Accuracy Of Motion Correction Research Articles

Retrospective motion correction for cardiac multi-parametric mapping with dictionary matching-based image synthesis and a low-rank constraint.

To develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric mapping. The proposed method constructs a hybrid loss that includes a dictionary-matching loss and a signal low-rankness loss, where the former registers the multi-contrast original images to a set of motion-free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non-MoCo, a pairwise registration method (Pairwise-MI), and a groupwise registration method (pTVreg) via a free-breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively. The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath-hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments. The proposed method significantly improves the motion correction accuracy and mapping quality compared with non-MoCo and alternative image-based methods.

Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.

Mechano-electrical interactions and heterogeneities in wild-type and drug-induced long QT syndrome rabbits.

Electromechanical reciprocity - comprising electro-mechanical (EMC) and mechano-electric coupling (MEC) - provides cardiac adaptation to changing physiological demands. Understanding electromechanical reciprocity and its impact on function and heterogeneity in pathological conditions - such as (drug-induced) acquired long QT syndrome (aLQTS) - might lead to novel insights in arrhythmogenesis. Our aim is to investigate how electrical changes impact on mechanical function (EMC) and vice versa (MEC) under physiological conditions and in aLQTS. To measure regional differences in EMC and MEC in vivo, we used tissue phase mapping cardiac MRI and a 24-lead ECG vest in healthy (control) and IKr-blocker E-4031-induced aLQTS rabbit hearts. MEC was studied in vivo by acutely increasing cardiac preload, and ex vivo by using voltage optical mapping (OM) in beating hearts at different preloads. In aLQTS, electrical repolarization (heart rate corrected RT-interval, RTn370) was prolonged compared to control (P<0.0001) with increased spatial and temporal RT heterogeneity (P<0.01). Changing electrical function (in aLQTS) resulted in significantly reduced diastolic mechanical function and prolonged contraction duration (EMC), causing increased apico-basal mechanical heterogeneity. Increased preload acutely prolonged RTn370 in both control and aLQTS hearts (MEC). This effect was more pronounced in aLQTS (P<0.0001). Additionally, regional RT-dispersion increased in aLQTS. Motion-correction allowed us to determine APD-prolongation in beating aLQTS hearts, but limited motion correction accuracy upon preload-changes prevented a clear analysis of MEC ex vivo. Mechano-induced RT-prolongation and increased heterogeneity were more pronounced in aLQTS than in healthy hearts. Acute MEC effects may play an additional role in LQT-related arrhythmogenesis, warranting further mechanistic investigations. KEY POINTS: Electromechanical reciprocity comprising excitation-contraction coupling (EMC) and mechano-electric feedback loops (MEC) is essential for physiological cardiac function. Alterations in electrical and/or mechanical heterogeneity are known to have potentially pro-arrhythmic effects. In this study, we aimed to investigate how electrical changes impact on the mechanical function (EMC) and vice versa (MEC) both under physiological conditions (control) and in acquired long QT syndrome (aLQTS). We show that changing the electrical function (in aLQTS) results in significantly altered mechanical heterogeneity via EMC and, vice versa, that increasing the preload acutely prolongs repolarization duration and increases electrical heterogeneity, particularly in aLQTS as compared to control. Our results substantiate the hypothesis that LQTS is an ‛electro-mechanical', rather than a 'purely electrical', disease and suggest that acute MEC effects may play an additional role in LQT-related arrhythmogenesis.

A scale space theory based motion correction approach for dynamic PET brain imaging studies

Aim/Introduction: Patient head motion poses a significant challenge when performing dynamic PET brain studies. In response, we developed a fast, robust, easily implementable and tracer-independent brain motion correction technique that facilitates accurate alignment of dynamic PET images.Materials and methods: Correction of head motion was performed using motion vectors derived by the application of Gaussian scale-space theory. A multiscale pyramid consisting of three different resolution levels (1/4x: coarse, 1/2x: medium, and 1x: fine) was applied to all image frames (37 frames, framing of 12 × 10s, 15 × 30s, 10 × 300s) of the dynamic PET sequence. Frame image alignment was initially performed at the coarse scale, which was subsequently used to initialise coregistration at the next finer scale, a process repeated until the finest possible scale, that is, the original resolution was reached. In addition, as tracer distribution changes during the dynamic frame sequence, a mutual information (MI) score was used to identify the starting frame for motion correction that is characterised by a sufficiently similar tracer distribution with the reference (last) frame. Validation of the approach was performed based on a simulated F18-fluoro-deoxy-glucose (FDG) dynamic sequence synthesised from the digital Zubal phantom. Inter-frame motion was added to each dynamic frame (except the reference frame). Total brain voxel displacement based on the added motion was constrained to 25 mm, which included both translation (0–15 mm in x, y and z) and rotation (0–0.3 rad for each Euler angle). Twenty repetitions were performed for each dataset with arbitrarily simulated motion, resulting in 20 synthetic datasets, each consisting of 36 dynamic frames (frame 37 was the reference frame). Assessment of motion correction accuracy across the dynamic sequence was performed based on the uncorrected/residual displacement remaining after the application of our algorithm. To investigate the clinical utility of the developed algorithm, three clinically cases that underwent list-mode PET imaging utilising different tracers ([18F]-fluoro-deoxy-glucose [18F]FDG [18F]-fluoroethyl-l-tyrosine [18F]FET [11C]-alpha-methyl-tryptophan [11C]AMT), each characterised by a different temporal tracer distribution were included in this study. Improvements in the Dice score coefficient (DSC) following frame alignment were evaluated as the correlation significance between the identified displacement for each frame of the clinical FDG, FET and AMT dynamic sequences.Results: Sub-millimetre accuracy (0.4 ± 0.2 mm) was achieved in the Zubal phantom for all frames after 5 min p. i., with early frames (30 s–180 s) displaying a higher residual displacement of ∼3 mm (3.2 ± 0.6 mm) due to differences in tracer distribution relative to the reference frame. The effect of these differences was also seen in MI scores; the MI plateau phase was reached at 35s p. i., 2.0 and 2.5 min p. i. At the coarse, medium and fine resolution levels, respectively. For the clinical images, a significant correlation between the identified (and corrected) displacement and the improvement in DSC score was seen in all dynamic studies (FET: R = 0.49, p &amp;lt; 0.001; FDG: R = 0.82, p &amp;lt; 0.001; AMT: R = 0.92, p &amp;lt; 0.001).Conclusion: The developed motion correction method is insensitive to any specific tracer distribution pattern, thus enabling improved correction of motion artefacts in a variety of clinical applications of extended PET imaging of the brain without the need for fiducial markers.

Marker-less and calibration-less motion correction method for brain PET.

Marker-less head motion correction methods have been well-studied; however, no reports discussing potential issues in positional calibration between a PET system and an external sensor remain limited. In this study, we develop a method for positional calibration between the PET system and an external range sensor to achieve practical head motion correction. The basic concept of the developed method involves using the subject's face model as a marker not only for head motion detection but also for the system positional calibration. The face model of the subject, which can be obtained easily using the range sensor, can also be calculated from a computed tomography (CT) image of the same subject. The CT image, which is acquired separately for attenuation correction in PET, has the same coordinates as the PET image because of the appropriate matching algorithm between CT and PET images. The proposed method was implemented in the helmet-type PET and the motion correction accuracy was assessed quantitatively using a mannequin head. The phantom experiments demonstrated the performance of the developed motion correction method; high-resolution images with no trace of the applied motion were obtained as if no motion was provided. Statistical analysis supported the visual assessment results in terms of the spatial resolution, contrast recovery; uniformity, and the results implied that motion with correction slightly improved image quality compared with the motionless case. The tolerance of the developed method against potential tracking errors had a minimum 10% difference in the amplitude of the rotation angle.

Dynamic cardiac PET motion correction using 3D normalized gradient fields in patients and phantom simulations.

A full rigid-body algorithm was developed using 3D normalized gradient fields including a multi-resolution step and sampling off the voxel grid to reduce interpolation artifacts. Optimization was performed using a weighted similarity metric that accounts for opposing gradients between images of blood pool and perfused tissue without the need for segmentation. Forty-three retrospective dynamic [N-13]-ammonia PET/CT rest/adenosine-stress patient studies were motion corrected and the mean motion parameters plotted at each frame time point. Motion correction accuracy was assessed using a comprehensive dynamic XCAT simulation incorporating published physiologic parameters of the heart's trajectory following adenosine infusion as well as corrupted attenuation correction commonly observed in clinical studies. Accuracy of the algorithm was assessed objectively by comparing the errors between isosurfaces and centers of mass of the motion corrected XCAT simulations. In the patient studies, the overall mean cranial-to-caudal translation was 7mm at stress over the duration of the adenosine infusion. Noninvasive clinical measures of relative flow reserve and myocardial flow reserve were highly correlated with their invasive analogues. Motion correction accuracy assessed with the XCAT simulations showed an error of <1mm in late perfusion frames that broadened gradually to <3mm in earlier frames containing blood pool. This work demonstrates that patients undergoing [N-13]-ammonia dynamic PET/CT exhibit a large cranial-to-caudal translation related to cardiac creep primarily at stress and to a lesser extent at rest, which can be accurately corrected by optimizing their 3D normalized gradient fields. Our approach provides a solution to the challenging condition where the image intensity and its gradients are opposed without the need for segmentation and remains robust in the presence of PET-CT mismatch.

Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning.

The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteers and patients who underwent clinically indicated cardiac MRI examinations. A U-net structure was used for the encoder and decoder parts of the network and the code space was regularized by an adversarial objective. The autoencoder learns the identity map for the free-breathing motion-corrupted images and preserves the structural content of the images, while the discriminator, which interacts with the output of the encoder, forces the encoder to remove motion artifacts. The network was first evaluated based on data that were artificially corrupted with simulated rigid motion with regard to motion-correction accuracy and the presence of any artificially created structures. Subsequently, to demonstrate the feasibility of the proposed approach in vivo, our network was trained on respiratory motion-corrupted images in an unpaired manner and was tested on volunteer and patient data. In the simulation study, mean structural similarity index scores for the synthesized motion-corrupted images and motion-corrected images were 0.76 and 0.93 (out of 1), respectively. The proposed method increased the Tenengrad focus measure of the motion-corrupted images by 12% in the simulation study and by 7% in the in vivo study. The average overall subjective image quality scores for the motion-corrupted images, motion-corrected images and breath-held images were 2.5, 3.5 and 4.1 (out of 5.0), respectively. Nonparametric-paired comparisons showed that there was significant difference between the image quality scores of the motion-corrupted and breath-held images (P < .05); however, after correction there was no significant difference between the image quality scores of the motion-corrected and breath-held images. This feasibility study demonstrates the potential of an adversarial autoencoder network for correcting respiratory motion-related image artifacts without requiring paired data.

A systematic performance evaluation of head motion correction techniques for3 commercial PET scanners using a reproducible experimental acquisition protocol.

Subject's motion during brain PET scan degrades spatial resolution and quantification of PET images. To suppress these effects, rigid-body motion correction systems have been installed in commercial PET scanners. In this study, we systematically compare the accuracy of motion correction among 3 commercial PET scanners using a reproducible experimental acquisition protocol. A cylindrical phantom with two 22Na point sources was placed on a customized base to enable two types of motion, 5° yaw and 15° pitch rotations. Repetitive PET scans (5min × 5 times) were performed at rest and under 2 motion conditions using 3 clinical PET scanners: the Eminence STARGATE G/L PET/CT (STARGATE) (Shimadzu Corp.), the SET-3000 B/X PET (SET-3000) (Shimadzu Corp.), and the Biograph mMR PET/MR (mMR) (Siemens Healthcare) systems. For STARGATE and SET-3000, the Polaris Vicra (Northern Digital Inc.) optical tracking system was used for frame-by-frame motion correction. For Biograph mMR, sequential MR images were simultaneously acquired with PET and used for LOR-based motion correction. All PET images were reconstructed by FBP algorithm with 1 × 1mm pixel size. To evaluate the accuracy of motion correction, FWHMs and spherical ROI values were analyzed. The percent differences (%diff) in averaged FWHMs of point sources at 4cm off-center between motion-corrected and static images were 0.77 ± 0.16 (STARGATE), 2.4 ± 0.34 (SET-3000), and 11 ± 1.0% (mMR) for a 5° yaw and 2.3 ± 0.37 (STARGATE) and 1.1 ± 0.60 (SET-3000) for a 15° pitch respectively. The averaged %diff between ROI values of motion-corrected images and static images were less than 2.0% for all conditions. In this study, we proposed a reproducible experimental framework to allow the systematic validation and comparison of multiple motion tracking and correction methodologies among different PET/CT and PET/MR commercial systems. Our proposed validation platform may be useful for future studies evaluating state-of-the-art motion correction strategies in clinical PET imaging.

Two-Stage Motion Correction for Super-Resolution Ultrasound Imaging in Human Lower Limb.

The structure of microvasculature cannot be resolved using conventional ultrasound (US) imaging due to the fundamental diffraction limit at clinical US frequencies. It is possible to overcome this resolution limitation by localizing individual microbubbles through multiple frames and forming a superresolved image, which usually requires seconds to minutes of acquisition. Over this time interval, motion is inevitable and tissue movement is typically a combination of large- and small-scale tissue translation and deformation. Therefore, super-resolution (SR) imaging is prone to motion artifacts as other imaging modalities based on multiple acquisitions are. This paper investigates the feasibility of a two-stage motion estimation method, which is a combination of affine and nonrigid estimation, for SR US imaging. First, the motion correction accuracy of the proposed method is evaluated using simulations with increasing complexity of motion. A mean absolute error of 12.2 was achieved in simulations for the worst-case scenario. The motion correction algorithm was then applied to a clinical data set to demonstrate its potential to enable in vivo SR US imaging in the presence of patient motion. The size of the identified microvessels from the clinical SR images was measured to assess the feasibility of the two-stage motion correction method, which reduced the width of the motion-blurred microvessels to approximately 1.5-fold.

A rigid motion correction method for helical computed tomography (CT)

We propose a method to compensate for six degree-of-freedom rigid motion in helical CT of the head. The method is demonstrated in simulations and in helical scans performed on a 16-slice CT scanner. Scans of a Hoffman brain phantom were acquired while an optical motion tracking system recorded the motion of the bed and the phantom. Motion correction was performed by restoring projection consistency using data from the motion tracking system, and reconstructing with an iterative fully 3D algorithm. Motion correction accuracy was evaluated by comparing reconstructed images with a stationary reference scan. We also investigated the effects on accuracy of tracker sampling rate, measurement jitter, interpolation of tracker measurements, and the synchronization of motion data and CT projections. After optimization of these aspects, motion corrected images corresponded remarkably closely to images of the stationary phantom with correlation and similarity coefficients both above 0.9. We performed a simulation study using volunteer head motion and found similarly that our method is capable of compensating effectively for realistic human head movements. To the best of our knowledge, this is the first practical demonstration of generalized rigid motion correction in helical CT. Its clinical value, which we have yet to explore, may be significant. For example it could reduce the necessity for repeat scans and resource-intensive anesthetic and sedation procedures in patient groups prone to motion, such as young children. It is not only applicable to dedicated CT imaging, but also to hybrid PET/CT and SPECT/CT, where it could also ensure an accurate CT image for lesion localization and attenuation correction of the functional image data.

Integration of cardiac and respiratory motion into MRI roadmaps fused with x‐ray

Volumetric roadmaps overlaid on live x-ray fluoroscopy may be used to enhance image guidance during interventional procedures. These roadmaps are often static and do not reflect cardiac or respiratory motion. In this work, the authors present a method for integrating cardiac and respiratory motion into magnetic resonance imaging (MRI)-derived roadmaps to fuse with live x-ray fluoroscopy images, and this method was tested in large animals. Real-time MR images were used to capture cardiac and respiratory motion. Nonrigid registration was used to calculate motion fields to deform a reference end-expiration, end-diastolic image to different cardiac and respiratory phases. These motion fields were fit to separate affine motion models for the aorta and proximal right coronary artery. Under x-ray fluoroscopy, an image-based navigator and ECG signal were used as inputs to deform the roadmap for live overlay. The in vivo accuracy of motion correction was measured in four swine as the ventilator tidal volume was varied. Motion correction reduced the root-mean-square error between the roadmaps and manually drawn centerlines, even under high tidal volume conditions. For the aorta, the error was reduced from 2.4 ± 1.5 mm to 2.2 ± 1.5 mm (p < 0.05). For the proximal right coronary artery, the error was reduced from 8.8 ± 16.2 mm to 4.3 ± 5.2 mm (p < 0.001). Using real-time MRI and an affine motion model it is feasible to incorporate physiological cardiac and respiratory motion into MRI-derived roadmaps to provide enhanced image guidance for interventional procedures. A method has been presented for creating dynamic 3D roadmaps that incorporate cardiac and respiratory motion. These roadmaps can be overlaid on live X-ray fluoroscopy to enhance image guidance for cardiac interventions.

Rapid PROPELLER‐MRI: A combination of iterative reconstruction and under‐sampling

To develop a technique that is able to reduce acquisition time and remove uneven blurring in reconstructed image for PROPELLER MRI. By using under-sampling and iterative reconstruction, this proposed technique will be less sensitive to subject motion. Numerical simulations, as well as experiments on a phantom and healthy human subjects were performed to demonstrate advantages of a combination of under-sampled acquisition and iterative reconstruction. Method of motion correction was modified to increase accuracy of motion correction for the under-sampled PROPELLER acquisition. It was demonstrated that the proposed approach achieved substantial acceleration of PROPELLER acquisition while maintaining its motion correction advantage. An effective method for reducing imaging time in PROPELLER was introduced in this study, which minimizes typical under-sampling artifacts without uneven spatial resolution and maintains the ability of motion correction.

Optimised Motion Tracking for Positron Emission Tomography Studies of Brain Function in Awake Rats

Positron emission tomography (PET) is a non-invasive molecular imaging technique using positron-emitting radioisotopes to study functional processes within the body. High resolution PET scanners designed for imaging rodents and non-human primates are now commonplace in preclinical research. Brain imaging in this context, with motion compensation, can potentially enhance the usefulness of PET by avoiding confounds due to anaesthetic drugs and enabling freely moving animals to be imaged during normal and evoked behaviours. Due to the frequent and rapid motion exhibited by alert, awake animals, optimal motion correction requires frequently sampled pose information and precise synchronisation of these data with events in the PET coincidence data stream. Motion measurements should also be as accurate as possible to avoid degrading the excellent spatial resolution provided by state-of-the-art scanners. Here we describe and validate methods for optimised motion tracking suited to the correction of motion in awake rats. A hardware based synchronisation approach is used to achieve temporal alignment of tracker and scanner data to within 10 ms. We explored the impact of motion tracker synchronisation error, pose sampling rate, rate of motion, and marker size on motion correction accuracy. With accurate synchronisation (<100 ms error), a sampling rate of >20 Hz, and a small head marker suitable for awake animal studies, excellent motion correction results were obtained in phantom studies with a variety of continuous motion patterns, including realistic rat motion (<5% bias in mean concentration). Feasibility of the approach was also demonstrated in an awake rat study. We conclude that motion tracking parameters needed for effective motion correction in preclinical brain imaging of awake rats are achievable in the laboratory setting. This could broaden the scope of animal experiments currently possible with PET.

Motion correction in periodically‐rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) and turboprop MRI

Periodically-rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) and Turboprop MRI are characterized by greatly reduced sensitivity to motion, compared to their predecessors, fast spin-echo (FSE) and gradient and spin-echo (GRASE), respectively. This is due to the inherent self-navigation and motion correction of PROPELLER-based techniques. However, it is unknown how various acquisition parameters that determine k-space sampling affect the accuracy of motion correction in PROPELLER and Turboprop MRI. The goal of this work was to evaluate the accuracy of motion correction in both techniques, to identify an optimal rotation correction approach, and determine acquisition strategies for optimal motion correction. It was demonstrated that blades with multiple lines allow more accurate estimation of motion than blades with fewer lines. Also, it was shown that Turboprop MRI is less sensitive to motion than PROPELLER. Furthermore, it was demonstrated that the number of blades does not significantly affect motion correction. Finally, clinically appropriate acquisition strategies that optimize motion correction are discussed for PROPELLER and Turboprop MRI.

Automated correction of fast motion artifacts for two-photon imaging of awake animals

Two-photon imaging of bulk-loaded calcium dyes can record action potentials (APs) simultaneously from dozens of spatially resolved neurons in vivo. Extending this technique to awake animals, however, has remained technically challenging due to artifacts caused by brain motion. Since in two-photon excitation microscopes image pixels are captured sequentially by scanning a focused pulsed laser across small areas of interest within the brain, fast displacements of the imaged area can distort the image nonuniformly. If left uncorrected, brain motion in awake animals will cause artifactual fluorescence changes, masking the small functional fluorescence increases associated with AP discharge. We therefore present a procedure for detection and correction of both fast and slow displacements in two-photon imaging of awake animals. Our algorithm, based on the Lucas–Kanade framework, operates directly on the motion-distorted imaging data, requiring neither external signals such as heartbeat nor a distortion-free template image. Motion correction accuracy was tested in silico over a wide range of simplified and realistic displacement trajectories and for multiple levels of fluorescence noise. Accuracy was confirmed in vivo by comparing solutions obtained from red and green fluorophores imaged simultaneously. Finally, the accuracy of AP detection from motion-displaced bulk-loaded calcium imaging is evaluated with and without motion correction, and we conclude that accurate motion correction as achieved by this procedure is both necessary and sufficient for single AP detection in awake animals.

Development and characterisation of aptamers for cancer therapy

Two-photon imaging of bulk-loaded calcium dyes can record action potentials (APs) simultaneously from dozens of spatially resolved neurons in vivo. Extending this technique to awake animals, however, has remained technically challenging due to artifacts caused by brain motion. Since in two-photon excitation microscopes image pixels are captured sequentially by scanning a focused pulsed laser across small areas of interest within the brain, fast displacements of the imaged area can distort the image nonuniformly. If left uncorrected, brain motion in awake animals will cause artifactual fluorescence changes, masking the small functional fluorescence increases associated with AP discharge. We therefore present a procedure for detection and correction of both fast and slow displacements in two-photon imaging of awake animals. Our algorithm, based on the Lucas–Kanade framework, operates directly on the motion-distorted imaging data, requiring neither external signals such as heartbeat nor a distortion-free template image. Motion correction accuracy was tested in silico over a wide range of simplified and realistic displacement trajectories and for multiple levels of fluorescence noise. Accuracy was confirmed in vivo by comparing solutions obtained from red and green fluorophores imaged simultaneously. Finally, the accuracy of AP detection from motion-displaced bulk-loaded calcium imaging is evaluated with and without motion correction, and we conclude that accurate motion correction as achieved by this procedure is both necessary and sufficient for single AP detection in awake animals.

Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system

Subject motion and associated artefacts limit the applicability of MRI and the achievable quality of the images acquired. In this paper, a fully integrated method for prospective correction of arbitrary rigid body motion employing an external motion tracking device is demonstrated for the first time. The position of the imaging volume is updated prior to every excitation of the spin system. The performance of the available tracking hardware and its connection to the MR imager is analyzed in detail. With the introduction of a novel calibration procedure the accuracy of motion correction is improved compared to previous approaches. Together with the high geometry update rate even freely moving objects can be imaged without motion related artefacts. The high performance and image quality improvement in case of subject motion are demonstrated for various imaging techniques such as gradient and spin echo, as well as echo planar imaging.

Factor Analysis of Medical Image Sequences Improves Evaluation of First-Pass MR Imaging Acquisitions for Myocardial Perfusion

Rationale and Objectives Factor analysis of medical image sequences (FAMIS) applied to gadolinium chelate–enhanced subsecond magnetic resonance (MR) imaging was evaluated as a postprocessing method for assessing myocardial perfusion in coronary artery disease (CAD). Materials and Methods To assess the accuracy of motion correction, five normal volunteers underwent MR imaging at rest. Thirteen patients with well-documented CAD and no myocardial infarction underwent MR imaging at rest and after dipyridamole administration. After motion correction, a single myocardial tissue factor (FAMISt) image was obtained with FAMIS for each raw MR imaging series acquisition. To evaluate how FAMIS could improve the analysis of these acquisitions, five readers visually assessed myocardial perfusion with FAMISt and raw MR images, and a multicase, multi–reader receiver operating characteristic analysis was performed. Results FAMISt images significantly improved detection of the perfusion defects when compared with raw MR images ( P = .002). Areas under the receiver operating characteristic curves ranged from 0.84 to 0.93 with FAMISt images and from 0.48 to 0.85 with raw MR images. Conclusion FAMIS applied to first-pass MR imaging series provided myocardial perfusion images that improve the objective assessment of myocardial perfusion in patients with CAD.