title: New RESOLVE-Based Diffusional Kurtosis Imaging in MRI-Visible Prostate Cancer: Effect of Reduced b Value on Image Quality and Diagnostic Effectiveness.
abstract: OBJECTIVE
  The purpose of this article was to investigate whether a new readout segmentation of long variable echo-trains (RESOLVE)-based diffusional kurtosis imaging (DKI) with reduced b value technique can affect image quality and diagnostic effectiveness in MRI-visible prostate cancer (PCA).
  
  
  METHODS
  Prostatic RESOLVE DKI (0-1400 s/mm2) was prospectively performed for 12 volunteers. The optimal protocol was then performed in 108 MRI-visible PCAs to determine whether it can compete against a preferred b-value set (0-2000 s/mm(2)) regarding image quality and diagnostic effectiveness. Images were interpreted by two independent radiologists using the prostate imaging reporting and data system (PI-RADS). Readers' concordance and diagnostic effectiveness were tested with the Fleiss kappa and area under the ROC curve (Az) analyses.
  
  
  RESULTS
  A b value of 1400 s/mm(2) generated a larger apparent diffusion coefficient of gaussian distribution (Dapp) (1.35 ± 0.31 vs 1.30 ± 0.30 mm(2)/s; p < 0.001) and apparent kurtosis coefficient (Kapp) (1.11 ± 0.26 vs 1.00 ± 0.21; p < 0.001) in PCA than did a b value of 2000 s/mm(2). Interreader agreement using PI-RADS was relatively low when Dapp and Kapp maps were excluded from image interpretations (κ = 0.39-0.41 vs κ = 0.66-0.68 with Dapp and Kapp maps). Interreader agreement in staging PCA was relatively high (κ > 0.80) and was not influenced by reducing the b value. The power of Dapp and Kapp to differentiate PCA from normal tissue (Az = 0.97-0.98), tissue with a Gleason score less than or equal to 3 + 4 from tissue with a Gleason score greater than 3 + 4 (Az = 0.77-0.82), and PCA stage lower than pT3 from stage pT3 and higher PCA (Az = 0.70-0.75) was not significantly degraded by reducing the b value.
  
  
  CONCLUSIONS
  We found that b values significantly influenced image quality, PI-RADS score, and DKI outputs but did not degrade the diagnostic effectiveness of DKI parameters to detect and classify PCA.
authors.affiliation: 1 Department of Radiology, the First Affiliated Hospital With Nanjing Medical University, 300 Guangzhou Rd, Nanjing, China, 210009.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Zhang
authors.affiliation: 1 Department of Radiology, the First Affiliated Hospital With Nanjing Medical University, 300 Guangzhou Rd, Nanjing, China, 210009.
authors.forename: Chen-Jiang
authors.initials: CJ
authors.lastname: Wu
authors.affiliation: 2 Department of Pathology, the First Affiliated Hospital With Nanjing Medical University, Nanjing, China.
authors.forename: Mei-Ling
authors.initials: ML
authors.lastname: Bao
authors.affiliation: 2 Department of Pathology, the First Affiliated Hospital With Nanjing Medical University, Nanjing, China.
authors.forename: Hai
authors.initials: H
authors.lastname: Li
authors.affiliation: 3 MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China.
authors.forename: Xu
authors.initials: X
authors.lastname: Yan
authors.affiliation: 1 Department of Radiology, the First Affiliated Hospital With Nanjing Medical University, 300 Guangzhou Rd, Nanjing, China, 210009.
authors.forename: Xi-Sheng
authors.initials: XS
authors.lastname: Liu
authors.affiliation: 1 Department of Radiology, the First Affiliated Hospital With Nanjing Medical University, 300 Guangzhou Rd, Nanjing, China, 210009.
authors.forename: Hai-Bin
authors.initials: HB
authors.lastname: Shi
mesh_terms: D000368:Aged; D001706:Biopsy; D003287:Contrast Media; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA
keywords: MRI; RESOLVE; b value; diffusional kurtosis imaging; prostate cancer; readout segmentation of long variable echo-trains