scholarly journals In vivo assessment of optimal b -value range for perfusion-insensitive apparent diffusion coefficient imaging

2012 ◽  
Vol 39 (8) ◽  
pp. 4832-4839 ◽  
Author(s):  
Moti Freiman ◽  
Stephan D. Voss ◽  
Robert V. Mulkern ◽  
Jeannette M. Perez-Rossello ◽  
Michael J. Callahan ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
B Value ◽  
Apparent Diffusion ◽  
Value Range ◽  
In Vivo Assessment

Importance of Fractional b Value for Calculating Apparent Diffusion Coefficient in DWI

2016 ◽  
Vol 207 (6) ◽  
pp. 1239-1243 ◽  
Author(s):  
Akio Ogura ◽  
Isamu Hatano ◽  
Kohki Osakabe ◽  
Natsumi Yamaguchi ◽  
Daisuke Koyama ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
B Value ◽  
Apparent Diffusion

scholarly journals Rapid in vivo apparent diffusion coefficient mapping of hyperpolarized 13 C metabolites

2014 ◽  
Vol 74 (3) ◽  
pp. 622-633 ◽  
Author(s):  
Bertram L. Koelsch ◽  
Galen D. Reed ◽  
Kayvan R. Keshari ◽  
Myriam M. Chaumeil ◽  
Robert Bok ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Apparent Diffusion ◽  
Apparent Diffusion Coefficient Mapping

Differentiation of prostate cancer and stromal hyperplasia in the transition zone with histogram analysis of the apparent diffusion coefficient

2017 ◽  
Vol 58 (12) ◽  
pp. 1528-1534 ◽  
Author(s):  
Liu Xiaohang ◽  
Zhou Bingni ◽  
Zhou Liangping ◽  
Peng Weijun ◽  
Yang Xiaoqun ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Transition Zone ◽  
B Value ◽  
Histogram Analysis ◽  
Apparent Diffusion ◽  
Adc Values ◽  
The Mean ◽  
Magnetic Resonance Imaging Mri

Background Prostate cancer and stromal hyperplasia (SH) in the transition zone (TZ) are difficult to discriminate by conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI). Purpose To investigate the apparent diffusion coefficient (ADC) of prostate cancer and SH in the TZ with histogram analysis and the ability of ADC metrics to differentiate between these two tissues. Material and Methods Thirty-three cancer and 29 SH lesions in the TZ of 54 patients undergoing preoperative DWI (b-value 0, 1000 s/mm2) were analyzed. All the lesions on the MR images were localized based on histopathologic correlations. The 10th, 25th, and 50th percentiles, and the mean ADC values were calculated for the two tissues and compared. The efficiencies of the 10th, 25th, and 50th ADC percentiles in differentiating the two tissues were compared with that of the mean ADC with receiver operating characteristic (ROC) analysis. Results The 10th, 25th, and 50th percentiles and mean ADC values (×10−3 mm2/s) were 0.86 ± 0.15, 0.89 ± 0.16, 0.94 ± 0.16, and 1.03 ± 0.17 in SH and 0.64 ± 0.12, 0.69 ± 0.12, 0.72 ± 0.16, and 0.83 ± 0.15 in TZ cancer, respectively. The parameters were all significantly lower in cancer than SH. The 10th ADC percentile yielded an area under the ROC curve (AUC) of 0.87 for the differentiation of carcinomas from SH, which was higher than the mean ADC (0.80) ( P < 0.05), and the AUCs of the 25th (0.82) and 50th (0.83) percentiles exhibited no differences from those of the mean ADC ( P > 0.05). Conclusion Histogram analysis of ADC values may potentially improve the differentiation of prostate cancer from SH in the TZ.

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