Respiratory Phase Research Articles

Respiratory-Correlated 4-Dimensional Magnetic Resonance Fingerprinting for Liver Cancer Radiation Therapy Motion Management

The objective of this study was to develop a respiratory-correlated (RC) 4-dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF) (RC-4DMRF) for liver tumor motion management in radiation therapy. Thirteen patients with liver cancer were prospectively enrolled in this study. k-space MRF signals of the liver were acquired during free-breathing using the fast acquisition with steady-state precession sequence on a 3T scanner. The signals were binned into 8 respiratory phases based on respiratory surrogates, and interphase displacement vector fields were estimated using a phase-specific low-rank optimization method. Hereafter, the tissue property maps, including T1 and T2 relaxation times, and proton density, were reconstructed using a pyramid motion-compensated method that alternatively optimized interphase displacement vector fields and subspace images. To evaluate the efficacy of RC-4DMRF, amplitude motion differences and Pearson correlation coefficients were determined to assess measurement agreement in tumor motion between RC-4DMRF and cine magnetic resonance imaging (MRI); mean absolute percentage errors of the RC-4DMRF-derived tissue maps were calculated to reveal tissue quantification accuracy using digital human phantom; and tumor-to-liver contrast-to-noise ratio of RC-4DMRF images was compared with that of planning CT and contrast-enhanced MRI (CE-MRI) images. A paired Student t test was used for statistical significance analysis with a P value threshold of .05. RC-4DMRF achieved excellent agreement in motion measurement with cine MRI, yielding the mean (± standard deviation) Pearson correlation coefficients of 0.95 ± 0.05 and 0.93 ± 0.09 and amplitude motion differences of 1.48 ± 1.06 mm and 0.81 ± 0.64 mm in the superior-inferior and anterior-posterior directions, respectively. Moreover, RC-4DMRF achieved high accuracy in tissue property quantification, with mean absolute percentage errors of 8.8%, 9.6%, and 5.0% for T1, T2, and proton density, respectively. Notably, the tumor contrast-to-noise ratio in RC-4DMRI-derived T1 maps (6.41 ± 3.37) was found to be the highest among all tissue property maps, approximately equal to that of CE-MRI (6.96 ± 1.01, P=.862), and substantially higher than that of planning CT (2.91 ± 1.97, P=.048). RC-4DMRF demonstrated high accuracy in respiratory motion measurement and tissue properties quantification, potentially facilitating tumor motion management in liver radiation therapy.

Volumetric and microstructural abnormalities of the amygdala in focal epilepsy with varied levels of SUDEP risk

Although the mechanisms of sudden unexpected death in epilepsy (SUDEP) are not yet well understood, generalised- or focal-to-bilateral tonic-clonic seizures (TCS) are a major risk factor. Previous studies highlighted alterations in structures linked to cardio-respiratory regulation; one structure, the amygdala, was enlarged in people at high risk of SUDEP and those who subsequently died. We investigated volume changes and the microstructure of the amygdala in people with epilepsy at varied risk for SUDEP since that structure can play a key role in triggering apnea and mediating blood pressure.The study included 53 healthy subjects and 143 patients with epilepsy, the latter separated into two groups according to whether TCS occur in years before scan. We used amygdala volumetry, derived from structural MRI, and tissue microstructure, derived from diffusion MRI, to identify differences between the groups. The diffusion metrics were obtained by fitting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. The analyses were performed at the whole amygdala level and at the scale of amygdaloid nuclei.Patients with epilepsy showed larger amygdala volumes and lower neurite density indices (NDI) than healthy subjects; the left amygdala volumes were especially enhanced. Microstructural changes, reflected by NDI differences, were more prominent on the left side and localized in the lateral, basal, central, accessory basal and paralaminar amygdala nuclei; basolateral NDI lowering appeared bilaterally. No significant microstructural differences appeared between epilepsy patients with and without current TCS.The central amygdala nuclei, with prominent interactions from surrounding nuclei of that structure, project to cardiovascular regions and respiratory phase switching areas of the parabrachial pons, as well as to the periaqueductal gray. Consequently, they have the potential to modify blood pressure and heart rate, and induce sustained apnea or apneusis. The findings here suggest that lowered NDI, indicative of reduced dendritic density, could reflect an impaired structural organization influencing descending inputs that modulate vital respiratory timing and drive sites and areas critical for blood pressure control.

Determining Obstruction in Endotracheal Tubes Using Physical Respiratory Signals

This study proposes a method for determining obstruction of the endotracheal tube (ET) and its degree and location. Respiratory signals were acquired using a three sensor (microphone, pressure, and flow) integrated sensor connector. Obstruction classification involved pre-processing and feature extraction. During pre-processing, one cycle of the respiratory signal was extracted using respiratory cycle extraction and phase segmentation. The signal was then divided into three phases: (i) inspiratory phase, (ii) expiratory phase, and (iii) between both the phases, where the intrapulmonary pressure increased, decreased, and remained constant, respectively. In the feature extraction process, the results were quantified using absolute value average and texture analyses. Artificial ET tubes were fabricated to simulate the presence of foreign substances in the ET tube; they had different degrees of obstruction (0%, 20%, 40%, and 50%) and obstruction positions (Sections 1, 2, and 3). The experiment was performed by connecting the sensor connector and artificial ET tube between the ventilator and test lung. Respiratory signals were obtained in 10 cases by cross connecting the artificial ET tubes. The degree and location of obstruction were classified according to the average absolute value and texture analyses of the flow data. The obstruction can be determined through the texture analysis results using the combined microphone and flow sensor data. The proposed method is simple in configuration, can be readily used in existing setups, and can be operated regardless of surrounding noise.

Deep learning-based fast volumetric imaging using kV and MV projection images for lung cancer radiotherapy: A feasibility study.

The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV/MV image pair. The proposed model is a combination of 2D and 3D networks. The proposed model consists of five major parts: (1) kV and MV feature extractors are used to extract deep features from the perpendicular kV and MV projections. (2) The feature-matching step is used to re-align the feature maps to their projection angle in a Cartesian coordinate system. By using a residual module, the feature map can focus more on the difference between the estimated and ground truth images. (3) In addition, the feature map is downsized to include more global semantic information for the 3D estimation, which is useful to reduce inhomogeneity. By using convolution-based reweighting, the model is able to further increase the uniformity of image. (4) To reduce the blurry noise of generated 3D volume, the Laplacian latent space loss calculated via the feature map that is extracted via specifically-learned Gaussian kernel is used to supervise the network. (5) Finally, the 3D volume is derived from the trained model. We conducted a proof-of-concept study using 50 patients with lung cancer. An orthogonal kV/MV pair was generated by ray tracing through CT of each phase in a 4D CT scan. Orthogonal kV/MV pairs from nine respiratory phases were used to train this patient-specific model while the kV/MV pair of the remaining phase was held for model testing. The results are based on simulation data and phantom results from a real Linac system. The mean absolute error (MAE) values achieved by our method were 57.5 HU and 77.4 HU within body and tumor region-of-interest (ROI), respectively. The mean achieved peak-signal-to-noise ratios (PSNR) were 27.6dB and 19.2dB within the body and tumor ROI, respectively. The achieved mean normalized cross correlation (NCC) values were 0.97 and 0.94 within the body and tumor ROI, respectively. A phantom study demonstrated that the proposed method can accurately re-position the phantom after shift. It is also shown that the proposed method using both kV and MV is superior to current method using kV or MV only in image quality. These results demonstrate the feasibility and accuracy of our proposed fast volumetric imaging method from an orthogonal kV/MV pair, which provides a potential solution for daily treatment setup and verification of patients receiving radiation therapy for lung cancer.

Characterization of motion dependent magnetic field inhomogeneity for DWI in the kidneys

PurposeDiffusion-weighted imaging (DWI) of the abdomen has increased dramatically for both research and clinical purposes. Motion and static field inhomogeneity related challenges limit image quality of abdominopelvic imaging with the most conventional echo-planar imaging (EPI) pulse sequence. While reversed phase encoded imaging is increasingly used to facilitate distortion correction, it typically assumes one motion independent magnetic field distribution. In this study, we describe a more generalized workflow for the case of kidney DWI in which the field inhomogeneity at multiple respiratory phases is mapped and used to correct all images in a multi-contrast DWI series. MethodsIn this HIPAA-compliant and IRB-approved prospective study, 8 volunteers (6 M, ages 28–51) had abdominal imaging performed in a 3 T MRI system (MAGNETOM Prisma; Siemens Healthcare, Erlangen, Germany) with ECG gating. Coronal oblique T2-weighted HASTE images were collected for anatomical reference. Sagittal phase-contrast (PC) MRI images through the left renal artery were collected to determine systolic and diastolic phases. Cardiac triggered oblique coronal DWI were collected at 10 b-values between 0 and 800 s/mm2 and 12 directions. DWI series were distortion corrected using field maps generated by forward and reversed phase encoded b = 0 images collected over the full respiratory cycle and matched by respiratory phase. Morphologic accuracy, intraseries spatial variability, and diffusion tensor imaging (DTI) metrics mean diffusivity (MD) and fractional anisotropy (FA) were compared for results generated with no distortion correction, correction with only one respiratory bin, and correction with multiple respiratory bins across the breathing cycle. ResultsComputed field maps showed significant variation in static field with kidney laterality, region, and respiratory phase. Distortion corrected images showed significantly better registration to morphologic images than uncorrected images; for the left kidney, the multiple bin correction outperformed one bin correction. Line profile analysis showed significantly reduced spatial variation with multiple bins than one bin correction. DTI metrics were mostly similar between correction methods, with some differences observed in MD between uncorrected and corrected datasets. ConclusionsOur results indicate improved morphology of kidney DWI and derived parametric maps as well as reduced variability over the full image series using the motion-resolved distortion correction. This work highlights some morphologic and quantitative metric improvements can be obtained for kidney DWI when distortion correction is performed in a respiratory-resolved manner.

Joint Cardiac T1 Mapping and Cardiac Cine Using Manifold Modeling

The main focus of this work is to introduce a single free-breathing and ungated imaging protocol to jointly estimate cardiac function and myocardial T1 maps. We reconstruct a time series of images corresponding to k-space data from a free-breathing and ungated inversion recovery gradient echo sequence using a manifold algorithm. We model each image in the time series as a non-linear function of three variables: cardiac and respiratory phases and inversion time. The non-linear function is realized using a convolutional neural networks (CNN) generator, while the CNN parameters, as well as the phase information, are estimated from the measured k-t space data. We use a dense conditional auto-encoder to estimate the cardiac and respiratory phases from the central multi-channel k-space samples acquired at each frame. The latent vectors of the auto-encoder are constrained to be bandlimited functions with appropriate frequency bands, which enables the disentanglement of the latent vectors into cardiac and respiratory phases, even when the data are acquired with intermittent inversion pulses. Once the phases are estimated, we pose the image recovery as the learning of the parameters of the CNN generator from the measured k-t space data. The learned CNN generator is used to generate synthetic data on demand by feeding it with appropriate latent vectors. The proposed approach capitalizes on the synergies between cine MRI and T1 mapping to reduce the scan time and improve patient comfort. The framework also enables the generation of synthetic breath-held cine movies with different inversion contrasts, which improves the visualization of the myocardium. In addition, the approach also enables the estimation of the T1 maps with specific phases, which is challenging with breath-held approaches.

Precise dosimetric comparison between GAMOS and the collapsed cone convolution algorithm of 4D DOSE accumulated in lung SBRT treatments

BackgroundIt is widely accepted that Monte Carlo dose calculations offers a higher precision that the commercially available dose calculation algorithms. This advantage may be especially relevant for lung Stereotactic Body Radiation Therapy (SBRT), as this is a precise technique applied to an area of big inhomogeneity. PurposeWe conducted a comparative study to reveal the differences between the doses calculated using the Collapsed Cone Convolution algorithm and the GAMOS/Geant4 Monte Carlo calculation for lung cancer patients treated with Stereotactic Body Radiation Therapy on an Elekta Versa HD linac. MethodsFor this study a set of ten patient treatments carried out at the Clínica Universidad de Navarra was selected. Theanalysis is based on the comparison of several dosimetric quantities for the Gross Tumor Volume (GTV) and several OrgansAt Risk (OARs), and also a gamma index calculation with distance-to-agreement set to 2 mm and dose difference to 3%, as recommended by ICRU to assess clinical impact. In order to guarantee a small uncertainty in the Monte Carlo calculation of the dosimetric quantities, we studied in detail the validity of different methods that may be used to determine this uncertainty.To compensate for lung movements, a 4D-Cone-beam Computed Tomography (CBCT) was acquired before treatment, whichallowed us to identify eight respiratory phases using a temporal binning. Using commercial MIM software®, we performed a deformable image registration between the eight CT respiration phases to construct the 4D doses. The same procedure was applied for the Treatment Planning System (TPS) dose files and for the Monte Carlo dose files. ResultsThe differences between the two algorithms reveal the known weaknesses of the Collapsed Cone Convolution (CCC) algorithm for the calculation of lateral doses and in regions of large density change. The comparison between the two algorithms for individual phase doses shows differences up to 5% of the GTV D95 or 3–4 Gy in some OARs, which may have a clinical impact. Nevertheless these differences are reduced for the 4D dose in most quantities under study. ConclusionsComparing the dose calculated with a Collapsed Cone Convolution algorithm with GAMOS/Geant4 for ten patients and eight respiratory phases, we found some differences that could have a clinical impact. When combining the eight temporal phases into a 4D dose using the MIM Deformable Image Registration software, the differences diminished substantially.Our statistical analysis concludes that dose uncertainty in the voxels with a maximum dose below a given percentage guarantees uncertainty in the dosimetric quantities below that figure.

In vivo measurements of lung function using respiratory-gated micro-computed tomography in a smoke-exposure model of chronic obstructive pulmonary disease.

We hypothesize that in vivo respiratory-gated micro computed tomography (micro-CT) imaging can noninvasively provide structural and functional information about the lungs in a cigarette-exposure model of chronic obstructive pulmonary disease in mice. Female C57BL/6 mice were exposed to cigarette smoke or ambient air for 1, 3, or 6 months. Each mouse received a respiratory-gated micro-CT scan at baseline and another scan following the exposure period, while anaesthetized and free-breathing. Images were obtained representing end-expiration and peak inspiration, and measurements were performed to characterize the lung structure and compute functional metrics. Following the final micro-CT session, the mice were euthanized and the lungs prepared for histology. Following 6 months of smoke-exposure, the mice exhibited larger increases in end-expiration lung volume and functional residual capacity, and a reduction in weight gain when compared with air-exposed mice. The histogram of CT numbers in the lung obtained during end-expiration also showed a shift to lower CT numbers following 6 months of smoke-exposure, indicating increased air content within the lungs. The metrics suggested air-trapping in the lung, which is consistent with emphysema. In the 3-month exposure group, only the reduction in weight gain was significant compared with the air-exposed group. Histological analysis confirmed that the 6-month smoke-exposed mice likely developed centrilobular emphysema as measured by the mean linear intercept. Respiratory-gated micro-CT imaging of free-breathing mice at multiple respiratory phases is noninvasive and provides additional information about lung structure and function that complements postmortem techniques and could be used to monitor changes over time.

Motion compensated self supervised deep learning for highly accelerated 3D ultrashort Echo time pulmonary MRI.

To investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions. A self-supervised eXtra dimension MBDL architecture (XD-MBDL) was developed that combined respiratory states to reconstruct a single high-quality 3D image. Non-rigid motion fields were incorporated into this architecture by estimating motion fields from a lower resolution motion resolved (XD-GRASP) reconstruction. Motion compensated XD-MBDL was evaluated on lung UTE datasets with and without contrast and compared to constrained reconstructions and variants of self-supervised MBDL that do not account for dynamic respiratory states or leverage motion correction. Images reconstructed using XD-MBDL demonstrate improved image quality as measured by apparent SNR (aSNR), contrast to noise ratio (CNR), and visual assessment relative to self-supervised MBDL approaches that do not account for dynamic respiratory states, XD-GRASP and a recently proposed motion compensated iterative reconstruction strategy (iMoCo). Additionally, XD-MBDL reduced reconstruction time relative to both XD-GRASP and iMoCo. A method was developed to allow self-supervised MBDL to combine multiple respiratory states to reconstruct a single image. This method was combined with graphics processing unit (GPU)-based image registration to further improve reconstruction quality. This approach showed promising results reconstructing a user-selected respiratory phase from free breathing 3D pulmonary UTE acquisitions.

An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals

<span lang="EN-US">This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.</span>

Effects of respiratory phases on the processing of emotional and non-emotional visual stimuli.

The number of studies investigating the relationship between respiratory phases and cognitive/neural processing of external events has been increasing, but the findings remain controversial. This registered report examined the effect of the respiratory phase on the discrimination accuracy of visual stimuli in the emotional and non-emotional domains. Forty-two healthy young participants were asked to choose fearful over neutral facial expressions and to choose high-contrast over low-contrast Gabor patches during spontaneous nasal respiration. Event-related potentials (ERPs) were also recorded for each type of stimulus presented during each respiratory phase. It was hypothesized that discrimination accuracy would be higher when the stimuli were presented during inhalation than during exhalation. It was also hypothesized that the amplitudes of ERPs elicited by the stimuli would be greater during inhalation than during exhalation. For comparison, the effect of the cardiac phase was examined, with the expectation that discrimination accuracy would be higher when the stimuli were presented during systole than during diastole. It was also hypothesized that the amplitudes of ERPs elicited by the stimuli would be greater during systole than during diastole. As expected, the results indicated that fear discrimination accuracy was higher during inhalation than exhalation and during systole than diastole. However, this was not the case for contrast discrimination. No differences in ERPs were observed between respiratory phases in either task. These results suggest that natural breathing in through the nose facilitates the discrimination of emotional stimuli, possibly via subcortical processes.

Deep learning for improving ZTE MRI images in free breathing

IntroductionDespite a growing interest in lung MRI, its broader use in a clinical setting remains challenging. Several factors limit the image quality of lung MRI, such as the extremely short T2 and T2* relaxation times of the lung parenchyma and cardiac and breathing motion. Zero Echo Time (ZTE) sequences are sensitive to short T2 and T2* species paving the way to improved “CT-like” MR images. To overcome this limitation, a retrospective respiratory gated version of ZTE (ZTE4D) which can obtain images in 16 different respiratory phases during free breathing was developed. Initial performance of ZTE4D have shown motion artifacts. To improve image quality, deep learning with fully convolutional neural networks (FCNNs) has been proposed. CNNs has been widely used for MR imaging, but it has not been used for improving free-breathing lung imaging yet. Our proposed pipeline facilitates the clinical work with patients showing difficulties/uncapable to perform breath-holding, or when the different gating techniques are not efficient due to the irregular respiratory pace. Materials and methodsAfter signed informed consent and IRB approval, ZTE4D free breathing and breath-hold ZTE3D images were obtained from 10 healthy volunteers on a 1.5 T MRI scanner (GE Healthcare Signa Artist, Waukesha, WI). ZTE4D acquisition captured all 16 phases of the respiratory cycle. For the ZTE breath-hold, the subjects were instructed to hold their breath in 5 different inflation levels ranging from full expiration to full inspiration. The training dataset consisting of ZTE-BH images of 10 volunteers was split into 8 volunteers for training, 1 for validation and 1 for testing. In total 800 ZTE breath-hold images were constructed by adding Gaussian noise and performing image transformations (translations, rotations) to imitate the effect of motion in the respiratory cycle, and blurring from varying diaphragm positions, as it appears for ZTE4D. These sets were used to train a FCNN model to remove the artificially added noise and transformations from the ZTE breath-hold images and reproduce the original quality of the images. Mean squared error (MSE) was used as loss function. The remaining 2 healthy volunteer's ZTE4D images were used to test the model and qualitatively assess the predicted images. ResultsOur model obtained a MSE of 0.09% on the training set and 0.135% on the validation set. When tested on unseen data the predicted images from our model improved the contrast of the pulmonary parenchyma against air filled regions (airways or air trapping). The SNR of the lung parenchyma was quantitatively improved by a factor of 1.98 and the CNR lung- blood, which is indicating the visibility of the intrapulmonary vessels, was improved by 4.2%. Our network was able to reduce ghosting artifacts, such as diaphragm movement and blurring, and enhancing image quality. DiscussionFree-breathing 3D and 4D lung imaging with MRI is feasible, however its quality is not yet acceptable for clinical use. This can be improved with deep learning techniques. Our FCNN improves the visual image quality and reduces artifacts of free-breathing ZTE4D. Our main goal was rather to remove ghosting artifacts from the ZTE4D images, to improve diagnostic quality of the images. As main results of the network, diaphragm contour increased with sharper edges by visual inspection and less blurring of the anatomical structures and lung parenchyma. ConclusionWith FCNNs, image quality of free breathing ZTE4D lung MRI can be improved and enable better visualization of the lung parenchyma in different respiratory phases.

High-resolution, respiratory-resolved coronary MRA using a Phyllotaxis-reordered variable-density 3D cones trajectory

PurposeTo develop a respiratory-resolved motion-compensation method for free-breathing, high-resolution coronary magnetic resonance angiography (CMRA) using a 3D cones trajectory. MethodsTo achieve respiratory-resolved 0.98 mm resolution images in a clinically relevant scan time, we undersample the imaging data with a variable-density 3D cones trajectory. For retrospective motion compensation, translational estimates from 3D image-based navigators (3D iNAVs) are used to bin the imaging data into four phases from end-expiration to end-inspiration. To ensure pseudo-random undersampling within each respiratory phase, we devise a phyllotaxis readout ordering scheme mindful of eddy current artifacts in steady state free precession imaging. Following binning, residual 3D translational motion within each phase is computed using the 3D iNAVs and corrected for in the imaging data. The noise-like aliasing characteristic of the combined phyllotaxis and cones sampling pattern is leveraged in a compressed sensing reconstruction with spatial and temporal regularization to reduce aliasing in each of the respiratory phases. ResultsIn initial studies of six subjects, respiratory motion compensation using the proposed method yields improved image quality compared to non-respiratory-resolved approaches with no motion correction and with 3D translational correction. Qualitative assessment by two cardiologists and quantitative evaluation with the image edge profile acutance metric indicate the superior sharpness of coronary segments reconstructed with the proposed method (P < 0.01). ConclusionWe have demonstrated a new method for free-breathing, high-resolution CMRA based on a variable-density 3D cones trajectory with modified phyllotaxis ordering and respiratory-resolved motion compensation with 3D iNAVs.

Do Ultrasound Based Quantitative Hepatic Fat Content Measurements Have Differences Between Respiratory Phases?

The recently developed ultrasound based tools using attenuation coefficient (AC) and scatter distribution coefficient (SDC) values can be used to quantify hepatic fat content in patients with non-alcoholic fatty liver disease (NAFLD). However, currently the impact of respiratory phase on these measurements is not known. The purpose of this study is to compare AC and SDC measurements acquired at peak inspiration and end expiration phases. AC and SDC measurements were obtained in 50 patients with NAFLD. Tissue Attenuation Imaging (TAI) and Tissue Scatter Distribution Imaging (TSI) tools were utilized to measure AC and SDC values, respectively. Five measurements were performed at respiratory phases using TAI and TSI tools and the median values were noted. Subgroup analyses were performed and Wilcoxon signed rank test was used for comparison of the measurements. The median values of the AC measurements at peak inspiration and end expiration phases were 0.87 dB/cm/MHz and 0.89 dB/cm/MHz, respectively. The median values of the SDC measurements at peak inspiration and end expiration phases were 97.91 and 96.62, respectively. There were no statistically significant differences in AC and SDC measurements between the respiratory phases except for AC measurements in BMI <30 kg/m2 subgroup. Our results revealed that respiratory phases have no impact on SDC measurements. However, while the AC measurements in BMI ≥30 kg/m2 subgroup showed no significant difference, there was a significant difference in AC measurements in BMI <30 kg/m2 subgroup between the respiratory phases.

Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning.

To introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoes (DENSE) and to use the model to develop a deep convolutional neural network to aid in first-order respiratory motion compensation for self-navigated free-breathing cine DENSE of the heart. The motion model includes conventional position shifts of magnetization and further describes the phase shift of the stimulated echo due to breathing. These image-domain effects correspond to linear and constant phase errors, respectively, in k-space. The model was validated using phantom experiments and Bloch-equation simulations and was used along with the simulation of respiratory motion to generate synthetic images with phase-shift artifacts to train a U-Net, DENSE-RESP-NET, to perform motion correction. DENSE-RESP-NET-corrected self-navigated free-breathing DENSE was evaluated in human subjects through comparisons with signal averaging, uncorrected self-navigated free-breathing DENSE, and breath-hold DENSE. Phantom experiments and Bloch-equation simulations showed that breathing-induced constant phase errors in segmented DENSE leads to signal loss in magnitude images and phase corruption in phase images of the stimulated echo, and that these artifacts can be corrected using the known respiratory motion and the model. For self-navigated free-breathing DENSE where the respiratory motion is not known, DENSE-RESP-NET corrected the signal loss and phase-corruption artifacts and provided reliable strain measurements for systolic and diastolic parameters. DENSE-RESP-NET is an effective method to correct for breathing-associated constant phase errors. DENSE-RESP-NET used in concert with self-navigation methods provides reliable free-breathing DENSE myocardial strain measurement.

Percutaneous Transluminal Caval-Flow Regulation PTCR®: A New Alternative Therapy to Reshape the Future Treatment of Heart Failure

Percutaneous Transluminal Caval-flow Regulation (PTCR) is an emerging alternative therapy to treat patients with acute heart failure (AHF). AHF represents the first cause of hospitalization in elderly persons and is the main determinant of the huge healthcare expenditure related to heart failure (HF). Despite therapeutic advances, the prognosis of AHF is poor, with in-hospital mortality ranging from 4% to 7%, 60 to 90-day mortality. To reverse this situation, a balloon catheter medical device has been designed to produce cyclic occlusions of the Inferior Vena Cava (IVC) supported by the phases of respiration, thus having subtotal occlusion during expiration and total occlusion during inspiration, producing an intermittent regulation of venous return or preload from the IVC to the right atrium. This PTCR procedure is minimally invasive. It is performed through the insertion of the balloon catheter via the femoral vein. This catheter is advanced to the IVC guided by echocardiogram or fluoroscopy to be placed prior to drainage of the hepatic vein. At this point, the balloon is inflated up to 70% to 80% of the diameter of the IVC in expiration, which has to be previously evaluated by echocardiography. Then inspiratory collapse (20 to 30% average) of the IVC diameter produces total occlusion during inspiration and a partial or subtotal occlusion during expiration, thereby regulating caval flow in an intermittent manner. This innovative procedure is aimed at regulating the hypervolemia present in the IVC, normalizing venous return, preload, intracardiac pressures, biventricular diastolic and systolic diameters, diastolic and systolic volume, thus, obtaining reduction of total cardiac burden (TCB) and producing a reversal of ventricular remodeling. In this manner the heart returns close to its original design, with improvement in ejection fraction (EF) and cardiac output.

Utilization of Diaphragm Motion to Predict the Displacement of Liver Tumors for Patients Treated with Carbon ion Radiotherapy.

Objectives: To establish and validate a linear model utilizing diaphragm motion (DM) to predict the displacement of liver tumors (DLTs) for patients who underwent carbon ion radiotherapy (CIRT). A total of 60 pairs of planning and reviewing four-dimensional computed tomography (4DCT) sets over 23 patients were used. Method: We constructed an averaged computed tomography (CT) set for each either planning or reviewing 4DCT within respiratory phases between 20% of exhale and inhale. A rigid image registration to align bony structures was performed between planning and reviewing 4DCT. The position changes on top of diaphragm in superior-inferior (SI) direction between 2 CTs to present DM were obtained. The translational vectors in SI from matching to present DLT were obtained. The linear model was built by training data for 23 imaging pairs. A distance model utilized the cumulative probability distribution (CPD) of DM or DLT and was compared with the linear model. We conducted the statistical regression analysis with receiver operating characteristic (ROC) testing data of 37 imaging pairs to validate the performance of our linear model. Results: The DM within 0.5 mm was true positive (TP) with an area under the ROC curve (AUC) of 0.983 to predict DLT. The error of predicted DLT within half of its mean value indicated the reliability of prediction method. The 23 pairs of data showed (4.5 ± 3.3) mm for trend of DM and (2.2 ± 1.6) mm for DLT. A linear model of DLT = 0.46*DM + 0.12 was established. The predicted DLT was (2.2 ± 1.5) mm with a prediction error of (0.3 ± 0.3) mm. The accumulated probability of observed and predicted DLT with < 5.0 mm magnitude was 93.2% and 94.5%, respectively. Conclusion: We utilized the linear model to set the proper beam gating for predicting DLT within 5.0 mm to treat patients. We will investigate a proper process on x-ray fluoroscopy images to establish a reliable model predicting DLT for DM observed in x-ray fluoroscopy in the following two years.

Motion Compensated Unsupervised Deep Learning for 5D MRI.

We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated free-breathing 5D MRI simplifies the scan planning, improves patient comfort, and offers several clinical benefits over breath-held 2D exams, including isotropic spatial resolution and the ability to reslice the data to arbitrary views. However, the current reconstruction algorithms for 5D MRI take very long computational time, and their outcome is greatly dependent on the uniformity of the binning of the acquired data into different physiological phases. The proposed algorithm is a more data-efficient alternative to current motion-resolved reconstructions. This motion-compensated approach models the data in each cardiac/respiratory bin as Fourier samples of the deformed version of a 3D image template. The deformation maps are modeled by a convolutional neural network driven by the physiological phase information. The deformation maps and the template are then jointly estimated from the measured data. The cardiac and respiratory phases are estimated from 1D navigators using an auto-encoder. The proposed algorithm is validated on 5D bSSFP datasets acquired from two subjects.

Radial artery pulse wave velocity: a new characterization technique and the instabilities associated with the respiratory phase and breath-holding

Objective. Pulse wave velocity (PWV) is a key diagnostic parameter of the cardiovascular system’s state. However, approaches aimed at PWV characterization often suffer from inevitable drawbacks. Statistical results demonstrating how closely PWV in the radial artery (RA) and the respiration phase correlate, as well as RA PWV evolution during breath-holding (BH), have not yet been presented in the literature. The aims of this study are (a) to propose a simple robust technique for measuring RA PWV, (b) to reveal the phase relation between the RA PWV and spontaneous breathing, and (c) to disclose the influence of BH on the RA PWV. Approach. The high-resolution remote breathing monitoring method Sorption-Enhanced Infrared Thermography (SEIRT) and the new technique aimed at measuring RA PWV described in this paper were used synchronously, and their measurement data were processed simultaneously. Main results. Spontaneous breathing leaves a synchronous ‘trace’ on the RA PWV. The close linear correlation of the respiration phase and the phase of concomitant RA PWV changes is statistically confirmed in five tested people (Pearson’s r is of the order of 0.5–0.8, P < 0.05). The BH appreciably affects the RA PWV. A phenomenon showing that the RA PWV is not indifferent to hypoxia is observed for the first time. Significance. The proposed technique for RA PWV characterization has high prospects in biomedical diagnostics. The presented pilot study deserves attention in the context of the mutual interplay between respiratory and cardiovascular systems. It may also be useful in cases where peripheral pulse wave propagation helps assess respiratory function.

Assessing the Complicated Venous Hemodynamics and Therapeutic Outcomes of Budd-Chiari Syndrome with Respiratory-gated 4D Flow MR Imaging During the Expiratory and Inspiratory Phases.

A man in his 50s with Budd-Chiari syndrome diagnosed with the suprahepatic inferior vena cava (IVC) obstruction on CT was assessed using 4D Flow MRI before and after balloon angioplasty. 4D Flow MRI acquired in two respiratory phases, depicted complex hemodynamic and respiratory variability, and a jet stream at the narrowed channel of the membranous IVC. Post-interventional 4D Flow MRI showed that the IVC blood flow increased with corrected flow directions in the infrarenal IVC.