Validation and quantification of left ventricular function during exercise and free breathing from real-time cardiac magnetic resonance images.

Exercise cardiovascular magnetic resonance (CMR) can unmask cardiac pathology not evident at rest. Real-time CMR in free breathing can be used, but respiratory motion may compromise quantification of left ventricular (LV) function. We aimed to develop and validate a post-processing algorithm that semi-automatically sorts real-time CMR images according to breathing to facilitate quantification of LV function in free breathing exercise. A semi-automatic algorithm utilizing manifold learning (Laplacian Eigenmaps) was developed for respiratory sorting. Feasibility was tested in eight healthy volunteers and eight patients who underwent ECG-gated and real-time CMR at rest. Additionally, volunteers performed exercise CMR at 60% of maximum heart rate. The algorithm was validated for exercise by comparing LV mass during exercise to rest. Respiratory sorting to end expiration and end inspiration (processing time 20 to 40 min) succeeded in all research participants. Bias ± SD for LV mass was 0 ± 5 g when comparing real-time CMR at rest, and 0 ± 7 g when comparing real-time CMR during exercise to ECG-gated at rest. This study presents a semi-automatic algorithm to retrospectively perform respiratory sorting in free breathing real-time CMR. This can facilitate implementation of exercise CMR with non-ECG-gated free breathing real-time imaging, without any additional physiological input.

Classification of Electrical Impedance Tomography Data Using Machine Learning.

Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a non- invasive imaging method that allows to analyze and quantify the distribution of ventilation in the lungs. In this article, we present a new approach to promote the use of EIT data and the implementation of new clinical applications for differential diagnosis, with the development of several machine learning models to discriminate between EIT data from healthy and nonhealthy subjects. EIT data from 16 subjects were acquired: 5 healthy and 11 non-healthy subjects (with multiple pulmonary conditions). Preliminary results have shown accuracy percentages of 66% in challenging evaluation scenarios. The results suggest that the pairing of EIT feature engineering methods with machine learning methods could be further explored and applied in the diagnostic and monitoring of patients suffering from lung diseases. Also, we introduce the use of a new feature in the context of EIT data analysis (Impedance Curve Correlation).

Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI.

BACKGROUND AND OBJECTIVE: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was to overcome these limitations through advanced MR simulations on a realistic computer model of human anatomy without using a real MRI scanner, without scanning patients and without having personnel and the associated expenses. METHODS: The 4D-XCAT model was used with the coreMRI simulation platform for generating artificial short-axis MR-images for training a neural-network to automatic delineate the LV endocardium and epicardium. Its performance was assessed on real MR-images acquired from eight healthy volunteers. The neural-network was also trained on real MR-images from a publicly available dataset and its performance was assessed on the same volunteers' data. RESULTS: The proposed solution demonstrated a performance of 94% (endocardium) and 90% DICE (epicardium) in real mid-ventricular slices, whereas a 10% addition of real MR-images in the artificial training dataset increased the performance to 97% DICE. The use of artificial MR-images that cover the entire LV yielded 85% (endocardium) and 88% DICE (epicardium) when combined with real MR data with an 80%-20% mix respectively. CONCLUSIONS: This study suggests a low-cost solution for constructing artificial training datasets for supervised learning techniques in the field of MR by using advanced MR simulations without the use of a real MRI scanner, without scanning patients and without having to use specialized personnel, such as technologists and radiologists.

Free-breathing fetal cardiac MRI with doppler ultrasound gating, compressed sensing, and motion compensation.

BACKGROUND: Fetal cardiovascular MRI complements ultrasound to assess fetal cardiovascular pathophysiology. PURPOSE: To develop a free-breathing method for retrospective fetal cine MRI using Doppler ultrasound (DUS) cardiac gating and tiny golden angle radial sampling (tyGRASP) for accelerated acquisition capable of detecting fetal movements for motion compensation. STUDY TYPE: Feasibility study. SUBJECTS: Nine volunteers (gestational week 34-40). Short-axis and four-chamber views were acquired during maternal free-breathing and breath-hold. FIELD STRENGTH/SEQUENCE: 1.5T cine balanced steady-state free precession. ASSESSMENT: A self-gated reconstruction method was improved for clinical application by using 1) retrospective DUS gating, and 2) motion detection and rejection/correction algorithms for compensating for fetal motion. The free-breathing reconstructions were qualitatively and quantitatively assessed, and DUS-gating was compared with self-gating in breath-hold reconstructions. A scoring of 1-4 for overall image quality, cardiac, and extracardiac diagnostic quality was used. STATISTICAL TESTS: Friedman's test was used to assess differences in qualitative scoring between observers. A Wilcoxon matched-pairs signed rank test was used to assess differences between breath-hold and free-breathing acquisitions and between observers' quantitative measurements. RESULTS: In all cases, 111 free-breathing and 145 breath-hold acquisitions, the automatically calculated DUS-based cardiac gating signal provided reconstructions of diagnostic quality (median score 4, range 1-4). Free-breathing did not affect the DUS-based cardiac gated retrospective radial reconstruction with respect to image or diagnostic quality (all P > 0.06). Motion detection with rejection/correction in k-space produced high-quality free-breathing DUS-based reconstructions [median 3, range (2-4)], whereas free-breathing self-gated methods failed in 80 out of 88 cases to produce a stable gating signal. DATA CONCLUSION: Free-breathing fetal cine cardiac MRI based on DUS gating and tyGRASP with motion compensation yields diagnostic images. This simplifies acquisition for the pregnant woman and thus could help increase fetal cardiac MRI acceptance in the clinic. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:260-272.

Fetal iGRASP cine CMR assisting in prenatal diagnosis of complicated cardiac malformation with impact on delivery planning.

Limited visualization of the fetal heart and vessels by fetal ultrasound due to suboptimal fetal position, patient habitus and skeletal calcification may lead to missed diagnosis, overdiagnosis and parental uncertainty. Counselling and delivery planning may in those cases also be tentative. The recent fetal cardiac magnetic resonance (CMR) reconstruction method utilizing tiny golden-angle iGRASP (iterative Golden-angle RAdial Sparse Parallel MRI) allows for cine imaging of the fetal heart for use in clinical practice. This case describes an unbalanced common atrioventricular canal where limited ultrasound image quality and visibility of the aortic arch precluded confirming or ruling out presence of a ventricular septal defect. Need of prostaglandins or neonatal intervention was thus uncertain. Cardiovascular magnetic resonance imaging confirmed ultrasound findings and added value by ruling out a significant ventricular septal defect and diagnosing arch hypoplasia. This confirmed the need of patient relocation for delivery at a paediatric cardiothoracic surgery centre and prostaglandins could be initiated before the standard postnatal ultrasound. The applied CMR method can thus improve diagnosis of complicated fetal cardiac malformation and has direct clinical impact.

Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.

OBJECTIVE: Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. METHODS: A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. RESULTS: The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p < 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was - 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004). CONCLUSION: Automated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements.

Self-gated fetal cardiac MRI with tiny golden angle iGRASP: A feasibility study.

PURPOSE: To develop and assess a technique for self-gated fetal cardiac cine magnetic resonance imaging (MRI) using tiny golden angle radial sampling combined with iGRASP (iterative Golden-angle RAdial Sparse Parallel) for accelerated acquisition based on parallel imaging and compressed sensing. MATERIALS AND METHODS: Fetal cardiac data were acquired from five volunteers in gestational week 29-37 at 1.5T using tiny golden angles for eddy currents reduction. The acquired multicoil radial projections were input to a principal component analysis-based compression stage. The cardiac self-gating (CSG) signal for cardiac gating was extracted from the acquired radial projections and the iGRASP reconstruction procedure was applied. In all acquisitions, a total of 4000 radial spokes were acquired within a breath-hold of less than 15 seconds using a balanced steady-state free precession pulse sequence. The images were qualitatively compared by two independent observers (on a scale of 1-4) to a single midventricular cine image from metric optimized gating (MOG) and real-time acquisitions. RESULTS: For iGRASP and MOG images, good overall image quality (2.8 ± 0.4 and 2.6 ± 1.3, respectively, for observer 1; 3.6 ± 0.5 and 3.4 ± 0.9, respectively, for observer 2) and cardiac diagnostic quality (3.8 ± 0.4 and 3.4 ± 0.9, respectively, for observer 1; 3.6 ± 0.5 and 3.6 ± 0.9, respectively, for observer 2) were obtained, with visualized myocardial thickening over the cardiac cycle and well-defined myocardial borders to ventricular lumen and liver/lung tissue. For iGRASP, MOG, and real time, left ventricular lumen diameter (14.1 ± 2.2 mm, 14.2 ± 1.9 mm, 14.7 ± 1.1 mm, respectively) and wall thickness (2.7 ± 0.3 mm, 2.6 ± 0.3 mm, 3.0 ± 0.4, respectively) showed agreement and no statistically significant difference was found (all P > 0.05). Images with iGRASP tended to have higher overall image quality scores compared with MOG and particularly real-time images, albeit not statistically significant in this feasibility study (P > 0.99 and P = 0.12, respectively). CONCLUSION: Fetal cardiac cine MRI can be performed with iGRASP using tiny golden angles and CSG. Comparison with other fetal cardiac cine MRI methods showed that the proposed method produces high-quality fetal cardiac reconstructions. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:207-217.

Cloud GPU-based simulations for SQUAREMR.

Quantitative Magnetic Resonance Imaging (MRI) is a research tool, used more and more in clinical practice, as it provides objective information with respect to the tissues being imaged. Pixel-wise T1 quantification (T1 mapping) of the myocardium is one such application with diagnostic significance. A number of mapping sequences have been developed for myocardial T1 mapping with a wide range in terms of measurement accuracy and precision. Furthermore, measurement results obtained with these pulse sequences are affected by errors introduced by the particular acquisition parameters used. SQUAREMR is a new method which has the potential of improving the accuracy of these mapping sequences through the use of massively parallel simulations on Graphical Processing Units (GPUs) by taking into account different acquisition parameter sets. This method has been shown to be effective in myocardial T1 mapping; however, execution times may exceed 30min which is prohibitively long for clinical applications. The purpose of this study was to accelerate the construction of SQUAREMR's multi-parametric database to more clinically acceptable levels. The aim of this study was to develop a cloud-based cluster in order to distribute the computational load to several GPU-enabled nodes and accelerate SQUAREMR. This would accommodate high demands for computational resources without the need for major upfront equipment investment. Moreover, the parameter space explored by the simulations was optimized in order to reduce the computational load without compromising the T1 estimates compared to a non-optimized parameter space approach. A cloud-based cluster with 16 nodes resulted in a speedup of up to 13.5 times compared to a single-node execution. Finally, the optimized parameter set approach allowed for an execution time of 28s using the 16-node cluster, without compromising the T1 estimates by more than 10ms. The developed cloud-based cluster and optimization of the parameter set reduced the execution time of the simulations involved in constructing the SQUAREMR multi-parametric database thus bringing SQUAREMR's applicability within time frames that would be likely acceptable in the clinic.