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Effect of restricted image data on automated coregistration algorithms: A method of investigation
ABSTRACTS
EFFECT OF RESTRICTED IMAGE DATA ON A U T O M A T E D COREGISTRATION ALGORITHMS: A METHOD OF INVESTIGATION. A n n a Barnes 1, David Wyper 1, Daniela Montaldi 2 and Jim Patterson 1
1 Institute of Neurological Sciences, Southern General Hospital, Glasgow and 2 University of Paisley, Scotland INTRODUCTION: A critical element in studies of neuro-activation, whether undertaken by SPECT, PET or fMRI, is the ability to co-register 'baseline' and 'activation' images with sufficient accuracy to exclude artefacts due to mis-registration. A variety of algorithms are available for co-registering functional images (PET, SPECT, fMRI) with other functional images or with their corresponding structural image (MRI or CT), Accurate co-registration of these images then makes it possible to study patterns of response in a group of subjects using either region of interest (ROD analysis 1 or statistical paratnetric mapping (SPM) 2. A frequent limitation of the data sets available for coregistration is that only a limited volume of the brain has been scanned. In this study we have investigated the effects of a reduced data set in the co-registration process and the resulting errors produced in subsequent regional cerebral blood flow (rCBF) measurements. METHODS: In order to investigate the consequences of using reduced image data sets with an automated coregistration algorithm, two identical scans (one a direct copy of the other) were co-registered under a series of different degrees of initial alignment and differing amounts of restricted data. The duplicate scan was mis-aligned with respect to the original scan and then reduced in volume from the full data set of 20 slices. The reduced and mis-aligned duplicate scan was then co-registered to the original reference scml. Using a template consisting of 14 different ROIs, at the level of the basal ganglia, mean activity values were recorded for both the reference and the duplicate scan after each co-registration. RESULTS: A difference image expressed in terms of percentage difference with respect to the reference scan was obtained by subtracting the normalised ROI values of the duplicate scml from the values taken from the original unaltered reference scan. In order to investigate the accuracy of the automated algorithm under each of its challenge conditions a sum over all ROIs of the percentage difference weighted by the ROI area was produced (see table 1). This value is referred to as an accuracy coefficient (small values indicating good co-registration accuracy ).
Volume of reduced scan Plane of mis-
10 slices @ 6ram width 12.89%
12 slices @ 6ram width 10.77%
15 slices @ 6Jmn width 4.61%
20 slices @ 6nun width 0.15%
Sagittal
12.66%
12.09%
4.66%
0.28%
Coronal
15.67%
13.08%
7.55%
0.23%
alignment Axial
Table 1. Tabulated data of accuracy co-efficients: 5 degree at~gle of mis-alignment ,and reduced volume with respect to the reference scan: repeated for 10,15,20 degrees in each plane rind similar results obtained. CONCLUSION: It is clear from the results that the enor produced by initial misalignment between two scans is fat outweighed by the error produced by using a reduced data set for automated image co-registration, The results show that using an automated algorithm to co-register reduced image data sets can produce residual errors independent of any changes in blood flow produced by a particular cognitive task. We suggest that where possible, matching whole brain data sets be used for co-registration by automated algorithm. The software used in this investigation was V2.9 Strichman Medical Equipment Neuro 900 on a Power Macintosh 7100/66 but the method outlined can be used to assess any co-registration algorithm. It is intended to repeat the study described above using other co-registration algorithms and to investigate the consequences for significance levels obtained using SPM analysis. REFERENCES 1. Montaldi, D., Brooks, DN., McColl, JH., Wyper, D., Patterson, J., Barton, E., McCulloch, J. J Neurol Neurosurg Psychiatry 1990, 53:33-38. 2. Friston, K J. J Cereb Blood Flow Metab 1995, 15:361-370.
$48
EFFECT OF RESTRICTED IMAGE DATA ON A U T O M A T E D COREGISTRATION ALGORITHMS: A METHOD OF INVESTIGATION. A n n a Barnes 1, David Wyper 1, Daniela Montaldi 2 and Jim Patterson 1
1 Institute of Neurological Sciences, Southern General Hospital, Glasgow and 2 University of Paisley, Scotland INTRODUCTION: A critical element in studies of neuro-activation, whether undertaken by SPECT, PET or fMRI, is the ability to co-register 'baseline' and 'activation' images with sufficient accuracy to exclude artefacts due to mis-registration. A variety of algorithms are available for co-registering functional images (PET, SPECT, fMRI) with other functional images or with their corresponding structural image (MRI or CT), Accurate co-registration of these images then makes it possible to study patterns of response in a group of subjects using either region of interest (ROD analysis 1 or statistical paratnetric mapping (SPM) 2. A frequent limitation of the data sets available for coregistration is that only a limited volume of the brain has been scanned. In this study we have investigated the effects of a reduced data set in the co-registration process and the resulting errors produced in subsequent regional cerebral blood flow (rCBF) measurements. METHODS: In order to investigate the consequences of using reduced image data sets with an automated coregistration algorithm, two identical scans (one a direct copy of the other) were co-registered under a series of different degrees of initial alignment and differing amounts of restricted data. The duplicate scan was mis-aligned with respect to the original scan and then reduced in volume from the full data set of 20 slices. The reduced and mis-aligned duplicate scan was then co-registered to the original reference scml. Using a template consisting of 14 different ROIs, at the level of the basal ganglia, mean activity values were recorded for both the reference and the duplicate scan after each co-registration. RESULTS: A difference image expressed in terms of percentage difference with respect to the reference scan was obtained by subtracting the normalised ROI values of the duplicate scml from the values taken from the original unaltered reference scan. In order to investigate the accuracy of the automated algorithm under each of its challenge conditions a sum over all ROIs of the percentage difference weighted by the ROI area was produced (see table 1). This value is referred to as an accuracy coefficient (small values indicating good co-registration accuracy ).
Volume of reduced scan Plane of mis-
10 slices @ 6ram width 12.89%
12 slices @ 6ram width 10.77%
15 slices @ 6Jmn width 4.61%
20 slices @ 6nun width 0.15%
Sagittal
12.66%
12.09%
4.66%
0.28%
Coronal
15.67%
13.08%
7.55%
0.23%
alignment Axial
Table 1. Tabulated data of accuracy co-efficients: 5 degree at~gle of mis-alignment ,and reduced volume with respect to the reference scan: repeated for 10,15,20 degrees in each plane rind similar results obtained. CONCLUSION: It is clear from the results that the enor produced by initial misalignment between two scans is fat outweighed by the error produced by using a reduced data set for automated image co-registration, The results show that using an automated algorithm to co-register reduced image data sets can produce residual errors independent of any changes in blood flow produced by a particular cognitive task. We suggest that where possible, matching whole brain data sets be used for co-registration by automated algorithm. The software used in this investigation was V2.9 Strichman Medical Equipment Neuro 900 on a Power Macintosh 7100/66 but the method outlined can be used to assess any co-registration algorithm. It is intended to repeat the study described above using other co-registration algorithms and to investigate the consequences for significance levels obtained using SPM analysis. REFERENCES 1. Montaldi, D., Brooks, DN., McColl, JH., Wyper, D., Patterson, J., Barton, E., McCulloch, J. J Neurol Neurosurg Psychiatry 1990, 53:33-38. 2. Friston, K J. J Cereb Blood Flow Metab 1995, 15:361-370.
$48