C Ngamsombat1,2,3, A L M Gonçalves Filho1,2,4, M G F Longo1,2,4, S F Cauley1,2,4, K Setsompop1,2,4,5, J E Kirsch1,2,4, Q Tian1,2,4, Q Fan1,2,4, D Polak2,6,7, W Liu8, W-C Lo7, R Gilberto González1,4, P W Schaefer1,4, O Rapalino1,4, J Conklin1,2,4, S Y Huang9,2,4,5. 1. From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.). 2. Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts. 3. Department of Radiology (C.N.), Faculty of Medicine, Siriraj Hospital, Mahidol University, Thailand. 4. Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts. 5. Harvard-MIT Division of Health Sciences and Technology (K.S., S.Y.H.), Massachusetts Institute of Technology, Cambridge, Massachusetts. 6. Department of Physics and Astronomy (D.P.), Heidelberg University, Heidelberg, Germany. 7. Siemens Healthcare GmbH, (D.P., W.-C.L.), Erlangen, Germany. 8. Siemens Shenzhen Magnetic Resonance Ltd (W.L.), Shenzhen, China. 9. From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.) susie.huang@mgh.harvard.edu.
Abstract
BACKGROUND AND PURPOSE: Our aim was to evaluate an ultrafast 3D-FLAIR sequence using Wave-controlled aliasing in parallel imaging encoding (Wave-FLAIR) compared with standard 3D-FLAIR in the visualization and volumetric estimation of cerebral white matter lesions in a clinical setting. MATERIALS AND METHODS: Forty-two consecutive patients underwent 3T brain MR imaging, including standard 3D-FLAIR (acceleration factor = 2, scan time = 7 minutes 50 seconds) and resolution-matched ultrafast Wave-FLAIR sequences (acceleration factor = 6, scan time = 2 minutes 45 seconds for the 20-channel coil; acceleration factor = 9, scan time = 1 minute 50 seconds for the 32-channel coil) as part of clinical evaluation for demyelinating disease. Automated segmentation of cerebral white matter lesions was performed using the Lesion Segmentation Tool in SPM. Student t tests, intraclass correlation coefficients, relative lesion volume difference, and Dice similarity coefficients were used to compare volumetric measurements among sequences. Two blinded neuroradiologists evaluated the visualization of white matter lesions, artifacts, and overall diagnostic quality using a predefined 5-point scale. RESULTS: Standard and Wave-FLAIR sequences showed excellent agreement of lesion volumes with an intraclass correlation coefficient of 0.99 and mean Dice similarity coefficient of 0.97 (SD, 0.05) (range, 0.84-0.99). Wave-FLAIR was noninferior to standard FLAIR for visualization of lesions and motion. The diagnostic quality for Wave-FLAIR was slightly greater than for standard FLAIR for infratentorial lesions (P < .001), and there were fewer pulsation artifacts on Wave-FLAIR compared with standard FLAIR (P < .001). CONCLUSIONS: Ultrafast Wave-FLAIR provides superior visualization of infratentorial lesions while preserving overall diagnostic quality and yields white matter lesion volumes comparable with those estimated using standard FLAIR. The availability of ultrafast Wave-FLAIR may facilitate the greater use of 3D-FLAIR sequences in the evaluation of patients with suspected demyelinating disease.
BACKGROUND AND PURPOSE: Our aim was to evaluate an ultrafast 3D-FLAIR sequence using Wave-controlled aliasing in parallel imaging encoding (Wave-FLAIR) compared with standard 3D-FLAIR in the visualization and volumetric estimation of cerebral white matter lesions in a clinical setting. MATERIALS AND METHODS: Forty-two consecutive patients underwent 3T brain MR imaging, including standard 3D-FLAIR (acceleration factor = 2, scan time = 7 minutes 50 seconds) and resolution-matched ultrafast Wave-FLAIR sequences (acceleration factor = 6, scan time = 2 minutes 45 seconds for the 20-channel coil; acceleration factor = 9, scan time = 1 minute 50 seconds for the 32-channel coil) as part of clinical evaluation for demyelinating disease. Automated segmentation of cerebral white matter lesions was performed using the Lesion Segmentation Tool in SPM. Student t tests, intraclass correlation coefficients, relative lesion volume difference, and Dice similarity coefficients were used to compare volumetric measurements among sequences. Two blinded neuroradiologists evaluated the visualization of white matter lesions, artifacts, and overall diagnostic quality using a predefined 5-point scale. RESULTS: Standard and Wave-FLAIR sequences showed excellent agreement of lesion volumes with an intraclass correlation coefficient of 0.99 and mean Dice similarity coefficient of 0.97 (SD, 0.05) (range, 0.84-0.99). Wave-FLAIR was noninferior to standard FLAIR for visualization of lesions and motion. The diagnostic quality for Wave-FLAIR was slightly greater than for standard FLAIR for infratentorial lesions (P < .001), and there were fewer pulsation artifacts on Wave-FLAIR compared with standard FLAIR (P < .001). CONCLUSIONS: Ultrafast Wave-FLAIR provides superior visualization of infratentorial lesions while preserving overall diagnostic quality and yields white matter lesion volumes comparable with those estimated using standard FLAIR. The availability of ultrafast Wave-FLAIR may facilitate the greater use of 3D-FLAIR sequences in the evaluation of patients with suspected demyelinating disease.
Authors: Daniel Polak; Stephen Cauley; Susie Y Huang; Maria Gabriela Longo; John Conklin; Berkin Bilgic; Ned Ohringer; Esther Raithel; Peter Bachert; Lawrence L Wald; Kawin Setsompop Journal: J Magn Reson Imaging Date: 2019-02-08 Impact factor: 4.813
Authors: Supada Prakkamakul; Thomas Witzel; Susie Huang; Daniel Boulter; Maria J Borja; Pamela Schaefer; Bruce Rosen; Keith Heberlein; Eva Ratai; Gilberto Gonzalez; Otto Rapalino Journal: J Neuroimaging Date: 2016-06-07 Impact factor: 2.486
Authors: M A Clarke; D Pareto; L Pessini-Ferreira; G Arrambide; M Alberich; F Crescenzo; S Cappelle; M Tintoré; J Sastre-Garriga; C Auger; X Montalban; N Evangelou; À Rovira Journal: AJNR Am J Neuroradiol Date: 2020-05-21 Impact factor: 3.825
Authors: M G F Longo; J Conklin; S F Cauley; K Setsompop; Q Tian; D Polak; M Polackal; D Splitthoff; W Liu; R G González; P W Schaefer; J E Kirsch; O Rapalino; S Y Huang Journal: AJNR Am J Neuroradiol Date: 2020-07-30 Impact factor: 3.825
Authors: Elizabeth M Sweeney; Russell T Shinohara; Navid Shiee; Farrah J Mateen; Avni A Chudgar; Jennifer L Cuzzocreo; Peter A Calabresi; Dzung L Pham; Daniel S Reich; Ciprian M Crainiceanu Journal: Neuroimage Clin Date: 2013-03-15 Impact factor: 4.881
Authors: M M Weeda; I Brouwer; M L de Vos; M S de Vries; F Barkhof; P J W Pouwels; H Vrenken Journal: Neuroimage Clin Date: 2019-11-05 Impact factor: 4.881
Authors: M Le; L Y W Tang; E Hernández-Torres; M Jarrett; T Brosch; L Metz; D K B Li; A Traboulsee; R C Tam; A Rauscher; V Wiggermann Journal: Neuroimage Clin Date: 2019-07-05 Impact factor: 4.881
Authors: Min Lang; Samuel Cartmell; Azadeh Tabari; Daniel Briggs; Oleg Pianykh; John Kirsch; Stephen Cauley; Wei-Ching Lo; Seretha Risacher; Augusto Goncalves Filho; Marc D Succi; Otto Rapalino; Pamela Schaefer; John Conklin; Susie Y Huang Journal: Acad Radiol Date: 2021-10-08 Impact factor: 3.173
Authors: H J Baek; Y J Heo; D Kim; S Y Yun; J W Baek; H W Jeong; H J Choo; J Y Lee; S-I Oh Journal: AJNR Am J Neuroradiol Date: 2022-05-26 Impact factor: 4.966