Retrospective Distortion and Motion Correction for Free‐Breathing <scp>DW‐MRI</scp> of the Kidneys Using Dual‐Echo <scp>EPI</scp> and Slice‐to‐Volume Registration

Coll‐Font, Jaume; Afacan, Onur; Hoge, W. Scott; Garg, Harsha K.; Shashi, Kumar; Marami, Bahram; Gholipour, Ali; Chow, Jeanne S.; Warfield, Simon K.; Kurugol, Sila

doi:10.1002/jmri.27473

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…Furthermore, only up to three cardiac phases were sampled due to time constraints. While motion was handled via retrospective registration, in this study distortion from magnetic field inhomogeneity was not corrected, as was done in several recent kidney DWI studies 38–40 . Thus, this will be the subject of future work.…”

Section: Discussionmentioning

confidence: 99%

“…While motion was handled via retrospective registration, in this study distortion from magnetic field inhomogeneity was not corrected, as was done in several recent kidney DWI studies. [38][39][40] Thus, this will be the subject of future work. While denoising showed demonstrable improvements in image texture, its impact on the contrast within the study (cardiac phase, tissue type, or pulse sequence dependence) is not yet clear.…”

Section: Limitationsmentioning

confidence: 99%

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Cardiac Phase and Flow Compensation Effects on REnal Flow and Microstructure AnisotroPy MRI in Healthy Human Kidney

Sigmund

Mikheev

Brinkmann³

et al. 2022

Magnetic Resonance Imaging

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Background Renal diffusion‐weighted imaging (DWI) involves microstructure and microcirculation, quantified with diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and hybrid models. A better understanding of their contrast may increase specificity. Purpose To measure modulation of DWI with cardiac phase and flow‐compensated (FC) diffusion gradient waveforms. Study Type Prospective. Population Six healthy volunteers (ages: 22–48 years, five females), water phantom. Field Strength/Sequence 3‐T, prototype DWI sequence with 2D echo‐planar imaging, and bipolar (BP) or FC gradients. 2D Half‐Fourier Single‐shot Turbo‐spin‐Echo (HASTE). Multiple‐phase 2D spoiled gradient‐echo phase contrast (PC) MRI. Assessment BP and FC water signal decays were qualitatively compared. Renal arteries and velocities were visualized on PC‐MRI. Systolic (peak velocity), diastolic (end stable velocity), and pre‐systolic (before peak velocity) phases were identified. Following mutual information‐based retrospective self‐registration of DWI within each kidney, and Marchenko‐Pastur Principal Component Analysis (MPPCA) denoising, combined IVIM‐DTI analysis estimated mean diffusivity (MD), fractional anisotropy (FA), and eigenvalues (λi) from tissue diffusivity (Dt), perfusion fraction (fp), and pseudodiffusivity (Dp, Dp,axial, Dp,radial), for each tissue (cortex/medulla, segmented on b0/FA respectively), phase, and waveform (BP, FC). Monte Carlo water diffusion simulations aided data interpretation. Statistical Tests Mixed model regression probed differences between tissue types and pulse sequences. Univariate general linear model analysis probed variations among cardiac phases. Spearman correlations were measured between diffusion metrics and renal artery velocities. Statistical significance level was set at P < 0.05. Results Water BP and FC signal decays showed no differences. Significant pulse sequence dependence occurred for λ1, λ3, FA, Dp, fp, Dp,axial, Dp,radial in cortex and medulla, and medullary λ2. Significant cortex/medulla differences occurred with BP for all metrics except MD (systole [P = 0.224]; diastole [P = 0.556]). Significant phase dependence occurred for Dp, Dp,axial, Dp,radial for BP and medullary λ1, λ2, λ3, MD for FC. FA correlated significantly with velocity. Monte Carlo simulations indicated medullary measurements were consistent with a 34 μm tubule diameter. Data Conclusion Cardiac gating and flow compensation modulate of measurements of renal diffusion. Evidence Level 2 Technical Efficacy Stage 2

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Cardiac Phase and Flow Compensation Effects on REnal Flow and Microstructure AnisotroPy MRI in Healthy Human Kidney

Sigmund

Mikheev

Brinkmann³

et al. 2022

Magnetic Resonance Imaging

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“…At present, some DWI sequences have widely been used to diagnose renal diseases and evaluate the renal function in a large number of acute and/or chronic kidney diseases and various renal tumors 3 – 7 , including breath-hold DWI (BH-DWI) 8 , free-breathing DWI (FB-DWI) 9 , navigator-triggered DWI (NT-DWI) 10 , respiratory-triggered DWI (RT-DWI) 11 , readout-segmented DWI (RS-DWI) 12 , and zoomit-DWI (Z-DWI) 13 . Zoomit-DWI is a reduced field-of-view (rFOV) single-shot DWI sequence, which is also called as field-of-view optimized and constrained undistorted single-shot DWI (FOCUS DWI) in GE and zonal oblique multislice (ZOOM) in Philips 14 , 15 .…”

Section: Introductionmentioning

confidence: 99%

Comparing the clinical utility of single-shot, readout-segmented and zoomit echo-planar imaging in diffusion-weighted imaging of the kidney at 3 T

Liu

Xie

et al. 2022

Sci Rep

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We compared the clinical utility of single-shot echo-planar imaging (SS-EPI) using different breathing schemes, readout-segmented EPI and zoomit EPI in the repeatability of apparent diffusion coefficient (ADC) measurements, cortico-medullary contrast to noise ratio (c-mCNR) and image quality. In this institutional review board-approved prospective study, some common clinically applicable diffusion-weighted imaging (b = 50, 400, 800 s/mm2) of kidney on 3.0 T MRI were performed on 22 volunteers using SS-EPI with breath-hold diffusion-weighted imaging (BH-DWI), free-breathing (FB-DWI), navigator-triggered (NT-DWI) and respiratory-triggered (RT-DWI), readout-segmented DWI (RS-DWI), and Zoomit DWI (Z-DWI). ADC and c-mCNR were measured in 12 anatomic locations (the upper, middle, and lower pole of the renal cortex and medulla), and image quality was assessed on these DWI sequences. A DWI with the optimal clinical utility was decided by systematically assessing the ADC repeatability, c-mCNR and image quality among the DWIs. For ADC measurements, Z-DWI had an excellent intra-observer agreement (intra-class correlation coefficients (ICCs): 0.876–0.944) and good inter-observer agreement (inter-class ICCs: 0.798–0.856) in six DWI sequences. Z-DWI had the highest ADC repeatability in most of the 12 anatomic locations of the kidneys (mean ADC absolute difference: 0.070–0.111 × 10−3 mm2/s, limit of agreement: 0.031–0.056 × 10−3 mm2/s). In all DWIs, Z-DWI yielded a slightly higher c-mCNR than other DWIs in most representative locations (P > 0.05), which was significantly higher than BH-DWI and FB-DWI in the middle pole of both kidneys and the upper pole of the left kidney (P < 0.05). In addition, Z-DWI yielded image quality that was similar to RT-DWI and NT-DWI (P > 0.05) and superior to BH-DWI, FB-DWI and RS-DWI (P < 0.05). Our results suggest that Z-DWI provides the highest ADC reproducibility, better c-mCNR and good image quality on 3.0 T MRI, making it the recommended sequence for clinical DWI of the kidney.

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“…However, prospective motion correction in some cases reduces scan efficiency, as motion-corrupt measurements must be re-acquired with additional RF excitations. Retrospective motion correction is also popular [13,5], as it relies on computational processing during reconstruction in lieu of reducing scan efficiency.…”

Section: Introductionmentioning

confidence: 99%

FSE Compensated Motion Correction for MRI Using Data Driven Methods

Levac¹,

Kumar²,

Kardonik³

et al. 2022

Preprint

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Magnetic Resonance Imaging (MRI) is a widely used medical imaging modality boasting great soft tissue contrast without ionizing radiation, but unfortunately suffers from long acquisition times. Long scan times can lead to motion artifacts, for example due to bulk patient motion such as head movement and periodic motion produced by the heart or lungs. Motion artifacts can degrade image quality and in some cases render the scans nondiagnostic. To combat this problem, prospective and retrospective motion correction techniques have been introduced. More recently, data driven methods using deep neural networks have been proposed. As a large number of publicly available MRI datasets are based on Fast Spin Echo (FSE) sequences, methods that use them for training should incorporate the correct FSE acquisition dynamics. Unfortunately, when simulating training data, many approaches fail to generate accurate motion-corrupt images by neglecting the effects of the temporal ordering of the k-space lines as well as neglecting the signal decay throughout the FSE echo train. In this work, we highlight this consequence and demonstrate a training method which correctly simulates the data acquisition process of FSE sequences with higher fidelity by including sample ordering and signal decay dynamics. Through numerical experiments, we show that accounting for the FSE acquisition leads to better motion correction performance during inference.

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Retrospective Distortion and Motion Correction for Free‐Breathing DW‐MRI of the Kidneys Using Dual‐Echo EPI and Slice‐to‐Volume Registration

Cited by 8 publications

References 36 publications

Cardiac Phase and Flow Compensation Effects on REnal Flow and Microstructure AnisotroPy MRI in Healthy Human Kidney

Cardiac Phase and Flow Compensation Effects on REnal Flow and Microstructure AnisotroPy MRI in Healthy Human Kidney

Comparing the clinical utility of single-shot, readout-segmented and zoomit echo-planar imaging in diffusion-weighted imaging of the kidney at 3 T

FSE Compensated Motion Correction for MRI Using Data Driven Methods

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