Vigorous-intensity exercise as a modulator of cardiac adipose tissue in women with obesity: a cross-sectional and randomized pilot study.

Cardiac adipose tissue (CAT) has become an important target for the reduction of disease risk. Supervised exercise programs have shown potential to "significantly" reduce CAT; however, the impact of different exercise modalities is not clear, and the relationships between CAT, physical activity (PA) levels and fitness (PFit) remain unknown. Therefore, the purpose of this study was to analyze the relationships between CAT, PA and PFit, and to explore the effects of different exercise modalities in a group of women with obesity. A total of 26 women (age: 23.41 ± 5.78 years-old) were enrolled in the cross-sectional study. PA, cardiorespiratory fitness, muscular strength, body composition and CAT were evaluated. The pilot intervention included 16 women randomized to a control (CON, n=5), high intensity interval training (HIIT, n = 5) and high-intensity circuit training (HICT, n=6) groups. Statistical analysis showed negative correlations between CAT and vigorous PA (VPA) (r s=-0.41, p=0.037); and between percent body fat (%BF), fat mass (FM), and all PA levels (r s=-0.41- -0.68, p<0.05); while muscle mass was positively associated with moderate-to-vigorous PA, and upper-body lean mass with all PA levels (r s =0.40-0.53, p<0.05). The HICT intervention showed significant improvements (p<0.05) in %BF, FM, fat free mass, and whole-body and lower extremities lean mass and strength after three weeks; however, only leg strength and upper extremities' FM improved significantly compared to CON and HICT. In conclusion, although all types of PA showed a positive influence on body fat content, only VPA significantly impacted on CAT volume. Moreover, three weeks of HICT induced positive changes in PFit in women with obesity. Further research is needed to explore VPA levels and high-intensity exercise interventions for short- and long-term CAT management.

Comparison of two-dimensional and three-dimensional U-Net architectures for segmentation of adipose tissue in cardiac magnetic resonance images.

The process of identifying cardiac adipose tissue (CAT) from volumetric magnetic resonance imaging of the heart is tedious, time-consuming, and often dependent on observer interpretation. Many 2-dimensional (2D) convolutional neural networks (CNNs) have been implemented to automate the cardiac segmentation process, but none have attempted to identify CAT. Furthermore, the results from automatic segmentation of other cardiac structures leave room for improvement. This study investigated the viability of a 3-dimensional (3D) CNN in comparison to a similar 2D CNN. Both models used a U-Net architecture to simultaneously classify CAT, left myocardium, left ventricle, and right myocardium. The multi-phase model trained with multiple observers' segmentations reached a whole-volume Dice similarity coefficient (DSC) of 0.925 across all classes and 0.640 for CAT specifically; the corresponding 2D model's DSC across all classes was 0.902 and 0.590 for CAT specifically. This 3D model also achieved a higher level of CAT-specific DSC agreement with a group of observers with a Williams Index score of 0.973 in comparison to the 2D model's score of 0.822.

Spectral analysis of ultrasound radiofrequency backscatter for the identification of epicardial adipose tissue.

Purpose: The coronary arteries are embedded in a layer of fat known as epicardial adipose tissue (EAT). The EAT influences the development of coronary artery disease (CAD), and increased EAT volume can be indicative of the presence and type of CAD. Identification of EAT using echocardiography is challenging and only sometimes feasible on the free wall of the right ventricle. We investigated the use of spectral analysis of the ultrasound radiofrequency (RF) backscatter for its potential to provide a more complete characterization of the EAT. Approach: Autoregressive (AR) models facilitated analysis of the short-time signals and allowed tuning of the optimal order of the spectral estimation process. The spectra were normalized using a reference phantom and spectral features were computed from both normalized and non-normalized data. The features were used to train random forests for classification of EAT, myocardium, and blood. Results: Using an AR order of 15 with the normalized data, a Monte Carlo cross validation yielded accuracies of 87.9% for EAT, 84.8% for myocardium, and 93.3% for blood in a database of 805 regions-of-interest. Youden's index, the sum of sensitivity, and specificity minus 1 were 0.799, 0.755, and 0.933, respectively. Conclusions: We demonstrated that spectral analysis of the raw RF signals may facilitate identification of the EAT when it may not otherwise be visible in traditional B-mode images.

Tissue classification in intercostal and paravertebral ultrasound using spectral analysis of radiofrequency backscatter.

Paravertebral and intercostal nerve blocks have experienced a resurgence in popularity. Ultrasound has become the gold standard for visualization of the needle during injection of the analgesic, but the intercostal artery and vein can be difficult to visualize. We investigated the use of spectral analysis of raw radiofrequency (RF) ultrasound signals for identification of the intercostal vessels and six other tissue types in the intercostal and paravertebral spaces. Features derived from the one-dimensional spectrum, two-dimensional spectrum, and cepstrum were used to train four different machine learning algorithms. In addition, the use of the average normalized spectrum as the feature set was compared with the derived feature set. Compared to a support vector machine (SVM) (74.2%), an artificial neural network (ANN) (68.2%), and multinomial analysis (64.1%), a random forest (84.9%) resulted in the most accurate classification. The accuracy using a random forest trained with the first 15 principal components of the average normalized spectrum was 87.0%. These results demonstrate that using a machine learning algorithm with spectral analysis of raw RF ultrasound signals has the potential to provide tissue characterization in intercostal and paravertebral ultrasound.

Development and evaluation of a method for segmentation of cardiac, subcutaneous, and visceral adipose tissue from Dixon magnetic resonance images.

Magnetic resonance imaging (MRI) has evolved into the gold standard for quantifying excess adiposity, but reliable, efficient use in longitudinal studies requires analysis of large numbers of images. The objective of this study is to develop and evaluate a segmentation method designed to identify cardiac, subcutaneous, and visceral adipose tissue (VAT) in Dixon MRI scans. The proposed method is evaluated using 10 scans from volunteer females 18- to 35-years old, with body mass indexes between 30 and 39.99 kg / m 2 . Cross-sectional area (CSA) for cardiac adipose tissue (CAT), subcutaneous adipose tissue (SAT), and VAT, is compared to manually-traced results from three observers. Comparisons of CSA are made in 191 images for CAT, 394 images for SAT, and 50 images for VAT. The segmentation correlated well with respect to average observer CSA with Pearson correlation coefficient ( R 2 ) values of 0.80 for CAT, 0.99 for SAT, and 0.99 for VAT. The proposed method provides accurate segmentation of CAT, SAT, and VAT and provides an option to support longitudinal studies of obesity intervention.

Spectral Analysis of Ultrasound Radiofrequency Backscatter for the Detection of Intercostal Blood Vessels.

Spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels during ultrasound-guided placement of paravertebral nerve blocks and intercostal nerve blocks. Autoregressive models were used for spectral estimation, and bandwidth, autoregressive order and region-of-interest size were evaluated. Eight spectral parameters were calculated and used to create random forests. An autoregressive order of 10, bandwidth of 6 dB and region-of-interest size of 1.0 mm resulted in the minimum out-of-bag error. An additional random forest, using these chosen values, was created from 70% of the data and evaluated independently from the remaining 30% of data. The random forest achieved a predictive accuracy of 92% and Youden's index of 0.85. These results suggest that spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels. (jokling@siue.edu) © 2018 World Federation for Ultrasound in Medicine and Biology.

Real-time plaque characterization and visualization with spectral analysis of intravascular ultrasound data.

Coronary artery disease is the number one cause of death in the United States and the Western world, and approximately 250,000 affected people die per year without ever being admitted to a hospital. One of the main reasons of such a high death-rate without any diagnosis is that more than 50 or heart-attacks) occur in patients with no prior history of known heart disease or symptoms. Coronary artery disease leads to the occlusion of arteries that are vital in providing nutrients to the heart muscles. The disease develops by progressive accumulation or formation of "plaque" within an artery. Certain types of plaques could occlude blood flow and yet might be "stable". These plaques usually have a high fibrous content, and are known as hard plaques. On the other hand, "unstable" or "soft" plaques might not cause much occlusion but could be vulnerable to rupture. Rupture of such plaques could lead to total or partial occlusion in arteries resulting in sudden cardiac death or heart-attack. In fact, 68 coronary arteries are less than 50.Intravascular ultrasound (IVUS) is a minimally invasive imaging modality that provides cross-section images of arteries in real-time, allowing visualization of atherosclerotic plaques in vivo. In standard IVUS gray-scale images, calcified regions of plaque and dense fibrous components generally reflect ultrasound energy well and thus appear bright and homogeneous on IVUS images. Conversely, regions of low echo reflectance in IVUS images are usually labeled "soft" or "mixed" plaque. However, this visual interpretation has been demonstrated to be very inconsistent in accurately determining plaque composition and does not allow real-time assessment of quantitative plaque constituents.Spectral analysis of the backscattered radiofrequency (RF) ultrasound signals allows detailed assessment of plaque composition. Advanced mathematical techniques can be employed to extract spectral information from these RF data to determine composition. The spectral content or signature of RF data reflected from tissue depends on density, compressibility, concentration, size, etc. A combination of spectral parameters were used to develop statistical classification schemes for analysis of in vivo IVUS data in real-time. The clinical data acquisition system is ECG gated and the analysis software developed by our group reconstructs IVUS gray-scale images from the acquired RF data. A combination of spectral parameters and active contour models is used for real-time 3D plaque segmentation followed by computation of color-coded tissue maps for each image cross-section and longitudinal views of the entire vessel. The "fly-through" mode allows one to visualize the complete length of the artery internally with the histology components at the lumen surface. In addition, vessel and plaque metrics such as areas and volumes of individual plaque components (collagen, fibro-lipid, calcium, lipid-core) are also available.

Validation of an automated system for luminal and medial-adventitial border detection in three-dimensional intravascular ultrasound.

The precise tomographic assessment of coronary artery disease by intravascular ultrasound (IVUS) is useful in quantitative studies. Such studies require identification of luminal and medial-adventitial (MA) borders in a sequence of IVUS images. We have developed a three-dimensional (3D) active-surface system for border detection that facilitates the analysis of many images with minimal user interaction. To assess the validity of the technique, luminal and MA borders in 529 end-diastolic images from nine coronary arterial segments (58.8 +/- 14.2 images per patient) were traced manually by four experienced observers. The computer-detected borders were compared with borders determined by the four observers using a modified Williams' index (WI), the ratio of inter-observer variability to computer-observer variability. While manual tracing required 49.2 +/- 12.1 min for analysis, the analysis system identified luminal (R2 = 0.92) and MA borders (R2 = 0.97) in 13.8 +/- 4.0 min, a decrease of 35.4 min (p < 0.000001). The computer minus observer differences in lumen area and MA area were -0.88 +/- 0.90 and -0.07 +/- 0.63 mm2. Therefore, the computer system underestimated both lumen and MA area, but this effect was very small in MA area. The WI values and 95% confidence intervals were 0.98 (0.89,1.06) for luminal border detection and 0.99 (0.95,1.04) for MA border detection. Plaque volume measurements, a common endpoint of clinical trials, also verified the accuracy of the technique (R2 = 0.98). The proposed 3D active-surface border detection system provides a faster and less-tedious alternative to manual tracing for assessment of coronary artery anatomy in vivo.

Early constriction or expansion of the external elastic membrane area determines the late remodeling response and cumulative lumen loss in transplant vasculopathy: an intravascular ultrasound study with 4-year follow-up.

BACKGROUND: Early constriction of the external elastic membrane (EEM) area has been observed after cardiac transplantation. The aim of this study was to compare the late disease process of transplant vasculopathy between coronary segments with early constrictive and expansive remodeling. METHODS: Serial intravascular ultrasound data obtained annually for 4 years after transplantation in 38 transplant recipients was available. In 135 matched segments from 59 coronary arteries ultrasound images were digitized at 1-mm intervals. Mean values of the external elastic membrane (EEM), lumen and intimal areas were calculated. On the basis of a decrease or increase in EEM area within the first year after transplantation, we defined segments with early constrictive remodeling (CR, n = 71) or early expansive remodeling (ER, n = 64). RESULTS: Annual changes in intimal area were similar between segments with early CR and ER throughout the follow-up period. However, during the second and third year, annual increases in EEM area were greater in segments with early CR than in segments with early ER (second year: 1.5 +/- 2.7 vs 0.6 +/- 2.8 mm(2), p = 0.052; third year: 1.3 +/- 2.5 vs -0.03 +/- 2.6 mm(2), p = 0.003). Despite this late expansion, segments with early CR showed a cumulative decrease in the EEM area and a greater lumen loss than segments with early ER (-2.5 +/- 3.4 vs -0.6 +/- 2.6 mm(2), p < 0.001). CONCLUSIONS: In transplant vasculopathy, the late remodeling response was different between segments with early constrictive and expansive remodeling, despite similar intimal thickening. Early constriction caused an overall decrease in EEM area and greater loss of lumen during follow-up.

Automated three-dimensional assessment of coronary artery anatomy with intravascular ultrasound scanning.

BACKGROUND: Angiography allows the definition of advanced, severe stages of coronary artery disease, but early atherosclerotic lesions, which do not lead to luminal stenosis, are not identified reliably. In contrast, intravascular ultrasound scanning allows the precise characterization and quantification of a wide range of atherosclerotic lesions, independent of the severity of luminal stenosis. METHODS: Three-dimensional (3-D) reconstruction of entire coronary segments is possible with the integration of sequential 2-dimensional tomographic images and allows volumetric analysis of coronary arteries. RESULTS: Automated systems able to recognize lumen and vessel borders and to display 3-D images are becoming available. CONCLUSION: These systems have the potential for on-line 3-D image reconstruction for clinical decision-making and fast routine volumetric analysis in research studies. This review describes 3-D intravascular ultrasound scanning acquisition, analysis, and processing, and the associated technical challenges.

Influence of coronary pulsation on volumetric intravascular ultrasound measurements performed without ECG-gating. Validation in vessel segments with minimal disease.

Volumetric analysis of coronary arteries can be performed using intravascular ultrasound (IVUS) images selected at 1 mm intervals without ECG gating. However, there are few data regarding the influence of coronary pulsation on this volumetric analysis. We developed two models of consecutive area measurements consisting of duplicated area measurements from short coronary segments and virtual measurements based on a sine function. These models allowed the re-calculation of volumes using different sets of frames from the same simulated segments. The variability of the volume determinations was evaluated by its percent standard deviation [%SD = (SD/the mean value) x 100]. The relation of the variability to the extent of external elastic membrane (EEM) area change during the cardiac cycle (amplitude) and heart rates (frequency) were examined. In 58 short coronary segments of 15 patients, consecutive IVUS images were measured [%EEM area change: 12.3 +/- 7.7%, heart rate 78 +/- 21 beats/min (bpm)]. In both models, %SD of the volume calculations was directly proportional to the %EEM area change and showed two peaks at heart rates of 60 +/- 2 and 90 +/- 2 bpm. In the model based on actual coronary measurements, the %SD of volume calculations of a segment with 10% EEM area change was 0.7% except for heart rates of 60 +/- 2 and 90 +/- 2 bpm. The variability of a volumetric analysis based upon measuring IVUS images at constant intervals without ECG gating is affected by coronary pulsation, extent of cross-sectional area changes, and heart rate. Despite these limitations, this method is feasible and provides reproducible volume measurements.

B-spline methods for interactive segmentation and modeling of lumen and vessel surfaces in three-dimensional intravascular ultrasound.

Intravascular ultrasound (IVUS) provides direct depiction of coronary anatomy, including the degree and extent of coronary plaque, useful in quantitative research or clinical studies. These studies require fast and accurate analysis of coronary morphometry in volumetric IVUS images. Semi-automated interaction techniques are important for this task due to limitations of automated processing. We present B-spline-based surface fitting and manipulation methods that provide a foundation for such interaction techniques. They can be integrated easily with previously developed segmentation algorithms to provide a semi-automated segmentation system for identification of luminal and vessel borders in volumetric IVUS images.