Sofia Chavez1, Joseph Viviano2, Mojdeh Zamyadi3, Peter B Kingsley4, Peter Kochunov5, Stephen Strother6, Aristotle Voineskos7. 1. Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada. Electronic address: sofia.chavez@camhpet.ca. 2. Centre for Addiction and Mental Health, Toronto, Canada. 3. Rotman Research Institute, Baycrest, Toronto, Canada. 4. Department of Radiology, North Shore University Hospital, Manhasset, USA. 5. Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland, School of Medicine, Baltimore, USA. 6. Rotman Research Institute, Baycrest, Toronto, Canada; Medical Biophysics Department, University of Toronto, Toronto, Canada. 7. Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Canada.
Abstract
PURPOSE: To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics. METHODS: A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting. RESULTS: The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available. CONCLUSIONS: A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs.
PURPOSE: To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics. METHODS: A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting. RESULTS: The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available. CONCLUSIONS: A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs.
Authors: T Jaermann; G Crelier; K P Pruessmann; X Golay; T Netsch; A M C van Muiswinkel; S Mori; P C M van Zijl; A Valavanis; S Kollias; P Boesiger Journal: Magn Reson Med Date: 2004-02 Impact factor: 4.668
Authors: Gary H Glover; Bryon A Mueller; Jessica A Turner; Theo G M van Erp; Thomas T Liu; Douglas N Greve; James T Voyvodic; Jerod Rasmussen; Gregory G Brown; David B Keator; Vince D Calhoun; Hyo Jong Lee; Judith M Ford; Daniel H Mathalon; Michele Diaz; Daniel S O'Leary; Syam Gadde; Adrian Preda; Kelvin O Lim; Cynthia G Wible; Hal S Stern; Aysenil Belger; Gregory McCarthy; Burak Ozyurt; Steven G Potkin Journal: J Magn Reson Imaging Date: 2012-02-07 Impact factor: 4.813
Authors: Olaf Dietrich; José G Raya; Scott B Reeder; Maximilian F Reiser; Stefan O Schoenberg Journal: J Magn Reson Imaging Date: 2007-08 Impact factor: 4.813
Authors: Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith Journal: Neuroimage Date: 2011-09-16 Impact factor: 6.556
Authors: Peter Kochunov; Erin W Dickie; Joseph D Viviano; Jessica Turner; Peter B Kingsley; Neda Jahanshad; Paul M Thompson; Meghann C Ryan; Els Fieremans; Dmitry Novikov; Jelle Veraart; Elliot L Hong; Anil K Malhotra; Robert W Buchanan; Sofia Chavez; Aristotle N Voineskos Journal: Hum Brain Mapp Date: 2017-11-27 Impact factor: 5.038
Authors: Colin Hawco; Joseph D Viviano; Sofia Chavez; Erin W Dickie; Navona Calarco; Peter Kochunov; Miklos Argyelan; Jessica A Turner; Anil K Malhotra; Robert W Buchanan; Aristotle N Voineskos Journal: Psychiatry Res Neuroimaging Date: 2018-06-09 Impact factor: 2.376
Authors: Kathryn E Keenan; Jana G Delfino; Kalina V Jordanova; Megan E Poorman; Prathyush Chirra; Akshay S Chaudhari; Bettina Baessler; Jessica Winfield; Satish E Viswanath; Nandita M deSouza Journal: Med Phys Date: 2021-09-29 Impact factor: 4.506
Authors: Joseph D Viviano; Robert W Buchanan; Navona Calarco; James M Gold; George Foussias; Nikhil Bhagwat; Laura Stefanik; Colin Hawco; Pamela DeRosse; Miklos Argyelan; Jessica Turner; Sofia Chavez; Peter Kochunov; Peter Kingsley; Xiangzhi Zhou; Anil K Malhotra; Aristotle N Voineskos Journal: Biol Psychiatry Date: 2018-04-13 Impact factor: 13.382
Authors: Meichen Yu; Kristin A Linn; Philip A Cook; Mary L Phillips; Melvin McInnis; Maurizio Fava; Madhukar H Trivedi; Myrna M Weissman; Russell T Shinohara; Yvette I Sheline Journal: Hum Brain Mapp Date: 2018-07-01 Impact factor: 5.038
Authors: Lindsay D Oliver; Colin Hawco; Philipp Homan; Junghee Lee; Michael F Green; James M Gold; Pamela DeRosse; Miklos Argyelan; Anil K Malhotra; Robert W Buchanan; Aristotle N Voineskos Journal: Biol Psychiatry Cogn Neurosci Neuroimaging Date: 2020-12-05
Authors: Anthony L Vaccarino; Moyez Dharsee; Stephen Strother; Don Aldridge; Stephen R Arnott; Brendan Behan; Costas Dafnas; Fan Dong; Kenneth Edgecombe; Rachad El-Badrawi; Khaled El-Emam; Tom Gee; Susan G Evans; Mojib Javadi; Francis Jeanson; Shannon Lefaivre; Kristen Lutz; F Chris MacPhee; Jordan Mikkelsen; Tom Mikkelsen; Nicholas Mirotchnick; Tanya Schmah; Christa M Studzinski; Donald T Stuss; Elizabeth Theriault; Kenneth R Evans Journal: Front Neuroinform Date: 2018-05-23 Impact factor: 4.081