Image-based Motion Correction Research Articles

Fast high-resolution prospective motion correction for single-voxel spectroscopy.

To develop a fast high-resolution image-based motion correction method using spiral navigators with multislice-to-volume registration. A semi-LASER sequence was modified to include a multislice spiral navigator for prospective motion correction (∼305 ms including acquisition, processing, and feedback) as well as shim and frequency navigators for prospective shim and frequency correction (∼100 ms for each). MR spectra were obtained in the prefrontal cortex in five healthy subjects at 3 T with and without prospective motion and shim correction. The effect of key navigator parameters (number of slices, image resolution, and excitation flip angle) on registration accuracy was assessed using simulations. Without prospective motion and shim correction, spectral quality degraded significantly in the presence of voluntary motion. In contrast, with prospective motion and shim correction, spectral quality was improved (metabolite linewidth = 6.7 ± 0.6 Hz, SNR= 67 ± 9) and in good agreement with baseline data without motion (metabolite linewidth = 6.9 ± 0.9 Hz, SNR = 73 ± 9). In addition, there was no significant difference in metabolites concentrations measured without motion and with prospective motion and shim correction in the presence of motion. Simulations showed that the registration precision was comparable when using three navigator slices with 3 mm resolution and when using the entire volume (all slices) with 8 mm resolution. The proposed motion correction scheme allows fast and precise prospective motion and shim correction for single-voxel spectroscopy at 3 T. With 3 mm resolution, only a few navigator slices are necessary to achieve excellent motion correction performance.

Deep learning based registration for head motion correction in positron emission tomography as a strategy for improved image quantification

Objectives: Positron emission tomography (PET) is affected by various kinds of patient movement during a scan. Frame-by-frame image registration is one of the most practiced motion correction techniques. In recent years, deep learning has shown a remarkable ability to quickly and accurately register images once trained. This paper studies the feasibility of using a deep learning framework to correct 3D positron emission tomography image volumes for head motion in routine positron emission tomography imaging to improve quantification in motion impacted data.Materials and Methods: A neural network was trained with 3D positron emission tomography image volumes in an unsupervised manner to predict transformation parameters required to perform image registration. A multi-step convolutional neural network (CNN) was combined with a spatial transform layer. Pairs of target and source images were used as input to the network. To prepare the training dataset, a previously published TOF-PEPT algorithm was applied to automatically detect static frames where the patient remained in a relatively steady position and transitional frames where they underwent abrupt motion. A single image volume was reconstructed for each static frame. The image reconstructed from the first static frame served as the target image with images from subsequent static frames being used as source images. The trained neural network predicted transformation parameters that could be used to perform frame-by-frame image-based motion correction but also enabled raw listmode positron emission tomography data correction where individual line-of-responses were repositioned. Line profiles and ROIs were drawn across the reconstructed image volumes to compare performance and quantitative results between standard registration tools and the deep learning technique. Corrected volumes were further compared to motion free images quantitatively using Dice indices.Results: In total, one hundred 3D positron emission tomography image volumes were used to train the network. Cross-validation was carried out using a 4:1 ratio for the training and test data. A conventional algorithm for affine registration from the Advanced Normalization Tools (ANTs) software package served as a baseline. To evaluate the correction performance, the mean Dice index and standardized uptake value (SUV) were used. Application of the algorithm to clinical data showed good performance with respect to registration accuracy as well as processing time. The neural network yielded a mean Dice index of ∼0.87 which was similar to the advanced Normalization Tools algorithm and did so ∼3x faster using a multi-core CPU and ∼20x faster with a GPU. Standardized uptake value analysis showed that quantitative results were 30%–60% higher in the motion-corrected images, and the neural network performed better than or close to the advanced Normalization Tools.Conclusion: The aim of this work was to study the quantitative impact of using a data-driven deep learning motion correction technique for positron emission tomography data and assess its performance. The results showed the technique is capable of producing high quality registrations that compensate for patient motion that occurs during a scan and improve quantitative accuracy.

Correction of head movement by frame-to-frame image realignment for receptor imaging in positron emission tomography studies with [11C]raclopride and [11C]FLB 457

Positron emission tomography (PET) scans of imaging receptors require 60-90-min dynamic acquisition for quantitative analysis. Head movement is often observed during scanning, which hampers the reliable estimation of quantitative parameters. This study evaluated image-based motion correction by frame-to-frame realignment for PET studies with [11C]raclopride and [11C]FLB 457 acquired by an Eminence SET-3000GCT/X and investigated the effect of this correction on the quantitative outcomes. First, an optimal method for estimating motion parameters was evaluated by computer simulation. Simulated emission sinograms were reconstructed to the PET images with or without attenuation correction using a µ-map of the transmission scan. Six motion parameters were estimated frame-by-frame by registering each frame of the PET images to several types of reference images and the reliability of registration was compared. Next, in [11C]raclopride and [11C]FLB 457 studies in normal volunteers, six motion parameters for each frame were estimated by the registration method determined from the simulation results. Head movement was corrected by realigning the PET images reconstructed with a motion-included µ-map in which a mismatch between the transmission and emission scans was corrected. After this correction, time-activity curves (TAC) for the striatum or cerebral cortex were obtained and the binding potentials of the receptors (BPND) were estimated using the simplified reference tissue model. In the simulations, the motion parameters could be reliably estimated by registering each frame of the non-attenuation-corrected PET images to their early-phase frame. The motion parameters in the human studies were also obtained using the same method. After correction, a discontinuity of TACs in the striatum and cerebral cortex was remarkably improved and the BPND values in these regions increased. Compared to the motion-corrected PET images reconstructed using the measured µ-map, the images reconstructed using the motion-included µ-map did not result in a remarkable improvement of BPND in the striatum of [11C]raclopride studies, while the BPND in the cerebral cortex changed in some [11C]FLB 457 studies in which large head movement was observed. In PET receptor imaging, head movement during dynamic scans can be corrected by frame-to-frame realignment. This method is easily applicable to clinical studies and provides reliable TACs and BPND.

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

The sensitivity to subject motion is one of the major challenges in functional MRI (fMRI) studies in which a precise alignment of images from different time points is required to allow reliable quantification of brain activation throughout the scan. Especially the long measurement times and laborious fMRI tasks add to the amount of subject motion found in typical fMRI measurements, even when head restraints are used. In case of moving subjects, prospective motion correction can maintain the relationship between spatial image information and subject anatomy by constantly adapting the image slice positioning to follow the subject in real time. Image-based prospective motion correction is well-established in fMRI studies and typically computes the motion estimates based on a volume-to-volume image registration, resulting in low temporal resolution. This study combines fMRI using simultaneous multislice imaging with multislice-to-volume-based image registration to allow sub-TR motion detection with subsequent real-time adaption of the imaging system. Simultaneous multislice imaging is widely used in fMRI studies and, together with multislice-to-volume-based image registration algorithms, enables computing suitable motion states after only a single readout by registering the simultaneously excited slices to a reference volume acquired at the start of the measurement. The technique is evaluated in three human BOLD fMRI studies (n = 1, 5, and 1) to explore different aspects of the method. It is compared to conventional, volume-to-volume-based prospective motion correction as well as retrospective motion correction methods. Results show a strong reduction in retrospectively computed residual motion parameters of up to 50% when comparing the two prospective motion correction techniques. An analysis of temporal signal-to-noise ratio as well as brain activation results shows high consistency between the results before and after additional retrospective motion correction when using the proposed technique, indicating successful prospective motion correction. The comparison of absolute tSNR values does not show an improvement compared to using retrospective motion correction alone. However, the improved temporal resolution may provide improved tSNR in the presence of more exaggerated intra-volume motion.

Image-Based Motion Compensation for High-Resolution Extremities Cone-Beam CT.

Cone-beam CT (CBCT) of the extremities provides high spatial resolution, but its quantitative accuracy may be challenged by involuntary sub-mm patient motion that cannot be eliminated with simple means of external immobilization. We investigate a two-step iterative motion compensation based on a multi-component metric of image sharpness. Motion is considered with respect to locally rigid motion within a particular region of interest, and the method supports application to multiple locally rigid regions. Motion is estimated by maximizing a cost function with three components: a gradient metric encouraging image sharpness, an entropy term that favors high contrast and penalizes streaks, and a penalty term encouraging smooth motion. Motion compensation involved initial coarse estimation of gross motion followed by estimation of fine-scale displacements using high resolution reconstructions. The method was evaluated in simulations with synthetic motion (1-4 mm) applied to a wrist volume obtained on a CMOS-based CBCT testbench. Structural similarity index (SSIM) quantified the agreement between motion-compensated and static data. The algorithm was also tested on a motion contaminated patient scan from dedicated extremities CBCT. Excellent correction was achieved for the investigated range of displacements, indicated by good visual agreement with the static data. 10-15% improvement in SSIM was attained for 2-4 mm motions. The compensation was robust against increasing motion (4% decrease in SSIM across the investigated range, compared to 14% with no compensation). Consistent performance was achieved across a range of noise levels. Significant mitigation of artifacts was shown in patient data. The results indicate feasibility of image-based motion correction in extremities CBCT without the need for a priori motion models, external trackers, or fiducials.

Image-based 3D non-rigid respiratory motion correction for free-breathing thoracic MR angiography

Background Respiratory motion compensation is usually required in free-breathing thoracic MR imaging applications to remove the motion induced blur and ghosting artifacts. Most respiratory motion compensation techniques are based on gating in which data is acquired only at one respiratory state (RS) and therefore suffer from extended yet unpredictable scan time. Other techniques compensate for motion by correcting the phase of motion corrupted k-space data so that data acquired in multiple respiratory states can be used in reconstruction for improved scan efficiency. However, these methods are often limited to correcting rigid-body motion and thus not suitable for thoracic imaging with large field-ofview. In this work, we propose a technical strategy of correcting 3D non-rigid motion and apply it to ferumoxytol enhanced free-breathing thoracic MRA.

Impact of image-based motion correction on dopamine D3/D2 receptor occupancy-comparison of groupwise and frame-by-frame registration approaches.

BackgroundImage registration algorithms are frequently used to align the reconstructed brain PET frames to remove subject head motion. However, in occupancy studies, this is a challenging task where competitive binding of a drug can further reduce the available signal for registration. The purpose of this study is to evaluate two kinds of algorithms—a conventional frame-by-frame (FBF) registration and a recently introduced groupwise image registration (GIR), for motion correction of a dopamine D3/D2 receptor occupancy study.MethodsThe FBF method co-registers all the PET frames to a common reference based on normalised mutual information as the spatial similarity. The GIR method incorporates a pharmacokinetic model and conducts motion correction by maximising a likelihood function iteratively on tracer kinetics and subject motion. Data from eight healthy volunteers scanned with [11C]-(+)-PHNO pre- and post-administration of a range of doses of the D3 antagonist GSK618334 were used to compare the motion correction performance.ResultsThe groupwise registration achieved improved motion correction results, both by visual inspection of the dynamic PET data and by the reduction of the variability in the outcome measures, and required no additional steps to exclude unsuccessfully realigned PET data for occupancy modelling as compared to frame-by-frame registration. Furthermore, for the groupwise method, the resultant binding potential estimates had reduced variation and bias for individual scans and improved half maximal effective concentration (EC50) estimates were obtained for the study as a whole.ConclusionsThese results indicate that the groupwise registration approach can provide improved motion correction of dynamic brain PET data as compared to frame-by-frame registration approaches for receptor occupancy studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s40658-015-0117-0) contains supplementary material, which is available to authorized users.

Image-based motion correction for optical mapping of cardiac electrical activity.

Optical mapping, with membrane-bound, voltage-sensitive dyes, is widely used for in vitro recording of cardiac electrical activity. The spatial registration of such maps is lost when the heart moves with respect to a fixed photodetector array and contraction can generate substantial artifact if background fluorescence is not uniformly distributed. While motion artifact is commonly suppressed with electromechanical uncoupling agents, there are circumstances where these are undesirable. This study outlines a novel image-based approach for retrospective motion artifact correction. Isolated Langendorff-supported rat hearts (n=8), stained with di-4-ANEPPS, were illuminated at 516±14nm and fluorescent emission (>565±10nm) was acquired with a charge multiplying CCD camera. Background fluorescence was segmented in successive frames and stabilized using a non-rigid image registration algorithm. The resultant image deformation was used to estimate material point movement on the heart surface, so that total fluorescence could be mapped frame-by-frame to appropriate reference pixels. Finally, residual motion artifact was identified and removed. The effectiveness of this correction method was evaluated over 18 experimental datasets. Signal-to-noise ratio was increased more than fourfold, and activation time and action potential duration (APD) could be estimated at 24% more pixels than in the raw data. The variability of all APD measures was substantially reduced (i.e. APD50 estimated as 83.8±45.8ms before correction was 52.1±4.7ms afterward). This approach provides a robust means of recovering optical action potentials in the presence of substantial motion artifact.

A new framework for interleaved scanning in cardiovascular MR: Application to image‐based respiratory motion correction in coronary MR angiography

To describe a new framework for interleaving scans and demonstrate its usefulness for image-based respiratory motion correction in whole heart coronary MR angiography (CMRA). Scan interleaving using the proposed approach was achieved by switching between separately defined, independent scans at arbitrary time points during their execution, using a generic function call. The scan interleaving framework was used to perform scan interleaving for image-based respiratory navigation of CMRA with spiral, radial, and Cartesian echo-planar imaging (EPI) navigator k-space trajectories. Eight healthy volunteers were scanned. Improved coronary vessel sharpness and visual scores were obtained using spiral and Cartesian EPI navigators compared with radial navigators. The usefulness of the proposed scan interleaving framework was demonstrated for image-based respiratory motion correction. It facilitated more direct comparisons of image navigator acquisitions with different k-space trajectories. Furthermore, we could demonstrate that spiral and Cartesian EPI navigators may be particularly suitable for image-based motion correction, as they provide improved motion correction and high navigator apparent signal-to-noise ratio while spending very little magnetization, thereby minimizing saturation effects.

Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer.

To investigate the impact of respiratory motion on localization, and quantification of lung lesions for the Gross Tumor Volume utilizing a fully automated Auto3Dreg program and dynamic NURBS-based cardiac-torso digitized phantom (NCAT). Respiratory motion may result in more than 30% underestimation of the SUV values of lung, liver and kidney tumor lesions. The motion correction technique adopted in this study was an image-based motion correction approach using, a voxel-intensity-based and a multi-resolution multi-optimization (MRMO) algorithm. The NCAT phantom was used to generate CT attenuation maps and activity distribution volumes for the lung regions. All the generated frames were co-registered to a reference frame using a time efficient scheme. Quantitative assessment including Region of Interest (ROI), image fidelity and image correlation techniques, as well as semi-quantitative line profile analysis and qualitatively overlaying non-motion and motion corrected image frames were performed. The largest motion was observed in the Z-direction. The greatest translation was for the frame 3, end inspiration, and the smallest for the frame 5 which was closet frame to the reference frame at 67% expiration. Visual assessment of the lesion sizes, 20-60mm at 3 different locations, apex, mid and base of lung showed noticeable improvement for all the foci and their locations. The maximum improvements for the image fidelity were from 0.395 to 0.930 within the lesion volume of interest. The greatest improvement in activity concentration underestimation was 7.7% below the true activity for the 20 mm lesion in comparison to 34.4% below, prior to correction. The discrepancies in activity underestimation were reduced with increasing the lesion sizes. Overlaying activity distribution on the attenuation map showed improved localization of the PET metabolic information to the anatomical CT images. The respiratory motion correction for the lung lesions has led to an improvement in the lesion size, localization and activity quantification with a potential application in reducing the size of the PET GTV for radiotherapy treatment planning applications and hence improving the accuracy of the regime in treatment of lung cancer.

Whole‐heart coronary MRA with 100% respiratory gating efficiency: Self‐navigated three‐dimensional retrospective image‐based motion correction (TRIM)

To develop a three-dimensional retrospective image-based motion correction technique for whole-heart coronary MRA with self-navigation that eliminates both the need to setup a diaphragm navigator and gate the acquisition. The proposed technique uses one-dimensional self-navigation to track the superior-inferior translation of the heart, with which the acquired three-dimensional radial k-space data is segmented into different respiratory bins. Respiratory motion is then estimated in image space using an affine transform model and subsequently this information is used to perform efficient motion correction in k-space. The performance of the proposed technique on healthy volunteers is compared with the conventional navigator gating approach as well as data binning using diaphragm navigator. The proposed method is able to reduce the imaging time to 7.1±0.5 min from 13.9±2.6 min with conventional navigator gating. The scan setup time is reduced as well due to the elimination of the navigator. The proposed method yields excellent image quality comparable with either conventional navigator gating or the navigator binning approach. We have developed a new respiratory motion correction technique for coronary MRA that enables 1 mm(3) isotropic resolution and whole-heart coverage with 7 min of scan time. Further tests on patient population are needed to determine its clinical usage.

Impact of subject head motion on quantitative brain 15O PET and its correction by image-based registration algorithm

Subject head motion during sequential (15)O positron emission tomography (PET) scans can result in artifacts in cerebral blood flow (CBF) and oxygen metabolism maps. However, to our knowledge, there are no systematic studies examining this issue. Herein, we investigated the effect of head motion on quantification of CBF and oxygen metabolism, and proposed an image-based motion correction method dedicated to (15)O PET study, correcting for transmission-emission mismatch and inter-scan mismatch of emission scans. We analyzed (15)O PET data for patients with major arterial steno-occlusive disease (n = 130) to determine the occurrence frequency of head motion during (15)O PET examination. Image-based motion correction without and with realignment between transmission and emission scans, termed simple and 2-step method, respectively, was applied to the cases that showed severe inter-scan motion. Severe inter-scan motion (>3 mm translation or >5° rotation) was observed in 27 of 520 adjacent scan pairs (5.2 %). In these cases, unrealistic values of oxygen extraction fraction (OEF) or cerebrovascular reactivity (CVR) were observed without motion correction. Motion correction eliminated these artifacts. The volume-of-interest (VOI) analysis demonstrated that the motion correction changed the OEF on the middle cerebral artery territory by 17.3 % at maximum. The inter-scan motion also affected CBV, CMRO2 and CBF, which were improved by the motion correction. A difference of VOI values between the simple and 2-step method was also observed. These data suggest that image-based motion correction is useful for accurate measurement of CBF and oxygen metabolism by (15)O PET.

Motion corrected sensitivity encoded isotropic projection reconstruction (SNIPR) for whole-heart coronary MRA

Background As has been shown recently, the use of undersampled 3D projection reconstruction (3DPR) and image-based motion correction with 100% acquisition efficiency [1,2] enables highly accelerated coronary MRA (CMRA) with high isotropic resolution. However, streaking artifact from undersampling significantly reduces the apparent signal-to-noise ratio (SNR). Moreover, affine motion correction distorts the k-space trajectory, hence introducing more streaking. In this work, the SNIPR reconstruction [3], a self-calibrated sensitivity encoding scheme for 3DPR, is integrated with image-based motion correction to reduce streaking artifacts and thus improve image quality.

Correction of head movements in positron emission tomography using point source tracking system: a simulation study

The motion of the head during brain positron emission tomography (PET) acquisitions has been identified as a source of artifact in the reconstructed image. In this study, a method is described to develop an image-based motion correction technique for correcting the post-acquisition data without using external optical motion-tracking system such as POLARIS. In this technique, GATE has been used to simulate PET brain scan using point sources mounted around the head to accurately monitor the position of the head during the time frames. The measurement of head motion in each frame showed a transformation in the image frame matrix, resulting in a fully corrected data set. Using different kinds of phantoms and motions, the accuracy of the correction method is tested and its applicability to experimental studies is demonstrated as well.

Real‐time optical motion correction for diffusion tensor imaging

Head motion is a fundamental problem in brain MRI. The problem is further compounded in diffusion tensor imaging because of long acquisition times, and the sensitivity of the tensor computation to even small misregistration. To combat motion artifacts in diffusion tensor imaging, a novel real-time prospective motion correction method was introduced using an in-bore monovision system. The system consists of a camera mounted on the head coil and a self-encoded checkerboard marker that is attached to the patient's forehead. Our experiments showed that optical prospective motion correction is more effective at removing motion artifacts compared to image-based retrospective motion correction. Statistical analysis revealed a significant improvement in similarity between diffusion data acquired at different time points when prospective correction was used compared to retrospective correction (P<0.001).

Combined high-resolution and real-time imaging: a technical feasibility study on coronary magnetic resonance angiography.

To propose a new approach to combining high-resolution and real-time imaging and to show its technical feasibility on the example of coronary magnetic resonance angiography. The insertion of fast two-dimensional (2D) acquisitions into time intervals that have not been utilized by triggered or gated 2D or three-dimensional (3D) acquisitions so far is suggested, as well as the immediate reconstruction and display of the additional data. For a technical validation of this concept, a 2D ventricular function protocol was interleaved into a cardiac-triggered and respiratory-gated 3D coronary angiography protocol. Dedicated hardware was employed to rapidly process the data originating from the former. Since the sampling of the latter was restricted to intervals with minimal motion, remaining periods of time could be used to simultaneously image the cardiac and respiratory motion. The technical feasibility of the proposed approach was demonstrated by successful measurements with the combined high-resolution and real-time protocol in volunteers. All examinations provided short axis views during the acquisition and angiograms of selected parts of the coronary system after its completion. The investigated concept allows high-resolution measurements to be complemented with real-time imaging functionality without affecting the scan time or image quality. In the particular application considered, an image-based patient monitoring or motion correction is enabled, indicating potential benefits of combining two very dissimilar methods of data acquisition in one measurement.