Abstract
PurposeTo compare prostate diffusional kurtosis imaging (DKI) metrics generated using phase‐corrected real data with those generated using magnitude data with and without noise compensation (NC).MethodsDiffusion‐weighted images were acquired at 3T in 16 prostate cancer patients, measuring 6 b‐values (0‐1500 s/mm2), each acquired with 6 signal averages along 3 diffusion directions, with noise‐only images acquired to allow NC. In addition to conventional magnitude averaging, phase‐corrected real data were averaged in an attempt to reduce rician noise‐bias, with a range of phase‐correction low‐pass filter (LPF) sizes (8‐128 pixels) tested. Each method was also tested using simulations. Pixelwise maps of apparent diffusion (D) and apparent kurtosis (K) were calculated for magnitude data with and without NC and phase‐corrected real data. Average values were compared in tumor, normal transition zone (NTZ), and normal peripheral zone (NPZ).ResultsSimulations indicated LPF size can strongly affect K metrics, where 64‐pixel LPFs produced accurate metrics. Relative to metrics estimated from magnitude data without NC, median NC K were lower (P < 0.0001) by 6/11/8% in tumor/NPZ/NTZ, 64‐LPF real‐data K were lower (P < 0.0001) by 4/10/7%, respectively.ConclusionCompared with magnitude data with NC, phase‐corrected real data can produce similar K, although the choice of phase‐correction LPF should be chosen carefully.
Highlights
Prostate cancer (PCa) is the most common cancer in men in developed countries.[1]
Relative to metrics estimated from magnitude data without noise compensation (NC), median NC K were lower (P < 0.0001) by 6/11/8% in tumor/ normal peripheral zone (NPZ)/normal transition zone (NTZ), 64-low-pass filter (LPF) real-data K were lower (P < 0.0001) by 4/10/7%, respectively
To reduce spurious increases in K arising from the effect of the rectified noise floor on fitted diffusional kurtosis imaging (DKI) data, a post hoc NC method of standard magnitude-averaged images has generally been used.[41]
Summary
Prostate cancer (PCa) is the most common cancer in men in developed countries.[1]. PCa encompasses a spectrum of low- to high-risk diseases, which are often poorly characterized by current diagnostic methods, leading to both overtreatment and undertreatment.[2]. Water diffusion in biological tissue is much more complex and often deviates substantially from Gaussian behavior.[11] several “non-Gaussian” signal models have been suggested to more accurately describe in vivo diffusion.[12,13,14,15,16,17,18,19,20,21,22] One such expanded model is diffusional kurtosis imaging (DKI),[12] which provides an estimate of the apparent diffusional kurtosis (K): a dimensionless parameter that quantifies the degree of non-Gaussian diffusion. Since the first DKI studies,[12,23] the technique has been applied extensively in neuroimaging[24,25,26] and other studies have demonstrated feasibility for body applications, including PCa.[27,28,29,30,31,32,33,34] Several investigations have compared DKI with standard DWI in PCa,[29,30,31,32,33,34] with disagreement over whether DKI brings additional value compared to standard DWI29-31 or not.[32,33,34] these findings may be influenced by methodological issues, including the choice of b-values and by whether and how noise compensation (NC) is performed, and this issue has yet to be fully investigated
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