Autoadaptive motion modelling for MR-based respiratory motion estimation

Cardio-respiratory motion compensation for coronary roadmapping in fluoroscopic imaging

BACKGROUND

Inferring the shape and position of coronary artery poses challenges when using fluoroscopic image guidance during percutaneous coronary intervention (PCI) procedure. Although angiography enables coronary artery visualization, the use of injected contrast agent raises concerns about radiation exposure and the risk of contrast-induced nephropathy. To address these issues, dynamic coronary roadmapping overlaid on fluoroscopic images can provide coronary visual feedback without contrast injection.

PURPOSE

This paper proposes a novel cardio-respiratory motion compensation method that utilizes cardiac state synchronization and catheter motion estimation to achieve coronary roadmapping in fluoroscopic images.

METHODS

For more accurate cardiac state synchronization, video frame interpolation is applied to increase the frame rate of the original limited angiographic images, resulting in higher framerate and more adequate roadmaps. The proposed method also incorporates a multi-length cross-correlation based adaptive electrocardiogram (ECG) matching to address irregular cardiac motion situation. Furthermore, a shape-constrained path searching method is proposed to extract catheter structure from both fluoroscopic and angiographic image. Then catheter motion is estimated using a cascaded matching approach with an outlier removal strategy, leading to a final corrected roadmap.

RESULTS

Evaluation of the proposed method on clinical x-ray images demonstrates its effectiveness, achieving a 92.8% F1 score for catheter extraction on 589 fluoroscopic and angiographic images. Additionally, the method achieves a 5.6-pixel distance error of the coronary roadmap on 164 intraoperative fluoroscopic images.

CONCLUSIONS

Overall, the proposed method achieves accurate coronary roadmapping in fluoroscopic images and shows potential to overlay accurate coronary roadmap on fluoroscopic image in assisting PCI.

Embedding-Alignment Fusion-Based Graph Convolution Network With Mixed Learning Strategy for 4D Medical Image Reconstruction

In recent years, 4D medical image involving structural and motion information of tissue has attracted increasing attention. The key to the 4D image reconstruction is to stack the 2D slices based on matching the aligned motion states. In this study, the distribution of the 2D slices with the different motion states is modeled as a manifold graph, and the reconstruction is turned to be the graph alignment. An embedding-alignment fusion-based graph convolution network (GCN) with a mixed-learning strategy is proposed to align the graphs. Herein, the embedding and alignment processes of graphs interact with each other to realize a precise alignment with retaining the manifold distribution. The mixed strategy of self- and semi-supervised learning makes the alignment sparse to avoid the mismatching caused by outliers in the graph. In the experiment, the proposed 4D reconstruction approach is validated on the different modalities including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). We evaluate the reconstruction accuracy and compare it with those of state-of-the-art methods. The experiment results demonstrate that our approach can reconstruct a more accurate 4D image.

Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy

Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hybrid MR-linac scanner holds the promise to efficiently deal with such respiratory motion through real-time adaptive MR-guided radiotherapy (MRgRT). For MRgRT, motion-fields should be estimated from MR-data and the radiotherapy plan should be adapted in real-time according to the estimated motion-fields. All of this should be performed with a total latency of maximally 200 ms, including data acquisition and reconstruction. A measure of confidence in such estimated motion-fields is highly desirable, for instance to ensure the patient's safety in case of unexpected and undesirable motion. In this work, we propose a framework based on Gaussian Processes to infer 3D motion-fields and uncertainty maps in real-time from only three readouts of MR-data. We demonstrated an inference frame rate up to 69 Hz including data acquisition and reconstruction, thereby exploiting the limited amount of required MR-data. Additionally, we designed a rejection criterion based on the motion-field uncertainty maps to demonstrate the framework's potential for quality assurance. The framework was validated in silico and in vivo on healthy volunteer data (n=5) acquired using an MR-linac, thereby taking into account different breathing patterns and controlled bulk motion. Results indicate end-point-errors with a 75th percentile below 1 mm in silico, and a correct detection of erroneous motion estimates with the rejection criterion. Altogether, the results show the potential of the framework for application in real-time MR-guided radiotherapy with an MR-linac.

Slice-specific tracking for free-breathing diffusion tensor cardiac MRI

BACKGROUND

Diffusion tensor cardiac magnetic resonance (DT-CMR) imaging has great potential to characterize myocardial microarchitecture. However, its accuracy is limited by respiratory and cardiac motion and long scan times. Here, we develop and evaluate a slice-specific tracking method to improve the efficiency and accuracy of DT-CMR acquisition during free breathing.

METHODS

Coronal images were obtained along with signals from a diaphragmatic navigator. Respiratory and slice displacements were obtained from the navigator signals and coronal images, respectively, and these displacements were fitted with a linear model to obtain the slice-specific tracking factors. This method was evaluated in DT-CMR examinations of 17 healthy subjects, and the results were compared with those obtained using a fixed tracking factor of 0.6. DT-CMR with breath-holding was used for reference. Quantitative and qualitative evaluation methods were used to analyze the performance of the slice-specific tracking method and the consistency between the obtained diffusion parameters.

RESULTS

In the study, the slice-specific tracking factors showed an upward trend from the basal to the apical slice. Residual in-plane movements were lower in slice-specific tracking than in fixed-factor tracking (RMSE: 2.748 ± 1.171 versus 5.983 ± 2.623, P < 0.001). The diffusion parameters obtained using slice-specific tracking were not significantly different from those obtained from breath-holding acquisition (P > 0.05).

CONCLUSION

In free-breathing DT-CMR imaging, the slice-specific tracking method reduced misalignment of the acquired slices. The diffusion parameters obtained using this approach were consistent with those obtained with the breath-holding technique.

Real-Time 2D MR Cine From Beam Eye's View With Tumor-Volume Projection to Ensure Beam-to-Tumor Conformality for MR-Guided Radiotherapy of Lung Cancer

Purpose

To minimize computation latency using a predictive strategy to retrieve and project tumor volume onto 2D MR beam eye’s view (BEV) cine from time-resolved four-dimensional magnetic resonance imaging (TR-4DMRI) libraries (inhalation/exhalation) for personalized MR-guided intensity-modulated radiotherapy (IMRT) or volumetric-modulated arc therapy (VMAT).

Methods

Two time-series forecasting algorithms, autoregressive (AR) modeling and deep-learning-based long short-term memory (LSTM), were applied to predict the diaphragm position in the next 2D BEV cine to identify a motion-matched and hysteresis-accounted image to retrieve the tumor volume from the inhalation/exhalation TR-4DMRI libraries. Three 40-s TR-4DMRI (2 Hz, 3 × 80 images) per patient of eight lung cancer patients were used to create patient-specific inhalation/exhalation 4DMRI libraries, extract diaphragmatic waveforms, and interpolate them to f = 4 and 8 Hz to match 2D cine frame rates. Along a (40•f)-timepoint waveform, 30•f training timepoints were moved forward to produce 3×(10•f-1) predictions. The accuracy of position prediction was assessed against the waveform ground truth. The accuracy of tumor volume projections was evaluated using the center-of-mass difference (∆COM) and Dice similarity index against the TR-4DMRI ground truth for both IMRT (six beam angles, 30° interval) and VMAT (240/480 beam angles, 1.5°/0.75° interval, at 4/8 Hz, respectively).

Results

The accuracy of the first-timepoint prediction is 0.36 ± 0.10 mm (AR) and 0.62 ± 0.21 mm (LSTM) at 4 Hz and 0.06 ± 0.02 mm (AR) and 0.18 ± 0.06 mm (LSTM) at 8 Hz. A 10%–20% random error in prediction-library matching increases the overall uncertainty slightly. For both IMRT and VMAT, the accuracy of projected tumor volume contours on 2D BEV cine is ∆COM = 0.39 ± 0.13 mm and DICE = 0.97 ± 0.02 at 4 Hz and ∆COM = 0.10 ± 0.04 mm and DICE = 1.00 ± 0.00 at 8Hz.

Conclusion

This study demonstrates the feasibility of accurately predicting respiratory motion during 2D BEV cine imaging, identifying a motion-matched and hysteresis-accounted tumor volume, and projecting tumor volume contour on 2D BEV cine for real-time assessment of beam-to-tumor conformality, promising for optimal personalized MR-guided radiotherapy.

Dosimetry impact of gating latency in cine magnetic resonance image guided breath-hold pancreatic cancer radiotherapy

Objective.We investigated dosimetry effect of gating latency in cine magnetic resonance image (cine MRI) guided breath-hold pancreatic cancer radiotherapy.Approach.The gating latency was calculated based on cine MRI obtained from 17 patients who received MRI guided radiotherapy. Because of the cine MRI-related latency, beam overshoot occurs when beam remains on while the tracking target already moves out of the target boundary. The number of beam on/off events was calculated from the cine MRI data. We generated both IMRT and VMAT plans for all 17 patients using 33 Gy prescription, and created motion plans by applying isocenter shift that corresponds to motion-induced tumor displacement. The GTV and PTV coverage and dose to nearby critical structures were compared between the motion and original plan to evaluate the dosimetry change caused by cine MRI latency.Main results.The time ratio of cine MRI imaging latency over the treatment duration is 6.6 ± 3.1%, the mean and median percentage of beam-on events <4 s are 67.0 ± 14.3% and 66.6%. When a gating boundary of 4 mm and a target-out threshold of 5% is used, there is no significant difference for GTV V33Gy between the motion and original plan (p = 0.861 and 0.397 for IMRT and VMAT planning techniques, respectively). However, the PTV V33Gy and stomach Dmax for the motion plans are significantly lower; duodenum V12.5 Gy and V18Gy are significantly higher when compared with the original plans, for both IMRT and VMAT planning techniques.Significance.The cine MRI gating latency can significantly decrease the dose delivered to the PTV, and increase the dose to the nearby critical structures. However, no significant difference is observed for the GTV coverage. The dosimetry impact can be mitigated by implementing additional beam-on control techniques which reduces unnecessary beam on events and/or by using faster cine MRI sequences which reduces the latency period.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Characterizing interactions between cardiac shape and deformation by non-linear manifold learning

In clinical routine, high-dimensional descriptors of the cardiac function such as shape and deformation are reduced to scalars (e.g. volumes or ejection fraction), which limit the characterization of complex diseases. Besides, these descriptors undergo interactions depending on disease, which may bias their computational analysis. In this paper, we aim at characterizing such interactions by unsupervised manifold learning. We propose to use a sparsified version of Multiple Manifold Learning to align the latent spaces encoding each descriptor and weighting the strength of the alignment depending on each pair of samples. While this framework was up to now only applied to link different datasets from the same manifold, we demonstrate its relevance to characterize the interactions between different but partially related descriptors of the cardiac function (shape and deformation). We benchmark our approach against linear and non-linear embedding strategies, among which the fusion of manifolds by Multiple Kernel Learning, the independent embedding of each descriptor by Diffusion Maps, and a strict alignment based on pairwise correspondences. We first evaluated the methods on a synthetic dataset from a 0D cardiac model where the interactions between descriptors are fully controlled. Then, we transfered them to a population of right ventricular meshes from 310 subjects (100 healthy and 210 patients with right ventricular disease) obtained from 3D echocardiography, where the link between shape and deformation is key for disease understanding. Our experiments underline the relevance of jointly considering shape and deformation descriptors, and that manifold alignment is preferable over fusion for our application. They also confirm at a finer scale the characteristic traits of the right ventricular diseases in our population.

Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy

Shape and location organ variability induced by respiration constitutes one of the main challenges during dose delivery in radiotherapy. Providing up-to-date volumetric information during treatment can improve tumor tracking, thereby increasing treatment efficiency and reducing damage to healthy tissue. We propose a novel probabilistic model to address the problem of volumetric estimation with scalable predictive horizon from image-based surrogates during radiotherapy treatments, thus enabling out-of-plane tracking of targets. This problem is formulated as a conditional learning task, where the predictive variables are the 2D surrogate images and a pre-operative static 3D volume. The model learns a distribution of realistic motion fields over a population dataset. Simultaneously, a seq-2-seq inspired temporal mechanism acts over the surrogate images yielding extrapolated-in-time representations. The phase-specific motion distributions are associated with the predicted temporal representations, allowing the recovery of dense organ deformation in multiple times. Due to its generative nature, this model enables uncertainty estimations by sampling the latent space multiple times. Furthermore, it can be readily personalized to a new subject via fine-tuning, and does not require inter-subject correspondences. The proposed model was evaluated on free-breathing 4D MRI and ultrasound datasets from 25 healthy volunteers, as well as on 11 cancer patients. A navigator-based data augmentation strategy was used during the slice reordering process to increase model robustness against inter-cycle variability. The patient data was used as a hold-out test set. Our approach yields volumetric prediction from image surrogates with a mean error of 1.67 ± 1.68 mm and 2.17 ± 0.82 mm in unseen cases of the patient MRI and US datasets, respectively. Moreover, model personalization yields a mean landmark error of 1.4 ± 1.1 mm compared to ground truth annotations in the volunteer MRI dataset, with statistically significant improvements over state-of-the-art.

Lung Respiratory Motion Estimation Based on Fast Kalman Filtering and 4D CT Image Registration

Respiratory motion estimation is an important part in image-guided radiation therapy and clinical diagnosis. However, most of the respiratory motion estimation methods rely on indirect measurements of external breathing indicators, which will not only introduce great estimation errors, but also bring invasive injury for patients. In this paper, we propose a method of lung respiratory motion estimation based on fast Kalman filtering and 4D CT image registration (LRME-4DCT). In order to perform dynamic motion estimation for continuous phases, a motion estimation model is constructed by combining two kinds of GPU-accelerated 4D CT image registration methods with fast Kalman filtering method. To address the high computational requirements of 4D CT image sequences, a multi-level processing strategy is adopted in the 4D CT image registration methods, and respiratory motion states are predicted from three independent directions. In the DIR-lab dataset and POPI dataset with 4D CT images, the average target registration error (TRE) of the LRME-4DCT method can reach 0.91 mm and 0.85 mm respectively. Compared with traditional estimation methods based on pair-wise image registration, the proposed LRME-4DCT method can estimate the physiological respiratory motion more accurately and quickly. Our proposed LRME-4DCT method fully meets the practical clinical requirements for rapid dynamic estimation of lung respiratory motion.

A hierarchical model of abdominal configuration changes extracted from golden angle radial magnetic resonance imaging

Abdominal organs are subject to a variety of physiological forces that superimpose their effects to influence local motion and configuration. These forces not only include breathing, but can also arise from cyclic antral contractions and a range of slow configuration changes. To elucidate each individual motion pattern as well as their combined effects, a hierarchical motion model was built for characterization of these 3 motion modes (characterized as deformation maps between states) using golden angle radial MR signals. Breathing motions are characterized first. Antral contraction states are then reconstructed after breathing motion-induced deformation are corrected; slow configuration change states are further extracted from breathing motion-corrected image reconstructions. The hierarchical model is established based on these multimodal states, which can be either individually shown or combined to demonstrate any arbitrary composited motion patterns. The model was evaluated using 20 MR scans acquired from 9 subjects. Poor reproducibility of breathing motions both within as well as between scan sessions was observed, with an average intra-subject difference of 1.6 cycles min-1 for average breathing frequencies of 12.0 cycles min-1. Antral contraction frequency distributions were more stable than breathing, but also presented poor reproducibility between scans with an average difference of 0.3 cycles min-1 for average frequencies of 3.2 cycles min-1. The magnitudes of motions beyond breathing were found to be significant, with 14.4 and 33.8 mm maximal motions measured from antral contraction and slow configuration changes, respectively. Hierarchical motion models have potential in multiple applications in radiotherapy, including improving the accuracy of dose delivery estimation, providing guidance for margin creation, and supporting advanced decisions and strategies for immobilization, treatment monitoring and gating.

Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution

Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free reconstructions from highly undersampled MRI data. In this work, we present for the first time a variational multi-task framework that allows joining three relevant tasks in MRI: reconstruction, registration and super-resolution. Our framework takes a set of multiple undersampled MR acquisitions corrupted by motion into a novel multi-task optimisation model, which is composed of an L² fidelity term that allows sharing representation between tasks, super-resolution foundations and hyperelastic deformations to model biological tissue behaviors. We demonstrate that this combination yields significant improvements over sequential models and other bi-task methods. Our results exhibit fine details and compensate for motion producing sharp and highly textured images compared to state of the art methods while keeping low CPU time. Our improvements are appraised on both clinical assessment and statistical analysis.

Evaluation of MRI-derived surrogate signals to model respiratory motion

An MR-Linac can provide motion information of tumour and organs-at-risk before, during, and after beam delivery. However, MR imaging cannot provide real-time high-quality volumetric images which capture breath-to-breath variability of respiratory motion. Surrogate-driven motion models relate the motion of the internal anatomy to surrogate signals, thus can estimate the 3D internal motion from these signals. Internal surrogate signals based on patient anatomy can be extracted from 2D cine-MR images, which can be acquired on an MR-Linac during treatment, to build and drive motion models. In this paper we investigate different MRI-derived surrogate signals, including signals generated by applying principal component analysis to the image intensities, or control point displacements derived from deformable registration of the 2D cine-MR images. We assessed the suitability of the signals to build models that can estimate the motion of the internal anatomy, including sliding motion and breath-to-breath variability. We quantitatively evaluated the models by estimating the 2D motion in sagittal and coronal slices of 8 lung cancer patients, and comparing them to motion measurements obtained from image registration. For sagittal slices, using the first and second principal components on the control point displacements as surrogate signals resulted in the highest model accuracy, with a mean error over patients around 0.80 mm which was lower than the in-plane resolution. For coronal slices, all investigated signals except the skin signal produced mean errors over patients around 1 mm. These results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice. This implies the signals should also be suitable for modelling the 3D respiratory motion of the internal anatomy.

A statistical weighted sparse-based local lung motion modelling approach for model-driven lung biopsy

PURPOSE

Lung biopsy is currently the most effective procedure for cancer diagnosis. However, respiration-induced location uncertainty presents a challenge in precise lung biopsy. To reduce the medical image requirements for motion modelling, in this study, local lung motion information in the region of interest (ROI) is extracted from whole chest computed tomography (CT) and CT-fluoroscopy scans to predict the motion of potentially cancerous tissue and important vessels during the model-driven lung biopsy process.

METHODS

The motion prior of the ROI was generated via a sparse linear combination of a subset of motion information from a respiratory motion repository, and a weighted sparse-based statistical model was used to preserve the local respiratory motion details. We also employed a motion prior-based registration method to improve the motion estimation accuracy in the ROI and designed adaptive variable coefficients to interactively weigh the relative influence of the prior knowledge and image intensity information during the registration process.

RESULTS

The proposed method was applied to ten test subjects for the estimation of the respiratory motion field. The quantitative analysis resulted in a mean target registration error of 1.5 (0.8) mm and an average symmetric surface distance of 1.4 (0.6) mm.

CONCLUSIONS

The proposed method shows remarkable advantages over traditional methods in preserving local motion details and reducing the estimation error in the ROI. These results also provide a benchmark for lung respiratory motion modelling in the literature.

Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets

Manifold alignment (MA) is a technique to map many high-dimensional datasets to one shared low-dimensional space. Here we develop a pipeline for using MA to reconstruct high-resolution medical images. We present two key contributions. First, we develop a novel MA scheme in which each high-dimensional dataset can be differently weighted preventing noisier or less informative data from corrupting the aligned embedding. We find that this generalisation improves performance in our experiments in both supervised and unsupervised MA problems. Second, we use the wave kernel signature as a graph descriptor for the unsupervised MA case finding that it significantly outperforms the current state-of-the-art methods and provides higher quality reconstructed magnetic resonance volumes than existing methods.

Dense motion propagation from sparse samples

There are many applications for which sparse, or partial sampling of dynamic image data can be used for articulating or estimating motion within the medical imaging area. In this new work, we propose a generalized framework for dense motion propagation from sparse samples which represents an example of transfer learning and manifold alignment, allowing the transfer of knowledge across imaging sources of different domains which exhibit different features. Many such examples exist in medical imaging, from mapping 2D ultrasound or fluoroscopy to 3D or 4D data or monitoring dynamic dose delivery from partial imaging data in radiotherapy. To illustrate this approach we animate, or articulate, a high resolution static MR image with 4D free breathing respiratory motion derived from low resolution sparse planar samples of motion. In this work we demonstrate that sparse sampling of dynamic MRI may be used as a viable approach to successfully build models of free- breathing respiratory motion by constrained articulation. Such models demonstrate high contrast, and high temporal and spatial resolution that have so far been hitherto unavailable with conventional imaging methods. The articulation is based on using a propagation model, in the eigen domain, to estimate complete 4D motion vector fields from sparsely sampled free-breathing dynamic MRI data. We demonstrate that this approach can provide equivalent motion vector fields compared to fully sampled 4D dynamic data, whilst preserving the corresponding high resolution/high contrast inherent in the original static volume. Validation is performed on three 4D MRI datasets using eight extracted slices from a fast 4D acquisition (0.7 s per volume). The estimated deformation fields from sparse sampling are compared to the fully sampled equivalents, resulting in an rms error of the order of 2 mm across the entire image volume. We also present exemplar 4D high contrast, high resolution articulated volunteer datasets using this methodology. This approach facilitates greater freedom in the acquisition of free breathing respiratory motion sequences which may be used to inform motion modelling methods in a range of imaging modalities and demonstrates that sparse sampling of free breathing data may be used within a manifold alignment and transfer learning paradigm to estimate fully sampled motion. The method may also be applied to other examples of sparse sampling to produce dense motion propagation.

Multislice motion modeling for MRI-guided radiotherapy gating

PURPOSE

On-board magnetic resonance imaging (MRI) greatly enhances real-time target tracking capability during radiotherapy treatments. However, multislice and volumetric MRI techniques are frame rate limited and introduce unacceptable latency between the target moving out of position and the beam being turned off. We present a technique to estimate continuous volumetric tissue motion using motion models built from a repeated acquisition of a stack of MR slices. Applications including multislice target visualization and out-of-slice motion estimation during MRI-guided radiotherapy are demonstrated.

METHODS

Eight healthy volunteer studies were performed using a 0.35 T MRI-guided radiotherapy system. Images were acquired at three frames per second in an interleaved fashion across ten adjacent sagittal slice positions covering 4.5 cm using a balanced steady-state-free precession sequence. A previously published five-dimensional (5D) linear motion model used for MRI-guided radiotherapy gating was extended to include multiple slices. This model utilizes an external respiratory bellows signal recorded during imaging to simultaneously estimate motion across all imaged slices. For comparison to an image-based approach, the manifold learning technique local linear embedding (LLE) was used to derive a respiratory surrogate for motion modeling. Manifolds for every slice were aligned during LLE in a group-wise fashion, enabling motion estimation outside the current imaged slice using a motion model, a process which we denote as mSGA. Additionally, a method is developed to evaluate out-of-slice motion estimates. The multislice motion model was evaluated in a single slice with each newly acquired image using a leave-one-out approach. Model-generated gating decision accuracy and beam-on positive predictive value (PPV) are reported along with the median and 95th percentile distance between model and ground truth target centroids.

RESULTS

The average model gating decision accuracy and PPV across all volunteer studies was 93.7% and 92.8% using the 5D model, and 96.8% and 96.1% using the mSGA model, respectively. The median and 95th percentile distance between model and ground truth target centroids was 0.91 and 2.90 mm, respectively, using the 5D model and 0.58 and 1.49 mm using the mSGA model, averaged over all eight subjects. The mSGA motion model provided a statistically significant improvement across all evaluation metrics compared to the external surrogate-based 5D model.

CONCLUSION

The proposed techniques for out-of-slice target motion estimation demonstrated accuracy likely sufficient for clinical use. Results indicate the mSGA model may provide higher accuracy, however, the external surrogate-based model allows for unbiased in vivo accuracy evaluation.

Discontinuity Preserving Liver MR Registration with 3D Active Contour Motion Segmentation

OBJECTIVE

The sliding motion of the liver during respiration violates the homogeneous motion smoothness assumption in conventional non-rigid image registration and commonly results in compromised registration accuracy. This paper presents a novel approach, registration with 3D active contour motion segmentation (RAMS), to improve registration accuracy with discontinuity-aware motion regularization.

METHODS

A Markov random field-based discrete optimization with dense displacement sampling and self-similarity context metric is used for registration, while a graph cuts-based 3D active contour approach is applied to segment the sliding interface. In the first registration pass, a mask-free L1 regularization on an image-derived minimum spanning tree is performed to allow motion discontinuity. Based on the motion field estimates, a coarse segmentation finds the motion boundaries. Next, based on MR signal intensity, a fine segmentation aligns the motion boundaries with anatomical boundaries. In the second registration pass, smoothness constraints across the segmented sliding interface are removed by masked regularization on a minimum spanning forest and masked interpolation of the motion field.

RESULTS

For in vivo breath-hold abdominal MRI data, the motion masks calculated by RAMS are highly consistent with manual segmentations in terms of Dice similarity and bidirectional local distance measure. These automatically obtained masks are shown to substantially improve registration accuracy for both the proposed discrete registration as well as conventional continuous non-rigid algorithms.

CONCLUSION/SIGNIFICANCE

The presented results demonstrated the feasibility of automated segmentation of the respiratory sliding motion interface in liver MR images and the effectiveness of using the derived motion masks to preserve motion discontinuity.

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.

An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy

In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time magnetic resonance (MR) imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 to 2.75 mm in MR and from 3.0 to 1.8 mm in X-ray compared with the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos.

MR-based respiratory and cardiac motion correction for PET imaging

PURPOSE

To develop a motion correction for Positron-Emission-Tomography (PET) using simultaneously acquired magnetic-resonance (MR) images within 90 s.

METHODS

A 90 s MR acquisition allows the generation of a cardiac and respiratory motion model of the body trunk. Thereafter, further diagnostic MR sequences can be recorded during the PET examination without any limitation. To provide full PET scan time coverage, a sensor fusion approach maps external motion signals (respiratory belt, ECG-derived respiration signal) to a complete surrogate signal on which the retrospective data binning is performed. A joint Compressed Sensing reconstruction and motion estimation of the subsampled data provides motion-resolved MR images (respiratory + cardiac). A 1-POINT DIXON method is applied to these MR images to derive a motion-resolved attenuation map. The motion model and the attenuation map are fed to the Customizable and Advanced Software for Tomographic Reconstruction (CASToR) PET reconstruction system in which the motion correction is incorporated. All reconstruction steps are performed online on the scanner via Gadgetron to provide a clinically feasible setup for improved general applicability. The method was evaluated on 36 patients with suspected liver or lung metastasis in terms of lesion quantification (SUVmax, SNR, contrast), delineation (FWHM, slope steepness) and diagnostic confidence level (3-point Likert-scale).

RESULTS

A motion correction could be conducted for all patients, however, only in 30 patients moving lesions could be observed. For the examined 134 malignant lesions, an average improvement in lesion quantification of 22%, delineation of 64% and diagnostic confidence level of 23% was achieved.

CONCLUSION

The proposed method provides a clinically feasible setup for respiratory and cardiac motion correction of PET data by simultaneous short-term MRI. The acquisition sequence and all reconstruction steps are publicly available to foster multi-center studies and various motion correction scenarios.