An Introduction to Dynamic Contrast-Enhanced MRI in Oncology

Gribbestad, Ingrid S.; Gjesdal, Kjell-Inge; Nilsen, G; Lundgren, Steinar; Hjelstuen, M.; Jackson, Alan

doi:10.1007/3-540-26420-5_1

Cited by 20 publications

(26 citation statements)

References 66 publications

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“…Contrast enhancement of breast lesions shows large physiologic variations, mostly depending on differences in vascular permeability (22,23) and other technical and physiological parameters, including type and dose of contrast material (24,25). Differences may depend on lesion histology, on the timing of imaging or on inhomogeneities within the lesions, such as those observed in necrotic areas or in fibrosis.…”

Section: Lesion Detectionmentioning

confidence: 99%

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Vignati

Giannini

Luca

et al. 2011

Magnetic Resonance Imaging

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Purpose: To describe and test a new fully automatic lesion detection system for breast DCE-MRI.Materials and Methods: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrastenhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (systemdetected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed.Results: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). Conclusion:The system showed promising results on MR datasets obtained from different scanners producing fatsat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

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Section: Lesion Detectionmentioning

confidence: 99%

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Vignati

Giannini

Luca

et al. 2011

Magnetic Resonance Imaging

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“…One is the shape of the curve and the other is how well the measured data fit the model curve. The shape of the curve is also conveyed by pharmacokinetic parameters (e.g., Kep Amp) (15)(16)(17), which can be color coded and overlaid on grayscale images, but the locally calculated values of the pharmacokinetic parameters may be irrelevant if the measured data do not fit the model curve well. Local GF information for the time-intensity curve is conveyed through auditory feedback while a reader draws a ROI.…”

Section: Discussionmentioning

confidence: 99%

“…2e), pharmacokinetic parameters (Amp: amplitude, Kep: redistribution rate constant, Kel: elimination rate constant; Tleg: time log; Fig. 2f) (15)(16)(17), image-slice information (Fig. 2g), and adjustable sound parameters ( Fig.…”

Section: Implementation Frameworkmentioning

confidence: 99%

Improvement in the reproducibility of region of interest using an auditory feedback loop: A pilot assessment using dynamic contrast‐enhanced (DCE) breast MR images

Chun

Clymer

Sammet

et al. 2007

Magnetic Resonance Imaging

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Purpose:To augment traditional visual data perception of complex multiparametric imaging data sets by adding auditory feedback to improve the delineation of regions of interest (ROIs) in tumor assessment in dynamic contrastenhanced (DCE) MRI. Materials and Methods:In addition to conventional display methodologies, we have created an application window which interfaces with audio output using dynamically loadable sound modules, providing goodness of fit (GF) information through auditory feedback. We have assessed effectiveness of conveying sound information with three independent readers on eight DCE-MR breast image data sets. The assessment was based on either conventional visual only mode or combined visual plus auditory mode. For statistical comparison between two sensory approaches, interobserver repeatability was measured with three different criteria.Results: Adding auditory feedback improves repeatability significantly (P Ͻ 0.01), and the enhanced sensory approach had higher repeatability than visual only mode in visually complex breast tumor cases. However, in easy and moderate cases, visual only mode was more reproducible than the combined mode with very high significance (P Ͻ 0.001). Conclusion:Adding auditory information to visual based image analysis for identifying tumor ROIs provides higher interobserver repeatability for analyzing complex multidimensional/multiparametric medical image data sets with visually difficult lesions to delineate. TODAY'S ADVANCED IMAGING scanner systems generate multiparametric and multimodality (computed tomography [CT], MR, positron emission tomography [PET], ultrasound [US]) information in a short amount of time.It is important to simultaneously access and to coherently communicate with all these different types of image information for better lesion characterization and for more accurate and reliable diagnostic image analysis in many clinical applications. As the complexity and combination of different types of acquired imaging data increase, conveying this information to a human observer in a meaningful way for diagnostic purpose becomes a bottleneck.The traditional method of analyzing medical images solely relies on the visual perception system, using grayscale values and color overlays to represent composite imaging data or showing different types of image data in a side by side display regarding which the observer must be trained to mentally align corresponding positions in the image data sets. Furthermore, the visual representation of complex medical images is often limited by screen size, two-dimensional (2D) display of three-dimensional (3D) spatial information, image resolution, and the capacity of the human visual system. Therefore, it is not easy to quickly access valuable information due to the complexity of multiparametric imaging data sets and the limits of visual perception system.Dynamic contrast-enhanced MRI (DCE-MRI) is widely used as a powerful noninvasive clinical tool for the diagnosis of tumors and for therapy monitoring of a variety of treatment...

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“…In the early stage of cancer development, the tumor is nourished by diffusion of oxygen and nutrients provided by nearby blood vessels [2], [3]. In this phase, referred to as avascular phase, the tumor cannot grow beyond 1-2 mm in diameter [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…In this phase, referred to as avascular phase, the tumor cannot grow beyond 1-2 mm in diameter [2], [3], [4]. The transition from a pre-vascular to a vascularized phase, known as "angiogenic switch", is required for the tumor to overcome this size limit.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

Turco

Wijkstra

Mischi

2016

IEEE Rev. Biomed. Eng.

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Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.

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An Introduction to Dynamic Contrast-Enhanced MRI in Oncology

Cited by 20 publications

References 66 publications

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Improvement in the reproducibility of region of interest using an auditory feedback loop: A pilot assessment using dynamic contrast‐enhanced (DCE) breast MR images

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

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