Michaela R DelPriore1, Debosmita Biswas1, Daniel S Hippe1, Mladen Zecevic1, Sana Parsian2, John R Scheel2, Habib Rahbar2, Savannah C Partridge3. 1. Department of Radiology, University of Washington, Seattle, Washington. 2. Department of Radiology, University of Washington, Seattle, Washington; Breast Imaging, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109. 3. Department of Radiology, University of Washington, Seattle, Washington; Breast Imaging, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109. Electronic address: scp3@uw.edu.
Abstract
RATIONALE AND OBJECTIVES: On unenhanced diffusion-weighted imaging (DWI), computing or synthesizing high b-value images from lower b-value acquisitions can enhance breast cancer visibility. This study aimed to evaluate relative lesion conspicuity on computed versus acquired diffusion-weighted images and investigate clinical characteristics influencing optimal b-values. MATERIALS AND METHODS: Women with newly diagnosed breast cancer were prospectively enrolled and underwent 3T breast MRI with DWI. Lesion contrast-to-noise ratio (CNR) was measured across a range of b-values (0-2500 s/mm2) for computed and acquired DWI. Three readers independently compared lesion visibility between computed and acquired DWI and selected the optimal b-value. Computed versus acquired DWI was compared quantitatively based on CNR by paired t-test and qualitatively based on reader preference using a sign test. Optimal b-values by qualitative and quantitative assessment were compared by paired t-test, and associations with clinical characteristics were assessed by Wilcoxon rank sum test. RESULTS: The study included 30 women (median age, 48 years); 28 with invasive carcinoma, 2 DCIS. Lesion CNR was higher on acquired versus computed images (p = 0.018), while lesion visibility by reader assessment was not different (p = 0.36). Optimal b-values selected by readers (mean, b = 1411 ± 383 s/mm2) were slightly higher than those based on peak CNR (b = 1233 ± 463 s/mm2, p = 0.023), and were higher for younger (≤50 years) versus older women (p = 0.002) and dense versus nondense breasts (p = 0.015). CONCLUSION: Lesion CNR on computed high b-value images was slightly reduced versus acquired images, but our study suggests that this did not significantly impact lesion visibility. Computing high b-value images offers extra flexibility to adjust b-value during interpretation.
RATIONALE AND OBJECTIVES: On unenhanced diffusion-weighted imaging (DWI), computing or synthesizing high b-value images from lower b-value acquisitions can enhance breast cancer visibility. This study aimed to evaluate relative lesion conspicuity on computed versus acquired diffusion-weighted images and investigate clinical characteristics influencing optimal b-values. MATERIALS AND METHODS: Women with newly diagnosed breast cancer were prospectively enrolled and underwent 3T breast MRI with DWI. Lesion contrast-to-noise ratio (CNR) was measured across a range of b-values (0-2500 s/mm2) for computed and acquired DWI. Three readers independently compared lesion visibility between computed and acquired DWI and selected the optimal b-value. Computed versus acquired DWI was compared quantitatively based on CNR by paired t-test and qualitatively based on reader preference using a sign test. Optimal b-values by qualitative and quantitative assessment were compared by paired t-test, and associations with clinical characteristics were assessed by Wilcoxon rank sum test. RESULTS: The study included 30 women (median age, 48 years); 28 with invasive carcinoma, 2 DCIS. Lesion CNR was higher on acquired versus computed images (p = 0.018), while lesion visibility by reader assessment was not different (p = 0.36). Optimal b-values selected by readers (mean, b = 1411 ± 383 s/mm2) were slightly higher than those based on peak CNR (b = 1233 ± 463 s/mm2, p = 0.023), and were higher for younger (≤50 years) versus older women (p = 0.002) and dense versus nondense breasts (p = 0.015). CONCLUSION: Lesion CNR on computed high b-value images was slightly reduced versus acquired images, but our study suggests that this did not significantly impact lesion visibility. Computing high b-value images offers extra flexibility to adjust b-value during interpretation.
Authors: Jung Hyun Park; Bo La Yun; Mijung Jang; Hye Shin Ahn; Sun Mi Kim; Soo Hyun Lee; Eunyoung Kang; Eun-Kyu Kim; So Yeon Park Journal: J Magn Reson Imaging Date: 2018-08-21 Impact factor: 4.813
Authors: M O Leach; C R M Boggis; A K Dixon; D F Easton; R A Eeles; D G R Evans; F J Gilbert; I Griebsch; R J C Hoff; P Kessar; S R Lakhani; S M Moss; A Nerurkar; A R Padhani; L J Pointon; D Thompson; R M L Warren Journal: Lancet Date: 2005 May 21-27 Impact factor: 79.321
Authors: Debosmita Biswas; Daniel S Hippe; Yi Wang; Michaela R DelPriore; Mladen Zečević; John R Scheel; Habib Rahbar; Savannah C Partridge Journal: Radiol Imaging Cancer Date: 2022-01
Authors: Thomas Sartoretti; Elisabeth Sartoretti; Michael Wyss; Manoj Mannil; Luuk van Smoorenburg; Barbara Eichenberger; Carolin Reischauer; Alex Alfieri; Christoph Binkert; Sabine Sartoretti-Schefer Journal: Br J Radiol Date: 2021-02-17 Impact factor: 3.039