Evaluation of Diffusion Kurtosis Imaging Versus Standard Diffusion Imaging for Detection and Grading of Peripheral Zone Prostate Cancer

2015 ◽  
Vol 50 (8) ◽  
pp. 483-489 ◽  
Author(s):  
Matthias C. Roethke ◽  
Tristan A. Kuder ◽  
Timur H. Kuru ◽  
Michael Fenchel ◽  
Boris A. Hadaschik ◽  
...  
2017 ◽  
Vol 28 (8) ◽  
pp. 3141-3150 ◽  
Author(s):  
Tristan Barrett ◽  
Mary McLean ◽  
Andrew N. Priest ◽  
Edward M. Lawrence ◽  
Andrew J. Patterson ◽  
...  

2021 ◽  
Author(s):  
Hiba Taha ◽  
Jordan A Chad ◽  
J. Jean Chen

Studies of healthy brain aging have reported diffusivity patterns associated with white matter degeneration using diffusion tensor imaging (DTI), which assumes that diffusion measured at the typical b-value (approximately 1000 s/mm2) is Gaussian. Diffusion kurtosis imaging (DKI) is an extension of DTI that measures non-Gaussian diffusion (kurtosis) to better capture microenvironmental changes by incorporating additional data at a higher b-value. In this study, using UK Biobank data (b values of 1000 and 2000 s/mm2), we investigate (1) the extent of novel information gained from adding diffusional kurtosis to diffusivity observations in aging, and (2) how conventional DTI metrics in aging compare with diffusivity metrics derived from DKI, which are corrected for kurtosis. We find a general pattern of lower kurtosis alongside higher diffusivity among older adults. We also find differences between diffusivity metrics derived from DTI and DKI, emphasizing the importance of accounting for non-Gaussian diffusion. This work highlights the utility of measuring diffusional kurtosis as a simple addition to conventional diffusion imaging of aging.


2020 ◽  
Vol 61 (10) ◽  
pp. 1431-1440
Author(s):  
Yuwei Jiang ◽  
Chunmei Li ◽  
Ying Liu ◽  
Kaining Shi ◽  
Wei Zhang ◽  
...  

Background There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present. Purpose To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM). Material and Methods Twenty-three patients were enrolled in this study. For DKI model and MEM, the Dapp, Kapp, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman’s test was used to evaluate the correlation of parameters and Gleason scores. Results The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and Dapp were significantly lower in prostate cancer than non-cancerous foci ( P < 0.001). The mean, 50th, 75th, and 90th percentiles of Kapp were significantly higher in prostate cancer ( P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased ( ρ = −0.583, P = 0.018), but the 90th Kapp increased ( ρ = 0.642, P = 0.007). Conclusion Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.


2013 ◽  
Vol 40 (3) ◽  
pp. 723-729 ◽  
Author(s):  
Chiharu Tamura ◽  
Hiroshi Shinmoto ◽  
Shigeyoshi Soga ◽  
Teppei Okamura ◽  
Hiroki Sato ◽  
...  

2019 ◽  
Vol 26 (10) ◽  
pp. 1328-1337 ◽  
Author(s):  
Maria Giovanna Di Trani ◽  
Marco Nezzo ◽  
Alessandra S. Caporale ◽  
Riccardo De Feo ◽  
Roberto Miano ◽  
...  

Medicine ◽  
2021 ◽  
Vol 100 (35) ◽  
pp. e27144
Author(s):  
Weigen Yao ◽  
Jiaju Zheng ◽  
Chunhong Han ◽  
Pengcong Lu ◽  
Lihua Mao ◽  
...  

2014 ◽  
Vol 32 (5) ◽  
pp. 421-427 ◽  
Author(s):  
Shiteng Suo ◽  
Xiaoxi Chen ◽  
Lianming Wu ◽  
Xiaofei Zhang ◽  
Qiuying Yao ◽  
...  

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