Signal-to-Noise Ratio Improvement in Dynamic Contrast-enhanced CT and MR Imaging with Automated Principal Component Analysis Filtering

Balvay, D.; Kachenoura, Nadjia; Espinoza, Sophie; Thomassin‐Naggara, Isabelle; Fournier, Laure; Clémеnt, Olivier; Cuenod, Charles‐André

doi:10.1148/radiol.10100231

Cited by 20 publications

(30 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, it is desirable to optimize the number of principal components for the best compromise between denoising efficiency and information conservation. 35 To this end, the FRI, which is a normalized SNR calculated for a residual signal (the difference between original and filtered tissue curve), of each square in a ROI was calculated to differentiate the informative contrastenhanced signals from random noise. 36 The tissue curves in a ROI were replaced with random white Gaussian noise signals generated with standard deviation of the background signals before the contrast medium arrived at the region and the reference FRI distribution from the noise signals was compared with the tissue FRI distribution.…”

Section: D Pca Filteringmentioning

confidence: 99%

“…9,[31][32][33][34] While pixelby-pixel analysis is known to be challenging because of poor signal-to-noise ratio (SNR) in the time-concentration curve of each pixel (tissue curve), recently, a method to improve SNR in DCE imaging with automated principal component analysis (PCA) filtering has been proposed. 35 The method uses the idea of fraction of residual information (FRI) criterion 36 to optimize the balance between efficiency of noise reduction and information conservation in the tissue curve. Therefore, the aim of this study is to investigate the effectiveness of the proposed method of PCA filtering combined with the AIF estimation technique on the pixel-by-pixel analysis of DCE-CT data at low temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

et al. 2015

View full text Add to dashboard Cite

show abstract

Section: D Pca Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

et al. 2015

View full text Add to dashboard Cite

show abstract

“…It extracts the principal components (PCs) – a set of new, uncorrelated variables – of the data set and ranks them according to how much of the data's variability they describe. By considering only the highest ranked PCs, that is, the ones that describe a large portion of the variance of the observations and are hence supposed to convey the essential features of the data, PCA can be used for both dimensionality reduction and denoising . We interpret each pixel ( i , j ) in a stack of spectral CEST images as a variable and the corresponding signal intensity S ij ( ω k ) for a certain saturation frequency ω k as an observation of that variable.…”

Section: Pca Denoising Of Spectral Cest Imagesmentioning

confidence: 99%

Sensitivity enhancement of (Hyper‐)CEST image series by exploiting redundancies in the spectral domain

Döpfert

Witte

Kunth

et al. 2014

Contrast Media & Molecular

View full text Add to dashboard Cite

CEST has proven to be a valuable technique for the detection of hyperpolarized xenon-based functionalized contrast agents. Additional information can be encoded in the spectral dimension, allowing the simultaneous detection of multiple different biosensors. However, owing to the low concentration of dissolved xenon in biological tissue, the signal-to-noise ratio (SNR) of Hyper-CEST data is still a critical issue. In this work, we present two techniques aiming to increase SNR by exploiting the typically high redundancy in spectral CEST image series: PCA-based post-processing and sub-sampled acquisition with low-rank reconstruction. Each of them yields a significant SNR enhancement, demonstrating the feasibility of the two approaches. While the first method is directly applicable to proton CEST experiments as well, the second one is particularly beneficial when dealing with hyperpolarized nuclei, since it distributes the non-renewable initial polarization more efficiently over the sampling points. The results obtained are a further step towards the detection of xenon biosensors with spectral Hyper-CEST imaging in vivo.

show abstract

“…In medical imaging Hotelling-filters have been used in computed tomography and magnetic resonance imaging [12]. In dynamic PET, similar noise-reduction techniques have been described working on raw data, not images, prior to tomographic reconstruction [13].…”

Section: Introductionmentioning

confidence: 99%

The 2D Hotelling filter - a quantitative noise-reducing principal-component filter for dynamic PET data, with applications in patient dose reduction

Axelsson

Sørensen

2013

BMC Med Phys

View full text Add to dashboard Cite

BackgroundIn this paper we apply the principal-component analysis filter (Hotelling filter) to reduce noise from dynamic positron-emission tomography (PET) patient data, for a number of different radio-tracer molecules. We furthermore show how preprocessing images with this filter improves parametric images created from such dynamic sequence.We use zero-mean unit variance normalization, prior to performing a Hotelling filter on the slices of a dynamic time-series. The Scree-plot technique was used to determine which principal components to be rejected in the filter process. This filter was applied to [11C]-acetate on heart and head-neck tumors, [18F]-FDG on liver tumors and brain, and [11C]-Raclopride on brain. Simulations of blood and tissue regions with noise properties matched to real PET data, was used to analyze how quantitation and resolution is affected by the Hotelling filter. Summing varying parts of a 90-frame [18F]-FDG brain scan, we created 9-frame dynamic scans with image statistics comparable to 20 MBq, 60 MBq and 200 MBq injected activity. Hotelling filter performed on slices (2D) and on volumes (3D) were compared.ResultsThe 2D Hotelling filter reduces noise in the tissue uptake drastically, so that it becomes simple to manually pick out regions-of-interest from noisy data. 2D Hotelling filter introduces less bias than 3D Hotelling filter in focal Raclopride uptake. Simulations show that the Hotelling filter is sensitive to typical blood peak in PET prior to tissue uptake have commenced, introducing a negative bias in early tissue uptake. Quantitation on real dynamic data is reliable. Two examples clearly show that pre-filtering the dynamic sequence with the Hotelling filter prior to Patlak-slope calculations gives clearly improved parametric image quality. We also show that a dramatic dose reduction can be achieved for Patlak slope images without changing image quality or quantitation.ConclusionsThe 2D Hotelling-filtering of dynamic PET data is a computer-efficient method that gives visually improved differentiation of different tissues, which we have observed improve manual or automated region-of-interest delineation of dynamic data. Parametric Patlak images on Hotelling-filtered data display improved clarity, compared to non-filtered Patlak slope images without measurable loss of quantitation, and allow a dramatic decrease in patient injected dose.

show abstract

Signal-to-Noise Ratio Improvement in Dynamic Contrast-enhanced CT and MR Imaging with Automated Principal Component Analysis Filtering

Abstract: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100231/-/DC1.

Cited by 20 publications

References 31 publications

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Sensitivity enhancement of (Hyper‐)CEST image series by exploiting redundancies in the spectral domain

The 2D Hotelling filter - a quantitative noise-reducing principal-component filter for dynamic PET data, with applications in patient dose reduction

Contact Info

Product

Resources

About