An Exploration of Motion-Sampling Interactions in 3D MRI for Neuroimaging.

Radial imaging techniques, such as projection-reconstruction (PR), are used in magnetic resonance imaging (MRI) for dynamic imaging, angiography, and short-T2 imaging. They are less sensitive to flow and motion artifacts, and support fast imaging with short echo times. However, aliasing and streaking artifacts are two main sources which degrade radial imaging quality. For a given fixed number of k-space projections, data distributions along radial and angular directions will influence the level of aliasing and streaking artifacts. Conventional radial k-space sampling trajectory introduces an aliasing artifact at the first principal ring of point spread function (PSF). In this paper, a shaking projection (SP) k-space sampling trajectory was proposed to reduce aliasing artifacts in MR images. SP sampling trajectory shifts the projection alternately along the k-space center, which separates k-space data in the azimuthal direction. Simulations based on conventional and SP sampling trajectories were compared with the same number projections. A significant reduction of aliasing artifacts was observed using the SP sampling trajectory. These two trajectories were also compared with different sampling frequencies. A SP trajectory has the same aliasing character when using half sampling frequency (or half data) for reconstruction. SNR comparisons with different white noise levels show that these two trajectories have the same SNR character. In conclusion, the SP trajectory can reduce the aliasing artifact without decreasing SNR and also provide a way for undersampling reconstruction. Furthermore, this method can be applied to three-dimensional (3D) hybrid or spherical radial k-space sampling for a more efficient reduction of aliasing artifacts.

Motion artifacts on coronary CT angiography images in patients with a pericardial effusion

MRI is increasingly being used for radiotherapy planning, simulation, and in-treatment-room motion monitoring. To provide more detailed temporal and spatial MR data for these tasks, we have recently developed a novel self-gated (SG) MRI technique with advantage of k-space phase sorting, high isotropic spatial resolution, and high temporal resolution. The current work describes the validation of this 4D-MRI technique using a MRI- and CT-compatible respiratory motion phantom and comparison to 4D-CT. The 4D-MRI sequence is based on a spoiled gradient echo-based 3D projection reconstruction sequence with self-gating for 4D-MRI at 3 T. Respiratory phase is resolved by using SG k-space lines as the motion surrogate. 4D-MRI images are reconstructed into ten temporal bins with spatial resolution 1.56 × 1.56 × 1.56 mm(3). A MRI-CT compatible phantom was designed to validate the performance of the 4D-MRI sequence and 4D-CT imaging. A spherical target (diameter 23 mm, volume 6.37 ml) filled with high-concentration gadolinium (Gd) gel is embedded into a plastic box (35 × 40 × 63 mm(3)) and stabilized with low-concentration Gd gel. The phantom, driven by an air pump, is able to produce human-type breathing patterns between 4 and 30 respiratory cycles/min. 4D-CT of the phantom has been acquired in cine mode, and reconstructed into ten phases with slice thickness 1.25 mm. The 4D images sets were imported into a treatment planning software for target contouring. The geometrical accuracy of the 4D MRI and CT images has been quantified using target volume, flattening, and eccentricity. The target motion was measured by tracking the centroids of the spheres in each individual phase. Motion ground-truth was obtained from input signals and real-time video recordings. The dynamic phantom has been operated in four respiratory rate (RR) settings, 6, 10, 15, and 20/min, and was scanned with 4D-MRI and 4D-CT. 4D-CT images have target-stretching, partial-missing, and other motion artifacts in various phases, whereas the 4D-MRI images are visually free of those artifacts. Volume percentage difference for the 6.37 ml target ranged from 5.3% ± 4.3% to 10.3% ± 5.9% for 4D-CT, and 1.47 ± 0.52 to 2.12 ± 1.60 for 4D-MRI. With an increase of respiratory rate, the target volumetric and geometric deviations increase for 4D-CT images while remaining stable for the 4D-MRI images. Target motion amplitude errors at different RRs were measured with a range of 0.66-1.25 mm for 4D-CT and 0.2-0.42 mm for 4D-MRI. The results of Mann-Whitney tests indicated that 4D-MRI significantly outperforms 4D-CT in phase-based target volumetric (p = 0.027) and geometric (p < 0.001) measures. Both modalities achieve equivalent accuracy in measuring motion amplitude (p = 0.828). The k-space self-gated 4D-MRI technique provides a robust method for accurately imaging phase-based target motion and geometry. Compared to 4D-CT, the current 4D-MRI technique demonstrates superior spatiotemporal resolution, and robust resistance to motion artifacts caused by fast target motion and irregular breathing patterns. The technique can be used extensively in abdominal targeting, motion gating, and toward implementing MRI-based adaptive radiotherapy.

Optimization of view ordering for motion artifact suppression

Classifying MRI motion severity using a stacked ensemble approach

We have previously developed a retrospective 4D‐MRI technique using body area as the respiratory surrogate, but generally, the reconstructed 4D MR images suffer from severe or mild artifacts mainly caused by irregular motion during image acquisition. Those image artifacts may potentially affect the accuracy of tumor target delineation or the shape representation of surrounding nontarget tissues and organs. So the purpose of this study is to propose an approach employing principal component analysis (PCA), combined with a linear polynomial fitting model, to remodel the displacement vector fields (DVFs) obtained from deformable image registration (DIR), with the main goal of reducing the motion artifacts in 4D MR images. Seven patients with hepatocellular carcinoma (2/7) or liver metastases (5/7) in the liver, as well as a patient with non‐small cell lung cancer (NSCLC), were enrolled in an IRB‐approved prospective study. Both CT and MR simulations were performed for each patient for treatment planning. Multiple‐slice, multiple‐phase, cine‐MRI images were acquired in the axial plane for 4D‐MRI reconstruction. Single‐slice 2D cine‐MR images were acquired across the center of the tumor in axial, coronal, and sagittal planes. For a 4D MR image dataset, the DVFs in three orthogonal direction (inferior–superior (SI), anterior–posterior (AP), and medial–lateral (ML)) relative to a specific reference phase were calculated using an in‐house DIR algorithm. The DVFs were preprocessed in three temporal and spatial dimensions using a polynomial fitting model, with the goal of correcting the potential registration errors introduced by three‐dimensional DIR. Then PCA was used to decompose each fitted DVF into a linear combination of three principal motion bases whose spanned subspaces combined with their projections had been validated to be sufficient to represent the regular respiratory motion. By wrapping the reference MR image using the remodeled DVFs, ‘synthetic’ MR images with reduced motion artifacts were generated at selected phase. Tumor motion trajectories derived from cine‐MRI, 4D CT, original 4D MRI, and ‘synthetic’ 4D MRI were analyzed in the SI, AP, and ML directions, respectively. Their correlation coefficient (CC) and difference (D) in motion amplitude were calculated for comparison. Of all the patients, the means and standard deviations (SDs) of CC comparing ‘synthetic’ 4D MRI and cine‐MRI were 0.98±0.01,0.98±0,01, and 0.99±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.59±0.09 mm,0.29±0.10 mm, and 0.15±0.05 mm in SI, AP and ML directions, respectively. The means and SDs of CC comparing ‘synthetic’ 4D MRI and 4D CT were 0.96±0.01,0.95±0.01, and 0.95±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.76±0.20 mm,0.33±0.14 mm, and 0.19±0.07 mm in SI, AP, and ML directions, respectively. The means and SDs of CC comparing ‘synthetic’ 4D MRI and original 4D MRI were 0.98±0.01,0.98±0.01, and 0.97±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.58±0.10 mm,0.30±0.09 mm, and 0.17±0.04 mm in SI, AP, and ML directions, respectively. In this study we have proposed an approach employing PCA combined with a linear polynomial fitting model to capture the regular respiratory motion from a 4D MR image dataset. And its potential usefulness in reducing motion artifacts and improving image quality has been demonstrated by the preliminary results in oncological patients.PACS numbers: 87.57.cp, 87.57.nj, 87.61.‐c

Magnetic particle imaging is a tracer based imaging technique to determine the spatial distribution of superparamagnetic iron oxide nanoparticles with a high spatial and temporal resolution. Due to physiological constraints, the imaging volume is restricted in size and larger volumes are covered by shifting object and imaging volume relative to each other. This results in reduced temporal resolution, which can lead to motion artifacts when imaging dynamic tracer distributions. A common source of such dynamic distributions are cardiac and respiratory motion in in-vivo experiments, which are in good approximation periodic. We present a raw data processing technique that combines data snippets into virtual frames corresponding to a specific state of the dynamic motion. The technique is evaluated on the basis of measurement data obtained from a rotational phantom at two different rotational frequencies. These frequencies are determined from the raw data without reconstruction and without an additional navigator signal. The reconstructed images give reasonable representations of the rotational phantom frozen in several different states of motion while motion artifacts are suppressed.

The purpose of our study is to evaluate the T2 weighted sequence with multishot radial sampling (Radial Acquisition Multi-shot) also known as Multivane sequence in Philips Healthcare, introduced by J.Pipe with the aim to minimize motion artifacts in Magnetic Resonance Imaging (MRI). In the field of prostate MRI the Multivane sequence is useful in non-cooperating patients and/or patients who, due to their clinical conditions (such as glaucoma, arrhythmia, and severe benign prostatic hypertrophy), have not been receiving intravenous antispasmodic agents, administered for limiting the motility of intestinal loops, particularly rectum. The Multivane sequence is based on the collection of data throughout parallel multiple lines in periodic rotation around the center of k-space and advanced mathematical reconstruction. As the data at the center of k-space (low frequency) containing signals with maximum amplitude will be continuously sampled, this trajectory will provide an excellent contrast-noise ratio (CNR) and spatial resolution, without motion artifacts responsible of "blurring" in the final image. Specifically, each given point of the periphery of the k-space will be sampled by a certain line and the next one and so on, and for the final image reconstruction, once multiple data will be estimated, different algorithms will be used to compensate for motion artifacts. In this study we compared the Radial Acquisition Multishot TSE Multivane (Philips Healthcare) with the classic T2W TSE sequences with linear Cartesian sampling. Multivane sequences have proven to be superior and therefore of greater utility compared to sequences with linear Cartesian data sampling, in patients who can not receiving spasmolytic agents.

A method for optimizing k-space sampling trajectory in compressive sampling MRI (CS-MRI) is presented. In k-space, most of the energies are concentrated around the center. When k-space is undersampled, it is required to take most of its higher energy samples for proper CS reconstruction. Therefore more samples are required around the center than the periphery. Using this prior knowledge on k-space energy distribution, a probability density function (PDF) was proposed to generate sampling trajectories. Sampling trajectories were generated for various PDF parameters. These sampling trajectories were applied on the spatial frequency data of fully acquired brain MR images. The optimum sampling trajectory was chosen based on the reconstruction performance. With this optimum trajectory, only 38% of k-space data were required for proper image reconstruction. It was also found that at least 20% of the higher energy samples around the center of k-space were fully required and the rest of the higher energy samples were to be acquired as closely as possible. The optimized sampling trajectory was applied on the simulated k-space data of virtual brain phantom and k-space data of quality assurance phantom. It was verified that the quality of CS reconstructed image matches with the fully reconstructed image.

A bolus chase three-dimensional (3D) MR digital subtraction angiography (MRDSA) technique was implemented using dynamic k-space filling. This technique permits rapid 3D arterial imaging of the entire lower extremity at multiple stations using a single intravenous injection. Image acquisition at the first (most proximal) station starts from the edge of k-space and ends in the center of k-space (edge-center order). Image acquisition for middle stations starts from the edge of k-space, arrives at the center of k-space at the middle of data acquisition, and ends at the edge of k-space (edge-center-edge order). Image acquisition for the last station starts from the center of k-space and ends at the edge of k-space (center-edge order). This dynamic k-space filling minimizes contrast dose and motion artifacts. Bolus chase 3D MRDSA was performed on four normal volunteers and three patients using a multiple-phase 3D fast gradient-echo sequence, 25-ml gadolinium dose, and a prototype stepping table. Total bolus chase 3D acquisition time was 46 s. Mask subtraction using both complex and magnitude subtraction was performed. Complex subtraction was found to be necessary for proper delineation of arteries below the aortic bifurcation. Diagnostic results were consistently obtained for all subjects.

Optimizing three-dimensional (3D) k-space sampling trajectories is important for efficient MRI yet presents a challenging computational problem. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization. We built a differentiable simulation model to enable gradient-based methods for sampling trajectory optimization. The algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method can optimize sampling trajectories synergistically with either model-based or learning-based reconstruction methods. We proposed several strategies to alleviate the severe nonconvexity and huge computation demand posed by the large scale. The corresponding code is available as an open-source toolbox. We applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the image quality of a 3D kooshball trajectory was improved from 0.29 to 0.22 (NRMSE) with Stochastic optimization framework for 3D NOn-Cartesian samPling trajectorY (SNOPY) optimization. In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY reduced the NRMSE of reconstructed images from 1.19 to 0.97 compared to the best empirical method (RSOS-GR). Optimizing the gradient waveform of a rotational EPI trajectory improved participants' rating of the PNS from "strong" to "mild." SNOPY provides an efficient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories.

Four-dimensional CT imaging is widely used to account for motion-related effects during radiotherapy planning of lung cancer patients. However, 4D CT often contains motion artifacts, cannot be used to measure motion variability, and leads to higher dose exposure. In this article, we propose using 4D MRI to acquire motion information for the radiotherapy planning process. From the 4D MRI images, we derive a time-continuous model of the average patient-specific respiratory motion, which is then applied to simulate 4D CT data based on a static 3D CT. The idea of the motion model is to represent the average lung motion over a respiratory cycle by cyclic B-spline curves. The model generation consists of motion field estimation in the 4D MRI data by nonlinear registration, assigning respiratory phases to the motion fields, and applying a B-spline approximation on a voxel-by-voxel basis to describe the average voxel motion over a breathing cycle. To simulate a patient-specific 4D CT based on a static CT of the patient, a multi-modal registration strategy is introduced to transfer the motion model from MRI to the static CT coordinates. Differences between model-based estimated and measured motion vectors are on average 1.39mm for amplitude-based binning of the 4D MRI data of three patients. In addition, the MRI-to-CT registration strategy is shown to be suitable for the model transformation. The application of our 4D MRI-based motion model for simulating 4D CT images provides advantages over standard 4D CT (less motion artifacts, radiation-free). This makes it interesting for radiotherapy planning.

To determine whether diagnostic quality thoracic computed tomography angiography (CTA) studies can be obtained without general anesthesia (GA) in infants and young children using dual-source computed tomography (DSCT) with turbo flash spiral mode (TFSM) and free-breathing technique. All consecutive infants and young children (≤ 6 years old) who underwent thoracic CTA studies from January 2018 to October 2020 for suspected congenital thoracic disorders were categorized into two groups: with GA (Group 1) and without GA (Group 2). All thoracic CTA studies were performed on a DSCT scanner using TFSM and free-breathing technique. Two pediatric thoracic radiologists independently evaluated motion artifact in three lung zones (upper, mid, and lower). Degree of motion artifact was graded 0-3 (0, none; 1, mild; 2, moderate; and 3, severe). Logistic models adjusted for age and gender were used to compare the degree of motion artifact between lung zones. Interobserver agreement between reviewers was evaluated with kappa statistics. There were a total of 73 pediatric patients (43 males (59%) and 30 females (41%); mean age, 1.4 years; range, 0-5.9 years). Among these 73 patients, 42 patients (58%) underwent thoracic CTA studies with GA (Group 1) and the remaining 31 patients (42%) underwent thoracic CTA studies without GA (Group 2). Overall, the degree of motion artifact was higher for Group 2 (without GA). However, only a very small minority (1/31, 3%) of Group 2 (without GA) thoracic CTA studies had severe motion artifact. There was no significant difference between the two groups with respect to the presence of severe motion artifact (odds ratio [OR] = 6, p = .222). When two groups were compared with respect to the presence of motion artifact for individual lung zones, motion artifact was significantly higher in the upper lung zone for Group 2 (without GA) (OR = 20, p = .043). Interobserver agreement for motion artifact was high, the average Kappa being 0.81 for Group 1 and 0.95 for Group 2. Although the degree of motion artifact was higher in the group without GA, only a small minority (3%) of thoracic CTA studies performed without GA had severe motion artifact, rendering the study nondiagnostic. Therefore, the results of this study support the use of thoracic CTA without GA using DSCT with TFSM and free-breathing in infants and young children. In addition, given that motion artifact was significantly higher in the upper lung zone without GA, increased stabilization in the upper chest and extremities should be considered.

To determine risk factors for transient severe motion (TSM) artifact on arterial phase of gadoxetic acid-enhanced MRI using a large cohort. A total of 2230 patients who underwent gadoxetic acid-enhanced MRI was consecutively included. Two readers evaluated respiratory motion artifact on arterial phase images using a 5-point grading scale. Clinical factors including demographic data, underlying disease, laboratory data, presence of ascites and pleural effusion, and previous experience of gadoxetic acid-enhanced MRI were investigated. Univariable and multivariable logistic regression analyses were performed to determine significant risk factors for TSM. Predictive value of TSM was calculated according to the number of significant risk factors. Overall incidence of TSM was 5.0% (111/2230). In the multivariable analysis, old age (≥ 65 years; odds ratio [OR] = 2.01 [95% CI, 1.31-3.07]), high body mass index (≥ 25 kg/m2; OR = 1.76 [1.18-2.63]), chronic obstructive pulmonary disease (OR = 6.11 [2.32-16.04]), and moderate to severe pleural effusion (OR = 3.55 [1.65-7.65]) were independent significant risk factors for TSM. Presence of hepatitis B (OR = 0.66 [0.43-0.99]) and previous experience of gadoxetic acid-enhanced MRI (OR = 0.52 [0.33-0.83]) were negative risk factors for TSM. When at least one of the significant factors was present, the predictive risk was 5.7% (109/1916), whereas it was 16.3% (17/104) when at least four factors were present. Knowing risk factors for transient severe motion artifact on gadoxetic acid-enhanced MRI can be clinically useful for providing diagnostic strategies more tailored to individual patients. • Old age, high body mass index, chronic obstructive pulmonary disease, and moderate to severe pleural effusion were independent risk factors for transient severe motion artifact on gadoxetic acid-enhanced MRI. • Patients with hepatitis B or previous experience of gadoxetic acid-enhanced MRI were less likely to show transient severe motion artifact. • As the number of risk factors for transient severe motion artifact increased, the predicted risk for it also showed a tendency to increase.

Breast MRI has emerged as an increasingly important tool in evaluating breast pathologies including detection and assessment of cancers, evaluation of implant integrity and as a problem-solving tool for inconclusive conventional breast imaging findings. MRI artefacts encountered during image interpretation may create diagnostic dilemmas. Many of these artefacts are patient-related and can be avoidable. Identification of these artefacts can be challenging in daily practice in particular to trainees or inexperienced radiologists. This article illustrates the principles and imaging appearance of the common patient-related artefacts in breast MRI, with discussion on how to minimize them. They include positioning-related artefacts, inhomogeneous fat suppression, susceptibility artefacts including those associated with the newly emerged non-radioactive wireless localization devices and superparamagnetic lymphatic tracer, as well as motion artefacts. Familiarization with these 4 major types of artefacts by radiologists is crucial in troubleshooting and achieving accurate image interpretation.