Volume quantification of 123I-DaTSCAN imaging by MatLab for the differentiation and grading of parkinsonism and essential tremor

Lyra, Maria; Striligas, J.; Gavrilleli, Maria; Lаgopati, Nefeli

doi:10.1109/cssr.2010.5773757

Cited by 4 publications

(5 citation statements)

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“…Although the visual interpretation of SPECT-based brain scans is the major approach used to interpret findings, quantitative analysis of the images is helpful to determine the diagnosis by providing an objective source of information independent of the image readers. For SPECT scans related to the dopamine system, quantitative analysis can be used to measure tracer uptake in the striatal area [ 16 , 19 – 26 ], the size of the properly functional area [ 27 ], or the combination of these factors as the “uptake-size index” [ 28 ]. Among these indices, the amount or concentration of tracer uptake in the striatal area is the most commonly adapted parameter because it reflects the activity of dopamine transporters or receptors that have been reported as reliable biomarkers for the PD diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fully Automated Quantification of the Striatal Uptake Ratio of [^99mTc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson’s Disease and the Temporal Regression of Striatal Tracer Uptake

Fang

Chiu

et al. 2015

BioMed Research International

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Purpose. We aimed at improving the existing methods for the fully automatic quantification of striatal uptake of [99mTc]-TRODAT with SPECT imaging. Procedures. A normal [99mTc]-TRODAT template was first formed based on 28 healthy controls. Images from PD patients (n = 365) and nPD subjects (28 healthy controls and 33 essential tremor patients) were spatially normalized to the normal template. We performed an inverse transform on the predefined striatal and reference volumes of interest (VOIs) and applied the transformed VOIs to the original image data to calculate the striatal-to-reference ratio (SRR). The diagnostic performance of the SRR was determined through receiver operating characteristic (ROC) analysis. Results. The SRR measured with our new and automatic method demonstrated excellent diagnostic performance with 92% sensitivity, 90% specificity, 92% accuracy, and an area under the curve (AUC) of 0.94. For the evaluation of the mean SRR and the clinical duration, a quadratic function fit the data with R 2 = 0.84. Conclusions. We developed and validated a fully automatic method for the quantification of the SRR in a large study sample. This method has an excellent diagnostic performance and exhibits a strong correlation between the mean SRR and the clinical duration in PD patients.

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Section: Introductionmentioning

confidence: 99%

Fully Automated Quantification of the Striatal Uptake Ratio of [^99mTc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson’s Disease and the Temporal Regression of Striatal Tracer Uptake

Fang

Chiu

et al. 2015

BioMed Research International

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“…Volume visualisation in nuclear medicine consists of a method for extracting information from volumetric data utilising and processing a nuclear medicine image (Lyra et al, 2010b). In MatLab, this can be achieved by constructing a 3D surface plot which uses the pixel identities for (x, y) axes and the pixel value is transformed into surface plot height and, consequently, colour.…”

Section: Intensity Volume and 3-d Visualisationmentioning

confidence: 99%

“…14. A Series of central 123I-DaTSCAN SPECT imaging slices of I-123/Ioflupane; uptake is highest in middle 4 slices, and these were summarized for region of interest analysis (Lyra et al 2010b).…”

Section: Matlab Mesh Plotmentioning

confidence: 99%

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MATLAB as a Tool in Nuclear Medicine Image Processing

Lyra¹,

Ploussi²,

Georgantzoglou³

2011

MATLAB - A Ubiquitous Tool for the Practical Engineer

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“…This leads to a 'bottom-up' contraction of the ventricles, forcing blood up and out into the pulmonary artery (right) and aorta (left). The atria then re-fill as the myocardium relaxes [10][11].…”

confidence: 99%

Increasing of Edges Recognition in Cardiac Scintography for Ischemic Patients

Abdallah

2015

JBEMi

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The detection of ischemia heart disease was usually scored by a trained nuclear medicine Physician by determining the ischemia location and size subjectively (by eyes). This subjective method will add to the 5% tolerance error, which might compromise the whole process of treatment especially in patients with severe conditions. The aim of this study is to increase the edge recognition in cardiac scintography images in patients with ischemic heart disease using L*a*b* color space and K-means clustering. First, we read the nuclear cardiac images. We then to convert the images form RGB color space to L*a*b* color space. Then we classify the colors in 'a*b*' space using K-means clustering. Then we label every pixel in the Image using the results from K-means. We then create images that segment the cardiac image by colour. Finally, we segment the cardiac image into a separate image. The sample of this study was (146 cases) and they showed increase enhancement. This segmentation technique (automatic scoring) and segmented images was adjudicated by three nuclear medicine physician as being comparable to other segmentation techniques created with manual editing (subjective scoring). This technique showed potentials increasing of detection of the myocardial ischemia rather than conventional one.

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Volume quantification of 123I-DaTSCAN imaging by MatLab for the differentiation and grading of parkinsonism and essential tremor

Cited by 4 publications

References 12 publications

Fully Automated Quantification of the Striatal Uptake Ratio of [^99mTc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson’s Disease and the Temporal Regression of Striatal Tracer Uptake

Fully Automated Quantification of the Striatal Uptake Ratio of [^99mTc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson’s Disease and the Temporal Regression of Striatal Tracer Uptake

MATLAB as a Tool in Nuclear Medicine Image Processing

Increasing of Edges Recognition in Cardiac Scintography for Ischemic Patients

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Volume quantification of 123I-DaTSCAN imaging by MatLab for the differentiation and grading of parkinsonism and essential tremor

Cited by 4 publications

References 12 publications

Fully Automated Quantification of the Striatal Uptake Ratio of [99mTc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson’s Disease and the Temporal Regression of Striatal Tracer Uptake

Fully Automated Quantification of the Striatal Uptake Ratio of [99mTc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson’s Disease and the Temporal Regression of Striatal Tracer Uptake

MATLAB as a Tool in Nuclear Medicine Image Processing

Increasing of Edges Recognition in Cardiac Scintography for Ischemic Patients

Contact Info

Product

Resources

About

Fully Automated Quantification of the Striatal Uptake Ratio of [^99mTc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson’s Disease and the Temporal Regression of Striatal Tracer Uptake

Fully Automated Quantification of the Striatal Uptake Ratio of [^99mTc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson’s Disease and the Temporal Regression of Striatal Tracer Uptake