WFRF:(Mehnert Andrew 1967)
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03130naa a2200361 4500 | |
| 001 | oai:research.chalmers.se:ae892640-33d7-4c34-b0de-ba516f2c97b4 | |
| 003 | SwePub | |
| 008 | 171007s2007 | |||||||||||000 ||eng| | |
| 020 | a 9781424407873 | |
| 024 | 7 | a https://doi.org/10.1109/IEMBS.2007.43522252 DOI |
| 024 | 7 | a https://research.chalmers.se/publication/1787292 URI |
| 040 | a (SwePub)cth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a kon2 swepub-publicationtype |
| 072 | 7 | a ref2 swepub-contenttype |
| 100 | 1 | a Gal, Yaniv4 aut |
| 245 | 1 0 | a An evaluation of four parametric models of contrast enhancement for dynamic magnetic resonance imaging of the breast |
| 264 | 1 | c 2007 |
| 520 | a This paper presents an empirical evaluation of the goodness-of-fit (GOF) of four parametric models of contrast enhancement for dynamic resonance imaging of the breast: the Tofts, Brix, and Hayton pharmacokinetic models, and a novel empiric model. The goodness-of-fit of each model was evaluated with respect to: (i) two model-fitting algorithms (Levenberg- Marquardt and Nelder-Mead) and two fitting tolerances; and (ii) temporal resolution. In the first case the GOF was measured using data from three dynamic contrast-enhanced (DCE) MRI data sets from routine clinical examinations: one case with benign enhancement, one with malignant enhancement, and one with normal findings. Results are presented for fits to both the whole breast volume and to a selected region of interest. In the second case the GOF was measured by first fitting the models to several temporally sub-sampled versions of a custom high temporal resolution data set (subset of the breast volume containing a malignant lesion), and then comparing the fitted results to the original full temporal resolution data. Our results demonstrate that under the various optimization conditions considered, in general, both the proposed empiric model and the Hayton model fit the data equally well and that both of these models fit the data better than the Tofts and Brix models. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datorseende och robotik0 (SwePub)102072 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Vision and Robotics0 (SwePub)102072 hsv//eng |
| 650 | 7 | a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe |
| 650 | 7 | a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng |
| 700 | 1 | a Mehnert, Andrew,d 1967u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)mehnert |
| 700 | 1 | a Bradley, Andrew4 aut |
| 700 | 1 | a McMahon, Kerry4 aut |
| 700 | 1 | a Crozier, Stuart4 aut |
| 710 | 2 | a Chalmers tekniska högskola4 org |
| 773 | 0 | t Proc. 2007 Annual International Conference of the IEEE Engineering in Medicine and Biology Societyg , s. 71 - 74q <71 - 74z 9781424407873 |
| 856 | 4 | u http://dx.doi.org/10.1109/IEMBS.2007.4352225y FULLTEXT |
| 856 | 4 8 | u https://doi.org/10.1109/IEMBS.2007.4352225 |
| 856 | 4 8 | u https://research.chalmers.se/publication/178729 |
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