Principal component analysis of breast DCE‐MRI adjusted with a model‐based method

Eyal, Erez; Badikhi, Daria; Furman‐Haran, Edna; Kelcz, Frederick; Kirshenbaum, Kevin J.; Degani, Hadassa

doi:10.1002/jmri.21950

Cited by 37 publications

(39 citation statements)

References 38 publications

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“…Analysis of BP.-Quantitative analysis of dynamic signal enhancement in the BP was performed by using the principal component analysis (PCA) method proposed by Eyal et al (22), which decomposes DCE MR images into eigenvalues, eigenvectors, and projection coefficient maps (Fig 1). The principal components were computed by using the covariances of the original data as described in detail by Eyal et al (22).…”

Section: Analysis Of Breastmentioning

confidence: 99%

Evaluation of the Kinetic Properties of Background Parenchymal Enhancement throughout the Phases of the Menstrual Cycle

Amarosa¹,

McKellop²,

Leite³

et al. 2013

Radiology

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Purpose:To develop and apply a semiautomatic method of segmenting fibroglandular tissue to quantify magnetic resonance (MR) imaging contrast material-enhancement kinetics of breast background parenchyma (BP) and lesions throughout the phases of the menstrual cycle in women with benign and malignant lesions. Materials and Methods:The institutional review board approved this retrospective HIPAA-compliant study, and informed consent was waived. From December 2008 to August 2011, 58 premenopausal women who had undergone contrast materialenhanced MR imaging and MR imaging-guided biopsy were identified. The longest time from the start of the last known period was 34 days. One lesion per patient (37 benign and 21 malignant) was analyzed. The patient groups were stratified according to the week of the menstrual cycle when MR imaging was performed. A method based on principal component analysis (PCA) was applied for quantitative analysis of signal enhancement in the BP and lesions by using the percentage of slope and percentage of enhancement. Linear regression and the Mann-Whitney U test were used to assess the association between the kinetic parameters and the week of the menstrual cycle. Results:In the women with benign lesions, percentages of slope and enhancement for both BP and lesions during week 2 were significantly (P , .05) lower than those in week 4. Percentage of enhancement in the lesion in week 2 was lower than that in week 3 (P , .05). The MR images of women with malignant lesions showed no significant difference between the weeks for any of the parameters. There was a strong positive correlation between lesion and BP percentage of slope (r = 0.72) and between lesion and BP percentage of enhancement (r = 0.67) in the benign group. There was also a significant (P = .03) difference in lesion percentage of slope between the benign and malignant groups at week 2. Conclusion:The PCA-based method can quantify contrast enhancement kinetics of BP semiautomatically, and kinetics of BP and lesions vary according to the week of the menstrual cycle in benign but not in malignant lesions.q RSNA, 2013

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Section: Analysis Of Breastmentioning

confidence: 99%

Evaluation of the Kinetic Properties of Background Parenchymal Enhancement throughout the Phases of the Menstrual Cycle

Amarosa¹,

McKellop²,

Leite³

et al. 2013

Radiology

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“…Recently, Eyal et al 27 used the principal eigenvectors derived from principal component analysis (PCA) to determine a parametric representation of breast DCE-MRI data for lesion classification. In contrast to SE, the feature matrix in PCA is a covariance matrix.…”

Section: Previous Related Work and Motivationmentioning

confidence: 99%

“…[27][28][29] Spectral embedding (SE), a type of NLDR, uses the eigenvectors corresponding to the minimum eigenvalues derived from the eigenvalue decomposition of a weighted affinity matrix, 30 where the affinity matrix represents the pairwise dissimilarity between all the objects to be classified, obtained via a Gaussian, exponential, or polynomial kernel in the original feature space. SE also allows for parametrically representing high-dimensional data in a reduced dimensional space, and several researchers have employed SE in the context of image partitioning 30,31 and clustering.…”

Section: Introductionmentioning

confidence: 99%

Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging

Agner

Madabhushi

2013

Medical Physics

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Purpose: Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. Methods: In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. Results: On a cohort of 50 breast DCE-MRI studies, PrEIm yielded overall better region and boundary-based statistics compared to the original DCE-MR image, FCM, and PCA based image representations. Additionally, SEAC outperformed a hybrid AC applied to both PCA and FCM image representations. Mean dice similarity coefficient (DSC) for SEAC was significantly better (DSC = 0.74 ± 0.21) than FCM+AC (DSC = 0.50 ± 0.32) and similar to PCA+AC (DSC = 0.73 ± 0.22). Boundary-based metrics of mean absolute difference and Hausdorff distance followed the same trends. Of the automated segmentation methods, breast lesion classification based on morphologic features derived from SEAC segmentation using a support vector machine classifier also performed better (AUC = 0.67 ± 0.05; p < 0.05) than FCM+AC (AUC = 0.50 ± 0.07), and PCA+AC (AUC = 0.49 ± 0.07). Conclusions: In this work, we presented SEAC, an accurate, general purpose AC segmentation tool that could be applied to any imaging domain that employs time series data. SE allows for projection of time series data into a PrEIm representation so tha...

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“…Furthermore, the PCA-based approach has been applied to the DCE images of breast cancer, in which the 2nd and 3rd eigenvectors were found to be indicators of the wash-in and wash-out of the contrast agent. 15 However, although these works have shown promising results for diagnostic purposes, to the best of our knowledge, no standard approach has been proposed so far to incorporate PCA into an automatic supportive tool for decision-making in therapy assessment.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, a modelfree approach to analyze the DCE-MRI data, e.g., the methods based on factor analysis, 8,9 independent component analysis (ICA), 10 and principal component analysis (PCA), [11][12][13][14] could potentially facilitate the development of the real-time decision-making supportive tools in diagnosis and therapy assessment. PCA has shown the potential to be a very robust and fast technique in analyzing the DCE-MRI data, [11][12][13][14][15][16] and especially in prostate [12][13][14] and breast 15,16 cancer. Eyal et al 12 applied PCA to dynamic intensity-scaled (IS) and enhancement-scaled (ES) datasets to develop and evaluate a method for image processing of the dynamic contrast enhanced MRI of the prostate cancer.…”

Section: Introductionmentioning

confidence: 99%

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

Farjam¹,

Tsien²,

Lawrence³

et al. 2013

Med. Phys.

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Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PCdefined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.

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Principal component analysis of breast DCE‐MRI adjusted with a model‐based method

Cited by 37 publications

References 38 publications

Evaluation of the Kinetic Properties of Background Parenchymal Enhancement throughout the Phases of the Menstrual Cycle

Evaluation of the Kinetic Properties of Background Parenchymal Enhancement throughout the Phases of the Menstrual Cycle

Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

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