Volume Quantification of 123I-DaTSCAN Imaging by MatLab for the Differentiation and Grading of Parkinsonism and Essential Tremor

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1 Volume Quantification of 123I-DaTSCAN Imaging by MatLab for the Differentiation and Grading of Parkinsonism and Essential Tremor Maria Lyra, John Striligas, Maria Gavrilleli, Nefeli Lagopati Radiation Physics Unit, A Radiology Department National & Kapodistrian University of Athens, Athens, Greece (Hellas), mlyra@med.uoa.gr Abstract 123I-DaTSCAN imaging studies have shown the ability to detect loss of striatal dopamine transporters. Aim of this work is to evaluate whether mathematical approach of striatum imaging data by Matlab program processing can differentiate between parkinsonian syndromes, of various stages, and essential tremor, and thus increase diagnostic accuracy. The extraction of parameters by digitized processing of 123I-DaTSCAN (123I-ioflupane) images to differentiate between parkinsonism and essential tremor and evaluation of SPECT imaging of the dopamine transporters (DAT) in vivo, will produce a display of pattern recognition. Eccentricity, major & minor axis length, orientation, area, equivalent diameter and integral intensity of both left & right ganglia were determined. A specific ratio of both ganglia parameters gives a classification indication and contributes in diagnosis decision. Keywords DaTSCAN; MatLab image processing; volume quantification; Parkinsomism; striatum I. INTRODUCTION 123I-DaTSCAN (123Ioflupane) is a cocaine analogue that binds to presynaptic dopamine. The dopamine transporter (DAT) is a plasma membrane protein expressed exclusively in dopamine neurons. Imaging of dopamine transporters, situated in the membrane of dopaminergic neurons could detect degeneration of the dopaminergic nigrostriatal pathway [1]. 123I-DaTSCAN imaging studies have shown the ability to detect loss of striatal dopamine transporters. A correct diagnosis of Parkinson s disease (PD) is dependent upon clinical interpretation of the characteristic asymmetrical Parkinsonism responsive to anti-parkinson therapy; there is no specific diagnostic test or biomarker. Clinical distinction of PD from essential tremor (ET) is sometimes difficult due to indefinite parkinsonian features or atypical presentation of PD such as with postural tremor. In ET the occurrence of asymmetry, rest tremor, or other isolated parkinsonian features may contribute to diagnostic doubt. Misdiagnosis is more likely by non specialists than by movement disorder experts. In post-mortem series, atypical parkinsonian syndromes (e.g. multiple system atrophy, progressive supra-nuclear palsy or cortico-basal degeneration) accounted for half of such misdiagnoses [2]-[3]. Abnormal DAT imaging among subjects with olfactory deficit or rapid eye movement sleep disorder, who later develop motor features of PD, suggests that the technique is sensitive even in the pre-motor phase. DAT imaging accurately differentiates between established PD and ET when clinical criteria are fulfilled [4]. It must be noticed that there is variability in size of striatum ~15%; caudate is fatter than putamen and specific uptake ratio decreases with age. Published studies in earlier disease are more limited, in particular regarding blinded imaging analysis and duration of follow-up [5]. The requirement to set a specific volume cut-off threshold is problematic due to the wide overlap between the two diseases, presumably resulting from the individual variation in absolute striatal size. Quantification of DAT single photon emission tomography (SPECT) imaging is suitable for discriminating Parkinsonian syndromes from ET movement disorder, facilitating an early diagnosis of the disease, following up the progression of the disease and assessing the effects of treatment strategies [6]. To objectively assess striatal DAT binding, quantification is mandatory. Nevertheless, the different degradations that are involved in the reconstruction process affect the quantification output. Thus, image /10/$ IEEE 163

2 degrading effects such as attenuation, the spatially variant point spread function scatter and the partial volume effect have to be compensated to achieve an accurate quantification [7]-[9]. In the 123 I decay scheme, there are a few high-energy photons that have a non-negligible contribution to the final image. The effect of this high-energy contamination has to be compensated to improve the image quality and quantification. Generally, partial volume effect causes an underestimation of activity. This effect is especially severe in small structures such as the basal ganglia [10]. To contribute to the differentiation of PD and ET we have extracted special parameters of the images for the characterization of ganglia by differences in morphology as well as in function. Measure properties of the SPECT images regions are calculated by an algorithm in Matlab. Our algorithm calculates shape measurements (Eccentricity, major & minor axis length, orientation, area, equivalent diameter) and pixel measurements (min, max, mean and integral Intensity). Reference [11] has presented a similar algorithm in the IEEE, 2009 International Conference on Advances in Recent Technologies in Communication and Computing. They extracted area, major and minor axis length, eccentricity, orientation, equiv diameter, solidity and perimeter from MR images for segmentation of brain tumours. II. METHOD 123I-DaTSCAN imaging studies have shown the ability to detect loss of striatal dopamine transporters. It has been suggested that the ratio of tracer accumulation in the putamen to that in the caudate nucleus may allow parkinsonian syndromes progression to be assessed [5]. Caudate nucleus and putamen -arise from the same mass of cells- complete the neo striatum or striatum. The head of the caudate nucleus appears to be continuous with the anterior part of the putamen. In SPECT imaging it is difficult to accurately discriminate to two above mentioned structures. By our work no attempt is tried to define the putamen image from that of caudate nucleus. We, on the contrary, estimate the morphofunctional differences of the left and right sub-region of striatum in order to obtain indications on differential diagnosis between PD and ET. A. Patients DaTscan imaging was completed in 2 groups of patients. Group 1 was consisted of 14 patients, with a diagnosis of Parkinsonism in various stages and group 2, included 8 patients with a diagnosis of ET. The study subjects received 400 mg of potassium perchlorate 1 hour before injection of tracer and 200 mg 12 and 24 hours after injection in order to reduce 123 I uptake in their thyroid gland. All patients (group 1 and group 2) were scanned during an 18-months period. 148 MBq DaTscan were injected intravenously. To minimise the potential for pain at the injection site during administration, a slow intravenous injection (15-20 seconds) was completed. The radioligand for striatal dopamine transporter imaging were supplied by GE Healthcare. Then, SPECT acquisition studies were performed. B. Data Acquisition Imaging of the 22 patients (group 1 and group 2) was completed by GE Starcam Brain neurotransmission SPECT imaging with 123 I was performed with low energy high resolution (LEHR) parallel collimator. Total radiation exposure effective dose- to the study subjects was 9 12 msv. The subjects were scanned 4 hours post injection (at peak time of the specific activity of 123I-DaTSCAN). Data were acquired with a full 360 o rotation (120 views for 40sec each) in a 128x128 matrix mode. Radius of rotation was 14 cm. Images were acquired in a step and shoot mode. The energy window (20%) was centred on 159 kev. The pixel size is made up from a combination of field of view, matrix size and zoom factor and it was 3.2mm. Unprocessed projection data were reviewed in cinematic display, prior to reconstruction, to assess the presence and degree of motion and other potential artefacts, in order to exclude (remove) angular projection data in a possible patient motion. C. Data Processing The GE Xeleris-2 image processing system was used to reconstruct angular projections data. Transaxial, sagittal and coronal slices were reconstructed using the filtered back-projection (FBP) technique (Butterworth: order of 8.0 and a cut-off frequency 0.75 cm 1). Chang s attenuation correction was applied (uniform attenuation coefficient of 0.12 cm 1 ). Scatter correction was not applied because of the fact that most radioactivity was expected to be concentrated into the striatum. The imaging resolution was 8 mm. Coronal slices were visually surveyed and the slices were consecutively summarized to the total slice thickness of 3.2 mm and four of them, on the level of the highest striatal radioactivity, were used for region of interest analysis (fig.1). In group of ET subjects, no degeneration signs were found scintigraphically (Fig.1) Figure 1. Series of central 123I-DaTSCAN SPECT imaging slices of ET subject. DATSCAN uptake is highest in middle slices of Striatum. 4 of them, on the level of the highest striatal radioactivity, were summarized for region of interest analysis. Degeneration of the nigrostriatal pathway was found scintigraphically in the first group (Fig.2). 164

3 Figure I-DaTSCAN SPECT imaging central slice of a PD subjects of group 1. Loss in uniformity and increased background and inhomogeneity are remarked in the image. Background intensity isocontours defines the threshold of the striatum specifically in each case (Fig.3). For the slices delineation a histogram technique for the lower threshold was used. A threshold for each patient was defined as the 60% of the maximum striatal count density. Figure 3. Isocontouring on central coronal slice. a) a PD subject with high eccentricity difference between the left and right sub region of the striatum b) low eccentricity difference of the 2 sub-regions in a ET subject. We have transferred the dicom images in a PC (2GHz dual core CPU, 2GB RAM) and saved in an uncompressed bitmap format. Images were processed by Interactive Data Language (IDL) tools for the creation of iso-intensity contours and determine threshold (Fig.3). The slices were processed using the Matlab (The Mathworks, Inc.) development system. The program was written using Matlab s scripting language and graphical user interface (GUI) developer. We have analyzed the SPECT coronal slices data producing a magnified striatum image in the dicom format file (fig.4). Quantification was undertaken by creating the mesh figures over the whole striatum (caudate nucleus, putamen). Mesh figure represents the Intensity Volume of the two regions of striatum (fig.5a, fig.6a, fig.7a). The images were executed in less than a 30 seconds including the pre-processing time. The proposed algorithm takes only 20 second to process the single image slice to create mesh plot and calculate the parameters. Figure 4. Magnified striatum images were obtained for processing, segmentation and data collection by Matlab. Major axis length, areas and integral intensity differences are calculated for the ET and PD subjects. The proposed algorithm was tested using the software MatLabR2010b with images of different views (transverse and coronal views) and of matrix sizes ( , 256x256). Then, parameters calculations of striatum right and left sub-regions (Fig.4) were extracted. Calculated factors for left and right part of the basic ganglia were area, major and minor axis, eccentricity, orientation, equivalent diameter and integral intensity and were used for evaluation. Area is defined as the actual number of pixels in the region. It is used in the algorithm with the integral intensity for comparison as a percentage difference. Major Axis Length defines the length (in pixels) of the major axis of the ellipse that has the same normalized second central moments as the region. The shorter the major axis length is the greater defect of dopamine transporters, an indication of positive PD. Minor Axis Length is the length (in pixels) of the minor axis of the ellipse that has the same normalized second central moments as the region. Eccentricity specifies the eccentricity of the ellipse that has the same second-moments as the region. The eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 and 1. 0 and 1 are degenerate cases; an ellipse whose eccentricity is 0 is actually a circle, while an ellipse whose eccentricity is 1 is a line segment. Eccentricity higher of 0.5 was calculated in slices of subjects diagnosed as PD. Orientation is the angle (in degrees ranging from -90 to 90 degrees) between the x-axis and the major axis of the ellipse that has the same second-moments as the region. High declination of the two sub regions in degrees is a feature in PD slices. Equivalent Diameter is the definition of the diameter of a circle with the same area as the region. It is used for the calculation of the equivalent sphere volume of each sub region of the striatum and the estimation of their dopamine transporters volume difference. Background intensity, on the image, plays a crucial role both in visual and quantitative diagnostic evaluation. 165

4 Mean background counts is a weighting factor for the extraction of the final classification index. The smallest sub-region is consistent with the size of imaging resolution (_0.8 cm_0.8 cm_0.8 cm). The volume of the striatum is 6 7 cm 3 in healthy controls. Each subregion of interest is the left or right part of the striatum. The total number of counts in these sub-regions was defined by the ratio of integral intensity per area of each striatum part. From the mean number of counts (counts/voxel) in each sub-region, the observed relative dispersion was calculated. This relative dispersion was used as a measure of the heterogeneity of regional striatal DAT density at each sub-region. The observed dispersions of the striatal tracer distributions were also calculated. The difference of major as well as minor axis length of the 2 striatum sub-regions was calculated as a percentage and is an indication of the striatum DAT transporters positions deformation. Orientation and eccentricity differences were calculated to give factors that characterize dispersion of DAT transporters to abnormal positions. III. RESULTS Iso-intensities Ellipse areas were created all around the striatum to create the mesh plot (Fig.5a, Fig.6a and Fig.7a). Data were extracted and Area, Eccentricity, major & minor axis length, Orientation, equivalent Diameter and Integral Intensity of both left & right ganglia were calculated (Fig.5b, Fig.6b, and Fig.7b) Loss in uniformity-mesh plotting distribution is used for grades extraction. The difference of intensity volume percentage of Mesh plotting of the striatum sub-regions images gives a semi quantitative index. Quantitative profile indices were extracted and ratios of specific binding were calculated of the images of left and right part of the striatum. These indices are calculated for both PD subjects and for ET diagnosed subjects. Specific binding ratio is lower in PD than in ET subjects. Striatal uptake ratios were graded up to 4 abnormal levels. The results of the proposed algorithm are gained by a parametric combination of each sub-region coronal slice and a correlation of the two sub-regions of the stratum gives final diagnostic factor for each patient. These factors of the striatum of PD and ET subjects are summarized and a range of (4,8+/-3,9) expresses PD grade while ET indices cover the (0,9+/-0,4) range. Figure 5. a) Intensity of dopamine receptors area is plotted in mesh plot. Processing by our algorithm gives the Intensity distribution and b) useful parameters of each sub-region of striatum are calculated for classification 166

5 2010 CSSR 2010 Initial Submission Figure 7. a) Mesh plotting of the striatum image similar indices of intensities over areas for both sub regions. b) Values of Eccentricity, major & minor axis length, Orientation, equivalent Diameter for both left & right ganglia were determined to be close for this ET subject. Figure 6. a) Subject 6. Loss in volume intensity is indicated in Mesh plotting distribution. Volume intensity difference as a percentage gives the abnormality of the striatum. b) Segmentation parameters were extracted. Differences of major and minor axis as percentage, eccentricity and orientation of the 2 sub regions, give diagnostic factors values for a PD subject. IV. DISCUSSION & CONCLUSION Semi-quantitative analysis of striatum mesh figures and caudate/putamen intensities (uptake) were consistent with the results of visual inspection and clinical diagnosis. Matlab processing and analysis of 123I-DaTSCAN images can contribute to the differentiation between essential tremor and Parkinsonism, especially in early staged Parkinson's disease. These days, detection of basic ganglia function and the exact location and orientation has become an extremely important task in the differential diagnosis of Parkinson Disease than that of Essential Tremor. Detection of striatum function and structure (relative to Dopamine Transporters) plays an important role in the planning and analysis of various treatments and follow up of Parkinson diseases. Because of this, development of efficient and accurate DaTSCAN image segmentation techniques has become an important area of research today, in neurology. Via IDL & Matlab program we integrated an array oriented language and a graphical display technique by profiles of the regions of interest data. Quantitative factors were extracted and ratios of specific binding were calculated of the striatum imaging of all subjects (PD & ET) and correlated. 167

6 Striatal uptake ratios were graded up to 4 levels. Group of ET subjects covered the 1 grade and intersect to abnormal grade 2. Our results show that quantitative analysis of Datscan images provides an objective way to extract and interpret image data. We have presented a method to differentiate PD diagnosis from ET diagnosis. The algorithm developed measure parameters from 123I-DatSCAN SPECT images that are characteristic for each patient and we can classify to two groups of PD and ET. Segmentation method gives striatum regions property functions using image processing toolbox. The parameters extracted are area, major and minor axis length, eccentricity, orientation, equivalent diameter and integral intensity for each striatum sub region. This method is quite versatile, fast and simple to use. Quantitative processing of 123I-DaTSCAN images is a useful diagnostic test in the differential diagnosis of tremor disorders. Semi-quantitative analysis of specific binding uptake was consistent with the results of visual inspection and clinical diagnosis. Studies have further demonstrated that, automated observant-independent software in DAT imaging adds to diagnostic accuracy in patients with suspected PD at the earliest stages of illness [12]. Reliable, rapid, easy-toadminister automated tools for quantitative analysis of DAT images would potentially improve diagnostic accuracy and enable more effective, widespread use of DAT imaging technology in clinical practice [13]. Other investigators have published a comparison of different methods of DatSCAN quantification by analyzing the relative SPECT imaging [14]. It is important to indicate that literature suggests an association between ET and PD. The risk of incident PD has been quantified in cases of ET [15]. Patients with ET were four times more likely than controls to develop incident PD during prospective follow-up. These data confirm and begin to quantify the link between these two diseases. The performance of a pattern recognition program can also aid to detect Parkinsonism in early stage. The proposed technique used for PD and ET differential diagnosis, can also be applied to other applications in nuclear medical imaging of small organs as thyroid gland for texture evaluation or in paediatric nuclear nephrology imaging using the parameters calculated by this algorithm to determine left and right kidney parenchyma situation. ACKNOWLEDGMENT All authors are thankful to the personnel of Medical Imaging Center in Athens for their assistance in acquiring the patients imaging data. REFERENCES [1] DaTSCAN, PDS information sheet FS4, 48, Parkinson;s Disease Society, 2001 [2] E. Tolosa, G. Wenning, W. Poewe, The diagnosis of Parkinson s disease, Lancet Neurol., Vol. 5, pp , 2006 [3] V.L. Marshall, C. B. Reininger, M. Marquardt, J.Patterson, D.M. Hadley, W.H. 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