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- Langkilde, Fredrik, 1990, et al.
(författare)
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Evaluation of fitting models for prostate tissue characterization using extended-range b-factor diffusion-weighted imaging.
- 2018
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Ingår i: Magnetic resonance in medicine. - : Wiley. - 1522-2594 .- 0740-3194. ; 79:4, s. 2346-2358
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Tidskriftsartikel (refereegranskat)abstract
- To compare the fitting and tissue discrimination performance of biexponential, kurtosis, stretched exponential, and gamma distribution models for high b-factor diffusion-weighted images in prostate cancer.Diffusion-weighted images with 15 b-factors ranging from b=0 to 3500 s/mm(2) were obtained in 62 prostate cancer patients. Pixel-wise signal decay fits for each model were evaluated with the Akaike Information Criterion (AIC). Parameter values for each model were determined within normal prostate and the index lesion. Their potential to differentiate normal from cancerous tissue was investigated through receiver operating characteristic analysis and comparison with Gleason score.The biexponential slow diffusion fraction fslow , the apparent kurtosis diffusion coefficient ADCK , and the excess kurtosis factor K differ significantly among normal peripheral zone (PZ), normal transition zone (TZ), tumor PZ, and tumor TZ. Biexponential and gamma distribution models result in the lowest AIC, indicating a superior fit. Maximum areas under the curve (AUCs) of all models ranged from 0.93 to 0.96 for the PZ and from 0.95 to 0.97 for the TZ. Similar AUCs also result from the apparent diffusion coefficient (ADC) of a monoexponential fit to a b-factor sub-range up to 1250 s/mm(2) . For kurtosis and stretched exponential models, single parameters yield the highest AUCs, whereas for the biexponential and gamma distribution models, linear combinations of parameters produce the highest AUCs. Parameters with high AUC show a trend in differentiating low from high Gleason score, whereas parameters with low AUC show no such ability.All models, including a monoexponential fit to a lower-b sub-range, achieve similar AUCs for discrimination of normal and cancer tissue. The biexponential model, which is favored statistically, also appears to provide insight into disease-related microstructural changes. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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| 3. |
- Mitsouras, Dimitris, et al.
(författare)
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Three-dimensional printing of MRI-visible phantoms and MR image-guided therapy simulation.
- 2017
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Ingår i: Magnetic resonance in medicine. - : Wiley. - 1522-2594 .- 0740-3194. ; 77:2, s. 613-622
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Tidskriftsartikel (refereegranskat)abstract
- To demonstrate the use of anatomic MRI-visible three-dimensional (3D)-printed phantoms and to assess process accuracy and material MR signal properties.A cervical spine model was generated from computed tomography (CT) data and 3D-printed using an MR signal-generating material. Printed phantom accuracy and signal characteristics were assessed using 120 kVp CT and 3 Tesla (T) MR imaging. The MR relaxation rates and diffusion coefficient of the fabricated phantom were measured and (1) H spectra were acquired to provide insight into the nature of the proton signal. Finally, T2 -weighted imaging was performed during cryoablation of the model.The printed model produced a CT signal of 102±8 Hounsfield unit, and an MR signal roughly 1/3(rd) that of saline in short echo time/short repetition time GRE MRI (456±36 versus 1526±121 arbitrary signal units). Compared with the model designed from the in vivo CT scan, the printed model differed by 0.13±0.11mm in CT, and 0.62±0.28mm in MR. The printed material had T2 ∼32 ms, T2*∼7 ms, T1 ∼193 ms, and a very small diffusion coefficient less than olive oil. MRI monitoring of the cryoablation demonstrated iceball formation similar to an in vivo procedure.Current 3D printing technology can be used to print anatomically accurate phantoms that can be imaged by both CT and MRI. Such models can be used to simulate MRI-guided interventions such as cryosurgeries. Future development of the proposed technique can potentially lead to printed models that depict different tissues and anatomical structures with different MR signal characteristics. Magn Reson Med, 2016. © 2016 Wiley Periodicals, Inc.
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