Motion-compensated 3D Research Articles

Respiratory motion modelling for MR-guided lung cancer radiotherapy: model development and geometric accuracy evaluation.

Objective.Respiratory motion of lung tumours and adjacent structures is challenging for radiotherapy. Online MR-imaging cannot currently provide real-time volumetric information of the moving patient anatomy, therefore limiting precise dose delivery, delivered dose reconstruction, and downstream adaptation methods.Approach.We tailor a respiratory motion modelling framework towards an MR-Linac workflow to estimate the time-resolved 4D motion from real-time data. We develop a multi-slice acquisition scheme which acquires thick, overlapping 2D motion-slices in different locations and orientations, interleaved with 2D surrogate-slices from a fixed location. The framework fits a motion model directly to the input data without the need for sorting or binning to account for inter- and intra-cycle variation of the breathing motion. The framework alternates between model fitting and motion-compensated super-resolution image reconstruction to recover a high-quality motion-free image and a motion model. The fitted model can then estimate the 4D motion from 2D surrogate-slices. The framework is applied to four simulated anthropomorphic datasets and evaluated against known ground truth anatomy and motion. Clinical applicability is demonstrated by applying our framework to eight datasets acquired on an MR-Linac from four lung cancer patients.Main results.The framework accurately reconstructs high-quality motion-compensated 3D images with 2 mm3isotropic voxels. For the simulated case with the largest target motion, the motion model achieved a mean deformation field error of 1.13 mm. For the patient cases residual error registrations estimate the model error to be 1.07 mm (1.64 mm), 0.91 mm (1.32 mm), and 0.88 mm (1.33 mm) in superior-inferior, anterior-posterior, and left-right directions respectively for the building (application) data.Significance.The motion modelling framework estimates the patient motion with high accuracy and accurately reconstructs the anatomy. The image acquisition scheme can be flexibly integrated into an MR-Linac workflow whilst maintaining the capability of online motion-management strategies based on cine imaging such as target tracking and/or gating.

Non-rigid motion-compensated 3D whole-heart T2 mapping in a hybrid 3T PET-MR system.

Simultaneous PET-MRI improves inflammatory cardiac disease diagnosis. However, challenges persist in respiratory motion and mis-registration between free-breathing 3D PET and 2D breath-held MR images. We propose a free-breathing non-rigid motion-compensated 3D T2 -mapping sequence enabling whole-heart myocardial tissue characterization in a hybrid 3T PET-MR system and provides non-rigid respiratory motion fields to correct also simultaneously acquired PET data. Free-breathing 3D whole-heart T2 -mapping was implemented on a hybrid 3T PET-MRI system. Three datasets were acquired with different T2 -preparation modules (0, 28, 55 ms) using 3-fold undersampled variable-density Cartesian trajectory. Respiratory motion was estimated via virtual 3D image navigators, enabling multi-contrast non-rigid motion-corrected MR reconstruction. T2 -maps were computed using dictionary-matching. Approach was tested in phantom, 8 healthy subjects, 14 MR only and 2 PET-MR patients with suspected cardiac disease and compared with spin echo reference (phantom) and clinical 2D T2 -mapping (in-vivo). Phantom results show a high correlation (R2 = 0.996) between proposed approach and gold standard 2D T2 mapping. In-vivo 3D T2 -mapping average values in healthy subjects (39.0 ± 1.4 ms) and patients (healthy tissue) (39.1 ± 1.4 ms) agree with conventional 2D T2 -mapping (healthy = 38.6 ± 1.2 ms, patients = 40.3 ± 1.7 ms). Bland-Altman analysis reveals bias of 1.8 ms and 95% limits of agreement (LOA) of -2.4-6 ms for healthy subjects, and bias of 1.3 ms and 95% LOA of -1.9 to 4.6 ms for patients. Validated efficient 3D whole-heart T2 -mapping at hybrid 3T PET-MRI provides myocardial inflammation characterization and non-rigid respiratory motion fields for simultaneous PET data correction. Comparable T2 values were achieved with both 3D and 2D methods. Improved image quality was observed in the PET images after MR-based motion correction.

A hybrid deformable registration method to generate motion-compensated 3D virtual MRI for fusion with interventional real-time 3D ultrasound.

Ultrasound is often the preferred modality for image-guided therapy or treatment in organs such as liver due to real-time imaging capabilities. However, the reduced conspicuity of tumors in ultrasound images adversely impacts the precision and accuracy of treatment delivery. This problem is compounded by deformable motion due to breathing and other physiological activity. This creates the need for a fusion method to align interventional US with pre-interventional modalities that provide superior soft-tissue contrast (e.g., MRI) to accurately target a structure-of-interest and compensate for liver motion. In this work, we developed a hybrid deformable fusion method to align 3D pre-interventional MRI and 3D interventional US volumes to target the structures-of-interest in liver accurately in real-time. The deformable multimodal fusion method involved an offline alignment of a pre-intervention MRI with a pre-intervention US volume using a traditional registration method, followed by real-time prediction of deformation using a trained deep-learning model between interventional US volumes across different respiratory states. This framework enables motion-compensated MRI-US image fusion in real-time for image-guided treatment. The proposed hybrid deformable registration method was evaluated on three healthy volunteers across the pre-intervention MRI and 20 US volume pairs in the free-breathing respiratory cycle. The mean Euclidean landmark distance of three homologous targets in all three volunteers was less than 3 mm for percutaneous liver procedures. Preliminary results show that clinically acceptable registration accuracies for near real-time, deformable MRI-US fusion can be achieved by our proposed hybrid approach. The proposed combination of traditional and deep-learning deformable registration techniques is thus a promising approach for motion-compensated MRI-US fusion to improve targeting in image-guided liver interventions.

Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute.

To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute. Undersampled motion-corrected reconstructions have enabled free-breathing isotropic 3D CMRA in ~5-10 min acquisition times. In this work, we propose a deep-learning-based SR framework, combined with non-rigid respiratory motion compensation, to shorten the acquisition time to less than 1 min. A generative adversarial network (GAN) is proposed consisting of two cascaded Enhanced Deep Residual Network generator, a trainable discriminator, and a perceptual loss network. A 16-fold increase in spatial resolution is achieved by reconstructing a high-resolution (HR) isotropic CMRA (0.9 mm3 or 1.2 mm3 ) from a low-resolution (LR) anisotropic CMRA (0.9 × 3.6 × 3.6 mm3 or 1.2 × 4.8 × 4.8 mm3 ). The impact and generalization of the proposed SRGAN approach to different input resolutions and operation on image and patch-level is investigated. SRGAN was evaluated on a retrospective downsampled cohort of 50 patients and on 16 prospective patients that were scanned with LR-CMRA in ~50 s under free-breathing. Vessel sharpness and length of the coronary arteries from the SR-CMRA is compared against the HR-CMRA. SR-CMRA showed statistically significant (P < .001) improved vessel sharpness 34.1% ± 12.3% and length 41.5% ± 8.1% compared with LR-CMRA. Good generalization to input resolution and image/patch-level processing was found. SR-CMRA enabled recovery of coronary stenosis similar to HR-CMRA with comparable qualitative performance. The proposed SR-CMRA provides a 16-fold increase in spatial resolution with comparable image quality to HR-CMRA while reducing the predictable scan time to <1 min.

Evaluation of accelerated motion-compensated 3d water/fat late gadolinium enhanced MR for atrial wall imaging

Objective3D late gadolinium enhancement (LGE) imaging is a promising non-invasive technique for the assessment of atrial fibrosis. However, current techniques result in prolonged and unpredictable scan times and high rates of non-diagnostic images. The purpose of this study was to compare the performance of a recently proposed accelerated respiratory motion-compensated 3D water/fat LGE technique with conventional 3D LGE for atrial wall imaging.Materials and methods18 patients (age: 55.7±17.1 years) with atrial fibrillation underwent conventional diaphragmatic navigator gated inversion recovery (IR)-prepared 3D LGE (dNAV) and proposed image-navigator motion-corrected water/fat IR-prepared 3D LGE (iNAV) imaging. Images were assessed for image quality and presence of fibrosis by three expert observers. The scan time for both techniques was recorded.ResultsImage quality scores were improved with the proposed compared to the conventional method (iNAV: 3.1 ± 1.0 vs. dNAV: 2.6 ± 1.0, p = 0.0012, with 1: Non-diagnostic to 4: Full diagnostic). Furthermore, scan time for the proposed method was significantly shorter with a 59% reduction is scan time (4.5 ± 1.2 min vs. 10.9 ± 3.9 min, p < 0.0001). The images acquired with the proposed method were deemed as inconclusive less frequently than the conventional images (expert 1/expert 2: 4/7 dNAV and 2/4 iNAV images inconclusive).DiscussionThe motion-compensated water/fat LGE method enables atrial wall imaging with diagnostic quality comparable to the current conventional approach with a significantly shorter scan of about 5 min.

3D whole-heart grey-blood late gadolinium enhancement cardiovascular magnetic resonance imaging

PurposeTo develop a free-breathing whole-heart isotropic-resolution 3D late gadolinium enhancement (LGE) sequence with Dixon-encoding, which provides co-registered 3D grey-blood phase-sensitive inversion-recovery (PSIR) and complementary 3D fat volumes in a single scan of < 7 min.MethodsA free-breathing 3D PSIR LGE sequence with dual-echo Dixon readout with a variable density Cartesian trajectory with acceleration factor of 3 is proposed. Image navigators are acquired to correct both inversion recovery (IR)-prepared and reference volumes for 2D translational respiratory motion, enabling motion compensated PSIR reconstruction with 100% respiratory scan efficiency. An intermediate PSIR reconstruction is performed between the in-phase echoes to estimate the signal polarity which is subsequently applied to the IR-prepared water volume to generate a water grey-blood PSIR image. The IR-prepared water volume is obtained using a water/fat separation algorithm from the corresponding dual-echo readout. The complementary fat-volume is obtained after water/fat separation of the reference volume. Ten patients (6 with myocardial scar) were scanned with the proposed water/fat grey-blood 3D PSIR LGE sequence at 1.5 T and compared to breath-held grey-blood 2D LGE sequence in terms of contrast ratio (CR), contrast-to-noise ratio (CNR), scar depiction, scar transmurality, scar mass and image quality.ResultsComparable CRs (p = 0.98, 0.40 and 0.83) and CNRs (p = 0.29, 0.40 and 0.26) for blood-myocardium, scar-myocardium and scar-blood respectively were obtained with the proposed free-breathing 3D water/fat LGE and 2D clinical LGE scan. Excellent agreement for scar detection, scar transmurality, scar mass (bias = 0.29%) and image quality scores (from 1: non-diagnostic to 4: excellent) of 3.8 ± 0.42 and 3.6 ± 0.69 (p > 0.99) were obtained with the 2D and 3D PSIR LGE approaches with comparable total acquisition time (p = 0.29). Similar agreement in intra and inter-observer variability were obtained for the 2D and 3D acquisition respectively.ConclusionThe proposed approach enabled the acquisition of free-breathing motion-compensated isotropic-resolution 3D grey-blood PSIR LGE and fat volumes. The proposed approach showed good agreement with conventional 2D LGE in terms of CR, scar depiction and scan time, while enabling free-breathing acquisition, whole-heart coverage, reformatting in arbitrary views and visualization of both water and fat information.

3D whole-heart isotropic-resolution motion-compensated joint T1 /T2 mapping and water/fat imaging.

To develop a free-breathing isotropic-resolution whole-heart joint T1 and T2 mapping sequence with Dixon-encoding that provides coregistered 3D T1 and T2 maps and complementary 3D anatomical water and fat images in a single ~9 min scan. Four interleaved dual-echo Dixon gradient echo volumes are acquired with a variable density Cartesian trajectory and different preparation pulses: 1) inversion recovery-preparation, 2) and 3) no preparations, and 4) T2 preparation. Image navigators are acquired to correct each echo for 2D translational respiratory motion; the 8 echoes are jointly reconstructed with a low-rank patch-based reconstruction. A water/fat separation algorithm is used to obtain water and fat images for each acquired volume. T1 and T2 maps are generated by matching the signal evolution of the water images to a simulated dictionary. Complementary bright-blood and fat volumes for anatomical visualization are obtained from the T2 -prepared dataset. The proposed sequence was tested in phantom experiments and 10 healthy subjects and compared to standard 2D MOLLI T1 mapping, 2D balance steady-state free precession T2 mapping, and 3D T2 -prepared Dixon coronary MR angiography. High linear correlation was found between T1 and T2 quantification with the proposed approach and phantom spin echo measurements (y = 1.1 × -11.68, R2 = 0.98; and y = 0.85 × +5.7, R2 = 0.99). Mean myocardial values of T1 /T2 = 1116 ± 30.5 ms/45.1 ± 2.38 ms were measured in vivo. Biases of T1 /T2 = 101.8 ms/-0.77 ms were obtained compared to standard 2D techniques. The proposed joint T1 /T2 sequence permitted the acquisition of motion-compensated isotropic-resolution 3D T1 and T2 maps and complementary coronary MR angiography and fat volumes, showing promising results in terms of T1 and T2 quantification and visualization of cardiac anatomy and pericardial fat.

A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography

PurposeTo enable fast reconstruction of undersampled motion-compensated whole-heart 3D coronary magnetic resonance angiography (CMRA) by learning a multi-scale variational neural network (MS-VNN) which allows the acquisition of high-quality 1.2 × 1.2 × 1.2 mm isotropic volumes in a short and predictable scan time. MethodsEighteen healthy subjects and one patient underwent free-breathing 3D CMRA acquisition with variable density spiral-like Cartesian sampling, combined with 2D image navigators for translational motion estimation/compensation. The proposed MS-VNN learns two sets of kernels and activation functions for the magnitude and phase images of the complex-valued data. For the magnitude, a multi-scale approach is applied to better capture the small calibre of the coronaries. Ten subjects were considered for training and validation. Prospectively undersampled motion-compensated data with 5-fold and 9-fold accelerations, from the remaining 9 subjects, were used to evaluate the framework. The proposed approach was compared to Wavelet-based compressed-sensing (CS), conventional VNN, and to an additional fully-sampled (FS) scan. ResultsThe average acquisition time (m:s) was 4:11 for 5-fold, 2:34 for 9-fold acceleration and 18:55 for fully-sampled. Reconstruction time with the proposed MS-VNN was ~14 s. The proposed MS-VNN achieves higher image quality than CS and VNN reconstructions, with quantitative right coronary artery sharpness (CS:43.0%, VNN:43.9%, MS-VNN:47.0%, FS:50.67%) and vessel length (CS:7.4 cm, VNN:7.7 cm, MS-VNN:8.8 cm, FS:9.1 cm) comparable to the FS scan. ConclusionThe proposed MS-VNN enables 5-fold and 9-fold undersampled CMRA acquisitions with comparable image quality that the corresponding fully-sampled scan. The proposed framework achieves extremely fast reconstruction time and does not require tuning of regularization parameters, offering easy integration into clinical workflow.

Respiratory motion-compensated high-resolution 3D whole-heart T1\u03c1 mapping

BackgroundCardiovascular magnetic resonance (CMR) T1ρ mapping can be used to detect ischemic or non-ischemic cardiomyopathy without the need of exogenous contrast agents. Current 2D myocardial T1ρ mapping requires multiple breath-holds and provides limited coverage. Respiratory gating by diaphragmatic navigation has recently been exploited to enable free-breathing 3D T1ρ mapping, which, however, has low acquisition efficiency and may result in unpredictable and long scan times. This study aims to develop a fast respiratory motion-compensated 3D whole-heart myocardial T1ρ mapping technique with high spatial resolution and predictable scan time.MethodsThe proposed electrocardiogram (ECG)-triggered T1ρ mapping sequence is performed under free-breathing using an undersampled variable-density 3D Cartesian sampling with spiral-like order. Preparation pulses with different T1ρ spin-lock times are employed to acquire multiple T1ρ-weighted images. A saturation prepulse is played at the start of each heartbeat to reset the magnetization before T1ρ preparation. Image navigators are employed to enable beat-to-beat 2D translational respiratory motion correction of the heart for each T1ρ-weighted dataset, after which, 3D translational registration is performed to align all T1ρ-weighted volumes. Undersampled reconstruction is performed using a multi-contrast 3D patch-based low-rank algorithm. The accuracy of the proposed technique was tested in phantoms and in vivo in 11 healthy subjects in comparison with 2D T1ρ mapping. The feasibility of the proposed technique was further investigated in 3 patients with suspected cardiovascular disease. Breath-hold late-gadolinium enhanced (LGE) images were acquired in patients as reference for scar detection.ResultsPhantoms results revealed that the proposed technique provided accurate T1ρ values over a wide range of simulated heart rates in comparison to a 2D T1ρ mapping reference. Homogeneous 3D T1ρ maps were obtained for healthy subjects, with septal T1ρ of 58.0 ± 4.1 ms which was comparable to 2D breath-hold measurements (57.6 ± 4.7 ms, P = 0.83). Myocardial scar was detected in 1 of the 3 patients, and increased T1ρ values (87.4 ± 5.7 ms) were observed in the infarcted region.ConclusionsAn accelerated free-breathing 3D whole-heart T1ρ mapping technique was developed with high respiratory scan efficiency and near-isotropic spatial resolution (1.7 × 1.7 × 2 mm3) in a clinically feasible scan time of ~ 6 mins. Preliminary patient results suggest that the proposed technique may find applications in non-contrast myocardial tissue characterization.

Motion-Compensated Spatio-Temporal Filtering for Multi-Image and Multimodal Super-Resolution

The classical multi-image super-resolution model assumes that the super-resolved image is related to the low-resolution frames by warping, convolution and downsampling. State-of-the-art algorithms either use explicit registration to fuse the information for each pixel in its trajectory or exploit spatial and temporal similarities. We propose to combine both ideas, making use of inter-frame motion and exploiting spatio-temporal redundancy with patch-based techniques. We introduce a non-linear filtering approach that combines patches from several frames not necessarily belonging to the same pixel trajectory. The selection of candidate patches depends on a motion-compensated 3D distance, which is robust to noise and aliasing. The selected 3D volumes are then sliced per frame, providing a collection of 2D patches which are finally averaged depending on their similarity to the reference one. This makes the upsampling strategy robust to flow inaccuracies and occlusions. Total variation and nonlocal regularization are used in the deconvolution stage. The experimental results demonstrate the state-of-the-art performance of the proposed method for the super-resolution of videos and light-field images. We also adapt our approach to multimodal sequences when some additional data at the desired resolution is available.

HVS-based scalable video watermarking

Scalable coding methods are widely considered as promising coding approaches for image/video transmission in heterogeneous environments. To copyright protection of scalable coded videos, scalable video watermarking was introduced which enables reconstructing various scales of watermark from watermarked videos. This paper proposes a novel human vision system-based, spread spectrum method to scalable video watermarking. The host video is decomposed using newly proposed spatio-temporal motion-compensated 3D wavelet decomposition, called k( $$\hbox {t}+2\hbox {D}$$ ) DWT decomposition, to frequency sub-bands. A scalable decomposition of the watermark is inserted into the entire frequency sub-bands of decomposed video. At each frequency sub-band, the watermark data are inserted into the selected coefficients of the sub-band frames in a way that the watermark embedding visual artifact occurs in the highly textured, highly contrasted, and very dark/bright areas of the video frames. The experimental results show that the watermarked test videos are highly transparent and robust against scalable video coding even at very low decoding bit-rates and some other video processing attacks. The proposed approach can guarantee copyright protection for scalable coded videos, especially over heterogeneous networks.

Technical note: Accelerated nonrigid motion-compensated isotropic 3D coronary MR angiography.

PurposeTo develop an accelerated and nonrigid motion‐compensated technique for efficient isotropic 3D whole‐heart coronary magnetic resonance angiography (CMRA) with Cartesian acquisition.MethodsHighly efficient whole‐heart 3D CMRA was achieved by combining image reconstruction from undersampled data using compressed sensing (CS) with a nonrigid motion compensation framework. Undersampled acquisition was performed using a variable‐density Cartesian trajectory with radial order (VD‐CAPR). Motion correction was performed in two steps: beat‐to‐beat 2D translational correction with motion estimated from interleaved image navigators, and bin‐to‐bin 3D nonrigid correction with motion estimated from respiratory‐resolved images reconstructed from undersampled 3D CMRA data using CS. Nonrigid motion fields were incorporated into an undersampled motion‐compensated reconstruction, which combines CS with the general matrix description formalism. The proposed approach was tested on 10 healthy subjects and compared against a conventional twofold accelerated 5‐mm navigator‐gated and tracked acquisition.ResultsThe proposed method achieves isotropic 1.2‐mm Cartesian whole‐heart CMRA in 5 min ± 1 min (~8× acceleration). The proposed approach provides good‐quality images of the left and right coronary arteries, comparable to those of a twofold accelerated navigator‐gated and tracked acquisition, but scan time was up to about four times faster. For both coronaries, no significant differences (P > 0.05) in vessel sharpness and length were found between the proposed method and reference scan.ConclusionThe feasibility of a highly efficient motion‐compensated reconstruction framework for accelerated 3D CMRA has been demonstrated in healthy subjects. Further investigation is required to assess the clinical value of the method.

Motion-adaptive 3D nonlocal means filter based on stochastic distance for low-dose X-ray fluoroscopy

Abstract Low-dose X-ray fluoroscopy avoids radiation risks, but X-ray fluoroscopic image sequences are contaminated by quantum noise. In this paper, a 3D nonlocal means (NLM) filter based on stochastic distance that incorporates motion information is proposed, which can be applied to the denoising of X-ray fluoroscopic image sequences. First, the stochastic distance is obtained for use as the NLM filter similarity measure. This facilitates the removal of Poisson noise between the patches to be denoised within motion-compensated 3D volumes for spatio-temporal filtering. Second, motion state detection and motion-adaptive weights are proposed, which preserve the details of the motion that can occur during medical procedures. Experimental results obtained by applying the proposed method to real X-ray fluoroscopic image sequence images are shown in visual and numerical comparison with state-of-the-art denoising methods for spatio-temporal filtering techniques. The quantitative and qualitative results confirm that the proposed novel frameworks outperform other techniques in terms of objective criteria, as well as the subjective visual perception of the real X-ray fluoroscopic image sequences.

Fully integrated 3D high-resolution multicontrast abdominal PET-MR with high scan efficiency.

To provide 3D multicontrast anatomical MR with high isotropic resolution and metabolic positron emission tomography (PET) images using a respiratory motion-compensated simultaneous PET-MR examination with high scan efficiency. Standard abdominal PET-MR examinations combine MR data obtained during multiple breath-holds with free-breathing PET acquisitions, limiting the achievable image resolution and potentially causing misalignment errors between breath-hold and free-breathing data. Here, a 3D free-breathing PET-MR acquisition is presented, yielding T1 and T2 -weighted MR images with an isotropic resolution of 1.5 mm3 . In addition, nonrigid respiratory motion information and respiratory-resolved attenuation-correction maps are obtained without an increase in scan time. Motion information is used in motion-compensated image reconstructions to improve MR and PET image quality while shortening scan times. The proposed approach was evaluated in 11 oncology patients and provided respiratory motion information with an accuracy of 1.3 ± 0.1 mm. Sharpness of anatomical features was increased by 19 ± 13% compared with the uncorrected MR images in a 54 ± 26% shorter scan time than a gated MR acquisition. The MR-based motion information improved uptake values (75 ± 94%) and resolution (16 ± 27%) of simultaneously acquired PET images. The proposed method provides motion-compensated 3D high-quality MR and PET images in a comprehensive and highly efficient examination. Magn Reson Med 79:900-911, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Automated 3D coronary sinus catheter detection using a scanning-beam digital x-ray system.

Scanning-beam digital x-ray (SBDX) is an inverse geometry x-ray fluoroscopy system capable of tomosynthesis-based 3D tracking of catheter electrodes concurrent with fluoroscopic display. To facilitate respiratory motion-compensated 3D catheter tracking, an automated coronary sinus (CS) catheter detection algorithm for SBDX was developed. The technique uses the 3D localization capability of SBDX and prior knowledge of the catheter shape. Candidate groups of points representing the CS catheter are obtained from a 3D shape-constrained search. A cost function is then minimized over the groups to select the most probable CS catheter candidate. The algorithm was implemented in MATLAB and tested offline using recorded image sequences of a chest phantom containing a CS catheter, ablation catheter, and fiducial clutter. Fiducial placement was varied to create challenging detection scenarios. Table panning and elevation was used to simulate motion. The CS catheter detection method had 98.1% true positive rate and 100% true negative rate in 2755 frames of imaging. Average processing time was 12.7 ms/frame on a PC with a 3.4 GHz CPU and 8 GB memory. Motion compensation based on 3D CS catheter tracking was demonstrated in a moving chest phantom with a fixed CS catheter and an ablation catheter pulled along a fixed trajectory. The RMS error in the tracked ablation catheter trajectory was 1.41 mm, versus 10.35 mm without motion compensation. A computationally efficient method of automated 3D CS catheter detection has been developed to assist with motion-compensated 3D catheter tracking and registration of 3D cardiac models to tracked catheters.

Joint removal of random and fixed-pattern noise through spatiotemporal video filtering.

We propose a framework for the denoising of videos jointly corrupted by spatially correlated (i.e., nonwhite) random noise and spatially correlated fixed-pattern noise. Our approach is based on motion-compensated 3D spatiotemporal volumes, i.e., a sequence of 2D square patches extracted along the motion trajectories of the noisy video. First, the spatial and temporal correlations within each volume are leveraged to sparsify the data in 3D spatiotemporal transform domain, and then the coefficients of the 3D volume spectrum are shrunk using an adaptive 3D threshold array. Such array depends on the particular motion trajectory of the volume, the individual power spectral densities of the random and fixed-pattern noise, and also the noise variances which are adaptively estimated in transform domain. Experimental results on both synthetically corrupted data and real infrared videos demonstrate a superior suppression of the random and fixed-pattern noise from both an objective and a subjective point of view.

PET Motion Compensation for Radiation Therapy Using a CT-Based Mid-Position Motion Model: Methodology and Clinical Evaluation

Four-dimensional positron emission tomography (4D PET) imaging of the thorax produces sharper images with reduced motion artifacts. Current radiation therapy planning systems, however, do not facilitate 4D plan optimization. When images are acquired in a 2-minute time slot, the signal-to-noise ratio of each 4D frame is low, compromising image quality. The purpose of this study was to implement and evaluate the construction of mid-position 3D PET scans, with motion compensated using a 4D computed tomography (CT)-derived motion model. All voxels of 4D PET were registered to the time-averaged position by using a motion model derived from the 4D CT frames. After the registration the scans were summed, resulting in a motion-compensated 3D mid-position PET scan. The method was tested with a phantom dataset as well as data from 27 lung cancer patients. PET motion compensation using a CT-based motion model improved image quality of both phantoms and patients in terms of increased maximum SUV (SUV(max)) values and decreased apparent volumes. In homogenous phantom data, a strong relationship was found between the amplitude-to-diameter ratio and the effects of the method. In heterogeneous patient data, the effect correlated better with the motion amplitude. In case of large amplitudes, motion compensation may increase SUV(max) up to 25% and reduce the diameter of the 50% SUV(max) volume by 10%. 4D CT-based motion-compensated mid-position PET scans provide improved quantitative data in terms of uptake values and volumes at the time-averaged position, thereby facilitating more accurate radiation therapy treatment planning of pulmonary lesions.

Highly efficient 3D motion-compensated abdomen MRI from undersampled golden-RPE acquisitions

A common approach to compensate for respiratory motion in free-breathing 3D magnetic resonance imaging (MRI) is navigator gating where MRI data is only acquired when the respiratory signal coincides within a small predefined acceptance window. However, this leads to poor scan efficiency and prolonged scan times. Here, we propose a method to reconstruct motion-compensated 3D MRI of the abdomen acquired during free-breathing with nearly 100% scan efficiency without increasing scan time. The approach is based on a self-gated golden-radial phase encoding sampling scheme that allows for the reconstruction of multiple undersampled 3D images at different respiratory positions. Non-rigid image registrations and time-wise motion field interpolations are employed to form 3D motion models that combine all low-quality images into one high-quality motion-compensated image. Our highly efficient technique allows reconstruction of 3D liver MRI with a high isotropic resolution of 1.75 mm from a short acquisition of 1.1 min. The approach is validated in 10 healthy volunteers by comparing image quality to data sets acquired with a self-gating approach. Our method reduces scan time by 56% compared to the gating technique while similar image quality is preserved.

Motion compensation for interventional navigation on 3D static roadmaps based on an affine model and gating

Current cardiac interventions are performed under 2D fluoroscopy, which comes along with well-known burdens to patients and physicians, such as x-ray exposure and the use of contrast agent. Furthermore, the navigation on complex structures such as the coronaries is complicated by the use of 2D images in which the catheter position is only visible while the contrast agent is introduced. In this work, a new method is presented, which circumvents these drawbacks and enables the cardiac interventional navigation on motion-compensated 3D static roadmaps. For this, the catheter position is continuously reconstructed within a previously acquired 3D roadmap of the coronaries. The motion compensation makes use of an affine motion model for compensating the respiratory motion and compensates the motion due to cardiac contraction by gating the catheter position. In this process, only those positions which have been acquired during the rest phase of the heart are used for the reconstruction. The method necessitates the measurement of the catheter position, which is done by using a magnetic tracking system. Nevertheless, other techniques, such as image-based catheter tracking, can be applied. This motion compensation has been tested on a dynamic heart phantom. The evaluation shows that the algorithm can reconstruct the catheter position on the 3D static roadmap precisely with a residual motion of 1.0 mm and less.

Very low bit-rate video coding using motion compensated 3-d wavelet transform

Very low bit-rate video coding plays an important role in the development of various visual communication services over the public switched telephone network. In this paper, a new motion-compensated 3D wavelet transform (MC-3DWT) video coding scheme is presented. The new coding scheme, which substantially reduces the coding complexity, has a good performance in average PSNR, compression ratios and visual quality of reconstructions when compared to the other coding methods based on wavelet transforms. The new MC-3DWT coding scheme is suitable for VLSI implementation.