PURPOSE: To propose and evaluate a new automated method for the identification of new/enlarging multiple sclerosis (MS) lesions on subtracted images (SI). The subtraction of serially acquired images has shown great potential in assessing new/enlarging brain magnetic resonance imaging (MRI) lesions in MS patients. However, this approach relies on the manual definition of lesions, which is labor-intensive and subject to operator-dependent variability. MATERIALS AND METHODS: An overestimated mask of candidate SI lesions was created and then these hyperintense voxel clusters were filtered using specific constraints for extent, shape, and intensity. The method was tested on normal and pathological MRI datasets. RESULTS: The automated method did not detect hyperintense voxels on SI of healthy controls. SI lesions were identified manually and automatically in a multicenter MS dataset of 19 patients with paired MRI over 36 weeks. Sensitivity of the method was high (0.91) and in agreement with the results of manually defined SI lesions (Cohen's k=0.82,95% confidence interval [CI]: 0.77–0.87). On a second multicenter MS dataset of 103 patients with paired MRI over 76 weeks, the number of SI lesions detected automatically correlated with the number of gadolinium-enhancing lesions(r=0.74). CONCLUSION: The proposed method is robust, accurate,and sensitive and may be used with confidence in Phase II MS trials.
PURPOSE: To propose and evaluate a new automated method for the identification of new/enlarging multiple sclerosis (MS) lesions on subtracted images (SI). The subtraction of serially acquired images has shown great potential in assessing new/enlarging brain magnetic resonance imaging (MRI) lesions in MS patients. However, this approach relies on the manual definition of lesions, which is labor-intensive and subject to operator-dependent variability. MATERIALS AND METHODS: An overestimated mask of candidate SI lesions was created and then these hyperintense voxel clusters were filtered using specific constraints for extent, shape, and intensity. The method was tested on normal and pathological MRI datasets. RESULTS: The automated method did not detect hyperintense voxels on SI of healthy controls. SI lesions were identified manually and automatically in a multicenter MS dataset of 19 patients with paired MRI over 36 weeks. Sensitivity of the method was high (0.91) and in agreement with the results of manually defined SI lesions (Cohen's k=0.82,95% confidence interval [CI]: 0.77–0.87). On a second multicenter MS dataset of 103 patients with paired MRI over 76 weeks, the number of SI lesions detected automatically correlated with the number of gadolinium-enhancing lesions(r=0.74). CONCLUSION: The proposed method is robust, accurate,and sensitive and may be used with confidence in Phase II MS trials.
Authors: Mike P Wattjes; Àlex Rovira; David Miller; Tarek A Yousry; Maria P Sormani; Maria P de Stefano; Mar Tintoré; Cristina Auger; Carmen Tur; Massimo Filippi; Maria A Rocca; Franz Fazekas; Ludwig Kappos; Chris Polman Journal: Nat Rev Neurol Date: 2015-09-15 Impact factor: 42.937
Authors: Aaron Carass; Snehashis Roy; Amod Jog; Jennifer L Cuzzocreo; Elizabeth Magrath; Adrian Gherman; Julia Button; James Nguyen; Ferran Prados; Carole H Sudre; Manuel Jorge Cardoso; Niamh Cawley; Olga Ciccarelli; Claudia A M Wheeler-Kingshott; Sébastien Ourselin; Laurence Catanese; Hrishikesh Deshpande; Pierre Maurel; Olivier Commowick; Christian Barillot; Xavier Tomas-Fernandez; Simon K Warfield; Suthirth Vaidya; Abhijith Chunduru; Ramanathan Muthuganapathy; Ganapathy Krishnamurthi; Andrew Jesson; Tal Arbel; Oskar Maier; Heinz Handels; Leonardo O Iheme; Devrim Unay; Saurabh Jain; Diana M Sima; Dirk Smeets; Mohsen Ghafoorian; Bram Platel; Ariel Birenbaum; Hayit Greenspan; Pierre-Louis Bazin; Peter A Calabresi; Ciprian M Crainiceanu; Lotta M Ellingsen; Daniel S Reich; Jerry L Prince; Dzung L Pham Journal: Neuroimage Date: 2017-01-11 Impact factor: 6.556
Authors: A Galletto Pregliasco; A Collin; A Guéguen; M A Metten; J Aboab; R Deschamps; O Gout; L Duron; J C Sadik; J Savatovsky; A Lecler Journal: AJNR Am J Neuroradiol Date: 2018-06-07 Impact factor: 3.825
Authors: Àlex Rovira; Mike P Wattjes; Mar Tintoré; Carmen Tur; Tarek A Yousry; Maria P Sormani; Nicola De Stefano; Massimo Filippi; Cristina Auger; Maria A Rocca; Frederik Barkhof; Franz Fazekas; Ludwig Kappos; Chris Polman; David Miller; Xavier Montalban Journal: Nat Rev Neurol Date: 2015-07-07 Impact factor: 42.937
Authors: Claudio Gasperini; Luca Prosperini; Mar Tintoré; Maria Pia Sormani; Massimo Filippi; Jordi Rio; Jacqueline Palace; Maria A Rocca; Olga Ciccarelli; Frederik Barkhof; Jaume Sastre-Garriga; Hugo Vrenken; Jette L Frederiksen; Tarek A Yousry; Christian Enzinger; Alex Rovira; Ludwig Kappos; Carlo Pozzilli; Xavier Montalban; Nicola De Stefano Journal: Neurology Date: 2018-12-26 Impact factor: 9.910
Authors: M Cabezas; J F Corral; A Oliver; Y Díez; M Tintoré; C Auger; X Montalban; X Lladó; D Pareto; À Rovira Journal: AJNR Am J Neuroradiol Date: 2016-06-09 Impact factor: 3.825