PURPOSE: To evaluate whether quantitative susceptibility (QSM) may be used as an alternative to computed tomography (CT) to detect calcification in prostate cancer patients. MATERIALS AND METHODS: Susceptibility map calculation was performed using 3D gradient echo magnetic resonance imaging (MRI) data from 26 patients measured at 3T who previously received a planning CT of the prostate. Phase images were unwrapped using Laplacian-based phase unwrapping, the background field was removed with the V-SHARP method, and susceptibility maps were calculated with the iLSQR method. Two blinded readers were asked to identify peri- and intraprostatic calcifications. RESULTS: Average mean and minimum susceptibility values (referenced to iliopsoas muscle) of calcifications were -0.249 ± 0.179 ppm and -0.551 ± 0.323 ppm, and average mean and maximum intensities in CT images were 319 ± 164 HU and 679 ± 392 HU. Twenty-one and 17 out of 22 prostatic calcifications were identified using susceptibility maps and magnitude images, respectively, as well as more than half of periprostatic phleboliths depicted by CT. Calcifications in the prostate and its periphery were quantitatively differentiable from noncalcified prostate tissue in CT (mean values for calcifications / for noncalcified tissue: 71 to 649 / -1 to 83 HU) and in QSM (mean values for calcifications / for noncalcified tissue: -0.641 to 0.063 / -0.046 to 0.181 ppm). Moreover, there was a significant correlation between susceptibility values and CT image intensities for calcifications (P < 0.004). CONCLUSION: Prostatic calcifications could be well identified with QSM. Susceptibility maps can be easily obtained from clinical prostate MR protocols that include a 3D gradient echo sequence, rendering it a promising technique for detection and quantification of intraprostatic calcifications. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:889-898.
PURPOSE: To evaluate whether quantitative susceptibility (QSM) may be used as an alternative to computed tomography (CT) to detect calcification in prostate cancerpatients. MATERIALS AND METHODS: Susceptibility map calculation was performed using 3D gradient echo magnetic resonance imaging (MRI) data from 26 patients measured at 3T who previously received a planning CT of the prostate. Phase images were unwrapped using Laplacian-based phase unwrapping, the background field was removed with the V-SHARP method, and susceptibility maps were calculated with the iLSQR method. Two blinded readers were asked to identify peri- and intraprostatic calcifications. RESULTS: Average mean and minimum susceptibility values (referenced to iliopsoas muscle) of calcifications were -0.249 ± 0.179 ppm and -0.551 ± 0.323 ppm, and average mean and maximum intensities in CT images were 319 ± 164 HU and 679 ± 392 HU. Twenty-one and 17 out of 22 prostatic calcifications were identified using susceptibility maps and magnitude images, respectively, as well as more than half of periprostatic phleboliths depicted by CT. Calcifications in the prostate and its periphery were quantitatively differentiable from noncalcified prostate tissue in CT (mean values for calcifications / for noncalcified tissue: 71 to 649 / -1 to 83 HU) and in QSM (mean values for calcifications / for noncalcified tissue: -0.641 to 0.063 / -0.046 to 0.181 ppm). Moreover, there was a significant correlation between susceptibility values and CT image intensities for calcifications (P < 0.004). CONCLUSION:Prostatic calcifications could be well identified with QSM. Susceptibility maps can be easily obtained from clinical prostate MR protocols that include a 3D gradient echo sequence, rendering it a promising technique for detection and quantification of intraprostatic calcifications. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:889-898.
Authors: Yi Wang; Pascal Spincemaille; Zhe Liu; Alexey Dimov; Kofi Deh; Jianqi Li; Yan Zhang; Yihao Yao; Kelly M Gillen; Alan H Wilman; Ajay Gupta; Apostolos John Tsiouris; Ilhami Kovanlikaya; Gloria Chia-Yi Chiang; Jonathan W Weinsaft; Lawrence Tanenbaum; Weiwei Chen; Wenzhen Zhu; Shixin Chang; Min Lou; Brian H Kopell; Michael G Kaplitt; David Devos; Toshinori Hirai; Xuemei Huang; Yukunori Korogi; Alexander Shtilbans; Geon-Ho Jahng; Daniel Pelletier; Susan A Gauthier; David Pitt; Ashley I Bush; Gary M Brittenham; Martin R Prince Journal: J Magn Reson Imaging Date: 2017-03-10 Impact factor: 4.813
Authors: Hyungseok Jang; Xing Lu; Michael Carl; Adam C Searleman; Saeed Jerban; Yajun Ma; Annette von Drygalski; Eric Y Chang; Jiang Du Journal: Magn Reson Med Date: 2018-10-16 Impact factor: 4.668
Authors: Sarah M Böker; Lisa C Adams; Yvonne Y Bender; Moritz Wagner; Torsten Diekhoff; Eva Fallenberg; Bernd Hamm; Marcus R Makowski Journal: Eur Radiol Date: 2017-12-19 Impact factor: 5.315
Authors: Christian Langkammer; Ferdinand Schweser; Karin Shmueli; Christian Kames; Xu Li; Li Guo; Carlos Milovic; Jinsuh Kim; Hongjiang Wei; Kristian Bredies; Sagar Buch; Yihao Guo; Zhe Liu; Jakob Meineke; Alexander Rauscher; José P Marques; Berkin Bilgic Journal: Magn Reson Med Date: 2017-07-31 Impact factor: 4.668