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

摘要

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 .