Automatic detection of inter-frame motion in echocardiographic images

Klingler, J.W.; Begeman, Michael S.; Fraker, Theodore D.; Andrews, L.T.

doi:10.1016/0301-5629(89)90176-2

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Left lateral rotation of the patient will deflate the left lung and avoid the pulmonary interference and improve the clarity of echocardiograms. Improving the quality of image can be done by using device-driven systems, which can acquire signal averaging of echocardiographic frames over multiple cardiac cycles (Ginghina et al 2009, Lang 2005, Klingler 1989 …”

Section: The Image Qualitymentioning

confidence: 99%

Doppler Echocardiographic Changes in Respiratory Diseases

Al‐Biltagi¹,

Bediwy²

2011

Establishing Better Standards of Care in Doppler Echocardiography, Computed Tomography and Nuclear Cardiology

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Section: The Image Qualitymentioning

confidence: 99%

Doppler Echocardiographic Changes in Respiratory Diseases

Al‐Biltagi¹,

Bediwy²

2011

Establishing Better Standards of Care in Doppler Echocardiography, Computed Tomography and Nuclear Cardiology

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“…Other studies have utilised the repeated rhythmic contractions of the heart to acquire and compound multiple partially decorrelated 2-D images of the same cardiac phase over consecutive cardiac cycles through a single acoustic window. The process has been referred to as temporal compounding (Abiko et al 1997;Amorim et al 2009;Klingler et al 1989;Olstad 2002;Perperidis et al 2009;Rigney and Wei 1988;Unser et al 1989;van Ocken et al 1981;Vitale et al 1993).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Enhancement of B-Mode Cardiac Ultrasound Image Sequences

Perperidis

Cusack

White

et al. 2017

Ultrasound in Medicine & Biology

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Limited contrast, along with speckle and acoustic noise, can reduce the diagnostic value of echocardiographic images. This study introduces dynamic histogram-based intensity mapping (DHBIM), a novel approach employing temporal variations in the cumulative histograms of cardiac ultrasound images to contrast enhance the imaged structures. DHBIM is then combined with spatial compounding to compensate for noise and speckle. The proposed techniques are quantitatively assessed (32 clinical data sets) employing (i) standard image quality measures and (ii) the repeatability of routine clinical measurements, such as chamber diameter and wall thickness. DHBIM introduces a mean increase of 120.9% in tissue/chamber detectability, improving the overall repeatability of clinical measurements by 17%. The integrated approach of DHBIM followed by spatial compounding provides the best overall enhancement of image quality and diagnostic value, consistently outperforming the individual approaches and achieving a 401.4% average increase in tissue/chamber detectability with an associated 24.3% improvement in the overall repeatability of clinical measurements.

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“…A limitation in these studies was that no spatial alignment was performed on the temporally aligned frames prior to intensity averaging. Klingler et al (1989) recognized the need for spatial alignment prior to compounding and described an empirical method to reject (but not compensate for) temporally aligned frames that demonstrated large spatial displacement with respect to the reference frame. Olstad (2002) extended the approach by Vitale et al (1993) by introducing a rigid spatial alignment to compensate for larger cardiac movements during image acquisition.…”

Section: Introductionmentioning

confidence: 99%

Temporal Compounding: A Novel Implementation and Its Impact on Quality and Diagnostic Value in Echocardiography

Perperidis

Cusack

White

et al. 2015

Ultrasound in Medicine & Biology

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Temporal compounding can be used to suppress acoustic noise in transthoracic cardiac ultrasound by spatially averaging partially decorrelated images acquired over consecutive cardiac cycles. However, the reliable spatial and temporal alignment of the corresponding frames in consecutive cardiac cycles is vital for effective implementation of temporal compounding. This study introduces a novel, efficient, accurate and robust technique for the spatiotemporal alignment of consecutive cardiac cycles with variable temporal characteristics. Furthermore, optimal acquisition parameters, such as the number of consecutive cardiac cycles used, are derived. The effect of the proposed implementation of temporal compounding on cardiac ultrasound images is quantitatively assessed (32 clinical data sets providing a representative range of image qualities and diagnostic values) using measures such as tissue signal-to-noise ratio, chamber signal-to-noise ratio, tissue/chamber contrast and detectability index, as well as a range of clinical measurements, such as chamber diameter and wall thickness, performed during routine echocardiographic examinations. Temporal compounding (as implemented) consistently improved the image quality and diagnostic value of the processed images, when compared with the original data by: (i) increasing tissue and cavity signal-to-noise ratios as well as tissue/cavity detectability index, (ii) improving the corresponding clinical measurement repeatability and inter-operator measurement agreement, while (iii) reducing the number of omitted measurements caused by data corruption.

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Automatic detection of inter-frame motion in echocardiographic images

Cited by 6 publications

References 4 publications

Doppler Echocardiographic Changes in Respiratory Diseases

Doppler Echocardiographic Changes in Respiratory Diseases

Dynamic Enhancement of B-Mode Cardiac Ultrasound Image Sequences

Temporal Compounding: A Novel Implementation and Its Impact on Quality and Diagnostic Value in Echocardiography

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