Giacomo Tarroni

Myocardial Perfusion: Near-automated Evaluation from Contrast-enhanced MR Images Obtained at Rest and during Vasodilator Stress

PURPOSE To develop and validate a technique for near-automated definition of myocardial regions of interest suitable for perfusion evaluation during vasodilator stress cardiac magnetic resonance (MR) imaging. MATERIALS AND METHODS The institutional review board approved the study protocol, and all patients provided informed consent. Image noise density distribution was used as a basis for endocardial and epicardial border detection combined with nonrigid registration. This method was tested in 42 patients undergoing contrast material-enhanced cardiac MR imaging (at 1.5 T) at rest and during vasodilator (adenosine or regadenoson) stress, including 15 subjects with normal myocardial perfusion and 27 patients referred for coronary angiography. Contrast enhancement-time curves were near-automatically generated and were used to calculate perfusion indexes. The results were compared with results of conventional manual analysis, using quantitative coronary angiography results as a reference for stenosis greater than 50%. Statistical analyses included the Student t test, linear regression, Bland-Altman analysis, and κ statistics. RESULTS Analysis of one sequence required less than 1 minute and resulted in high-quality contrast enhancement curves both at rest and stress (mean signal-to-noise ratios, 17±7 [standard deviation] and 22±8, respectively), showing expected patterns of first-pass perfusion. Perfusion indexes accurately depicted stress-induced hyperemia (increased upslope, from 6.7 sec(-1)±2.3 to 15.6 sec(-1)±5.9; P<.0001). Measured segmental pixel intensities correlated highly with results of manual analysis (r=0.95). The derived perfusion indexes also correlated highly with (r up to 0.94) and showed the same diagnostic accuracy as manual analysis (area under the receiver operating characteristic curve, up to 0.72 vs 0.73). CONCLUSION Despite the dynamic nature of contrast-enhanced image sequences and respiratory motion, fast near-automated detection of myocardial segments and accurate quantification of tissue contrast is feasible at rest and during vasodilator stress. This technique, shown to be as accurate as conventional manual analysis, allows detection of stress-induced perfusion abnormalities.

Left ventricular modelling: a quantitative functional assessment tool based on cardiac magnetic resonance imaging.

We present the development and testing of a semi-automated tool to support the diagnosis of left ventricle (LV) dysfunctions from cardiac magnetic resonance (CMR). CMR short-axis images of the LVs were obtained in 15 patients and processed to detect endocardial and epicardial contours and compute volume, mass and regional wall motion (WM). Results were compared with those obtained from manual tracing by an expert cardiologist. Nearest neighbour tracking and finite-element theory were merged to calculate local myocardial strains and torsion. The method was tested on a virtual phantom, on a healthy LV and on two ischaemic LVs with different severity of the pathology. Automated analysis of CMR data was feasible in 13/15 patients: computed LV volumes and wall mass correlated well with manually extracted data. The detection of regional WM abnormalities showed good sensitivity (77.8%), specificity (85.1%) and accuracy (82%). On the virtual phantom, computed local strains differed by less than 14 per cent from the results of commercial finite-element solver. Strain calculation on the healthy LV showed uniform and synchronized circumferential strains, with peak shortening of about 20 per cent at end systole, progressively higher systolic wall thickening going from base to apex, and a 10° torsion. In the two pathological LVs, synchronicity and homogeneity were partially lost, anomalies being more evident for the more severely injured LV. Moreover, LV torsion was dramatically reduced. Preliminary testing confirmed the validity of our approach, which allowed for the fast analysis of LV function, even though future improvements are possible.

Estimation of prenatal aorta intima-media thickness from ultrasound examination

Prenatal events such as intrauterine growth restriction and increased cardiovascular risk in later life have been shown to be associated with an increased intima-media thickness (aIMT) of the abdominal aorta in the fetus. In order to assess and manage atherosclerosis and cardiovascular disease risk in adults and children, in recent years the measurement of abdominal and carotid artery thickness has gained a growing appeal. Nevertheless, no computer aided method has been proposed for the analysis of prenatal vessels from ultrasound data, yet. To date, these measurements are being performed manually on ultrasound fetal images by skilled practitioners. The aim of the presented study is to introduce an automatic algorithm that identifies abdominal aorta and estimates its diameter and aIMT from routine third trimester ultrasonographic fetal data.The algorithm locates the aorta, then segments it and, by modeling the arterial wall longitudinal sections by means of a gaussian mixture, derives a set of measures of the aorta diameter (aDiam) and of the intima-media thickness (aIMT). After estimating the cardiac cycle, the mean diameter and the aIMT at the end-diastole phase are computed.Considering the aIMT value for each subject, the correlation between automatic and manual end-diastolic aIMT measurements is 0.91 in a range of values 0.44-1.10 mm, corresponding to both normal and pathological conditions. The automatic system yields a mean relative error of 19%, that is similar to the intra-observer variability (14%) and much lower that the inter-observer variability (42%).The correlation between manual and automatic measurements and the small error confirm the ability of the proposed system to reliably estimate aIMT values in prenatal ultrasound sequences, reducing measurement variability and suggesting that it can be used for an automatic assessment of aIMT.

A supervised learning approach for the robust detection of heart beat in plethysmographic data.

Wearable devices equipped with photoplethysmography (PPG) sensors are gaining an increased interest in the context of biometric signal monitoring within clinical, e-health and fitness settings. When used in everyday life and during exercise, PPG traces are heavily affected by artifacts originating from motion and from a non constant positioning and contact of the PPG sensor with the skin. Many algorithms have been developed for the estimation of heart-rate from photoplethysmography signals. We remark that they were mainly conceived and tested in controlled settings and, in turn, do not provide robust performance, even during moderate exercise. Only a few of them have been designed for signals acquired at rest and during fitness. However, they provide the required resilience to motion artifacts at the cost of using computationally demanding signal processing tools. At variance with other methods from the literature, we propose a supervised learning approach, where a classifier is trained on a set of labelled data to detect the presence of heart beats at each position of a PPG signal, with only little preprocessing and postprocessing. We show that the results obtained on the TROIKA dataset using our approach are comparable with those shown in the original paper, providing a classification error of 14% in the detection of heart beat positions, that reduces to 2.86% on the heart-rate estimates after the postprocessing step.

Prosthetic component segmentation with blur compensation: a fast method for 3D fluoroscopy

A new method for prosthetic component segmentation from fluoroscopic images is presented. The hybrid approach we propose combines diffusion filtering, region growing and level-set techniques without exploiting any a priori knowledge of the analyzed geometry. The method was evaluated on a synthetic dataset including 270 images of knee and hip prosthesis merged to real fluoroscopic data simulating different conditions of blurring and illumination gradient. The performance of the method was assessed by comparing estimated contours to references using different metrics. Results showed that the segmentation procedure is fast, accurate, independent on the operator as well as on the specific geometrical characteristics of the prosthetic component, and able to compensate for amount of blurring and illumination gradient. Importantly, the method allows a strong reduction of required user interaction time when compared to traditional segmentation techniques. Its effectiveness and robustness in different image conditions, together with simplicity and fast implementation, make this prosthetic component segmentation procedure promising and suitable for multiple clinical applications including assessment of in vivo joint kinematics in a variety of cases.

Fully-automated identification and segmentation of aortic lumen from fetal ultrasound images.

Intrauterine growth restriction (IUGR) is a fetal condition that has been linked to an increase in cardiovascular mortality in the adult life. IUGR induces cardiovascular remodeling, including a decrease in aortic intima-media thickness (aIMT) which can be evaluated using fetal ultrasound imaging, potentially improving IUGR assessment and cardiovascular risk management. A necessary step for aIMT quantification is the identification of a region-of-interest (ROI) containing the lumen. This step is usually performed manually, even within the few semi-automated approaches to aIMT estimation. The aims of this study were to develop and test a fully-automated technique for lumen identification and segmentation from ultrasound images as a basis for aIMT quantification. The technique relies on convolution with a set of discriminative kernels learned from a training dataset using an AdaBoost classifier followed by segmentation based on anisotropic filtering and level-set methods. This approach was tested on 50 images acquired from 5 subjects: automatically extracted mean lumen width values were compared to reference ones manually obtained by an experienced interpreter. Results (R = 0.97) show that the proposed technique is accurate, suggesting that it could serve as a basis for fully-automated approaches to aIMT quantification.

Regadenoson cardiovascular magnetic resonance myocardial perfusion imaging predicts need for future revascularization

Summary Regadenoson is a new vasodilator myocardial stress agent that is easier-to-use and more tolerable than adenosine. We demonstrate that, in patients undergoing cardiovascular magnetic resonance myocardial perfusion imaging, regadenoson is safe and effective in producing hyperemia and identifying the need for future revascularization. Background Regadenoson (Lexiscan; Astellas) is a new vasodilator myocardial stress agent that selectively activates the A2A receptor. Unlike adenosine, regadenoson is easier to administer and results in fewer side effects. Although extensively studied in patients undergoing nuclear myocardial perfusion imaging (MPI), its performance in cardiovascular magnetic resonance (CMR) MPI remains unknown. The aim of this study was to assess the safety and tolerability of regadenoson and determine its ability to produce hyperemia and predict subsequent coronary revascularization in patients undergoing CMR-MPI. Methods 120 patients were prospectively enrolled to receive CMR-MPI (Achieva, Philips 1.5T) with regadenoson. Patients with contraindications to CMR-MPI or regadenoson were excluded. Short-axis slices were obtained at three levels of the left ventricle (LV) during first pass of Gadolinium-DTPA(0.075 mmol/kg at 4 ml/sec) for 50 consecutive heart beats. Images were acquired using a hybrid gradient echo/echo planar imaging sequence. Imaging was performed 1 minute after injection of regadenoson (0.4mg) and then repeated 15 minutes after injection of aminophylline (125mg) under resting conditions. Perfusion defects were defined as subendocardial hypointensity in a coronary distribution at stress, involving ≥25% wall thickness, and persisting for ≥ 2h eart beats following peak enhancement of the LV cavity. In a subgroup of patients (n=99), custom software was used to generate time intensity curves and to compare the myocardial upslope of the midventricular slice during stress and rest. All subjects were followed for 3 months for the occurrence of coronary revascularization. Results Overall, 51/120 (43%) of patients were female with an average age of 55±15 years and body mass index of 29 ±6 kg/m2. Baseline patient characteristics include: coronary artery disease (33%), diabetes (38%), hypertension (56%), and hypercholesterolemia (95%). The average resting blood pressure and heart rate were 124/ 61mmHg and 70bpm, respectively. Peak heart rate after regadenoson administration was 98bpm (p<0.001). Most patients (87%) experienced side effects from regadenoson including shortness of breath (34%), flushing (23%), and chest discomfort (17%). No EKG changes or residual side effects persisted in any patient at completion of study. The average myocardial upslope increased significantly between rest and stress conditions (9.1±5.9 vs. 12.8±8.1, p<0.001), reflecting the expected hyperemic effect of regadenoson. Perfusion defects were visually apparent in 33/120 (28%) patients. Revascularization occurred in 8/120 (7%) patients (Figure 1). The presence

A novel approach to aortic intima-media thickness quantification from fetal ultrasound images

Intrauterine fetal growth restriction (IUGR) is linked to increased cardiovascular mortality during adulthood. IUGR induces an increase in aortic intima-media thickness (aIMT) which can be detected from fetal ultrasound images, potentially improving IUGR assessment and thus cardiovascular risk management. Unfortunately this measurement currently relies on tedious and error-prone manual tracing. The aims of this study were to develop and test a novel near-automated technique for aIMT quantification from ultrasound images. The proposed technique uses a level-set method (with a functional relying on a distance-based term) to identify blood-intima and media-adventitia interfaces as a basis for aIMT estimation. This approach was tested on images acquired from 10 subjects, and automatically extracted aIMT values were compared to reference values manually obtained by two interpreters. Results indicate that the accuracy of the proposed technique is close to that of manual tracing, suggesting that it could be adopted as a basis for fast and reliable near-automated aIMT estimation.

Semiautomatic detection of villi in confocal endoscopy for the evaluation of celiac disease

Celiac Disease (CD) is an immune-mediated enteropathy, diagnosed in the clinical practice by intestinal biopsy and the concomitant presence of a positive celiac serology. Confocal Laser Endomicroscopy (CLE) allows skilled and trained experts to potentially perform in vivo virtual histology of small-bowel mucosa. In particular, it allows the qualitative evaluation of mucosa alteration such as a decrease in goblet cells density, presence of villous atrophy or crypt hypertrophy. We present a semi-automatic method for villi detection from confocal endoscopy images, whose appearance change in case of villous atrophy. Starting from a set of manual seeds, a first rough segmentation of the villi is obtained by means of mathematical morphology operations. A merge and split procedure is then performed, to ensure that each seed originates a different region in the final segmentation. A border refinement process is finally performed, evolving the shape of each region according to local gradient intensities. Mean and median Dice coefficients for 290 villi originating from 66 images when compared to manually obtained ground truth are 80.71% and 87.96% respectively.

Automated Estimation of Aortic Intima-Media Thickness from Fetal Ultrasound

Intima-media thickness (aIMT) of the abdominal aorta has proven to be an early marker for atherosclerosis and cardiovascular diseases risk assessment in young adults and children. Despite recent studies have highlighted the potential usefulness of its estimation at the fetal stage from ultrasound images, this relies on error-prone and tedious manual tracing. In this study, an automated technique for aIMT estimation from fetal ultrasound images is presented and tested against manual tracing. The proposed technique is based on narrow-band level-set methods applied to the regions surrounding the aortic lumen in order to segment the portions between the blood-intima and media-adventitia interfaces and thus estimate the aIMT. This approach was tested on images acquired from 11 subject at a mean gestational age of 29 weeks. Automatically extracted aIMT values were compared to reference values manually extracted by two interpreters using Pearson’s correlation coefficients, Bland-Altman and linear regression analyses. The results indicate that the accuracy of the proposed technique is comparable to that of manual tracing. As a consequence, this approach could be potentially adopted as an alternative to manual analysis for the automated estimation of aIMT.