A visual analytics approach to diagnosis of breast DCE-MRI data

Glaíer, Sylvia; Preim, Uta; Tönnies, Klaus D.; Preim, Bernhard

doi:10.1016/j.cag.2010.05.016

Cited by 24 publications

(19 citation statements)

References 12 publications

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“…By arranging several maps side-by-side, we fulfill the requirement to show multiple parameters. Here, we considered the alternative to display more than one quantitative parameter per map by color-mixing or by encoding more than one property per tile with an approach as shown in [12]. However, we chose the side-by-side version, since a complex encoding strategy would decrease comprehensibility and complicate pattern identification for the users.…”

Section: Requirements and Design Choicesmentioning

confidence: 98%

Stent Maps — Comparative Visualization for the Prediction of Adverse Events of Transcatheter Aortic Valve Implantations

Born

Sündermann

Russ

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Transcatheter aortic valve implantation (TAVI) is a minimally-invasive method for the treatment of aortic valve stenosis in patients with high surgical risk. Despite the success of TAVI, side effects such as paravalvular leakages can occur postoperatively. The goal of this project is to quantitatively analyze the co-occurrence of this complication and several potential risk factors such as stent shape after implantation, implantation height, amount and distribution of calcifications, and contact forces between stent and surrounding structure. In this paper, we present a two-dimensional visualization (stent maps), which allows (1) to comprehensively display all these aspects from CT data and mechanical simulation results and (2) to compare different datasets to identify patterns that are typical for adverse effects. The area of a stent map represents the surface area of the implanted stent - virtually straightened and uncoiled. Several properties of interest, like radial forces or stent compression, are displayed in this stent map in a heatmap-like fashion. Important anatomical landmarks and calcifications are plotted to show their spatial relation to the stent and possible correlations with the color-coded parameters. To provide comparability, the maps of different patient datasets are spatially adjusted according to a corresponding anatomical landmark. Also, stent maps summarizing the characteristics of different populations (e.g. with or without side effects) can be generated. Up to this point several interesting patterns have been observed with our technique, which remained hidden when examining the raw CT data or 3D visualizations of the same data. One example are obvious radial force maxima between the right and non-coronary valve leaflet occurring mainly in cases without leakages. These observations confirm the usefulness of our approach and give starting points for new hypotheses and further analyses. Because of its reduced dimensionality, the stent map data is an appropriate input for statistical group evaluation and machine learning methods.

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Section: Requirements and Design Choicesmentioning

confidence: 98%

Stent Maps — Comparative Visualization for the Prediction of Adverse Events of Transcatheter Aortic Valve Implantations

Born

Sündermann

Russ

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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“…The use of MRI as a diagnostic tool for detecting breast cancer began in the 1970s (2,6). With the introduction of contrast agents, advances in surface coil technology, and development of new imaging protocols, MRI has emerged as a promising modality for detection, diagnosis, and staging of breast cancer (1, 6 -9).…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

“…The heterogeneity of tumor vascularization, the proximity of necrotic and vital tumor tissue, and the subjectiveness of ROI placement complicate the interpretation of the kinetics and may even result in unrevealed malignant tissue. This could happen, if an ROI covers malignant and benign tissue, with an average curve shape indicating benignity (2,7,8,10). Whereas it is still not standardized the method for generating kinetic curves is accepted as both inter-and intraobserver variability (7,8,10,11,20,21).…”

Section: Dynamic Contrast Enhanced Mri (Dce Mri)mentioning

confidence: 99%

Nuclear Magnetic Resonance as a Diagnostic Tool in Breast Cancer

Hnilicová¹,

Dobrota²

2012

Acta Medica Martiniana

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A b s t r a c tThe early detection and treatment of breast cancer is of direct benefit to patients. Magnetic resonance imaging (MRI) is a promising modality for detection, diagnosis, and staging of breast cancer. MRI enables two methods: the diffusion-weighted MRI (DW MRI) and the dynamic contrast enhanced MRI (DCE MRI). DW MRI reflects the diffusion of water molecules in the extracellular fluid space and allows the estimation of cellularity and tissue structure. The value of the diffusion of water in tissue is called the apparent diffusion coefficient (ADC). ADC values in malignant lesions are smaller than in benign tissue. DCE MRI yields appropriate pharmacokinetic data of physiological parameters that relate to tissue perfusion, microvascular vessel wall permeability and extracellular volume fraction. Gadolinium based contrast agent is usually used in breast DCE MRI diagnostics. Changes in the post-contrast signal intensity help to distinguish lesions according to characteristically enhanced accumulation of contrast agent. Malignant lesions are characterized by a faster and stronger signal enhancement than benign lesions which relate to their neoangiogenesis. Over the last few years, there has been appreciable interest in the use of magnetic resonance spectroscopy (MRS) for the non-invasive analysis of breast tisue metabolites. One of the spectroscopic hallmarks of the neoplastic process appears to be the presence of total choline signal in the in vivo spectrum. Despite the fact that MRI and MRS achieve excellent results, they are still not so frequently used in comparison to mammography and breast ultrasound.

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“…The accuracy of early detection and/or diagnosis using the nonparametric DCE-MRI measurements has been tested and improved in a number of CAD systems [52,56,[105][106][107][108][109][110][111][112][113][114][115][116][117]. An approach for the extraction and visualization of perfusion parameters of breast DCE-MRI was proposed by Glaßer et al [105]. To reveal the most suspicious region and the heterogeneity of the tumor, their study employed voxel-wise parametric maps of relative enhancement of breast tumors.…”

Section: Clinical Applications Of Nonparametric Approachesmentioning

confidence: 99%

“…These studies try to correlate DCE-MRI measurements with diseases. The early DCE-MRI-based diagnosis was explored in different clinical studies, including head and neck [89], cardiac [90][91][92][93][94][95][96][97][98], pelvic [99], rectal [100], pancreatic cancer [101], liver [102], lung [103], colon [104], breast [52,56,[105][106][107][108][109][110][111][112][113][114][115][116][117], renal [3,[118][119][120][121][122][123][124][125][126][127][128][129], and prostate [75,77,[130][131]…”

Section: Clinical Applications Of Nonparametric Approachesmentioning

confidence: 99%

Analysis of contrast-enhanced medical images.

Khalifa¹

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DEDICATIONThis dissertation is dedicated to my mother. Words cannot express how much I wish that she was still with me to see the completion of this endeavor.iii ACKNOWLEDGMENTSIn the name of Allah the most merciful, the most compassionate. All deepest thanks are due to Almighty Allah for the uncountable gifts given to me.I would like to thank my dissertation advisor, Dr. Ayman El-Baz, for his continuous encouragement, guidance, and support over the course of my Ph.D. study. I thank him for giving me the opportunity to work in his research group and providing me the opportunity to work on promising applications and meet interesting people. Without his expertise, guidance, and commitment, my research would not have been possible. I truly appreciate his optimism whenever things looked impossible.I also express my deepest gratitude to Dr. Karla Conn Welch, my dissertation coadvisor, and Dr. Tamer Inanc, Dr. Cindy Harnett, Dr. Olfa Nasraoui, and Dr. Jake Wildstrom for being on my dissertation committee with enthusiasm and taking interest in my research in the midst of many other responsibilities and commitments.I also have to thank Dr. Georgy Gimel'farb for his useful discussions and valuable comments and feedback. He has never hesitated to share his experience in Markov-Gibbs random field and the field of image processing. I also would like to thank Dr. Mohamed Abou El-Ghar of the radiology department, Urology and Nephrology Center, University of Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection,(ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer.However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfuvi sion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building n...

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A visual analytics approach to diagnosis of breast DCE-MRI data

Cited by 24 publications

References 12 publications

Stent Maps — Comparative Visualization for the Prediction of Adverse Events of Transcatheter Aortic Valve Implantations

Stent Maps — Comparative Visualization for the Prediction of Adverse Events of Transcatheter Aortic Valve Implantations

Nuclear Magnetic Resonance as a Diagnostic Tool in Breast Cancer

Analysis of contrast-enhanced medical images.

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