Dong Wang, Lori R Arlinghaus, Thomas E Yankeelov, Xiaoping Yang, David S Smith
{"title":"压缩感知动态增强乳房MRI中时间正则化的定量评价。","authors":"Dong Wang, Lori R Arlinghaus, Thomas E Yankeelov, Xiaoping Yang, David S Smith","doi":"10.1155/2017/7835749","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled <i>k</i>-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast.</p><p><strong>Methods: </strong>We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV <sub><i>α</i></sub><sup>2</sup>), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters <i>K</i><sup>trans</sup> (volume transfer constant) and <i>v</i><sub><i>e</i></sub> (extravascular-extracellular volume fraction) across a population of random sampling schemes.</p><p><strong>Results: </strong>NN produced the lowest image error (SER: 29.1), while TV/TGV <sub><i>α</i></sub><sup>2</sup> produced the most accurate <i>K</i><sup>trans</sup> (CCC: 0.974/0.974) and <i>v</i><sub><i>e</i></sub> (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate <i>K</i><sup>trans</sup> (CCC: 0.842) and <i>v</i><sub>e</sub> (CCC: 0.799).</p><p><strong>Conclusion: </strong>TV/TGV <sub><i>α</i></sub><sup>2</sup> should be used as temporal constraints for CS DCE-MRI of the breast.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2017/7835749","citationCount":"8","resultStr":"{\"title\":\"Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast.\",\"authors\":\"Dong Wang, Lori R Arlinghaus, Thomas E Yankeelov, Xiaoping Yang, David S Smith\",\"doi\":\"10.1155/2017/7835749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled <i>k</i>-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast.</p><p><strong>Methods: </strong>We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV <sub><i>α</i></sub><sup>2</sup>), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters <i>K</i><sup>trans</sup> (volume transfer constant) and <i>v</i><sub><i>e</i></sub> (extravascular-extracellular volume fraction) across a population of random sampling schemes.</p><p><strong>Results: </strong>NN produced the lowest image error (SER: 29.1), while TV/TGV <sub><i>α</i></sub><sup>2</sup> produced the most accurate <i>K</i><sup>trans</sup> (CCC: 0.974/0.974) and <i>v</i><sub><i>e</i></sub> (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate <i>K</i><sup>trans</sup> (CCC: 0.842) and <i>v</i><sub>e</sub> (CCC: 0.799).</p><p><strong>Conclusion: </strong>TV/TGV <sub><i>α</i></sub><sup>2</sup> should be used as temporal constraints for CS DCE-MRI of the breast.</p>\",\"PeriodicalId\":47063,\"journal\":{\"name\":\"International Journal of Biomedical Imaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1155/2017/7835749\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Biomedical Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2017/7835749\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/8/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Biomedical Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2017/7835749","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/8/28 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast.
Purpose: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast.
Methods: We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV α2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes.
Results: NN produced the lowest image error (SER: 29.1), while TV/TGV α2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799).
Conclusion: TV/TGV α2 should be used as temporal constraints for CS DCE-MRI of the breast.
期刊介绍：
The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to):
Digital radiography and tomosynthesis
X-ray computed tomography (CT)
Magnetic resonance imaging (MRI)
Single photon emission computed tomography (SPECT)
Positron emission tomography (PET)
Ultrasound imaging
Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography
Neutron imaging for biomedical applications
Magnetic and optical spectroscopy, and optical biopsy
Optical, electron, scanning tunneling/atomic force microscopy
Small animal imaging
Functional, cellular, and molecular imaging
Imaging assays for screening and molecular analysis
Microarray image analysis and bioinformatics
Emerging biomedical imaging techniques
Imaging modality fusion
Biomedical imaging instrumentation
Biomedical image processing, pattern recognition, and analysis
Biomedical image visualization, compression, transmission, and storage
Imaging and modeling related to systems biology and systems biomedicine
Applied mathematics, applied physics, and chemistry related to biomedical imaging
Grid-enabling technology for biomedical imaging and informatics
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