Application of Karhunen-Loeve transform in nuclear cardiology: spatio-temporal smoothing and quantitative image analysis

Blagosklonov, Oleg; Sabbah, Ahmad; Verdenet, J.; Cardot, J

doi:10.1109/cic.2000.898516

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Furthermore, GBP combine with KLT may be used for quantitative analysis of global ventricular contractile function [5]. Use of this method for evaluation of right ventricular contraction is more difficult because of non round form of this ventricle, that complicate the modelling and application of mathematical methods for assessing of the contraction.…”

Section: Quantificationmentioning

confidence: 99%

Assessment Myocardial Perfusion and Contraction by Karhunen-Loeve Transform on Scintigraphic Images

Blagosklonov

Comas

Berthout³

et al. 2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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Theory and previous studies showed that KLT (an application of principal component transform for imaging) can be use for analysis of cardiac function. This paper presents the results of our studies concerning the applications of KLT for images smoothing, quantification of myocardial contraction, and improvement of inter-observer reproducibility in cardiac imaging. The paper also describes the use of 4D cardiac phantom to quantify Karhunen-Loeve images.

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Section: Quantificationmentioning

confidence: 99%

Assessment Myocardial Perfusion and Contraction by Karhunen-Loeve Transform on Scintigraphic Images

Blagosklonov

Comas

Berthout³

et al. 2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Data were stored in a 64x64 matrix; the raw scintigraphic data were preprocessed with a backprojection algorithm and a Butterworth filter (frequency 0.25, range 5) without attenuation correction was applied. Then, the series was smoothed and denoised using Karhunen-Loeve transform; we used the 3 first KLT images for reconstruction [3]. The processing was done using MATLAB version 5.3 (The MathWorks, inc.) on standard PC (PIII-733, 256Mb RAM)…”

Section: Patients and Image Acquisitionmentioning

confidence: 99%

Automatic contours detection in myocardial GSPECT

Comas

Berthout

Sabbah

et al. 2003

Computers in Cardiology, 2003

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IntroductionAcquisition of gated single-photon emission tomography of the myocardium (gSPECT) is routinely used in clinical practice to provide information on myocardial perfusion and contractile function [1]. After reconstruction, a rigorous left ventricular contouring on each cycle image and axis of the heart is required. We developed a program for automatic contour detection; it was applied without knowledge on counts statistics, chosen axis, tracer distribution or artefacts. 2.Material and methods Patients and image acquisitionThis study was performed on 110 explorations at stress (8 images per cardiac cycle) and concerned the central scans of the 3 heart axes and the summed image. 99mTc-gSPECT studies were performed in supine patients. The exercise test consisted of physical exercise combined with pharmacological vasodilatation. After exercise stress test, a dose of 15 MBq/kg of 99mTc was administered intravenously. A rotating dual-head gamma camera was used, equipped with low-energy high-resolution collimators; the energy window was centered on 140 keV ± 15%. The system detectors were at 90°. The gamma camera was rotated through a 180° arc in a contour orbit around the patient's chest from 45° RAO at 6° increments for 40-60 s each (32 views), 8 time bins [2]. 1.33 zoom was applied. Data were stored in a 64x64 matrix; the raw scintigraphic data were preprocessed with a backprojection algorithm and a Butterworth filter (frequency 0.25, range 5) without attenuation correction was applied. Then, the series was smoothed and denoised using Karhunen-Loeve transform; we used the 3 first KLT images for reconstruction [3]. The processing was done using MATLAB version 5.3 (The MathWorks, inc.) on standard PC (PIII-733, 256Mb RAM) PropertiesThe following topographical properties of the myocardial reconstructed images are involved: -The heart takes more than a half of the image surface; with many pixels, a histogram analysis can be used. -The resulting pictures are denoised, and the "useful" intensities are 5 to 10 times higher than the noise, both in rest and stress studies. This assumption justifies the thresholding.-Polar representation of the contours is applicable because of the elliptical form of the left ventricle and its central position on the images. -The artefacts correspond to some tissues with abnormal fixation of the pharmaceutical tracer, and they are outside of the cardiac zone. Morphological operations (erosion, dilation, skeleton) are therefore possible.Skeletonization is a process for reducing foreground regions in a binary image to a skeletal remnant that largely preserves the extent and connectivity of the original region while eliminating most of the original foreground pixels.The skeleton can be produced in two ways. The first is to use some kind of morphological thinning that sequentially erodes pixels from the boundary (while preserving the end points of line segments) until no more thinning is possible; at this point what is left approximates the skeleton. The alternative method is to...

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“…Application of KLT filtering in dynamic medical imaging was first described by (Sychra et al 1994). It has been used in cardiac nuclear scintigraphy (Blagosklonov et al 2000, Narayanan et al 1999 to filter cine image series representing one single cardiac cycle. Image correlation is limited in this case and so is the de-noising capability of the KLT filter.…”

Section: Introductionmentioning

confidence: 99%

Application of the Karhunen–Loeve transform temporal image filter to reduce noise in real-time cardiac cine MRI

Ding

Chung²,

Raman

et al. 2009

Phys. Med. Biol.

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Real-time dynamic magnetic resonance imaging (MRI) typically sacrifices the signal-to-noise ratio (SNR) to achieve higher spatial and temporal resolution. Spatial and/or temporal filtering (e.g., low-pass filtering or averaging) of dynamic images improves the SNR at the expense of edge sharpness. We describe the application of a temporal filter for dynamic MR image series based on the Karhunen-Loeve transform (KLT) to remove random noise without blurring stationary or moving edges and requiring no training data. In this paper, we present several properties of this filter and their effects on filter performance, and propose an automatic way to find the filter cutoff based on the autocorrelation of the eigenimages. Numerical simulation and in vivo real-time cardiac cine MR image series spanning multiple cardiac cycles acquired using multi-channel sensitivity-encoded MRI, i.e., parallel imaging, are used to validate and demonstrate these properties. We found that in this application, the noise standard deviation was reduced to 42% of the original with no apparent image blurring by using the proposed filter cutoff. Greater noise reduction can be achieved by increasing the length of the image series. This advantage of KLT filtering provides flexibility in the form of another scan parameter to trade for SNR.

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Application of Karhunen-Loeve transform in nuclear cardiology: spatio-temporal smoothing and quantitative image analysis

Cited by 7 publications

References 9 publications

Assessment Myocardial Perfusion and Contraction by Karhunen-Loeve Transform on Scintigraphic Images

Assessment Myocardial Perfusion and Contraction by Karhunen-Loeve Transform on Scintigraphic Images

Automatic contours detection in myocardial GSPECT

Application of the Karhunen–Loeve transform temporal image filter to reduce noise in real-time cardiac cine MRI

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