Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging

Malte Steinhoff; Kay Nehrke; Alfred Mertins; Peter Börnert

doi:10.1002/nbm.4185

Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging

Malte Steinhoff^*, Kay Nehrke, Alfred Mertins, Peter Börnert

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Signalverarbeitung

Abstract

Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence.

Originalsprache	Englisch
Aufsatznummer	e4185
Zeitschrift	NMR in Biomedicine
Jahrgang	33
Ausgabenummer	12
ISSN	0952-3480
DOIs	https://doi.org/10.1002/nbm.4185
Publikationsstatus	Veröffentlicht - 01.12.2020

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10.1002/nbm.4185

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@article{101062e7e09c4586b1e36820fd43f4a6,

title = "Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging",

abstract = "Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence.",

author = "Malte Steinhoff and Kay Nehrke and Alfred Mertins and Peter B{\"o}rnert",

note = "Funding Information: The in vivo experiments were executed on a 3 Tesla Philips Ingenia Scanner (Philips Healthcare, Best, The Netherlands) using a head coil with 13 channels. The data was obtained from six healthy volunteers. Informed consent was obtained according to the rules of the institution. The multi-shot echo-planar brain DWI experiments were performed using conventional Stejskal-Tanner diffusion encoding within a spin echo sequence54 and magnetization-prepared fat suppression. The DWI data was obtained in both full and partial Fourier acquisitions with four and six shots for a b-value of 1000?s/mm2 in three orthogonal directions. The subjects were asked to perform random in-plane motion from shot to shot within the head coil. DTI experiments were executed with four and five shots for a b-value of 1000?s/mm2 in 15 diffusion directions. Here, both static and gross motion-corrupted data was acquired. CSMs were acquired by precalibration. Relevant parameter settings are listed in Table?2. The multi-shot datasets were reconstructed using the three presented algorithms. The data rejection criterion was set to ?={0.4, 0.55, 0.7} for segmentations of {4,5,6}, respectively, if not stated otherwise. For DTI, the joint images from the individual multi-shot reconstructions for each diffusion direction were aligned with the non-DWI (b=0?s/mm2) image using an affine motion model. The registration, tensor estimation and fractional anisotropy (FA) calculations1 were performed using the Dipy library.55 The proposed algorithm was compared with the MC-SENSE+CG reference scheme and another variant of the SEDIMENT framework. All algorithms start with the CG-SENSE initialization, followed by macroscopic motion estimation, shot alignment and physiological motion estimation as shown in Figure?2. MC-SENSE+CG is a non-iterative algorithm that solves the joint multi-shot diffusion problem in Equation?(4)using CG. The method can be interpreted as SENSE+CG26 extended by macroscopic motion estimation. Phase unwrapping and 2D median filtering were used for physiological motion estimation. MC-SENSE+CG is also basically similar to AMUSE,30 as it obtains shot estimates using SENSE, extracts the motion parameters and calculates the final image once. AMUSE further corrects for diffusion contrast variations caused by rotational motion, which is neglected in this work. Regarding the scope of this work, we further excluded a dedicated PF scheme for MC-SENSE+CG. Prior-MC SEDIMENT (prior macroscopic motion-corrected SEDIMENT) adapts the iterative procedure of SEDIMENT, but skips the macroscopic motion estimation after the initial estimate. This variant was implemented to evaluate the necessity of combined iterative physiological and macroscopic motion correction. The repetitive registration should yield performance gains to justify the increased computational load. The algorithms were evaluated both in simulations and in vivo. The simulations were performed using the BrainWeb phantom53 from the T1-weighted normal brain database and 1?1?1?mm2 resolution. The phantom was padded to a matrix size of 256?256/NINI in the readout and phase-encoding directions, respectively, ensuring equal but shifted trajectories for all EPI shots. The simulation data was prepared in five steps according to the forward model. First, the motion-induced phase variations were created and applied for each shot as random functions of second spatial order as presented by Hu et al.31 Second, shot-wise rigid in-plane motion was uniformly sampled from a range of ?5 pix (?5 mm) and ?10? and the shot data was transformed accordingly. Third, 12 2D Gaussian sensitivity maps were arranged circularly around the image center. The disturbed shot data was multiplied by the CSMs to obtain multi-shot multi-coil data. Fourth, complex Gaussian noise with zero mean and equal variance for the real and imaginary parts was added in image space according to the predefined SNR. Finally, the data was undersampled in k-space according to the shot trajectories (optionally including PF acquisition). The BrainWeb phantom was prepared for {2,3,4,5,6} shots and SNRs of {5,10,15,20} without PF trajectories. The simulation data was reconstructed by the three algorithms without data rejection. The nRMSE and reconstruction time were used to measure performance. Total performance was measured as the average over 10 random simulation cases for each shot-SNR pair. The in vivo experiments were executed on a 3 Tesla Philips Ingenia Scanner (Philips Healthcare, Best, The Netherlands) using a head coil with 13 channels. The data was obtained from six healthy volunteers. Informed consent was obtained according to the rules of the institution. The multi-shot echo-planar brain DWI experiments were performed using conventional Stejskal-Tanner diffusion encoding within a spin echo sequence54 and magnetization-prepared fat suppression. The DWI data was obtained in both full and partial Fourier acquisitions with four and six shots for a b-value of 1000?s/mm2 in three orthogonal directions. The subjects were asked to perform random in-plane motion from shot to shot within the head coil. DTI experiments were executed with four and five shots for a b-value of 1000?s/mm2 in 15 diffusion directions. Here, both static and gross motion-corrupted data was acquired. CSMs were acquired by precalibration. Relevant parameter settings are listed in Table?2. The multi-shot datasets were reconstructed using the three presented algorithms. The data rejection criterion was set to ?={0.4, 0.55, 0.7} for segmentations of {4,5,6}, respectively, if not stated otherwise. For DTI, the joint images from the individual multi-shot reconstructions for each diffusion direction were aligned with the non-DWI (b=0?s/mm2) image using an affine motion model. The registration, tensor estimation and fractional anisotropy (FA) calculations1 were performed using the Dipy library.55 The reconstructions were conducted using Python 3.6.5 on a system with a 2.7 GHz Intel Core i7 4-core CPU and 16 GB RAM. The preparations of the CSMs included masking and coil compression. The sensitivities were masked6 by a threshold in advance of the presented reconstructions. The threshold was set to 10% of the body coil magnitude maximum value. In addition, binary closing (10 iterations) and binary dilations (5 iterations) were performed using the SciPy library. In simulations, the phantom was used as a reference image for thresholding after smoothing with a 2D Gaussian filter (?=15). Coil compression was performed by principal component analysis with a 99% threshold using the singular value decomposition in NumPy. This resulted in a reduction from 13 to 7 coils. Non-uniform sampling on EPI ramps was adjusted by gridding in advance. Fourier transforms were performed using the FFT of the NumPy library. The PSF-based undersampling was used whenever possible (not for PF acquisition) to avoid FFTs including data projections and CG gradient computations. The CG calculations were stopped by either the residual norm criterion7 or a maximum number of iterations. The residual norm tolerance was set to 10?4. The maximum iteration count was empirically set to 12 and 10 for single- and multi-shot reconstructions, respectively. Coil sensitivity normalization7 was applied for all CG methods. The macroscopic motion was estimated using the rigid registration described in the fast elastic image registration56 framework. After an exhaustive presearch, the registration performs a Gauss-Newton scheme with Armijo's step size rule. A normalized gradient field57 metric was used to stabilize the registration against intensity variations in the g-factor areas.6 To stabilize convergence, the shots were aligned at their joint average location after each registration by subtracting the mean rigid parameters of all included shots (in I).39,58 Furthermore, registration parameters below 0.01 pix (about 10 ?m) and 0.01? were ignored and set to zero. Registration accuracies below this threshold were assumed immoderate, hampering convergence. The rigid shot alignment with ?i was performed using a k-space formulation that avoids gridding.39 Translations were applied in k-space by multiplying phase ramps according to the Fourier shift theorem. Rotations were implemented as a concatenation of three shears applied in k-space.59 The action of ?i was thus implemented by subsequent rotational and translational operators. The SEDIMENT phase filters were k-space window functions avoiding phase unwrapping. For physiological motion estimation, the shot phases ?i were smoothed by a 2D triangular window in k-space using 2D-FFTs.29 For full Fourier acquisition, the window size was scaled to half the image size. For PF acquisitions, the range in the phase-encoding direction was limited to the symmetric area. MC-SENSE+CG used the phase unwrapper and 2D median filter from the SciPy library. The median filter kernel was set to 9?9 pixels applied on unwrapped full resolution phases. Phase estimation was disabled for non-DWI datasets (b0=0?s/mm2). PF projection phases ?i were obtained using a 2D window with 1D Hann shape in the phase-encoding direction scaled to the size of the symmetric sampling area in the k-space center. The iterative algorithms were stopped either by a convergence criterion or by a maximum iteration count. The mean-square error ?(k)=??(k)??(k?1)?22/??(k?1)?22 of subsequent iterations was used as the convergence criterion,29 where k is the iteration number. In this work, convergence was assumed when ?(k) dropped below the tolerance ?=10?6 or when a number of 200 iterations was exceeded. Publisher Copyright: {\textcopyright} 2019 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2020",

month = dec,

day = "1",

doi = "10.1002/nbm.4185",

language = "English",

volume = "33",

journal = "NMR in Biomedicine",

issn = "0952-3480",

number = "12",

}

TY - JOUR

T1 - Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging

AU - Steinhoff, Malte

AU - Nehrke, Kay

AU - Mertins, Alfred

AU - Börnert, Peter

N1 - Funding Information: The in vivo experiments were executed on a 3 Tesla Philips Ingenia Scanner (Philips Healthcare, Best, The Netherlands) using a head coil with 13 channels. The data was obtained from six healthy volunteers. Informed consent was obtained according to the rules of the institution. The multi-shot echo-planar brain DWI experiments were performed using conventional Stejskal-Tanner diffusion encoding within a spin echo sequence54 and magnetization-prepared fat suppression. The DWI data was obtained in both full and partial Fourier acquisitions with four and six shots for a b-value of 1000?s/mm2 in three orthogonal directions. The subjects were asked to perform random in-plane motion from shot to shot within the head coil. DTI experiments were executed with four and five shots for a b-value of 1000?s/mm2 in 15 diffusion directions. Here, both static and gross motion-corrupted data was acquired. CSMs were acquired by precalibration. Relevant parameter settings are listed in Table?2. The multi-shot datasets were reconstructed using the three presented algorithms. The data rejection criterion was set to ?={0.4, 0.55, 0.7} for segmentations of {4,5,6}, respectively, if not stated otherwise. For DTI, the joint images from the individual multi-shot reconstructions for each diffusion direction were aligned with the non-DWI (b=0?s/mm2) image using an affine motion model. The registration, tensor estimation and fractional anisotropy (FA) calculations1 were performed using the Dipy library.55 The proposed algorithm was compared with the MC-SENSE+CG reference scheme and another variant of the SEDIMENT framework. All algorithms start with the CG-SENSE initialization, followed by macroscopic motion estimation, shot alignment and physiological motion estimation as shown in Figure?2. MC-SENSE+CG is a non-iterative algorithm that solves the joint multi-shot diffusion problem in Equation?(4)using CG. The method can be interpreted as SENSE+CG26 extended by macroscopic motion estimation. Phase unwrapping and 2D median filtering were used for physiological motion estimation. MC-SENSE+CG is also basically similar to AMUSE,30 as it obtains shot estimates using SENSE, extracts the motion parameters and calculates the final image once. AMUSE further corrects for diffusion contrast variations caused by rotational motion, which is neglected in this work. Regarding the scope of this work, we further excluded a dedicated PF scheme for MC-SENSE+CG. Prior-MC SEDIMENT (prior macroscopic motion-corrected SEDIMENT) adapts the iterative procedure of SEDIMENT, but skips the macroscopic motion estimation after the initial estimate. This variant was implemented to evaluate the necessity of combined iterative physiological and macroscopic motion correction. The repetitive registration should yield performance gains to justify the increased computational load. The algorithms were evaluated both in simulations and in vivo. The simulations were performed using the BrainWeb phantom53 from the T1-weighted normal brain database and 1?1?1?mm2 resolution. The phantom was padded to a matrix size of 256?256/NINI in the readout and phase-encoding directions, respectively, ensuring equal but shifted trajectories for all EPI shots. The simulation data was prepared in five steps according to the forward model. First, the motion-induced phase variations were created and applied for each shot as random functions of second spatial order as presented by Hu et al.31 Second, shot-wise rigid in-plane motion was uniformly sampled from a range of ?5 pix (?5 mm) and ?10? and the shot data was transformed accordingly. Third, 12 2D Gaussian sensitivity maps were arranged circularly around the image center. The disturbed shot data was multiplied by the CSMs to obtain multi-shot multi-coil data. Fourth, complex Gaussian noise with zero mean and equal variance for the real and imaginary parts was added in image space according to the predefined SNR. Finally, the data was undersampled in k-space according to the shot trajectories (optionally including PF acquisition). The BrainWeb phantom was prepared for {2,3,4,5,6} shots and SNRs of {5,10,15,20} without PF trajectories. The simulation data was reconstructed by the three algorithms without data rejection. The nRMSE and reconstruction time were used to measure performance. Total performance was measured as the average over 10 random simulation cases for each shot-SNR pair. The in vivo experiments were executed on a 3 Tesla Philips Ingenia Scanner (Philips Healthcare, Best, The Netherlands) using a head coil with 13 channels. The data was obtained from six healthy volunteers. Informed consent was obtained according to the rules of the institution. The multi-shot echo-planar brain DWI experiments were performed using conventional Stejskal-Tanner diffusion encoding within a spin echo sequence54 and magnetization-prepared fat suppression. The DWI data was obtained in both full and partial Fourier acquisitions with four and six shots for a b-value of 1000?s/mm2 in three orthogonal directions. The subjects were asked to perform random in-plane motion from shot to shot within the head coil. DTI experiments were executed with four and five shots for a b-value of 1000?s/mm2 in 15 diffusion directions. Here, both static and gross motion-corrupted data was acquired. CSMs were acquired by precalibration. Relevant parameter settings are listed in Table?2. The multi-shot datasets were reconstructed using the three presented algorithms. The data rejection criterion was set to ?={0.4, 0.55, 0.7} for segmentations of {4,5,6}, respectively, if not stated otherwise. For DTI, the joint images from the individual multi-shot reconstructions for each diffusion direction were aligned with the non-DWI (b=0?s/mm2) image using an affine motion model. The registration, tensor estimation and fractional anisotropy (FA) calculations1 were performed using the Dipy library.55 The reconstructions were conducted using Python 3.6.5 on a system with a 2.7 GHz Intel Core i7 4-core CPU and 16 GB RAM. The preparations of the CSMs included masking and coil compression. The sensitivities were masked6 by a threshold in advance of the presented reconstructions. The threshold was set to 10% of the body coil magnitude maximum value. In addition, binary closing (10 iterations) and binary dilations (5 iterations) were performed using the SciPy library. In simulations, the phantom was used as a reference image for thresholding after smoothing with a 2D Gaussian filter (?=15). Coil compression was performed by principal component analysis with a 99% threshold using the singular value decomposition in NumPy. This resulted in a reduction from 13 to 7 coils. Non-uniform sampling on EPI ramps was adjusted by gridding in advance. Fourier transforms were performed using the FFT of the NumPy library. The PSF-based undersampling was used whenever possible (not for PF acquisition) to avoid FFTs including data projections and CG gradient computations. The CG calculations were stopped by either the residual norm criterion7 or a maximum number of iterations. The residual norm tolerance was set to 10?4. The maximum iteration count was empirically set to 12 and 10 for single- and multi-shot reconstructions, respectively. Coil sensitivity normalization7 was applied for all CG methods. The macroscopic motion was estimated using the rigid registration described in the fast elastic image registration56 framework. After an exhaustive presearch, the registration performs a Gauss-Newton scheme with Armijo's step size rule. A normalized gradient field57 metric was used to stabilize the registration against intensity variations in the g-factor areas.6 To stabilize convergence, the shots were aligned at their joint average location after each registration by subtracting the mean rigid parameters of all included shots (in I).39,58 Furthermore, registration parameters below 0.01 pix (about 10 ?m) and 0.01? were ignored and set to zero. Registration accuracies below this threshold were assumed immoderate, hampering convergence. The rigid shot alignment with ?i was performed using a k-space formulation that avoids gridding.39 Translations were applied in k-space by multiplying phase ramps according to the Fourier shift theorem. Rotations were implemented as a concatenation of three shears applied in k-space.59 The action of ?i was thus implemented by subsequent rotational and translational operators. The SEDIMENT phase filters were k-space window functions avoiding phase unwrapping. For physiological motion estimation, the shot phases ?i were smoothed by a 2D triangular window in k-space using 2D-FFTs.29 For full Fourier acquisition, the window size was scaled to half the image size. For PF acquisitions, the range in the phase-encoding direction was limited to the symmetric area. MC-SENSE+CG used the phase unwrapper and 2D median filter from the SciPy library. The median filter kernel was set to 9?9 pixels applied on unwrapped full resolution phases. Phase estimation was disabled for non-DWI datasets (b0=0?s/mm2). PF projection phases ?i were obtained using a 2D window with 1D Hann shape in the phase-encoding direction scaled to the size of the symmetric sampling area in the k-space center. The iterative algorithms were stopped either by a convergence criterion or by a maximum iteration count. The mean-square error ?(k)=??(k)??(k?1)?22/??(k?1)?22 of subsequent iterations was used as the convergence criterion,29 where k is the iteration number. In this work, convergence was assumed when ?(k) dropped below the tolerance ?=10?6 or when a number of 200 iterations was exceeded. Publisher Copyright: © 2019 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence.

AB - Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence.

UR - http://www.scopus.com/inward/record.url?scp=85076214075&partnerID=8YFLogxK

U2 - 10.1002/nbm.4185

DO - 10.1002/nbm.4185

M3 - Journal articles

C2 - 31814181

AN - SCOPUS:85076214075

SN - 0952-3480

VL - 33

JO - NMR in Biomedicine

JF - NMR in Biomedicine

IS - 12

M1 - e4185

ER -

Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging

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