Hye Shin Ahn1, Sung Hun Kim2, Ji Youn Kim3, Chang Suk Park4, Robert Grimm5, Yohan Son6. 1. Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea. 2. Department of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea. 3. Department of Radiology, College of Medicine, Yeouido St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea. 4. Department of Radiology, College of Medicine, Incheon St. Mary's Hospital, The Catholic University of Korea, Icheon, Republic of Korea. 5. MR Applications Development, Siemens Healthcare, Erlangen, Germany. 6. Siemens Healthineers Ltd., Seoul, Republic of Korea.
Abstract
PURPOSE: To compare the image quality of acquired diffusion-weighted imaging (DWI) and computed DWI and evaluate the lesion detectability and likelihood of malignancy in these datasets. MATERIALS AND METHODS: This prospective study was approved by our institutional review board. A total of 29 women (mean age, 43.5 years) underwent DWI between August 2018 and April 2019 for 32 breast cancers and 16 benign breast lesions. Three radiologists independently reviewed the acquired DWI with b-values of 1000 and 2000 s/mm2 (A-b1000 and A-b2000) and the computed DWI with a b-value of 2000 s/mm2 (C-b2000). Image quality was scored and compared between the three DWI datasets. Lesion detectability was recorded, and the lesion's likelihood for malignancy was scored using a five-point scale. RESULTS: The A-b1000 images were superior to the A-b2000 and C-b2000 images in chest distinction, fat suppression, and overall image quality. The A-b2000 and C-b2000 images showed comparable scores for all image quality parameters. C-b2000 showed the highest values for lesion detection among all readers, although there was no statistical difference in sensitivity, specificity, positive predictive value, negative predictive value, and accuracy between the DWI datasets. The malignancy scores of the DWI images were not significantly different among the three readers. CONCLUSIONS: A-b1000 DWI is suitable for breast lesion evaluations, considering its better image quality and comparable diagnostic values compared to that of A-b2000 and C-b2000 images. The additional use of computed high b-value DWI may have the potential to increase the detectability of breast masses.
PURPOSE: To compare the image quality of acquired diffusion-weighted imaging (DWI) and computed DWI and evaluate the lesion detectability and likelihood of malignancy in these datasets. MATERIALS AND METHODS: This prospective study was approved by our institutional review board. A total of 29 women (mean age, 43.5 years) underwent DWI between August 2018 and April 2019 for 32 breast cancers and 16 benign breast lesions. Three radiologists independently reviewed the acquired DWI with b-values of 1000 and 2000 s/mm2 (A-b1000 and A-b2000) and the computed DWI with a b-value of 2000 s/mm2 (C-b2000). Image quality was scored and compared between the three DWI datasets. Lesion detectability was recorded, and the lesion's likelihood for malignancy was scored using a five-point scale. RESULTS: The A-b1000 images were superior to the A-b2000 and C-b2000 images in chest distinction, fat suppression, and overall image quality. The A-b2000 and C-b2000 images showed comparable scores for all image quality parameters. C-b2000 showed the highest values for lesion detection among all readers, although there was no statistical difference in sensitivity, specificity, positive predictive value, negative predictive value, and accuracy between the DWI datasets. The malignancy scores of the DWI images were not significantly different among the three readers. CONCLUSIONS: A-b1000 DWI is suitable for breast lesion evaluations, considering its better image quality and comparable diagnostic values compared to that of A-b2000 and C-b2000 images. The additional use of computed high b-value DWI may have the potential to increase the detectability of breast masses.
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: Christopher C Riedl; Nikolaus Luft; Clemens Bernhart; Michael Weber; Maria Bernathova; Muy-Kheng M Tea; Margaretha Rudas; Christian F Singer; Thomas H Helbich Journal: J Clin Oncol Date: 2015-02-23 Impact factor: 44.544
Authors: Elizabeth A M O'Flynn; Matthew Blackledge; David Collins; Katherine Downey; Simon Doran; Hardik Patel; Sam Dumonteil; Wing Mok; Martin O Leach; Dow-Mu Koh Journal: J Magn Reson Imaging Date: 2016-01-13 Impact factor: 4.813
Authors: Hubert Bickel; Stephan H Polanec; Georg Wengert; Katja Pinker; Wolfgang Bogner; Thomas H Helbich; Pascal A Baltzer Journal: J Magn Reson Imaging Date: 2019-05-28 Impact factor: 4.813