共查询到20条相似文献,搜索用时 24 毫秒
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Three‐dimensional T2‐weighted imaging for liver MRI: Clinical values of tissue‐specific variable refocusing flip‐angle turbo spin echo imaging 下载免费PDF全文
Yukihisa Takayama MD PhD Akihiro Nishie MD PhD Yoshiki Asayama MD PhD Yasuhiro Ushijima MD PhD Nobuhiro Fujita MD PhD Dai Shimamoto MD Takashi Yoshiura MD PhD Makoto Obara PhD Atsushi Takemura BSc Masami Yoneyama MT Hiroshi Honda MD PhD 《Journal of magnetic resonance imaging : JMRI》2015,41(2):339-346
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M. A. Cloos N. Boulant M. Luong G. Ferrand E. Giacomini D. Le Bihan A. Amadon 《Magnetic resonance in medicine》2012,67(1):72-80
With Transmit SENSE, we demonstrate the feasibility of uniformly exciting a volume such as the human brain at 7T through the use of an original minimalist transmit k‐space coverage, referred to as “kT‐points.” Radio‐frequency energy is deposited only at a limited number of k‐space locations in the vicinity of the center to counteract transmit sensitivity inhomogeneities. The resulting nonselective pulses are short and need little energy compared to adiabatic or other B‐robust pulses available in the literature, making them good candidates for short‐repetition time 3D sequences at high field. Experimental verification was performed on three human volunteers at 7T by means of an 8‐channel transmit array system. On average, whereas the standard circularly polarized excitation resulted in a 33%‐flip angle spread (standard deviation over mean) throughout the brain, and a static radio‐frequency shim showed flip angle variations of 17% and up, application of kT‐point‐based excitations demonstrated excellent flip angle uniformity (8%) for a small target flip angle and with sub‐millisecond durations. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc. 相似文献
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Contrast enrichment of spinal cord MR imaging using a ratio of T1‐weighted and T2‐weighted signals 下载免费PDF全文
Masatoshi Teraguchi MD Hiroshi Yamada MD PhD Munehito Yoshida MD PhD Yoshiaki Nakayama MD Tomoyoshi Kondo MD PhD Hidefumi Ito MD PhD Masaki Terada MD PhD Yoshiki Kaneoke MD PhD 《Journal of magnetic resonance imaging : JMRI》2014,40(5):1199-1207
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Neha Bhooshan Maryellen Giger Li Lan Hui Li Angelica Marquez Akiko Shimauchi Gillian M. Newstead 《Magnetic resonance in medicine》2011,66(2):555-564
A multiparametric computer‐aided diagnosis scheme that combines information from T1‐weighted dynamic contrast–enhanced (DCE)‐MRI and T2‐weighted MRI was investigated using a database of 110 malignant and 86 benign breast lesions. Automatic lesion segmentation was performed, and three categories of lesion features (geometric, T1‐weighted DCE, and T2‐weighted) were automatically extracted. Stepwise feature selection was performed considering only geometric features, only T1‐weighted DCE features, only T2‐weighted features, and all features. Features were merged with Bayesian artificial neural networks, and diagnostic performance was evaluated by ROC analysis. With leave‐one‐lesion‐out cross‐validation, an area under the ROC curve value of 0.77 ± 0.03 was achieved with T2‐weighted‐only features, indicating high diagnostic value of information in T2‐weighted images. Area under the ROC curve values of 0.79 ± 0.03 and 0.80 ± 0.03 were obtained for geometric‐only features and T1‐weighted DCE‐only features, respectively. When all features were considered, an area under the ROC curve value of 0.85 ± 0.03 was achieved. We observed P values of 0.006, 0.023, and 0.0014 between the geometric‐only, T1‐weighted DCE‐only, and T2‐weighted‐only features and all features conditions, respectively. When ranked, the P values satisfied the Holm–Bonferroni multiple‐comparison test; thus, the improvement of multiparametric computer‐aided diagnosis was statistically significant. A computer‐aided diagnosis scheme that combines information from T1‐weighted DCE and T2‐weighted MRI may be advantageous over conventional T1‐weighted DCE‐MRI computer‐aided diagnosis. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc. 相似文献
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Matthew L. Zierhut PhD Esin Ozturk‐Isik PhD Albert P. Chen PhD Ilwoo Park BS Daniel B. Vigneron PhD Sarah J. Nelson Dr rer Nat 《Journal of magnetic resonance imaging : JMRI》2009,30(3):473-480
Purpose
To investigate the signal‐to‐noise‐ratio (SNR) and data quality of time‐reduced three‐dimensional (3D) proton magnetic resonance spectroscopic imaging (1H MRSI) techniques in the human brain at 3 Tesla.Materials and Methods
Techniques that were investigated included ellipsoidal k‐space sampling, parallel imaging, and echo‐planar spectroscopic imaging (EPSI). The SNR values for N‐acetyl aspartate, choline, creatine, and lactate or lipid peaks were compared after correcting for effective spatial resolution and acquisition time in a phantom and in the brains of human volunteers. Other factors considered were linewidths, metabolite ratios, partial volume effects, and subcutaneous lipid contamination.Results
In volunteers, the median normalized SNR for parallel imaging data decreased by 34–42%, but could be significantly improved using regularization. The normalized signal to noise loss in flyback EPSI data was 11–18%. The effective spatial resolutions of the traditional, ellipsoidal, sensitivity encoding (SENSE) sampling scheme, and EPSI data were 1.02, 2.43, 1.03, and 1.01 cm3, respectively. As expected, lipid contamination was variable between subjects but was highest for the SENSE data. Patient data obtained using the flyback EPSI method were of excellent quality.Conclusion
Data from all 1H 3D‐MRSI techniques were qualitatively acceptable, based upon SNR, linewidths, and metabolite ratios. The larger field of view obtained with the EPSI methods showed negligible lipid aliasing with acceptable SNR values in less than 9.5 min without compromising the point‐spread function. J. Magn. Reson. Imaging 2009;30:473–480. © 2009 Wiley‐Liss, Inc. 相似文献13.
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Andrew B. Rosenkrantz MD Martin Kopec MD Xiangtian Kong MD Jonathan Melamed MD George Dakwar MD James S. Babb PhD Bachir Taouli MD 《Journal of magnetic resonance imaging : JMRI》2010,31(6):1387-1394
Purpose:
To assess the value of quantitative T2 signal intensity (SI) and apparent diffusion coefficient (ADC) to differentiate prostate cancer from post‐biopsy hemorrhage, using prostatectomy as the reference.Materials and Methods:
Forty‐five men with prostate cancer underwent prostate magnetic resonance imaging (MRI), including axial T1‐weighted imaging (T1WI), T2WI, and single‐shot echo‐planar image (SS EPI) diffusion‐weighted imaging. Two observers measured, in consensus, normalized T2 signal intensity (SI) (nT2, relative to muscle T2 SI), ADC, and normalized ADC (nADC, relative to urine ADC) on peripheral zone (PZ) tumors, benign PZ hemorrhage, and non‐hemorrhagic benign PZ. Tumor maps from prostatectomy were used as the reference. Mixed model analysis of variance was performed to compare parameters among the three tissue classes, and Pearson's correlation coefficient was utilized to assess correlation between parameters and tumor size and Gleason score. Receiver‐operating characteristic (ROC)‐curve analysis was used to determine the performance of nT2, ADC, and nADC for diagnosis of prostate cancer.Results:
nT2, ADC, and nADC were significantly lower in tumor compared with hemorrhagic and non‐hemorrhagic benign PZ (P < 0.0001). There was a weak but significant correlation between ADC and Gleason score (r = ?0.30, P = 0.0119), and between ADC and tumor size (r = ?0.40, P = 0.0027), whereas there was no correlation between nT2 and Gleason score and tumor size. The areas under the curve to distinguish tumor from benign hemorrhagic and non‐hemorrhagic PZ were 0.97, 0.96, and 0.933 for nT2, ADC, and nADC, respectively.Conclusion:
Quantitative T2 SI and ADC/nADC values may be used to reliably distinguish prostate cancer from post‐biopsy hemorrhage. J. Magn. Reson. Imaging 2010;31:1387–1394. © 2010 Wiley‐Liss, Inc.15.
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Juliane Budde G. Shajan Jens Hoffmann Kâmil Uğurbil Rolf Pohmann 《Magnetic resonance in medicine》2011,65(2):544-550
The effect of susceptibility differences on an MR image is known to increase with field strength. Magnetic field inhomogeneities within the voxels influence the apparent transverse relaxation time T2*, while effects due to different precession frequencies between voxels caused by local field variations are evident in the image phase, and susceptibility‐weighted imaging highlights the veins and deep brain structures. Here, these three contrast mechanisms are examined at a field strength of 9.4 T. The T2* maps generated allow the identification of white matter structures not visible in conventional images. Phase images with in‐plane resolutions down to 130 μm were obtained, showing high gray/white matter contrast and allowing the identification of internal cortical structures. The susceptibility‐weighted images yield excellent visibility of small venous structures and attain an in‐plane resolution of 175 μm. Magn Reson Med, 2011. © 2010 Wiley‐Liss, Inc. 相似文献