An interactive interface for seizure focus localization using SPECT image analysis

Rossman, Mark; Adjouadi, Malek; Ayala, Melvin; Yaylali, Ilker

doi:10.1016/j.compbiomed.2004.09.001

Cited by 8 publications

(10 citation statements)

References 8 publications

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“…No mention was made of postoperative outcome or pathology and the Tc-HMPAO injections varied within the time of the ictus up to 30 seconds following its electrographic cessation. Moreover, HMPAO tends to underestimate large blood flow measures hampering the expression of subtracted SPECT images and resulting in poorly displayed ictal onset zones (Rossman et al, 2006). …”

Section: Discussionmentioning

confidence: 99%

“…Patterns of blood perfusion have been studied both qualitatively (Hwang et al, 2001; Kaiboriboon et al, 2002; Lee et al, 2000a; O’Brien et al, 1998; O’Brien et al, 1999; Shin et al, 2001; Sojkova et al, 2003; Tan et al, 2008; Vera et al, 1999; Won Chul et al, 2001; Zaknun et al, 2008; Zubal et al, 1995) and quantitatively (Aubert-Broche et al, 2003; Blumenfeld et al, 2004; Chang et al, 2002; Grova et al, 2005; Hogan et al, 1997; Houston et al, 1994; Kaiboriboon et al, 2005; Lee et al, 2000b; Lee et al, 2005; McNally et al, 2005; Pagani et al, 2002; Rossman et al, 2006; Stoeckel et al, 2001; Van Paesschen et al, 2003) for localization of epileptogenic sites. Quantitative analysis of cerebral blood perfusion patterns using single photon emission computed tomography (SPECT) imaging holds promise for providing more accurate anatomically-specific information compared to that of qualitative assessment (Aubert-Broche et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Quantitative analysis of cerebral blood perfusion patterns using single photon emission computed tomography (SPECT) imaging holds promise for providing more accurate anatomically-specific information compared to that of qualitative assessment (Aubert-Broche et al, 2005). The analysis may be performed by comparing the two cerebral hemispheres for perfusion asymmetry (Aubert-Broche et al, 2003; Aubert-Broche et al, 2005; Hogan et al, 1997), by comparing ictal and interictal images (Blumenfeld et al, 2004; Kaiboriboon et al, 2005; Lee et al, 2005; Rossman et al, 2006; Shin et al, 2001; Van Paesschen et al, 2003) and by comparing epileptic and nonepileptic subjects (Houston et al, 1994; Lee et al, 2005; Stoeckel et al, 2001). Stratification by age group in nonepileptic subjects has been reported to provide more reasonable age-related comparisons with patient groups (Pagani et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, an atlas, usually a T1-weighted or SPECT image template, may be coregistered with all SPECT volumes (Blumenfeld et al, 2004; Kaiboriboon et al, 2005; Lee et al, 2005; Pagani et al, 2002). Meaningful changes in blood perfusion within the brain may then be declared using voxel-based analysis (Aubert-Broche et al, 2003; Blumenfeld et al, 2004; Chang et al, 2002; Houston et al, 1994; Kaiboriboon et al, 2005; Lee et al, 2000b; Lee et al, 2005; McNally et al, 2005; Patterson et al, 1997; Rossman et al, 2006; Van Paesschen et al, 2003) or analysis based on region-of-interest (ROI) or volume-of-interest (VOI) (Aubert-Broche et al, 2005; Blumenfeld et al, 2004; Grova et al, 2005; Hogan et al, 1997; Pagani et al, 2002). …”

Section: Introductionmentioning

confidence: 99%

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Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy

Jafari-Khouzani¹,

Elisevich²,

Karvelis³

et al. 2011

Epilepsy Research

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This study assesses the utility of compartmental analysis of SPECT data in lateralizing ictal onset in cases of a putative mesial temporal lobe epilepsy (mTLE). An institutional archival review provided 46 patients (18M, 28F) operated for a putative mTLE who achieved an Engel class Ia postoperative outcome. This established the standard to assure a true ictal origin. Ictal and interictal SPECT images were separately coregistered to T1-weighted (T1W) magnetic resonance (MR) image using a rigid transformation and the intensities matched with an l1 norm minimization technique. The T1W MR image was segmented into separate structures using an atlas-based automatic segmentation technique with the hippocampi manually segmented to improve accuracy. Mean ictal-interictal intensity difference values were calculated for select subcortical structures and the accuracy of lateralization evaluated using a linear classifier. Hippocampal SPECT analysis yielded the highest lateralization accuracy (91%) followed by the amygdala (87%), putamen (67%) and thalamus (61%). Comparative FLAIR and volumetric analyses yielded 89% and 78% accuracies, respectively. A multi-modality analysis did not generate a higher accuracy (89%). A quantitative anatomically compartmented approach to SPECT analysis yields a particularly high lateralization accuracy in the case of mTLE comparable to that of quantitative FLAIR MR imaging. Hippocampal segmentation in this regard correlates well with ictal origin and shows good reliability in the preoperative analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy

Jafari-Khouzani¹,

Elisevich²,

Karvelis³

et al. 2011

Epilepsy Research

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show abstract

“…From the first view, plots involving dimensions 3,6,7,8,9 in Figure 5.5 show in general a wider spread of clusters and better separation. Most of these clusters are somewhat organized and separated from each other.…”

Section: Dimensionality Analysismentioning

confidence: 99%

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

Ayala¹

Self Cite

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This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface.A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function's slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes.The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth's parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty.It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

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A Windows‐based interface for teaching image processing

Ayala

Adjouadi

Cabrerizo

et al. 2010

Comp Applic In Engineering

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The use of image processing in research represents a challenge to the scientific community interested in its various applications but is not familiar with this area of expertise. In academia as well as in industry, fundamental concepts such as image transformations, filtering, noise removal, morphology, convolution/deconvolution among others require extra efforts to be understood. Additionally, algorithms for image reading and visualization in computers are not always easy to develop by inexperienced researchers. This type of environment has lead to an adverse situation where most students and researchers develop their own image processing code for operations which are already standards in image processing, a redundant process which only exacerbates the situation. The research proposed in this article, with the aim to resolve this dilemma, is to propose a user-friendly computer interface that has a dual objective which is to free students and researchers from the learning time needed for understanding/applying diverse imaging techniques but to also provide them with the option to enhance or reprogram such algorithms with direct access to the software code. The interface was thus developed with the intention to assist in understanding and performing common image processing operations through simple commands that can be performed mostly by mouse clicks. The visualization of pseudo code after each command execution makes the interface attractive, while saving time and facilitating to users the learning of such practical concepts. ß

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An interactive interface for seizure focus localization using SPECT image analysis

Cited by 8 publications

References 8 publications

Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy

Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

A Windows‐based interface for teaching image processing

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