• Cointepas, Yann
• Rochefort, Ludovic De
• Badagbon, Jacques
• Cam, Davy
• Bottlaender, Michel
• Roche, Stéphane
• Vignaud, Alexandre
• Guevara, Miguel
• Mangin, Jean-François

Abstract

Introduction:Iron accumulates in the brain over time, and an abnormal load is related to neurodegenerescence [1]. A quantitative measure of the iron load could then be used as a biomarker for evaluating the healthy brain aging as well as the pathologies severity [1][2]. QSM provides an iron load quantitative metric [3], and by means of it the accumulation of iron has been analyzed, mostly at 3T [1]. Ultra-high magnetic fields provide higher sensibility and resolution [4], but they are more vulnerable to effects from strong field variations due to air/tissue interface [2]. Also, an improper coil combination can generate artifacts in the phase reconstruction, leading to unexploitable QSM (Fig 1A). There are some available software that allow to reconstruct QSM from 3D multi-echo gradient echo (MGRE). They are mostly adapted to process data at 3T and only few offer an automatic user-independent pipeline [5]. Moreover they do not provide a means to overcome the possible artifacts in the input data. Here we propose a pipeline for obtaining QSM from 3D MGRE DICOM data without the need of in-house powerful calculation machines, since the computation takes place in a ISO27001-certified Openstack cloud infrastructure (de.NBI Clouda). It considers a phase pre-processing to reduce the presence of artifacts (from the background field and/or coil combination), and use the filtered field for the MEDI algorithm [6]. The pipeline was conceived as part of the QSM4SENIORb study, that aims at studying the accumulation of iron in a longitudinal way by using QSM in the SENIOR database [7]. We take advantage of these high resolution data for testing the pipeline. Methods: (I) Participants: 84 volunteers were selected from the SENIOR database [7].(II) Image acquisition: The MRI data was acquired at NeuroSpincd (France) on a Magnetom 7 Tesla scanner (Siemens Healthineers, Germany) 1Tx/32Rx Nova Medical head coil. MGRE was performed (TA=9:48 min, FoV=256 mm, voxel size=0.8 mm isotropic, TR=37 ms, TE=1.68 ms, ∆TE=3.05 ms, number of echoes=10, flip angle=30°, acceleration factor GRAPPA=3, 196 sagittal partitions, bandwidth=740 Hz/px, monopolar readouts) and reconstructed using Virtual Coil Combination (VCC) [8]. The T1-weighted MP2RAGE was also acquired (TR= 6000 ms; TE=2.96 ms; voxel size=0.75 mm isotropic).(III) Cloud computing: A virtual machine (14 cores, 32 Go RAM and 200 Go volume) was deployed and configured automatically in the de.NBI cloud. Imaging data were encrypted on transit and at rest. (IV) Data processing: The method consists in pre-filtering the phase data from the MGRE acquisition by using the magnitude and phase from the ten available echoes. It is based on the method described in [9] and illustrated in Fig 2. To isolate the brain internal field variations (∆B_"in" ) the conjugate gradients algorithm of the normal equation ∆W_∆^2 ∆B_"in" =∆W_∆^2 ∆B is calculated, where W_∆ is a diagonal matrix weighting each estimation of the Laplacian by the inverse of its error standard deviation. The unwrapping is done by forcing the point by point difference between ]-π, π] when calculating ∆B using the modulo function. The Laplacian of the field is calculated from the Laplacian of the phase for each echo k (∆B_k) and its error (W_∆k) combined in the least-squares sense. The gradient norm of the field is also computed similarly. A mask for excluding voxels suffering from spatial deformation (leading to a ∆B_ that cannot be correctly measured) is also computed from the gradient norm of the field by applying a whole-brain mask (computed using ANTs from the T1-weighted and registered by a rigid transformation to the T2 space)over it, followed by a thresholding and morphological operations (opening, connected components and closing). Finally, the conjugate gradient algorithm is used to compute B_ from the normal equation (stopped at maximum iterations: 512 or the relative norm less than 10-3).Results and Discussions: Figure 1 shows an example of a cloud-computed QSM from the reconstructed unwrapped filtered phase and the customized mask (D). The refinement of the brain mask (B) allows to remove from the QSM computation some unreliable voxel values. The computation of a filtered phase map (C) helps overcoming the presence of artifacts such as open-ended fringe lines arising from a poor coil combination, providing a cleaner input into the MEDI

Topics

• impedance spectroscopy
• phase
• laser emission spectroscopy
• mass spectrometry
• iron
• aging
• isotropic
• aging