Respiratory Motion Correction Research Articles

Highly efficient image navigator based 3D whole-heart cardiac MRA at 0.55T.

To develop and evaluate a highly efficient free-breathing and contrast-agent-free three-dimensional (3D) whole-heart Cardiac Magnetic Resonance Angiography (CMRA) sequence at 0.55T. Free-breathing whole-heart CMRA has been previously proposed at 1.5 and 3T. Direct application of this sequence to 0.55T is not possible due to changes in the magnetic properties of the tissues. To enable free-breathing CMRA at 0.55T, pulse sequence design and acquisition parameters of a previously proposed whole-heart CMRA framework are optimized via Bloch simulations. Image navigators (iNAVs) are used to enable nonrigid respiratory motion-correction and 100% respiratory scan efficiency. Patch-based low-rank denoising is employed to accelerate the scan and account for the reduced signal-to-noise ratio at 0.55T. The proposed approach was evaluated on 11 healthy subjects. Image quality was assessed by a clinical expert (1: poor to 5: excellent) for all intrapericardiac structures. Quantitative evaluation was performed by assessing the vessel sharpness of the proximal right coronary artery (RCA). Optimization resulted in an imaging flip angle of , fat saturation flip angle of , and six k-space lines for iNAV encoding. The relevant cardiac structures and main coronary arteries were visible in all subjects, with excellent image quality (mean ) and minimal artifacts (mean ), with RCA vessel sharpness ( ) comparable to previous studies at 1.5T. The proposed approach enables 3D whole-heart CMRA at 0.55T in a 6-min scan ( ), providing excellent image quality, minimal artifacts, and comparable vessel sharpness to previous 1.5T studies. Future work will include the evaluation of the proposed approach in patients with cardiovascular disease.

3D B1+ corrected simultaneous myocardial T1 and T1ρ mapping with subject-specific respiratory motion correction and water-fat separation.

To develop a 3D free-breathing cardiac multi-parametric mapping framework that is robust to confounders of respiratory motion, fat, and B1+ inhomogeneities and validate it for joint myocardial T1 and T1ρ mapping at 3T. An electrocardiogram-triggered sequence with dual-echo Dixon readout was developed, where nine cardiac cycles were repeatedly acquired with inversion recovery and T1ρ preparation pulses for T1 and T1ρ sensitization. A subject-specific respiratory motion model relating the 1D diaphragmatic navigator to the respiration-induced 3D translational motion of the heart was constructed followed by respiratory motion binning and intra-bin 3D translational and inter-bin non-rigid motion correction. Spin history B1+ inhomogeneities were corrected with optimized dual flip angle strategy. After water-fat separation, the water images were matched to the simulated dictionary for T1 and T1ρ quantification. Phantoms and 10 heathy subjects were imaged to validate the proposed technique. The proposed technique achieved strong correlation (T1: R2 = 0.99; T1ρ: R2 = 0.98) with the reference measurements in phantoms. 3D cardiac T1 and T1ρ maps with spatial resolution of 2 × 2 × 4 mm were obtained with scan time of 5.4 ± 0.5 min, demonstrating comparable T1 (1236 ± 59 ms) and T1ρ (50.2 ± 2.4 ms) measurements to 2D separate breath-hold mapping techniques. The estimated B1+ maps showed spatial variations across the left ventricle with the septal and inferior regions being 10%-25% lower than the anterior and septal regions. The proposed technique achieved efficient 3D joint myocardial T1 and T1ρ mapping at 3T with respiratory motion correction, spin history B1+ correction and water-fat separation.

INav-based, Automated Coronary Magnetic Resonance Angiography for the Detection of Coronary Artery Stenosis (iNav-AUTO CMRA)

BackgroundCoronary computed tomography angiography (CCTA) is recommended as the first line diagnostic imaging modality in low to intermediate risk individuals suspected of stable coronary artery disease (CAD). However, CCTA exposes patients to ionising radiation and potentially nephrotoxic contrast agents. Invasive coronary angiography (ICA) is the gold-standard investigation to guide coronary revascularisation strategy, however, invasive procedures incur an inherent risk to the patient. Coronary magnetic resonance angiography (Coronary MRA) avoids these issues. Nevertheless, clinical implementation is currently limited due to extended scanning durations, inconsistent image quality, and consequent lack of diagnostic accuracy. Several technical Coronary MRA innovations including advanced respiratory motion correction with 100% scan efficiency (no data rejection), fast image acquisition with motion-corrected undersampled image reconstruction and deep-learning (DL)-based automated planning have been implemented and now await clinical validation in multi-centre trials. MethodsThe objective of the iNav-AUTO CMRA prospective multi-centre study is to evaluate the diagnostic accuracy of a newly developed, state-of-the-art, standardised, and automated Coronary MRA framework compared to CCTA in 230 patients undergoing clinical investigation for CAD. The study protocol mandates the administration of oral beta-blockers to decrease heart rate to below 60bpm and the use of sublingual nitroglycerine spray to induce vasodilation. Additionally, the study incorporates the utilisation of standardised postprocessing with sliding-thin-slab multiplanar reformatting, in combination with evaluation of the source images, to optimize the visualisation of coronary artery stenosis. DiscussionIf proven effective, Coronary MRA could provide a non-invasive, needle-free, yet also clinically viable, alternative to CCTA. Trial RegistrationThis study is registered at clinicaltrials.gov (NCT05473117).

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components

Objective. Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging. Approach. The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyer et al (2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system. Main results. The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods. Significance. This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment. In a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Panorama PET/CT scanner. Ninety-nine patients, who also had respiration monitoring device (VSM), formed the validation set. The remaining data of the 638 patients were used to train neural networks used in the uRMC. The uRMC primarily consists of three key components: (1) data-driven respiratory signal extraction, (2) attenuation map generation, and (3) PET-CT alignment. SUV metrics were calculated within 906 lesions for three approaches, i.e., data-driven uRMC (proposed), VSM-based uRMC, and OSEM without motion correction (NMC). RM magnitude of major organs were estimated. uRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUVmax and SUVmean across 906 lesions. Sub-group analysis showed significant SUV increases in small and medium-sized lesions with uRMC. Minor differences were found between VSM-based and data-driven uRMC methods, with the SUVmax was found statistically marginal significant or insignificant between the two methods. The study observed varied motion amplitudes in major organs, typically ranging from 10 to 20mm. A data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.

Respiratory motion-corrected T1 mapping of the abdomen.

The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency. Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping. Image-based self-navigation allowed for binning and reconstruction of respiratory-resolved images, which were used for the estimation of respiratory motion fields. Finally, motion-corrected T1 maps were calculated from the data applying the estimated motion fields. The method was evaluated in five healthy volunteers. For the assessment of the image-based navigator, we compared it to a simultaneously acquired ultrawide band radar signal. Motion-corrected T1 maps were evaluated qualitatively and quantitatively for different scan times. For all volunteers, the motion-corrected T1 maps showed fewer motion artifacts in the liver as well as sharper kidney structures and blood vessels compared to uncorrected T1 maps. Moreover, the relative error to the reference breathhold T1 maps could be reduced from up to 25% for the uncorrected T1 maps to below 10% for the motion-corrected maps for the average value of a region of interest, while the scan time could be reduced to 6-8s. The proposed approach allows for respiratory motion-corrected T1 mapping in the abdomen and ensures accurate T1 maps without the need for any breathholds.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner’s reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

Inversion recovery and saturation recovery pulmonary vein MR angiography using an image based navigator fluoro trigger and variable-density 3D cartesian sampling with spiral-like order.

Contrast enhanced pulmonary vein magnetic resonance angiography (PV CE-MRA) has value in atrial ablation pre-procedural planning. We aimed to provide high fidelity, ECG gated PV CE-MRA accelerated by variable density Cartesian sampling (VD-CASPR) with image navigator (iNAV) respiratory motion correction acquired in under 4min. We describe its use in part during the global iodinated contrast shortage. VD-CASPR/iNAV framework was applied to ECG-gated inversion and saturation recovery gradient recalled echo PV CE-MRA in 65 patients (66 exams) using .15mmol/kg Gadobutrol. Image quality was assessed by three physicians, and anatomical segmentation quality by two technologists. Left atrial SNR and left atrial/myocardial CNR were measured. 12 patients had CTA within 6months of MRA. Two readers assessed PV ostial measurements versus CTA for intermodality/interobserver agreement. Inter-rater/intermodality reliability, reproducibility of ostial measurements, SNR/CNR, image, and anatomical segmentation quality was compared. The mean acquisition time was 3.58 ± 0.60min. Of 35 PV pre-ablation datasets (34 patients), mean anatomical segmentation quality score was 3.66 ± 0.54 and 3.63 ± 0.55 as rated by technologists 1 and 2, respectively (p = 0.7113). Good/excellent anatomical segmentation quality (grade 3/4) was seen in 97% of exams. Each rated one exam as moderate quality (grade 2). 95% received a majority image quality score of good/excellent by three physicians. Ostial PV measurements correlated moderate to excellently with CTA (ICCs range 0.52-0.86). No difference in SNR was observed between IR and SR. High quality PV CE-MRA is possible in under 4min using iNAV bolus timing/motion correction and VD-CASPR.

Extended MRI-based PET motion correction for cardiac PET/MRI

PurposeA 2D image navigator (iNAV) based 3D whole-heart sequence has been used to perform MRI and PET non-rigid respiratory motion correction for hybrid PET/MRI. However, only the PET data acquired during the acquisition of the 3D whole-heart MRI is corrected for respiratory motion. This study introduces and evaluates an MRI-based respiratory motion correction method of the complete PET data.MethodsTwelve oncology patients scheduled for an additional cardiac 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI and 15 patients with coronary artery disease (CAD) scheduled for cardiac 18F-Choline (18F-FCH) PET/MRI were included. A 2D iNAV recorded the respiratory motion of the myocardium during the 3D whole-heart coronary MR angiography (CMRA) acquisition (~ 10 min). A respiratory belt was used to record the respiratory motion throughout the entire PET/MRI examination (~ 30–90 min). The simultaneously acquired iNAV and respiratory belt signal were used to divide the acquired PET data into 4 bins. The binning was then extended for the complete respiratory belt signal. Data acquired at each bin was reconstructed and combined using iNAV-based motion fields to create a respiratory motion-corrected PET image. Motion-corrected (MC) and non-motion-corrected (NMC) datasets were compared. Gating was also performed to correct cardiac motion. The SUVmax and TBRmax values were calculated for the myocardial wall or a vulnerable coronary plaque for the 18F-FDG and 18F-FCH datasets, respectively.ResultsA pair-wise comparison showed that the SUVmax and TBRmax values of the motion corrected (MC) datasets were significantly higher than those for the non-motion-corrected (NMC) datasets (8.2 ± 1.0 vs 7.5 ± 1.0, p < 0.01 and 1.9 ± 0.2 vs 1.2 ± 0.2, p < 0.01, respectively). In addition, the SUVmax and TBRmax of the motion corrected and gated (MC_G) reconstructions were also higher than that of the non-motion-corrected but gated (NMC_G) datasets, although for the TBRmax this difference was not statistically significant (9.6 ± 1.3 vs 9.1 ± 1.2, p = 0.02 and 2.6 ± 0.3 vs 2.4 ± 0.3, p = 0.16, respectively). The respiratory motion-correction did not lead to a change in the signal to noise ratio.ConclusionThe proposed respiratory motion correction method for hybrid PET/MRI improved the image quality of cardiovascular PET scans by increased SUVmax and TBRmax values while maintaining the signal-to-noise ratio.Trial registration METC162043 registered 01/03/2017.

Augmented Reality Navigation System for Biliary Interventional Procedures With Dynamic Respiratory Motion Correction.

Biliary interventional procedures require physicians to track the interventional instrument tip (Tip) precisely with X-ray image. However, Tip positioning relies heavily on the physicians' experience due to the limitations of X-ray imaging and the respiratory interference, which leads to biliary damage, prolonged operation time, and increased X-ray radiation. We construct an augmented reality (AR) navigation system for biliary interventional procedures. It includes system calibration, respiratory motion correction and fusion navigation. Firstly, the magnetic and 3D computed tomography (CT) coordinates are aligned through system calibration. Secondly, a respiratory motion correction method based on manifold regularization is proposed to correct the misalignment of the two coordinates caused by respiratory motion. Thirdly, the virtual biliary, liver and Tip from CT are overlapped to the corresponding position of the patient for dynamic virtual-real fusion. Our system is respectively evaluated and achieved an average alignment error of 0.75 ± 0.17 mm and 2.79 ± 0.46 mm on phantoms and patients. The navigation experiments conducted on phantoms achieve an average Tip positioning error of 0.98 ± 0.15 mm and an average fusion error of 1.67 ± 0.34 mm after correction. Our system can automatically register the Tip to the corresponding location in CT, and dynamically overlap the 3D virtual model onto patients to provide accurate and intuitive AR navigation. This study demonstrates the clinical potential of our system by assisting physicians during biliary interventional procedures. Our system enables dynamic visualization of virtual model on patients, reducing the reliance on contrast agents and X-ray usage.

Intra-bin correction and inter-bin compensation of respiratory motion in free-running five-dimensional whole-heart magnetic resonance imaging

BackgroundFree-running cardiac and respiratory motion-resolved whole-heart five-dimensional (5D) cardiovascular magnetic resonance (CMR) can reduce scan planning and provide a means of evaluating respiratory-driven changes in clinical parameters of interest. However, respiratory-resolved imaging can be limited by user-defined parameters which create trade-offs between residual artifact and motion blur. In this work, we develop and validate strategies for both correction of intra-bin and compensation of inter-bin respiratory motion to improve the quality of 5D CMR. MethodsEach component of the reconstruction framework was systematically validated and compared to the previously established 5D approach using simulated free-running data (N = 50) and a cohort of 32 patients with congenital heart disease. The impact of intra-bin respiratory motion correction was evaluated in terms of image sharpness while inter-bin respiratory motion compensation was evaluated in terms of reconstruction error, compression of respiratory motion, and image sharpness. The full reconstruction framework (intra-acquisition correction and inter-acquisition compensation of respiratory motion [IIMC] 5D) was evaluated in terms of image sharpness and scoring of image quality by expert reviewers. ResultsIntra-bin motion correction provides significantly (p < 0.001) sharper images for both simulated and patient data. Inter-bin motion compensation results in significant (p < 0.001) lower reconstruction error, lower motion compression, and higher sharpness in both simulated (10/11) and patient (9/11) data. The combined framework resulted in significantly (p < 0.001) sharper IIMC 5D reconstructions (End-expiration (End-Exp): 0.45 ± 0.09, End-inspiration (End-Ins): 0.46 ± 0.10) relative to the previously established 5D implementation (End-Exp: 0.43 ± 0.08, End-Ins: 0.39 ± 0.09). Similarly, image scoring by three expert reviewers was significantly (p < 0.001) higher using IIMC 5D (End-Exp: 3.39 ± 0.44, End-Ins: 3.32 ± 0.45) relative to 5D images (End-Exp: 3.02 ± 0.54, End-Ins: 2.45 ± 0.52). ConclusionThe proposed IIMC reconstruction significantly improves the quality of 5D whole-heart MRI. This may be exploited for higher resolution or abbreviated scanning. Further investigation of the diagnostic impact of this framework and comparison to gold standards is needed to understand its full clinical utility, including exploration of respiratory-driven changes in physiological measurements of interest.

Combined free-running four-dimensional anatomical and flow magnetic resonance imaging with native contrast using Synchronization of Neighboring Acquisitions by Physiological Signals

Four-dimensional (4D) flow magnetic resonance imaging (MRI) often relies on the injection of gadolinium- or iron-oxide-based contrast agents to improve vessel delineation. In this work, a novel technique is developed to acquire and reconstruct 4D flow data with excellent dynamic visualization of blood vessels but without the need for contrast injection. Synchronization of Neighboring Acquisitions by Physiological Signals (SyNAPS) uses pilot tone (PT) navigation to retrospectively synchronize the reconstruction of two free-running three-dimensional radial acquisitions, to create co-registered anatomy and flow images. Thirteen volunteers and two Marfan syndrome patients were scanned without contrast agent using one free-running fast interrupted steady-state (FISS) sequence and one free-running phase-contrast MRI (PC-MRI) sequence. PT signals spanning the two sequences were recorded for retrospective respiratory motion correction and cardiac binning. The magnitude and phase images reconstructed, respectively, from FISS and PC-MRI, were synchronized to create SyNAPS 4D flow datasets. Conventional two-dimensional (2D) flow data were acquired for reference in ascending (AAo) and descending aorta (DAo). The blood-to-myocardium contrast ratio, dynamic vessel area, net volume, and peak flow were used to compare SyNAPS 4D flow with Native 4D flow (without FISS information) and 2D flow. A score of 0-4 was given to each dataset by two blinded experts regarding the feasibility of performing vessel delineation. Blood-to-myocardium contrast ratio for SyNAPS 4D flow magnitude images (1.5±0.3) was significantly higher than for Native 4D flow (0.7±0.1, p<0.01) and was comparable to 2D flow (2.3±0.9, p=0.02). Image quality scores of SyNAPS 4D flow from the experts (M.P.: 1.9±0.3, E.T.: 2.5±0.5) were overall significantly higher than the scores from Native 4D flow (M.P.: 1.6±0.6, p=0.03, E.T.: 0.8±0.4, p<0.01) but still significantly lower than the scores from the reference 2D flow datasets (M.P.: 2.8±0.4, p<0.01, E.T.: 3.5±0.7, p<0.01). The Pearson correlation coefficient between the dynamic vessel area measured on SyNAPS 4D flow and that from 2D flow was 0.69±0.24 for the AAo and 0.83±0.10 for the DAo, whereas the Pearson correlation between Native 4D flow and 2D flow measurements was 0.12±0.48 for the AAo and 0.08±0.39 for the DAo. Linear correlations between SyNAPS 4D flow and 2D flow measurements of net volume (r2 =0.83) and peak flow (r2 =0.87) were larger than the correlations between Native 4D flow and 2D flow measurements of net volume (r2 =0.79) and peak flow (r2 =0.76). The feasibility and utility of SyNAPS were demonstrated for joint whole-heart anatomical and flow MRI without requiring electrocardiography gating, respiratory navigators, or contrast agents. Using SyNAPS, a high-contrast anatomical imaging sequence can be used to improve 4D flow measurements that often suffer from poor delineation of vessel boundaries in the absence of contrast agents.

Highly efficient free-breathing 3D whole-heart imaging in 3-min: single center study in adults with congenital heart disease

BackgroundThree dimensional, whole-heart (3DWH) MRI is an established non-invasive imaging modality in patients with congenital heart disease (CHD) for the diagnosis of cardiovascular morphology and for clinical decision making. Current techniques utilise diaphragmatic navigation (dNAV) for respiratory motion correction and gating and are frequently limited by long acquisition times. This study proposes and evaluates the diagnostic performance of a respiratory gating-free framework, which considers respiratory image-based navigation (iNAV), and highly accelerated variable density Cartesian sampling in concert with non-rigid motion correction and low-rank patch-based denoising (iNAV-3DWH-PROST). The method is compared to the clinical dNAV-3DWH sequence in adult patients with CHD. MethodsIn this prospective single center study, adult patients with CHD who underwent the clinical dNAV-3DWH MRI were also scanned with the iNAV-3DWH-PROST. Diagnostic confidence (4-point Likert scale) and diagnostic accuracy for common cardiovascular lesions was assessed by three readers. Scan times and diagnostic confidence were compared using the Wilcoxon-signed rank test. Co-axial vascular dimensions at three anatomic landmarks were measured, and agreement between the research and the corresponding clinical sequence was assessed with Bland-Altman analysis. ResultsThe study included 60 participants (mean age ± [SD]: 33 ± 14 years; 36 men). The mean acquisition time of iNAV-3DWH-PROST was significantly lower compared with the conventional clinical sequence (3.1 ± 0.9 min vs 13.9 ± 3.9 min, p < 0.0001). Diagnostic confidence was higher for the iNAV-3DWH-PROST sequence compared with the clinical sequence (3.9 ± 0.2 vs 3.4 ± 0.8, p < 0.001), however there was no significant difference in diagnostic accuracy. Narrow limits of agreement and mean bias less than 0.08 cm were found between the research and the clinical vascular measurements. ConclusionsThe iNAV-3DWH-PROST framework provides efficient, high quality and robust 3D whole-heart imaging in significantly shorter scan time compared to the standard clinical sequence.

Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner.

18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.

Improved [18F]FDG PET/CT Diagnostic Accuracy for Infective Endocarditis Using Conventional Cardiac Gating or Combined Cardiac and Respiratory Motion Correction (CardioFreezeTM).

Infective endocarditis (IE) is a serious and diagnostically challenging condition. [18F]FDG PET/CT is valuable for evaluating suspected IE, but it is susceptible to motion-related artefacts. This study investigated the potential benefits of cardiac motion correction for [18F]FDG PET/CT. In this prospective study, patients underwent [18F]FDG PET/CT for suspected IE, combined with a conventional cardiac gating sequence, a data-driven cardiac and respiratory gating sequence (CardioFreezeTM), or both. Scans were performed in adherence to EANM guidelines and assessors were blinded to patients' clinical contexts. Final diagnosis of IE was established based on multidisciplinary consensus after a minimum of 4 months follow-up and surgical findings, whenever performed. Seven patients participated in the study, undergoing both an ungated [18F] FDG-PET/CT and a scan with either conventional cardiac gating, CardioFreezeTM, or both. Cardiac motion correction improved the interpretability of [18F]FDG PET/CT in four out of five patients with valvular IE lesions, regardless of the method of motion correction used, which was statistically significant by Wilcoxon's signed rank test: p = 0.046. In one patient the motion-corrected sequence confirmed the diagnosis of endocarditis, which had been missed on non-gated PET. The performance of the two gating sequences was comparable. In conclusion, in this exploratory study, cardiac motion correction of [18F]FDG PET/CT improved the interpretability of [18F]FDG PET/CT. This may improve the sensitivity of PET/CT for suspected IE. Further larger comparative studies are necessary to confirm the additive value of these cardiac motion correction methods.

Improved visualization of free-running cardiac magnetic resonance by respiratory phase using principal component analysis

Rationale and objectivesTo support cardiac MR acquisitions during breathing without ECG, we developed software to mitigate the effects of respiratory displacement of the heart. The algorithm resolves respiratory motions and cardiac cycles from DICOM files. The new software automatically detects heartbeats from expiration and inspiration to decrease apparent respiratory motion. Materials and methodsOur software uses principal component analysis to resolve respiratory motions from cardiac cycles. It groups heartbeats from expiration and inspiration to decrease apparent respiratory motion. The respiratory motion correction was evaluated on short-axis views (acquired with compressed sensing) of 11 healthy subjects and 8 cardiac patients. Two expert radiologists, blinded to the processing, assessed the dynamic images in terms of blood-myocardial contrast, endocardial interface definition, and motion artifacts. ResultsThe smallest correlation coefficients between end-systolic frames of the original dynamic scans averaged 0.79. After segregation of cardiac cycles by respiratory phase, the mean correlation coefficients between cardiac cycles were 0.94±0.03 at end-expiration and 0.90±0.08 at end-inspiration. The improvements in correlation coefficients were significant in paired t-tests for healthy subjects and heart patients at end-expiration. Clinical assessment preferred cardiac cycles during end-expiration, which maintained or enhanced scores in 90% of healthy subjects and 83% of the heart patients. Performance remained high with arrhythmia and irregular breathing present. ConclusionHeartbeats collected from end-expiration mitigate respiratory motion and are accessible by applying the new software to DICOM files from real-time CMR. Inspiratory heartbeats are also accessible for examination of arrhythmias or abnormalities at end-inspiration.

Effect of different respiratory motion correction methods on PET image quality in chest PET/MRI

Objective: To investigate the effect of different respiratory motion correction methods on PET images during chest PET/MRI scans. Methods: The data of 35 patients (24 males and 11 females, aged from 29 to 84 year) of pulmonary lesions with significantly high uptake in thoracic PET/MRI scan were retrospective collected from Jingling Hospital. Four different methods were used to reconstruct the PET data. Group A was the full-time 20 min without respiratory motion correction static acquisition (Static) as a control, group B was the end-expiration static collection (Q.Static), and group C was the multi-bins respiratory gating (Gated-Respiratory). In addition, the influence of the time being considered, group D was added for reconstruction in the first 1/3 period (6 min 40 s) of group A. Then, the maximum value (L-SUVmax) and the mean value (L-SUVmean) of the SUV of the lesion, the mean value (B-SUVmean) and the standard deviation (B-SUVsd) of the SUV of the background under each reconstruction results were measured, and for each lesion the signal-to-noise ratio (L-SNR) was calculated. In order to exclude the interference of the background, the mean of the relative SUV (L-dSUVmean) of the L-SUVmean relative to the B-SUVmean was also calculated. Finally, One-Way Repeated Measures ANOVA was used, and the post-hoc pairwise comparison between groups was tested by Bonferroni's modified test. Results: There was statistically significant difference among group B or group C compared to group A and group D in L-SUVmax, L-SUVmean and L-dSUVmean [L-SUVmax:group B vs group A or group D was 8.06±3.57 vs 7.73±3.45 or 7.61±3.50, group C vs group A or group D was 8.04±3.56 vs 7.73±3.45 or 7.61±3.50 (all P<0.05); L-SUVmean: group B vs group A or group D was 4.12±1.78 vs 3.98±1.72 or 3.91±1.71, group C vs group A or group D was 4.13±1.78 vs 3.98±1.72 or 3.91±1.71 (all P<0.05); L-dSUVmean: group B vs group A or group D was 3.52±0.16 vs 3.39±0.18 or 3.31±0.18, group C vs group A or group D was 3.53±0.18 vs 3.39±0.18 or 3.31±0.18 (all P<0.05)], but there was no statistically significant difference between group B and group C (all P>0.05). There were statistically significant differences between group D and group A in B-SUVsd (0.07±0.00 vs 0.07±0.00, P=0.023) and L-SNR (69.80±44.57 vs 85.35±68.98, P=0.001). There was no statistically significant difference between group D and group A in L-SUVmax, L-SUVmean, B-SUVmean and L-dSUVmean (all P>0.05). Conclusions: There was no significant difference in PET image quality between the Q.static and Gated-Respiratory group, both of the two groups were better than the Static group which with no Gated-Respiratory motion correction. If non-respiratory gated Static is used, the PET acquisition time is recommended to be 6 min 40 s.

Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12mm, compared to 2.6% for the CoM method and 8.5% for no correction. We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.

A new compressed sensing cine cardiac MRI sequence with free-breathing real-time acquisition and fully automated motion-correction: A comprehensive evaluation

PurposeThe purpose of this study was to compare a new free-breathing compressed sensing cine (FB-CS) cardiac magnetic resonance imaging (CMR) to the standard reference multi-breath-hold segmented cine (BH-SEG) CMR in an unselected population. Materials and methodsFrom January to April 2021, 52 consecutive adult patients who underwent both conventional BH-SEG CMR and new FB-CS CMR with fully automated respiratory motion correction were retrospectively enrolled. There were 29 men and 23 women with a mean age of 57.7 ± 18.9 (standard deviation [SD]) years (age range: 19.0–90.0 years) and a mean cardiac rate of 74.6 ± 17.9 (SD) bpm. For each patient, short-axis stacks were acquired with similar parameters providing a spatial resolution of 1.8 × 1.8 × 8.0 mm3 and 25 cardiac frames. Acquisition and reconstruction times, image quality (Likert scale from 1 to 4), left and right ventricular volumes and ejection fractions, left ventricular mass, and global circumferential strain were assessed for each sequence. ResultsFB-CS CMR acquisition time was significantly shorter (123.8 ± 28.4 [SD] s vs. 267.2 ± 39.3 [SD] s for BH-SEG CMR; P < 0.0001) at the penalty of a longer reconstruction time (271.4 ± 68.7 [SD] s vs. 9.9 ± 2.1 [SD] s for BH-SEG CMR; P < 0.0001). In patients without arrhythmia or dyspnea, FB-CS CMR provided subjective image quality that was not different from that of BH-SEG CMR (P = 0.13). FB-CS CMR improved image quality in patients with arrhythmia (n = 18; P = 0.002) or dyspnea (n = 7; P = 0.02), and the edge sharpness was improved at end-systole and end-diastole (P = 0.0001). No differences were observed between the two techniques in ventricular volumes and ejection fractions, left ventricular mass or global circumferential strain in patients in sinus rhythm or with cardiac arrhythmia. ConclusionThis new FB-CS CMR addresses respiratory motion and arrhythmia-related artifacts without compromising the reliability of ventricular functional assessment.

Respiratory and cardiac motion correction in positron emission tomography using elastic motion approach for simultaneous abdomen and thorax positron emission tomography-magnetic resonance imaging.

Cardiac and respiratory motions in clinical positron emission tomography (PET) are a major contributor to inaccurate PET quantification and lesion characterisation. In this study, an elastic motion-correction (eMOCO) technique based on mass preservation optical flow is adapted and investigated for positron emission tomography-magnetic resonance imaging (PET-MRI) applications. The eMOCO technique was investigated in a motion management QA phantom and in twenty-four patients who underwent PET-MRI for dedicated liver imaging and nine patients for cardiac PET-MRI evaluation. Acquired data were reconstructed with eMOCO and gated motion correction techniques at cardiac, respiratory and dual gating modes, and compared to static images. Standardized uptake value (SUV), signal-to-noise ratio (SNR) of lesion activities from each gating mode and correction technique were measured and their means/standard deviation (SD) were compared using 2-ways ANOVA analysis and post-hoc Tukey's test. Lesions' SNR are highly recovered from phantom and patient studies. The SD of the SUV resulted from the eMOCO technique was statistically significantly less (P<0.01) than the SD resulted from conventional gated and static SUVs at the liver, lung and heart. The eMOCO technique was successfully implemented in PET-MRI in a clinical setting and produced the lowest SD compared to gated and static images, and hence provided the least noisy PET images. Therefore, the eMOCO technique can potentially be used on PET-MRI for improved respiratory and cardiac motion correction.