Motion detection and correction for carotid MRI using a markerless optical system

Several barriers prevent the use of whole-brain T2 mapping in routine use despite increasing interest in this parameter. One of the main barriers is the long scan time resulting in patient discomfort and motion corrupted data. To address this challenge, a method for accurate whole-brain T2 mapping with a limited acquisition time and motion correction capabilities is investigated. A 3D radial multi-echo spin-echo sequence was implemented with optimized sampling trajectory enabling the estimation of intra-scan motion, subsequently used to correct the raw data. Motion corrected echo images are then reconstructed with linear subspace constrained reconstruction. Experiments were carried out on phantom and volunteers at 3T to evaluate the accuracy of the T2 estimation, the sensitivity to lesions and the efficiency of the correction on motion corrupted data. Whole-brain T2 mapping acquired in less than 7 min enabled the depiction of lesions in the white matter with longer T2. Data retrospectively corrupted with typical motion traces of pediatric patients highly benefited from the motion correction by reducing the error in T2 estimates within the lesions. All datasets acquired on seven volunteers, with deliberate motion, also showed that motion corrupted T2 maps could be improved with the retrospective motion correction both at the voxel level and the structure level. A whole-brain T2 mapping sequence with retrospective intra-scan motion correction and reasonable acquisition time is proposed. The method necessitates advanced iterative reconstruction strategies but no additional navigator, external device, or increased scan time is required.

Motion-compensated PET of awake animals has the potential to greatly improve translational neurological investigations by enabling brain function to be studied during learning tasks and complex behaviors. Previously we have demonstrated the feasibility of performing motion-compensated brain PET on rodents, obtaining the necessary head motion data using marker-based techniques. However, markerless motion tracking would simplify animal experiments and potentially provide more accurate pose estimates over a greater range of motion. Previously we have described a markerless stereo motion tracking system and associated algorithms and validated the approach in phantoms. In this work we performed a pilot study to demonstrate motion-compensated <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F_FDG brain imaging in an awake, unrestrained rat using head pose measurements obtained from the markerless tracking system. Motion compensation clearly worked, resulting in easily identifiable structures in the head. However, it was also obvious that considerable residual error remained after correction. Post analysis of the motion estimates indicated that the residual error was the result of occasional spurious pose estimates, most likely caused by features on non-rigid parts of the head contributing to the pose estimation. Moreover, the line-of-response rebinning used for motion correction resulted in a large proportion of lost events, leading to noisy and inconsistent projection data. The latter is avoided by using a direct list mode reconstruction. In summary, markerless tracking continues to show promise for motion-compensated imaging of awake animals, but further optimization is required to match the accuracy and consistency of marker-based tracking.

FreeSurfer based cortical mapping and T1-relaxometry with MPnRAGE: Test-retest reliability with and without retrospective motion correction

Prospective motion correction in functional MRI using simultaneous multislice imaging and multislice-to-volume image registration

To evaluate the feasibility of motion correction in glutamate chemical exchange saturation transfer (GluCEST) imaging, using a rat model of epileptic seizure. Epileptic seizure was induced in six male Wistar rats by intraperitoneal injection of kainic acid (KA). CEST data were obtained using a 7.0T Bruker MRI scanner before and 3h after KA injection. Retrospective motion correction was performed in CEST images using a gradient-based motion correction (GradMC) algorithm. GluCEST signals in the hippocampal regions were quantitatively evaluated with and without motion correction. Calculated GluCEST signals differed significantly between the pre-KA injection group, regardless of motion-correction implementation, and the post-KA injection group with motion correction (3.662 ± 1.393 % / 3.726 ± 1.982 % for pre-KA injection group with/without motion correction vs. 6.996 ± 1.684 % for post-KA injection group with motion correction; all P < 0.05). Our results clearly show that GradMC can be used in CEST imaging for efficient correction of seizure-like motion. The GradMC can be further implemented in various CEST imaging techniques to increase the accuracy of analysis.

To prospectively evaluate the feasibility and biventricular assessment accuracy of a free-breathing cardiac cine imaging technique (RTCSCineMoCo) combined with highly accelerated real-time (RT) acquisition, compressed sensing (CS) reconstruction, and fully automated non-rigid respiratory motion correction. We evaluated 80 patients scheduled for clinical cardiac MRI. Cardiac cine images of the same long-axis and short-axis stacks were acquired using three techniques: (1) SegBH: standard segmented cine with breath-hold; (2) RTCSCineMoCo; (3) RTCSCine: single-shot RT CS cine at 3.0 T. Image quality (IQ) was evaluated using a qualitative 5-point Likert scale and the European CMR registry standardized criteria. Quantitative parameters including left (LV) and right ventricular (RV) ejection fractions (EF), end-diastolic volumes (EDV), end-systolic volumes (ESV), stroke volumes (SV), and LV mass (LVM) were measured and compared. RTCSCineMoCo and SegBH had equivalent IQ scores (4.4 ± 0.7 vs. 4.2 ± 0.8, p = 0.066), while RTCSCine had a significantly lower IQ score than SegBH (4.0 ± 0.8 vs. 4.2 ± 0.8, p = 0.031). In a quantitative analysis, RTCSCineMoCo and SegBH yielded similar measurements for all parameters, while the majority of RTCSCine parameters were significantly different compared with SegBH, except for LVEDV. RTCSCineMoCo is a promising method for robust free-breathing cardiac cine imaging, achieving better IQ and more precise quantitative analysis results for both ventricles compared with RTCSCine. • RTCSCineMoCo is a promising method for free-breathing cardiac MR cine imaging in daily practice. • RTCSCineMoCo provided better IQ and more precise quantitative measurements compared with RTCSCine, by extending RT data acquisition to multiple heartbeats, performing non-rigid respiratory motion correction, and signal averaging. • RTCSCineMoCo may be suitable for routine clinical use for vulnerable patients who may otherwise pose a challenge to image successfully with the conventional segmented cine technique.

Motion correction of echo-planar imaging (EPI) data used in functional MRI (fMRI) is an essential pre-processing step performed prior to statistical analysis. At ultra-high resolution fMRI, current requirements regarding translational and rotational motion may no longer be acceptable. This prompts the need for a systematic investigation of the effects of motion correction procedures with in-vivo fMRI data. Here we systematically evaluated the effect of retrospective motion correction with freely available fMRI analysis software packages (FSL, AFNI and SPM) on activation maps using fMRI data acquired with prospective motion detection, to identify and quantify confounding effects of retrospective motion correction, and to evaluate its dependence on spatial resolution and motion correction algorithms.Brain activation maps were obtained for two different resolutions, an ultra-high, i.e. 0.653 mm3, and a more widely used 2.03 mm3 isotropic resolutions at 7T. The EPI data were acquired using simultaneous non-image-based optical moiré phase tracking (MPT) of physical motion. The results showed that image-based motion detection, performed by SPM8 software package, may be erroneous in high-field fMRI data with partial brain coverage and can introduce spurious motion leading to false-positive and false-negative activation. Further analyses demonstrated that limited acquisition field of view has the dominant influence on the effect.

Navigators for Motion Detection in the 1.5T MRI-linac

Purpose 18F-fluorocholine is a positron emission tomography (PET) tracer earlier found to be a marker of macrophage content in carotid plaques. We aimed to assess the feasibility of 18F-choline PET-MRI to non-invasively localize vulnerable coronary plaques, using optical coherence tomography (OCT) as reference standard. Methods Patients with recent myocardial infarction who were scheduled for a secondary angiography of a non-culprit vessel, underwent 18F-fluorocholine coronary PET-MRI. Subsequently, OCT was performed during the secondary angiography. Maximum target-to-background (TBRmax) values of 18F-fluorocholine uptake were determined in two vessel sections that contained either vulnerable or stable plaques as defined by OCT. The OCT-based definition of a vulnerable plaque was a fibrous cap thickness &lt;70µm. To enhance the detectability of coronary plaques using PET, three different motion-correction strategies were used: multigate respiratory gating motion correction (MRG-MOCO), extended MR-based motion correction (eMR-MOCO), and extended MR-based motion correction with ECG gating (eMR-MOCO-ECG). Results Fifteen patients were included in this study, of which eleven entered final analyses. Data of the other four subjects could only be partially analysed. TBRmax values were as follows for three different reconstructions in vulnerable versus stable plaques: MRG-MOCO: mean TBRmax 1.45 vs 1.35, p=0.52; eMR-MOCO mean TBRmax 1.47 vs 1.27, p= 0.26, eMR-MOCO-ECG: mean TBRmax 1.49 vs 1.26, p= 0.21. Conclusion 18F-fluorocholine uptake in vulnerable atherosclerotic plaques in coronary arteries was not significantly different from uptake in stable plaques even though advanced motion-correction methods were applied. That may be caused by multiple factors, such as small coronary plaque size and remaining cardiac motion.

Background/Objectives: 18F-fluorocholine is a positron emission tomography (PET) tracer earlier found to be a marker of macrophage content in carotid plaques. We aimed to assess the feasibility of 18F-choline PET-MRI to non-invasively localize vulnerable coronary plaques, using optical coherence tomography (OCT) as a reference standard. Methods: Patients with recent myocardial infarction who were scheduled for a secondary angiography of a non-culprit vessel underwent 18F-fluorocholine coronary PET-MRI. Subsequently, OCT was performed during the secondary angiography. Maximum target-to-background (TBRmax) values of 18F-fluorocholine uptake were determined in two vessel sections that contained either vulnerable or stable plaques as defined by OCT. The OCT-based definition of a vulnerable plaque was a fibrous cap thickness < 70 µm. To enhance the detectability of coronary plaques using PET, three different motion-correction strategies were used: multigate respiratory gating motion correction (MRG-MOCO), extended MR-based motion correction (eMR-MOCO), and extended MR-based motion correction with ECG gating (eMR-MOCO-ECG). Results: Fifteen patients were included in this study. One patient needed to be excluded due to extravasation of the tracer. In another patient, no region with only a stable plaque could be identified. TBRmax values were as follows for three different reconstructions in vulnerable versus stable plaques: MRG-MOCO: mean TBRmax 1.45 vs. 1.35, p = 0.52 (n = 13); eMR-MOCO: mean TBRmax 1.47 vs. 1.27, p = 0.26 (n = 11); eMR-MOCO-ECG: mean TBRmax 1.49 vs. 1.26, p = 0.21 (n = 11). Conclusions: 18F-fluorocholine uptake in vulnerable atherosclerotic plaques in coronary arteries was not significantly different from uptake in stable plaques, even though advanced motion-correction methods were applied. That may be caused by multiple factors, such as small coronary plaque size, tracer biology, or remaining cardiac motion.

In myocardial T1 mapping, undesirable motion poses significant challenges because uncorrected motion can affect T1 estimation accuracy and cause incorrect diagnosis. In this study, we propose and evaluate a motion correction method for myocardial T1 mapping using self-supervised deep learning based registration with contrast separation (SDRAP). A sparse coding based method was first proposed to separate the contrast component from T1 -weighted (T1w) images. Then, a self-supervised deep neural network with cross-correlation (SDRAP-CC) or mutual information as the registration similarity measurement was developed to register contrast separated images, after which signal fitting was performed on the motion corrected T1w images to generate motion corrected T1 maps. The registration network was trained and tested in 80 healthy volunteers with images acquired using the modified Look-Locker inversion recovery (MOLLI) sequence. The proposed SDRAP was compared with the free form deformation (FFD) registration method regarding (1) Dice similarity coefficient (DSC) and mean boundary error (MBE) of myocardium contours, (2) T1 value and standard deviation (SD) of T1 fitting, (3) subjective evaluation score for overall image quality and motion artifact level, and (4) computation time. Results showed that SDRAP-CC achieved the highest DSC of 85.0 ± 3.9% and the lowest MBE of 0.92 ± 0.25 mm among the methods compared. Additionally, SDRAP-CC performed the best by resulting in lower SD value (28.1 ± 17.6 ms) and higher subjective image quality scores (3.30 ± 0.79 for overall quality and 3.53 ± 0.68 for motion artifact) evaluated by a cardiologist. The proposed SDRAP took only 0.52 s to register one slice of MOLLI images, achieving about sevenfold acceleration over FFD (3.7 s/slice).

BackgroundCine cardiovascular magnetic resonance (CMR) is challenging in patients who cannot perform repeated breath holds. Real-time, free-breathing acquisition is an alternative, but image quality is typically inferior. There is a clinical need for techniques that achieve similar image quality to the segmented cine using a free breathing acquisition. Previously, high quality retrospectively gated cine images have been reconstructed from real-time acquisitions using parallel imaging and motion correction. These methods had limited clinical applicability due to lengthy acquisitions and volumetric measurements obtained with such methods have not previously been evaluated systematically.MethodsThis study introduces a new retrospective reconstruction scheme for real-time cine imaging which aims to shorten the required acquisition. A real-time acquisition of 16-20s per acquired slice was inputted into a retrospective cine reconstruction algorithm, which employed non-rigid registration to remove respiratory motion and SPIRiT non-linear reconstruction with temporal regularization to fill in missing data. The algorithm was used to reconstruct cine loops with high spatial (1.3-1.8 × 1.8-2.1 mm2) and temporal resolution (retrospectively gated, 30 cardiac phases, temporal resolution 34.3 ± 9.1 ms). Validation was performed in 15 healthy volunteers using two different acquisition resolutions (256 × 144/192 × 128 matrix sizes). For each subject, 9 to 12 short axis and 3 long axis slices were imaged with both segmented and real-time acquisitions. The retrospectively reconstructed real-time cine images were compared to a traditional segmented breath-held acquisition in terms of image quality scores. Image quality scoring was performed by two experts using a scale between 1 and 5 (poor to good). For every subject, LAX and three SAX slices were selected and reviewed in the random order. The reviewers were blinded to the reconstruction approach and acquisition protocols and scores were given to segmented and retrospective cine series. Volumetric measurements of cardiac function were also compared by manually tracing the myocardium for segmented and retrospective cines.ResultsMean image quality scores were similar for short axis and long axis views for both tested resolutions. Short axis scores were 4.52/4.31 (high/low matrix sizes) for breath-hold vs. 4.54/4.56 for real-time (paired t-test, P = 0.756/0.011). Long axis scores were 4.09/4.37 vs. 3.99/4.29 (P = 0.475/0.463). Mean ejection fraction was 60.8/61.4 for breath-held acquisitions vs. 60.3/60.3 for real-time acquisitions (P = 0.439/0.093). No significant differences were seen in end-diastolic volume (P = 0.460/0.268) but there was a trend towards a small overestimation of end-systolic volume of 2.0/2.5 ml, which did not reach statistical significance (P = 0.052/0.083).ConclusionsReal-time free breathing CMR can be used to obtain high quality retrospectively gated cine images in 16-20s per slice. Volumetric measurements and image quality scores were similar in images from breath-held segmented and free breathing, real-time acquisitions. Further speedup of image reconstruction is still needed.

Image quality of positron emission tomography (PET) reconstructions is degraded by subject motion occurring during the acquisition. Magnetic resonance (MR)-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g. navigators) to detect motion. However, handling irregular respiratory motion and bulk motion remains challenging. In this work, we propose an MR-based motion correction method relying on subspace-based real-time MR imaging to estimate motion fields used to correct PET reconstructions. We take advantage of the low-rank characteristics of dynamic MR images to reconstruct high-resolution MR images at high frame rates from highly undersampled k-space data. Reconstructed dynamic MR images are used to determine motion phases for PET reconstruction and estimate phase-to-phase nonrigid motion fields able to capture complex motion patterns such as irregular respiratory and bulk motion. MR-derived binning and motion fields are used for PET reconstruction to generate motion-corrected PET images. The proposed method was evaluated on in vivo data with irregular motion patterns. MR reconstructions accurately captured motion, outperforming state-of-the-art dynamic MR reconstruction techniques. Evaluation of PET reconstructions demonstrated the benefits of the proposed method in terms of motion artifacts reduction, improving the contrast-to-noise ratio by up to a factor 3 and achieveing a target-to-background ratio up to 90% superior compared to standard/uncorrected methods. The proposed method can improve the image quality of motion-corrected PET reconstructions in clinical applications.

Motion-induced artefacts in MRI are a common occurrence but can obscure pathologies or be falsely identified as pathological. Reacquiring motion-corrupted scans is expensive, and thus retrospective and prospective motion correction methods have been introduced. Although motion correction shows promise, there is a lack of exhaustive testing on its efficacy with respect to full clinical cerebral MRI protocols. Here we present a dataset (n = 22) to facilitate future research, which includes data with and without intentional motion, and with and without prospective motion correction, across six MRI sequences included in a full clinical cerebral MRI protocol. Motion was captured by an external tracking device, and the dataset includes the motion data as derived motion transforms. For standardization, all image data are fully BIDS-compliant. Raw k-space data are available as well. As the dataset pairs motion-free data with motion-corrupted data, it can be used to develop or test different motion-correction or k-space reconstruction methods.

Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change across different dynamic frames over time, which can cause difficulties for image registration-based motion correction, particularly for early dynamic frames. In this paper, we developed an automatic deep learning-based motion correction (DeepMC) method for dynamic cardiac PET. In this study we focused on the detection and correction of inter-frame rigid translational motion caused by voluntary body movement and pattern change of respiratory motion. A bidirectional-3D LSTM network was developed to fully utilize both local and nonlocal temporal information in the 4D dynamic image data for motion detection. The network was trained and evaluated over motion-free patient scans with simulated motion so that the motion ground-truths are available, where one million samples based on 65 patient scans were used in training, and 600 samples based on 20 patient scans were used in evaluation. The proposed method was also evaluated using additional 10 patient datasets with real motion. We demonstrated that the proposed DeepMC obtained superior performance compared to conventional registration-based methods and other convolutional neural networks (CNN), in terms of motion estimation and MBF quantification accuracy. Once trained, DeepMC is much faster than the registration-based methods and can be easily integrated into the clinical workflow. In the future work, additional investigation is needed to evaluate this approach in a clinical context with realistic patient motion.