Proc. SPIE. 7260, Medical Imaging 2009: Computer-Aided Diagnosis
KEYWORDS: Principal component analysis, Data modeling, Magnetic resonance imaging, Scanners, Feature selection, Raster graphics, In vivo imaging, Alzheimer's disease, Factor analysis, Intercontinental ballistic missiles
We propose a single, quantitative metric called the disease evaluation factor (DEF) and assess its efficiency at
estimating disease burden in normal, control subjects (CTRL) and probable Alzheimer's disease (AD) patients. The study group
consisted in 75 patients with a diagnosis of probable AD and 75 age-matched normal CTRL without neurological or
neuropsychological deficit. We calculated a reference eigenspace of MRI appearance from reference data, in which our CTRL
and probable AD subjects were projected. We then calculated the multi-dimensional hyperplane separating the CTRL and
probable AD groups. The DEF was estimated via a multidimensional weighted distance of eigencoordinates for a given subject
and the CTRL group mean, along salient principal components forming the separating hyperplane. We used quantile plots,
Kolmogorov-Smirnov and χ<sup>2</sup> tests to compare the DEF values and test that their distribution was normal. We used a linear
discriminant test to separate CTRL from probable AD based on the DEF factor, and reached an accuracy of 87%. A quantitative
biomarker in AD would act as an important surrogate marker of disease status and progression.
In successful brain tumor surgery, the neurosurgeon's objectives are threefold: (1) reach the target, (2) remove
it and (3) preserve eloquent tissue surrounding it. Surgical Planning (SP) consists in identifying optimal access
route(s) to the target based on anatomical references and constrained by functional areas. Preoperative
images are essential input in Multi-modal Image Guided NeuroSurgery systems (MIGNS) and update of these
images, with precision and accuracy, is crucial to approach the anatomical reality in the Operating Room (OR).
Intraoperative brain deformation has been previously identified by many research groups and related update
of preoperative images has also been studied. We present a study of three surgical cases with tumors accompanied
with edema and where corticosteroids were administered and monitored during a preoperative stage
[<i>t</i><sub>0</sub>, <i>t</i><sub>1</sub> = <i>t</i><sub>0</sub> + 10 days]. In each case we observed a significant change in the Region Of Interest (ROI) and in
anatomical references around it. This preoperative brain shift could induce error for localization during intervention
(time <i>t</i><sub>S</sub>) if the SP is based on the <i>t</i><sub>0</sub> preoperative images. We computed volume variation, distance maps based on closest point (CP) for different components of the ROI, and displacement of center of mass (CM) of
the ROI. The matching between sets of homologous landmarks from t<sub>0</sub> to <i>t</i><sub>1</sub> was performed by an expert. The
estimation of the landmarks displacement showed significant deformations around the ROI (landmarks shifted
with mean of 3.90 ± 0.92 mm and maximum of 5.45 <i>mm</i> for one case resection). The CM of the ROI moved
about 6.92 <i>mm</i> for one biopsy. Accordingly, there was a sizable difference between SP based at <i>t</i><sub>0</sub> <i>vs</i> SP based
at <i>t</i><sub>1</sub>, up to 7.95 <i>mm</i> for localization of reference access in one resection case. When compared to the typical
MIGNS system accuracy (2 <i>mm</i>), it is recommended that preoperative images be updated within the interval time [<i>t</i><sub>1</sub>,<i>t</i><sub>S</sub>] in order to minimize the error correspondence between the anatomical reality and the preoperative data. This should help maximize the accuracy of registration between the preoperative images and the patient in the OR.
<i>Background</i>: Reported error rates for initial clinical diagnosis in parkinsonian disorders can reach up to 35%. Reducing this initial error rate is an important research goal. The objective of this work is to evaluate the ability of an automated MR-based classification technique in the differential diagnosis of Parkinson's disease (PD), multiple systems atrophy (MSA) and progressive supranuclear palsy (PSP).
<i>Methods</i>: A total of 172 subjects were included in this study: 152 healthy subjects, 10 probable PD patients and 10 age-matched patients with diagnostic of either probable MSA or PSP. T1-weighted (T1w) MR images were acquired and subsequently corrected, scaled, resampled and aligned within a common referential space. Tissue transformation and deformation features were then automatically extracted. Classification of patients was performed using forward, stepwise linear discriminant analysis within a multidimensional transformation/deformation feature space built from healthy subjects data. Leave-one-out classification was used to avoid over-determination.
<i>Findings</i>: There were no age difference between groups. Highest accuracy (agreement with long-term clinical follow-up) of 85% was achieved using a single MR-based deformation feature.
<i>Interpretation</i>: These preliminary results demonstrate that a classification approach based on quantitative parameters of 3D brainstem morphology extracted automatically from T1w MRI has the potential to perform differential diagnosis of PD versus MSA/PSP with high accuracy.
Conference Committee Involvement (3)
10 February 2009 | Lake Buena Vista (Orlando Area), Florida, United States
19 February 2008 | San Diego, California, United States