Retrospective blind motion correction of MR images

We present a retrospective method, which significantly reduces ghosting and blurring artifacts due to subject motion. No modifications to the sequence (as in [2, 3]), or the use of additional equipment (as in [1]) are required. Our method iteratively searches for the transformation, that applied to the lines in k-space -- yields the sparsest Laplacian filter output in the spatial domain

Using GAN for learning joint task/response distribution in fMRI

This is a proof-of-principle study on using generative adversarial network (GAN) to synthesize functional Magnetic Resonance Imaging (fMRI) data. We trained GAN to model the joint distribution of motor task functional magnetic resonance imaging (fMRI) data and the corresponding task labels. Synthesized images by the trained GAN successfully replicated the task relevant fMRI signal in the motor cortex. This result shows a potential for using GAN to augment fMRI data

Automatic Detection of Motion Artifacts in MR Images using CNNS

Considerable practical interest exists in being able to automatically determine whether a recorded magnetic resonance image is affected by motion artifacts caused by patient movements during scanning. Existing approaches usually rely on the use of navigators or external sensors to detect and track patient motion during image acquisition. In this work, we present an algorithm based on convolutional neural networks that enables fully automated detection of motion artifacts in MR scans without special hardware requirements. The approach is data driven and uses the magnitude of MR images in the spatial domain as input. We evaluate the performance of our algorithm on both synthetic and real data and observe adequate performance in terms of accuracy and generalization to different types of data. Our proposed approach could potentially be used in clinical practice to tag an MR image as motion-free or motion-corrupted immediately after a scan is finished. This process would facilitate the acquisition of high-quality MR images that are often indispensable for accurate medical diagnosis

Improving performance of linear field generation with multi-coil setup by optimizing coils position

Purpose/Introduction: Recent publications[1],[2] report a high capability of a multi-coil setup to generate equivalent linear fields. Hence, the spatial encoding which is performed by scanner’s built-in linear gradient, can be accomplished with a multi-coil setup and therefore can be used for imaging in parallel with shimming[3]. The accuracy of the linear field produced by multi-coil is the benchmark key factor. Increasing the number of individual coils brings more degrees of freedom and a better generation of linear fields with the cost of a more complex setup to fabricate and maintain. Here, it is demonstrated how optimization of coil position results in generating superior linear fields with a limited number of coils. Subjects and Methods: Recent reports[1],[2] have used 48 and 84 coils which were arranged with a layout of 6*8 and 6*14 respectively. For comparison, we simulated a local multi-coil setup with 16, 24, 32, 48 and 84 circular shaped coils. All coils are placed on a cylinder with a diameter/length of 360/300 mm which is large enough to house an RF coil. We used optimization-based search of coil positions that result in three linear orthogonal fields for the FOV of 200*200*200 mm around the isocenter. Simulation started from arranging individual coils in a regular fashion on the cylinder surface. The magnetic field for each coil was calculated using the Biot–Savart law with no constraint for the floating current in the coils. The optimization was performed using the fmincon function in MATLAB. Results: As an initial position for the optimization, we placed the coils in a symmetric configuration such that they cover the whole cylinder surface. Figure 1 shows the arrangement of 16 individual coils before and after optimization to produce the linear field. Figure 2 demonstrates a comparison between the ideal linear fields, the generated linear field from 16 symmetrically positioned coils and the generated linear fields from 16 position-optimized coils. Figure 3 illustrates how position optimization can improve the quality of the linear fields generated by a multi-coil setup. As a cost function, we used cross-correlation between optimized field and the ideal linear field and also the l2-norm of their difference. Discussion/Conclusion: According to Fig. 3, position optimized arrangement for 16, 24 and 32 coils can bring the same or even better quality compared to the symmetric arrangement for 32, 48 and 84 coils respectively. This proves the importance of the optimal coil configuration to acquire high fidelity linear field with less coils

Image Quality Improvement by Applying Retrospective Motion Correction on Quantitative Susceptibility Mapping and R2*

The aim of this study was to quantitatively assess the improvement of image quality on motion corrupted quantitative susceptibility mapping (QSM) and the effective transverse relaxation rate (R2*) maps, after applying retrospective motion correction. Image quality was assessed using the following metrics: SNR in different brain tissues, histogram analysis, and linear correlation between susceptibility and R2* values in subcortical structures

Autofocusing-based correction of B0 fluctuation-induced ghosting

Long-TE gradient-echo images are prone to ghosting artifacts. Such degradation is primarily due to magnetic field variations caused by breathing or motion. The effect of these fluctuations amounts to different phase offsets in each acquired k-space line. A common remedy is to measure the problematic phase offsets using an extra non-phase-encoded scan before or after each imaging readout. In this work, we attempt to estimate the phase offsets directly from the raw image data by optimization-based search of phases that minimize an image distortion measure. This eliminates the need for any sequence modifications and additional scan time

Learning-based solution to phase error correction in T2*-weighted GRE scans

Long-TE gradient recalled-echo (GRE) scans are prone to phase artifacts due to B0 inhomogeneity. We propose a learning-based approach that does not rely on navigator readouts and allows to infer phase error offsets directly from corrupted data. Our method does not need to be pre-trained on a database of medical images that match a contrast/acquisition protocol of the input image. A sufficient input is a raw multi-coil spectrum of the image that needs to be corrected. We train a convolutional neural network to predict phase offsets for each k-space line of a 2D image. We synthesize training examples online by reconvolving the corrupted spectrum with point spread functions (PSFs) of the coil sensitivity profiles and superimposing artificial phase errors, which we attempt to predict. We evaluate our approach on “in vivo” data acquired with GRE sequence, and demonstrate an improvement in image quality after phase error correction

Multi-rigid motion correction of MR images

Purpose/Introduction: Much work was done over the last years to solve the problem of non-rigid motion correction in MRI. Prospective methods are limited to global motion correction, and thus to affine transformations [3]. Retrospective methods can address multi-rigid motion but need a motion reference usually a navigator [1]. We propose a retrospective method for multirigid motion correction that does not require any external motion reference. Subjects and Methods: We extend our blind motion-correction framework [2] to cover multi-rigid motion (multiple rigid bodies in FoV). Our method is based on an analytic forward model of multi-rigid motion in the MR scanner. As input, the algorithm requires a splitting of a given 2D/3D image into patches of arbitrary shape. We allow for both translational and rotational motion in each patch. To recover the image we perform alternating optimization with respect to image and motion parameters using a gradient-based approach. The objective consists of a quadratic data fidelity term and two regularization terms. The data fidelity ensures that reconstructed result fits the observation. We use total variation as a regularizer for the image, and for motion we put an quadratic penalty on the difference of consecutive motion parameters, thus penalizing rapid changes in the motion trajectory. We are able to efficiently explore the unknown parameter space of motion/image with the use of derivative-driven non-linear optimization. To ensure feasible computation times we implement our algorithm to run on modern graphic cards (GPUs). Results: We evaluated our algorithm on data acquired with a Siemens 3T Trio scanner. Using a wrist coil, we imaged two hands of a subject, which were moving against each other. In a second acquisition we imaged a single hand with moving index finger, while the hand was stationary. In both cases we used two patches for multi-rigid splitting, and motion involved both translation and rotation. Figure 1 shows that our algorithm significantly improves the image quality. Discussion/Conclusion: Our experimental results demonstrate the feasibility of blind multi-rigid motion correction using only image raw data. Although our framework is general enough to be applied to arbitrary patch-splittings, runtime remains an issue. Future work will concentrate on coupling motion parameters across patches to reduce the computational burden. This will allow to use denser patch-splittings, and thus address more realistic motion

Fast B0 first order inhomogeneity estimation using radial acquisition

B0 field inhomogeneity is a major source of distortion in MR images. Current approaches to dynamic shimming require extra acquisition time or external hardware. We propose a method that estimates first order shim errors by using projections of radial acquisition. The errors can be estimated from three projections multiple times in each measurement, which makes the method highly robust. The proposed method is evaluated in simulation and in vivo. Obtained results show a strong agreement between applied and measured first order shim errors