectra by LCModel and AMARES , 2004, Magnetic resonance in medicine.

[24]  M. Hanmandlu,et al.  The brain MR Image segmentation techniques and use of diagnostic packages. , 2010, Academic radiology.

[25]  Ali N. Akansu,et al.  Emerging applications of wavelets: A review , 2010, Phys. Commun..

[26]  M Filippi,et al.  MR imaging in white matter diseases of the brain and spinal cord , 2005 .

[27]  Robert Ivor John,et al.  Computer aided fuzzy medical diagnosis , 2004, Inf. Sci..

[28]  Mark Holden,et al.  A Review of Geometric Transformations for Nonrigid Body Registration , 2008, IEEE Transactions on Medical Imaging.

[29]  Javad Alirezaie,et al.  A novel Diffusion-Weighted Image analysis system for pediatric metabolic brain diseases , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.