Готові списки джерел за темами / Dynamic contrast

Добірка наукової літератури з теми "Dynamic contrast"

Автор: Grafiati
Опубліковано: 29 квітня 2023

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Статті в журналах з теми "Dynamic contrast"

1

Halder, Amiya. "Dynamic Contrast Enhancement Algorithm." International Journal of Computer Applications 74, no. 12 (July 26, 2013): 1–4. http://dx.doi.org/10.5120/12934-9879.

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2

Husband, J. E. "Fast dynamic contrast MRI." European Journal of Cancer 35 (September 1999): S306. http://dx.doi.org/10.1016/s0959-8049(99)81654-2.

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3

Miles, K. A. "Dynamic contrast enhanced MR." Clinical Radiology 51, no. 1 (January 1996): 78. http://dx.doi.org/10.1016/s0009-9260(96)80231-5.

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4

Buckley, D., S. Blackband, and R. Kerslake. "Dynamic contrast enhanced MR." Clinical Radiology 51, no. 1 (January 1996): 78–79. http://dx.doi.org/10.1016/s0009-9260(96)80232-7.

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5

Victor, Jonathan D., Mary M. Conte, and Keith P. Purpura. "Dynamic shifts of the contrast-response function." Visual Neuroscience 14, no. 3 (May 1997): 577–87. http://dx.doi.org/10.1017/s0952523800012232.

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AbstractWe recorded visual evoked potentials in response to square-wave contrast-reversal checkerboards undergoing a transition in the mean contrast level. Checkerboards were modulated at 4.22 Hz (8.45-Hz reversal rate). After each set of 16 cycles of reversals, stimulus contrast abruptly switched between a “high” contrast level (0.06 to 1.0) to a “low” contrast level (0.03 to 0.5). Higher contrasts attenuated responses to lower contrasts by up to a factor of 2 during the period immediately following the contrast change. Contrast-response functions derived from the initial second following a conditioning contrast shifted by a factor of 2–4 along the contrast axis. For low-contrast stimuli, response phase was an advancing function of the contrast level in the immediately preceding second. For high-contrast stimuli, response phase was independent of the prior contrast history. Steady stimulation for periods as long as 1 min produced only minor effects on response amplitude, and no detectable effects on response phase. These observations delineate the dynamics of a contrast gain control in human vision.
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6

Padhani, A. R. "Dynamic contrast-enhanced MR imaging." Cancer Imaging 1, no. 1 (2000): 52–63. http://dx.doi.org/10.1102/1470-7330/00/010052+12.

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7

Kelemen, Zsolt, Ruikang Zhang, Lionel Gissot, Raja Chouket, Yannick Bellec, Vincent Croquette, Ludovic Jullien, Jean-Denis Faure, and Thomas Le Saux. "Dynamic Contrast for Plant Phenotyping." ACS Omega 5, no. 25 (June 16, 2020): 15105–14. http://dx.doi.org/10.1021/acsomega.0c00957.

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Lenkinski, Robert E. "Dynamic contrast-enhanced MR studies." Academic Radiology 10, no. 9 (September 2003): 961–62. http://dx.doi.org/10.1016/s1076-6332(03)00296-4.

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Buxton, Richard B. "Dynamic models of BOLD contrast." NeuroImage 62, no. 2 (August 2012): 953–61. http://dx.doi.org/10.1016/j.neuroimage.2012.01.012.

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10

Brix, Gunnar, Ursula Lechel, Markus Petersheim, Radko Krissak, and Christian Fink. "Dynamic Contrast-Enhanced CT Studies." Investigative Radiology 46, no. 1 (January 2011): 64–70. http://dx.doi.org/10.1097/rli.0b013e3181f33b35.

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Дисертації з теми "Dynamic contrast"

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Cardenas, Rodriguez Julio César. "New Models and Contrast Agents for Dynamic Contrast-Enhanced MRI." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/222845.

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Angiogenesis is a fundamental driver of tumor biology and many other important aspect of human health. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been shown to be a valuable biomarker for the indirect assessment of angiogenesis. However, DCE-MRI is very specialized technique that has limitations. In this dissertation new models and contrast agents to address some of these limitations are presented. Chapter 1 presents an introduction to DCE-MRI, the rationale to asses tumor biology with this technique, the MRI pulses sequences and the standard pharmacokinetic modeling used for the analysis of DCE- MRI data. Chapter 2 describes the application of DCE-MRI to asses the response to the hypoxia-activated drug TH-302. It is shown that DCE-MRI can detect a response after only 24 hours of initiating therapy. In Chapter 3, a new model for the analysis of DCE-MRI is presented, the so-called Linear Reference Region Model (LRRM). This new model improves upon existing models and it was demonstrated that it is ~620 faster than current algorithms and 5 times less sensitive to noise, and more importantly less sensitive to temporal resolution which enables the analysis of DCE-MRI data obtained in the clinical setting, which opens a new area of study in clinical MRI. Chapter 4 describes the extension of the LRRM to estimate the absolute permeability of two fluorinated contrast agents; we call this approach the Reference Agent Model (RAM). In order to make this new model an experimental reality, a novel pulse sequence and contrast agents (CA) for ¹⁹F MRI were developed. Two contributions to the field of DCE-MRI are presented in this chapter, the first simultaneous ¹⁹F-DCE-MRI detection of two fluorinated CA in a mouse model of breast cancer, and the estimation of their relative permeability. RAM eliminates some of the physiological variables that affect DCE-MRI, which may improve its sensitivity and specificity. Finally, new potential applications of LRRM and RAM are discussed in Chapter 5.
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Olesko, Brian M. "Dynamic contrast sensitivity : methods and measurements /." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09052009-040416/.

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Garpebring, Anders. "Contributions to quantitative dynamic contrast-enhanced MRI." Doctoral thesis, Umeå universitet, Radiofysik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-49773.

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Background: Dynamic contrast-enhanced MRI (DCE-MRI) has the potential to produce images of physiological quantities such as blood flow, blood vessel volume fraction, and blood vessel permeability. Such information is highly valuable, e.g., in oncology. The focus of this work was to improve the quantitative aspects of DCE-MRI in terms of better understanding of error sources and their effect on estimated physiological quantities. Methods: Firstly, a novel parameter estimation algorithm was developed to overcome a problem with sensitivity to the initial guess in parameter estimation with a specific pharmacokinetic model. Secondly, the accuracy of the arterial input function (AIF), i.e., the estimated arterial blood contrast agent concentration, was evaluated in a phantom environment for a standard magnitude-based AIF method commonly used in vivo. The accuracy was also evaluated in vivo for a phase-based method that has previously shown very promising results in phantoms and in animal studies. Finally, a method was developed for estimation of uncertainties in the estimated physiological quantities. Results: The new parameter estimation algorithm enabled significantly faster parameter estimation, thus making it more feasible to obtain blood flow and permeability maps from a DCE-MRI study. The evaluation of the AIF measurements revealed that inflow effects and non-ideal radiofrequency spoiling seriously degrade magnitude-based AIFs and that proper slice placement and improved signal models can reduce this effect. It was also shown that phase-based AIFs can be a feasible alternative provided that the observed difficulties in quantifying low concentrations can be resolved. The uncertainty estimation method was able to accurately quantify how a variety of different errors propagate to uncertainty in the estimated physiological quantities. Conclusion: This work contributes to a better understanding of parameter estimation and AIF quantification in DCE-MRI. The proposed uncertainty estimation method can be used to efficiently calculate uncertainties in the parametric maps obtained in DCE-MRI.
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4

Kubassova, Olga. "Analysis of dynamic contrast enhanced MRI datasets." Thesis, University of Leeds, 2007. http://etheses.whiterose.ac.uk/1359/.

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The purpose of this research is to perform automated analysis of 4D dynamic contrast enhanced MRI datasets (DCE-MRI) of the habd and wrist relating to rheumatoid arthritis (RA) studies. In DCE-MRI, sequences of images are acquired from the joints over time, during which a contrast agent pre-injected into a patient enhances disease affected tissues. Measurement of this enhancement, which is specific to voxels representing particular tissue types, allows assessment of the patient's condition. Currently, analysis of DCE-MRI data is performed using semi-automated or manula techniques, which are time-consuming and subjective. These approaches involve no pre-processing techniques that can compensate for patient motion and hardware instability, or locate the tissue of interest. In this thesis we present a solution for fully automated objective assessment of DCE-MRI data acquired from RA patients. Analysis begins with application of a registration technique that permits compensation for patient motion. Secondly, independent automatic algorithms for accurate segmentation of both bone interiors, joint exteriors, and blood vessels from data volumes of the metacarpophalangeal joints are introduced. Performance of the segmentation algorithms is evaluated with both state-of-the-art and novel techniques developed as a part of this thesis. We have utilised and enhanced a supervised approach and developed a family of unsupervised metrics for automated evaluation of segmentation outputs. Lastly, the datasets are interpreted using a model-based approach, which permits understanding of the behaviour of tissues undergoing the medical procedure, and allows for a robust and accurate extraction of various parameters that quantify the extent of inflammation in RA patients. The algorithms proposed have been demonstrated on datasets acquired with both low and high field scanners, from different joints, using various pulse sequences. They are user-independent, time efficient, and generate easily reproducible and objective results. Expert observers found our results promising for possibly future diagnosis and monitoring of RA patients.
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Ingrisch, Michael. "Quantification of cerebral hemodynamics with dynamic contrast-enhanced MRI." Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-149513.

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Che, Ahmad Azlan. "Dynamic contrast-enhanced MRI of breast cancer at 3T." Thesis, University of Aberdeen, 2011. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=165831.

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3T MRI provides higher signal-to-noise ratio images compared to lower field machines. However, a major drawback of 3T MRI is a higher B1 transmission-field inhomogeneity across the field-of-view compared to imaging at lower fields. B1-field mapping was performed on volunteers using a Philips 3.0T MR scanner and a typical head-first prone patient positioning technique. The B1-field transmitted in the breasts was found to be reduced towards the right side of the body. In some volunteers, the B1-field was reduced to about one-half of the nominal field in the right breast. To minimize the B1 inhomogeneity artefacts, a saturation recovery snapshot FLASH (SRSF) imaging sequence was proposed. Different saturation techniques were assessed. The best saturation efficiency was produced by Hoffmann’s saturation method. By using Hoffmann’s SRSF sequence, the error in the enhancement ratio (ER) can be reduced to about one half compared to imaging obtained using typical FLASH sequence in the presence of a 50% B1-field reduction. Other techniques i.e. bilateral power optimization and a dedicated patient support system were also tested. Both of these approaches produced substantial reductions of the B1 inhomogeneity seen with the standard technique. To address the effects of the native T1 (T10) of different tissues on DCE-MRI, novel enhancement factor indices calculated using SRSF sequence images were introduced and assessed. Computer simulations and gel phantom experiments showed that less error was observed in the indices calculated compared to the ER calculated using the conventional and widely used FLASH sequence. Furthermore, the effect of B1-field inhomogeneity on the novel indices is also reduced. One of the indices proposed is directly related to the contrast agent concentration. The theory and results presented show that the SRSF pulse sequence and the quantification techniques proposed have the potential to improve the accuracy of breast DCE-MRI at 3T.
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7

Fransson, Samuel. "Validation and Robustness Analysis of Dynamic Contrast Enhanced MRI." Thesis, Umeå universitet, Institutionen för fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-107987.

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In Dynamic Contrast Enhanced MRI there are several steps from the initial signal to obtaining the pharmacokinetic parameters for tumor characterization. The aim of this work was to validate the steps in the flow of data focusing on T1-mapping, Contrast Agent (CA)-quantification and the pharmacokinetical (PK) model, using a digital phantom of a head. In the Digital Phantom tissues are assigned necessary values to obtain both a regular and contrast enhanced (using Parker AIF) representation and simulating an SPGR signal. The data analysis was performed in a software called MICE, as well as the Digital Phantom developed at the department of Radiation Sciences at Umeå University. The method of variable flip angles for the T1-mapping was analyzed with respect to SNR and number of flip angles, finding that the median value in each tissue is correct and stable. A "two point" inversion recovery sequence was tested with optimal combination of inversion times for white matter and CSF and obtaining correct T1-values when the inversion times were close to the tissue T1, otherwise with large deviations seen. Three different methods for CA-quantification were analyzed and a large underestimation was found assuming a linearity between signal and CA-concentration mainly for vessels at about 60%, but also for other tissue such as white matter at about 15%, improving when the assumption was removed. Still there was a noticeable underestimation of 30% and 10% and the quantification was improved further, achieving a near perfect agreement with the reference concentration, taking the T2*-effect into account. Applying Kety-model, discarding the vp-term, Ktrans was found to be stable with respect to noise in the tumor rim but ve noticeably underestimated with about 50%. The effect of different bolus arrival time, shifting the AIF required in the PK-model with respect to the CA-concentration, was tested with values up to 5 s, obtaining up to about 5% difference in Ktrans as well as the effect of a vascular transport function obtained by the means of an effective mean transit time up to 5 s and up to about 5% difference in Ktrans.
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8

Dickie, Ben. "Predicting cancer patient survival using dynamic contrast enhanced MRI." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/predicting-cancer-patient-survival-using-dynamic-contrast-enhanced-mri(146dfe97-f892-4cdf-b916-633e9247093e).html.

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This thesis describes the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to study the prognostic role of microvascular physiology and heterogeneity in locally advanced cancers of the cervix, bladder, and head and neck. To increase the utility of DCE-MRI parameters for prognostication and use in heterogeneity analyses, a novel model fitting approach was developed to reduce the error in two-compartment exchange model (2CXM) parameter estimates. Using this method, precision of 2CXM parameters was increased in 35 of 42 experimental conditions (improvements between 4.7% and 50%) and bias reduced in 30 of 42 conditions (reductions between 1.8% and 49%). The prognostic value of plasma flow, permeability surface area product, and contrast agent volume transfer constant were assessed in a cervix cancer dataset. Plasma flow was the most prognostic parameter (HR = 0.25, P = 0.0086), followed by the volume transfer constant (HR = 0.33, P = 0.031), then the permeability surface area product (HR = 0.43, P = 0.090). Inclusion of plasma flow in survival modelling significantly increased the ability to discriminate between patients with short and long disease-free survival, compared to clinicopathologic factors alone (P = 0.043). The universal prognostic value of microvascular heterogeneity was assessed in cervix, bladder, and head and neck datasets. Following estimation of 2CXM parameters for each patient, a selection of previously published heterogeneity biomarkers were computed and entered into a random survival forest variable selection algorithm. Two variables (vvas, Atrans) were identified as universally prognostic and significantly improved discriminative ability of survival models compared to clinicopathologic factors alone (P < 0.001). Gaussian process models were used to decompose statistical and spatial aspects of intratumoural microvascular heterogeneity. When applied to the three cancer datasets described above, statistical variance in plasma flow (P = 0.00025) was universally prognostic and showed greater discriminative ability compared with spatial scale and average microvascular function parameters. The results of this thesis demonstrate that joint fitting reduces error in DCE-MRI parameters. DCE-MRI estimates of plasma flow appear to hold greater prognostic value than the volume transfer constant and permeability surface area product, and microvascular heterogeneity has potential to provide universal prognostic value. The biomarkers vvas, Atrans, and variance in plasma flow, were identified as universally prognostic. Future work should test the reproducibility of these biomarkers for prognostication in independent datasets.
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9

Williams, Emma Jane. "Modelling of MRI dynamic susceptibility contrast in the brain." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625081.

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Yip, Ka Yue. "Contrast enhancement for tone-mapped high dynamic range images /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?ECED%202009%20YIP.

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Книги з теми "Dynamic contrast"

1

D, Jackson Alan Ph, Buckley D. 1969-, and Parker, G. J. M. 1971-, eds. Dynamic contrast-enhanced magnetic resonance imaging in oncology. Berlin: Springer, 2003.

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2

Bard, Robert L. Dynamic Contrast-Enhanced MRI Atlas of Prostate Cancer. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78423-4.

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Jackson, Alan, David L. Buckley, and Geoffrey J. M. Parker, eds. Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b137553.

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4

Dynamic computer tomography: Basic principles and clinical applications. Berlin: Springer-Verlag, 1985.

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5

Macleod, W. B. Involuntary unemployment in dynamic contract equilibria. Southampton: University of Southampton, Dept. of Economics, 1986.

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6

Hart, Oliver D. Contract renegotiation and coasian dynamics. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1987.

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7

Rockcastle, Siobhan, and Marilyne Andersen. Annual Dynamics of Daylight Variability and Contrast. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5233-0.

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8

Prat, Julien. Dynamic incentive contracts under parameter uncertainty. Cambridge, MA: National Bureau of Economic Research, 2010.

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9

Laffont, Jean-Jacques. The dynamics of incentive contracts. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1986.

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10

Lockwood, Ben. Dynamic equilibrium: Game theory, contracts and search. [s.l.]: typescript, 1986.

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Частини книг з теми "Dynamic contrast"

1

Claussen, Claus, and Bernd Lochner. "Contrast Media." In Dynamic Computed Tomography, 21–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-69733-3_4.

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2

Claussen, Claus, and Bernd Lochner. "Bolus Geometry and Dynamics: Contrast Studies with Intravenous Contrast Media." In Dynamic Computed Tomography, 33–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-69733-3_5.

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3

Chatterjee, Aritrick, Federico Pineda, Gregory S. Karczmar, and Aytekin Oto. "Dynamic Contrast-Enhanced Imaging." In Prostate MRI Essentials, 75–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45935-2_6.

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4

Moroz, Jennifer, and Stefan A. Reinsberg. "Dynamic Contrast-Enhanced MRI." In Preclinical MRI, 71–87. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7531-0_5.

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Giannatempo, Giuseppe Maria, Tommaso Scarabino, Teresa Popolizio, Tullio Parracino, Ettore Serricchio, and Annalisa Simeone. "3.0 T Perfusion MRI Dynamic Susceptibility Contrast and Dynamic Contrast-Enhanced Techniques." In High Field Brain MRI, 113–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44174-0_9.

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Verstraete, K. L., and J. L. Bloem. "Dynamic Contrast-Enhanced Magnetic Resonance Imaging." In Imaging of Soft Tissue Tumors, 73–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30792-3_6.

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Verstraete, K. L., and H. J. van der Woude. "Dynamic Contrast-enhanced Magnetic Resonance Imaging." In Imaging of Soft Tissue Tumors, 83–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-07856-3_6.

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Verstraete, K. L., and H. J. van der Woude. "Dynamic Contrast-Enhanced Magnetic Resonance Imaging." In Imaging of Soft Tissue Tumors, 89–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-07859-4_6.

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O’Connor, James P. B., Geoff J. M. Parker, and Alan Jackson. "Dynamic Contrast-Enhanced Magnetic Resonance Imaging." In Encyclopedia of Cancer, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27841-9_1756-4.

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O’Connor, James P. B., Geoff J. M. Parker, and Alan Jackson. "Dynamic Contrast-Enhanced Magnetic Resonance Imaging." In Encyclopedia of Cancer, 1439–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46875-3_1756.

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Тези доповідей конференцій з теми "Dynamic contrast"

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Rho, Junsuk. "Dynamic metasurfaces for holography and structural coloration." In High Contrast Metastructures XII, edited by Jonathan A. Fan, Connie J. Chang-Hasnain, and Weimin Zhou. SPIE, 2023. http://dx.doi.org/10.1117/12.2647173.

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Paniagua-Domínguez, Ramón, Shiqiang Li, Xuewu Xu, Mingyu Sun, Rasna Maruthiyodan Veetil, Xinan Liang, and Arseniy I. Kuznetsov. "Dynamic control at visible wavelengths of all-dielectric metasurfaces embedded in liquid crystals (Conference Presentation)." In High Contrast Metastructures VIII, edited by Connie J. Chang-Hasnain, Weimin Zhou, and Andrei Faraon. SPIE, 2019. http://dx.doi.org/10.1117/12.2507714.

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Vesely, Pavel. "Phase-contrast microscopy in the in-vitro study of neoplastic cell dynamic morphology and behavior as related to malignancy." In Phase Contrast and Differential Interference Contrast Imaging Techniques and Applications, edited by Maksymilian Pluta and Mariusz Szyjer. SPIE, 1994. http://dx.doi.org/10.1117/12.171869.

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Li, Shi-Qiang, Xuewu Xu, Rasna Maruthiyodan Veetil, Parikshit Moitra, Xinan Liang, Vytautas Valuckas, Ramón Paniagua-Domínguez, and Arseniy I. Kuznetsov. "Dynamic control of visible light with dielectric nanoantennas: towards next-gen spatial light modulators (Conference Presentation)." In High Contrast Metastructures IX, edited by Connie J. Chang-Hasnain, Weimin Zhou, and Andrei Faraon. SPIE, 2020. http://dx.doi.org/10.1117/12.2547188.

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Jafari-Khouzani, Kourosh, Elizabeth Gerstner, Bruce Rosen, and Jayashree Kalpathy-Cramer. "Upsampling dynamic contrast enhanced MRI." In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015). IEEE, 2015. http://dx.doi.org/10.1109/isbi.2015.7164047.

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6

Wong, Philip, Ivan Kosik, and Jeffrey J. L. Carson. "Dynamic contrast-enhanced 3D photoacoustic imaging." In SPIE BiOS, edited by Alexander A. Oraevsky and Lihong V. Wang. SPIE, 2013. http://dx.doi.org/10.1117/12.2004647.

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7

Hillman, Elizabeth M. C., Matthew B. Bouchard, Sean A. Burgess, Kirk Gossage, James Mansfield, and Richard M. Levenson. "Dynamic Molecular Imaging: Anatomical co-registration and dynamic contrast enhancement." In Biomedical Optics. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/biomed.2008.bwe1.

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8

Oh, Young-Tack, Eunsook Ko, and Hyunjin Park. "TDM-Stargan: Stargan Using Time Difference Map to Generate Dynamic Contrast-Enhanced Mri from Ultrafast Dynamic Contrast-Enhanced Mri." In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761463.

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9

Oh, Young-Tack, Eunsook Ko, and Hyunjin Park. "TDM-Stargan: Stargan Using Time Difference Map to Generate Dynamic Contrast-Enhanced Mri from Ultrafast Dynamic Contrast-Enhanced Mri." In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761463.

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10

Khorin, Pavel, and Sergey Karpeev. "Phase contrast Zernike method with dynamic transparent application." In optical-technologies-in-telecommunications-2018, edited by Anton V. Bourdine, Vladimir A. Burdin, Oleg G. Morozov, Albert H. Sultanov, and Vladimir A. Andreev. SPIE, 2019. http://dx.doi.org/10.1117/12.2527258.

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Звіти організацій з теми "Dynamic contrast"

1

Maidment, Andrew. Dynamic Contrast-Enhanced Digital Breast Tomosynthesis. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada568012.

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2

Maidment, Andrew D. Dynamic Contrast-Enhanced Digital Breast Tomosynthesis. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada591085.

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3

Pineda, Federico. Improvements in Diagnostic Accuracy with Quantitative Dynamic Contrast Enhanced MRI. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada574372.

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4

Pineda, Federico, and Gregory Karczmar. Improvements in Diagnostic Accuracy with Quantitative Dynamic Contrast-Enhanced MRI. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada604831.

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5

Pineda, Federico. Improvements in Diagnostic Accuracy with Quantitative Dynamic Contrast-Enhanced MRI. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada558442.

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6

Pineda, Federico, and Gregory Karczmar. Improvements in Diagnostic Accuracy with Quantitative Dynamic Contrast-Enhanced MRI. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada615005.

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7

Khalsa, Kimberly A., and Jeffery A. Fessler. Regularized Reconstruction of Dynamic Contrast-Enhanced MR Images for Evaluation of Breast Lesions. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada535361.

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8

Khalsa, Kimberly A. Regularized Reconstruction of Dynamic Contrast-Enhanced MR Images for Evaluation of Breast Lesions. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada542286.

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9

Khalsa, Kimberly A. Regularized Reconstruction of Dynamic Contrast-Enhanced MR Images for Evaluation of Breast Lesions. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada514609.

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10

Clayton, Daniel, Daniel Guerrero, David Schwellenbach, Craig Kruschwitz, Dan Stutman, and Kevin Tritz. X-Ray Phase Contrast Imaging for Dynamic Material Mix Experiments, LAO-003-17, Year 3 of 3. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1764722.

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