scholarly journals Diffusion-weighted imaging of normal and malignant prostate tissue at 3.0T

2006 ◽  
Vol 23 (2) ◽  
pp. 130-134 ◽  
Author(s):  
Martin D. Pickles ◽  
Peter Gibbs ◽  
Muthyala Sreenivas ◽  
Lindsay W. Turnbull
Keyword(s):  
Diffusion Weighted Imaging ◽  
Prostate Tissue ◽  
Diffusion Weighted ◽  
Malignant Prostate

scholarly journals Evaluation of fitting models for prostate tissue characterization using extended‐range b‐factor diffusion‐weighted imaging

2017 ◽  
Vol 79 (4) ◽  
pp. 2346-2358 ◽  
Author(s):  
Fredrik Langkilde ◽  
Thiele Kobus ◽  
Andriy Fedorov ◽  
Ruth Dunne ◽  
Clare Tempany ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Tissue Characterization ◽  
Prostate Tissue ◽  
Diffusion Weighted ◽  
Extended Range

scholarly journals Conspicuity of Peripheral Zone Prostate Cancer on Computed Diffusion-Weighted Imaging: Comparison of cDWI1500, cDWI2000, and cDWI3000

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Metin Vural ◽  
Gökhan Ertaş ◽  
Aslıhan Onay ◽  
Ömer Acar ◽  
Tarık Esen ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Contrast Ratio ◽  
Peripheral Zone ◽  
Lesion Detection ◽  
Prostate Tissue ◽  
Detection Rates ◽  
Diffusion Weighted ◽  
Image Set ◽  
Average Contrast ◽  
Lesion Conspicuity

Introduction and Objective. Disadvantages associated with direct highb-value measurements may be avoided with use of computed diffusion-weighted imaging (cDWI). The purpose of this study is to assess the diagnostic performance of cDWI image sets calculated for highb-values of 1500, 2000, and 3000 s/mm2.Materials and Methods. Twenty-eight patients who underwent multiparametric MRI of the prostate and radical prostatectomy consecutively were enrolled in this retrospective study. Using a software developed at our institute, cDWI1500, cDWI2000, and cDWI3000image sets were generated by fitting a monoexponential model. Index lesions on cDWI image sets were scored by two radiologists in consensus considering lesion conspicuity, suppression of background prostate tissue, distortion, image set preferability, and contrast ratio measurements were performed.Results. Lesion detection rates are the same for computedb-values of 2000 and 3000 s/mm2and are better thanb-values of 1500 s/mm2. Best lesion conspicuity and best background prostate tissue suppression are provided by cDWI3000image set. cDWI2000image set provides the best zonal anatomical delineation and less distortion and was chosen as the most preferred image set. Average contrast ratio measured on these image sets shows almost a linear relation with theb-values. Conclusion. cDWI2000image set with similar conspicuity and the same lesion detection rate, but better zonal anatomical delineation, and less distortion, was chosen as the preferable image set.

scholarly journals Relationship between kurtosis and bi-exponential characterization of high b-value diffusion-weighted imaging: application to prostate cancer

2018 ◽  
Vol 59 (12) ◽  
pp. 1523-1529 ◽  
Author(s):  
Roshan A Karunamuni ◽  
Joshua Kuperman ◽  
Tyler M Seibert ◽  
Natalie Schenker ◽  
Rebecca Rakow-Penner ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Clinical Care ◽  
B Value ◽  
Prostate Tissue ◽  
Normal Prostate ◽  
Excess Kurtosis ◽  
High B Value ◽  
Diffusion Weighted ◽  
Normal Prostate Tissue ◽  
Suspicious Lesions

Background High b-value diffusion-weighted imaging has application in the detection of cancerous tissue across multiple body sites. Diffusional kurtosis and bi-exponential modeling are two popular model-based techniques, whose performance in relation to each other has yet to be fully explored. Purpose To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions. Material and Methods This retrospective study analyzed patients with normal prostate tissue (n = 12) or suspicious lesions (n = 13, one lesion per patient), as determined by a radiologist whose clinical care included a high b-value diffusion series. The observed signal intensity was modeled using a bi-exponential decay, from which the signal fraction of the slow-moving component was derived ( SFs). In addition, the excess kurtosis was calculated using the signal fractions and ADCs of the two exponentials ( KCOMP). As a comparison, the kurtosis was also calculated using the cumulant expansion for the diffusion signal ( KCE). Results Both K and KCE were found to increase with SFs within the range of SFs commonly found within the prostate. Voxel-wise receiver operating characteristic performance of SFs, KCE, and KCOMP in discriminating between suspicious lesions and normal prostate tissue was 0.86 (95% confidence interval [CI] = 0.85 – 0.87), 0.69 (95% CI = 0.68–0.70), and 0.86 (95% CI = 0.86–0.87), respectively. Conclusion In a two-component diffusion environment, KCOMP is a scaled value of SFs and is thus able to discriminate suspicious lesions with equal precision . KCE provides a computationally inexpensive approximation of kurtosis but does not provide the same discriminatory abilities as SFs and KCOMP.

MRT des Prostatakarzinoms: Diffusion Weighted Imaging im Vergleich mit der Histologie

2009 ◽  
Vol 181 (S 01) ◽  
Author(s):  
J Yamamura ◽  
G Salomon ◽  
J Graessner ◽  
A Hohenstein ◽  
M Graefen ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Diffusion Weighted

Diffusion-Weighted Imaging Features of Urachal Abscess

2008 ◽  
Author(s):  
Ozgur Kilickesmez ◽  
Arda Kayhan ◽  
Bengi Gürses ◽  
Neslihan Tasdelen ◽  
Baki Ekci ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Imaging Features ◽  
Diffusion Weighted ◽  
Urachal Abscess
Sign in / Sign up

Export Citation Format

Share Document