Locus Coeruleus magnetic resonance imaging: a comparison between native-space and template-space approach

Locus Coeruleus (LC) is the main noradrenergic nucleus of the brain, which is involved in many physiological functions including cognition; its impairment may be crucial in the neurobiology of a variety of brain diseases. Locus Coeruleus-Magnetic Resonance Imaging (LC-MRI) allows to identify in vivo LC in humans. Thus, a variety of research teams have been using LC-MRI to estimate LC integrity in normal aging and in patients affected by neurodegenerative disorders, where LC integrity my work as a biomarker. A number of variations between LC-MRI studies exist, concerning post-acquisition analysis and whether this had been performed within MRI native space or in ad hoc-built MRI template space. Moreover, the reproducibility and reliability of this tool is still to be explored. Therefore, in the present study, we analyzed a group of neurologically healthy, cognitively intact elderly subjects, using both a native space- and a template space-based LC-MRI analysis. We found a good inter-method agreement, particularly considering the LC Contrast Ratio. The template space-based approach provided a higher spatial resolution, lower operator-dependency, and allowed the analysis of LC topography. Our ad hoc-developed LC template showed LC morphological data that were in line with templates published very recently. Remarkably, present data significantly overlapped with a recently published LC "metaMask", that had been obtained by averaging the results of a variety of previous LC-MRI studies. Thus, such a template space-based approach may pave the way to a standardized LC-MRI analysis and to be used in future clinic-anatomical correlations.

The triglyceride/HDL cholesterol ratio and TyG index to predict coronary artery calcium, epicardial fat and outcome in the general population

Background

The ratio between triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) and the triglyceride-glucose (TyG) index predict the severity of coronary atherosclerosis and outcome in patients with chronic coronary syndrome. We investigated the relationship between TG/HDL-C and TyG and coronary artery calcium (CAC), the volume of the pro-atherogenic epicardial fat, and survival in a primary prevention setting.

Methods

Between May 2010 and October 2011, subjects aged between 45 and 75 years living in Montignoso (Tuscany, Italy) and free from known cardiovascular disease were invited to participate to a screening including a computed tomography (CT) scan.

Results

Study participants (n=1,382) were aged 61 years (interquartile interval 54–68), 45% were men, and their 10-year risk of death or myocardial infarction based on the Framingham risk score was 5% (2–10%). CAC and epicardial fat volume (EFV) increased significantly across quartiles of TG/HDL-C and TyG. The TG/HDL-C and TyG displayed weak correlations with CAC and stronger correlations with EFV. The TG/HDL-C and TyG did not predict CAC independently from other baseline variables, while they both independently predicted EFV. Over 10 years (9.5–10.5), 103 individuals died (8%), and 36 patients experienced the composite endpoint of cardiovascular death or urgent revascularization during 10.1 years (9.6–10.6). The risk of all-cause death and the composite cardiovascular endpoint increased with TG/HDL-C and TyG. TG/HDL-C and TyG were univariable predictors of all-cause death and the composite cardiovascular endpoint. Nonetheless, TG/HDL-C and TyG lost their prognostic value for the composite cardiovascular endpoint when adjusting for CAC.

Conclusions

In subjects from the general population, the TG/HDL-C and TyG predict CAC and EFV. TG/HDL-C and TyG are also predictive of cardiovascular death or urgent coronary revascularization, although not independently from CAC.

Funding Acknowledgement

Type of funding sources: None.

Gamma glutamyl transferase and its fractions to predict coronary artery calcium and outcome in the general population

Background

Gamma glutamyl transferase (GGT) is a risk factor for plaque destabilization, particularly its fraction with the highest molecular weight (big-GGT, b-GGT). We explored the interplay between GGT, computed tomography (CT) findings that predict cardiovascular risk (coronary artery calcium [CAC] and epicardial fat volume [EFV]), and 10-year outcome.

Methods

Between May 2010 and October 2011, subjects aged between 45 and 75 years living in a city of Tuscany (Italy) and free from known cardiovascular disease were invited to participate to a screening including a CT scan.

Results

A subgroup of study participants (n=898, 65%) had total GGT and GGT fractions quantified (median age 65 years [25th-75th percentile: 55–70 yrs], 46% males, median 10-year risk of myocardial infarction or death based on the Framingham score 6% [2–11%]). GGT predicted CAC (Exp(B) 0.099, p=0.002) and EFV (Exp(B) 0.102, p=0.003) in a model including age, gender, diabetes, current or previous smoking status, low-density lipoprotein cholesterol, high-sensitivity C-reactive protein, and aspirin therapy. Over 10.3 years (9.6–10.8), 74 individuals died (8%), and 36 (4%) experienced the composite of cardiovascular death or coronary revascularization. The risk of all-cause death and the composite endpoint increased quite steeply with GGT values, with thresholds of 19 UI and 20 IU, respectively (Figure). GGT predicted both endpoints independently from the Framingham 10-year risk (hazard ratio [HR] 1.01, 95% CI 1.01–1.02, p=0.004, and HR 1.01, 95% CI 1.01–1.02, p=0.002, respectively), as well as a model including CT findings. bGGT had the highest area under the curve value to predict the composite endpoint (0.586).

Conclusions

In a general population setting, plasma GGT independently predicts CAC and EFV, and the risk of all-cause death or a composite cardiovascular endpoint.

Funding Acknowledgement

Type of funding sources: None.

Application of Monte Carlo Algorithms to Cardiac Imaging Reconstruction

Monte Carlo algorithms have a growing impact on nuclear medicine reconstruction processes. One of the main limitations of myocardial perfusion imaging (MPI) is the effective mitigation of the scattering component, which is particularly challenging in Single Photon Emission Computed Tomography (SPECT). In SPECT, no timing information can be retrieved to locate the primary source photons. Monte Carlo methods allow an event-by-event simulation of the scattering kinematics, which can be incorporated into a model of the imaging system response. This approach was adopted in the late Nineties by several authors, and recently took advantage of the increased computational power made available by high-performance CPUs and GPUs. These recent developments enable a fast image reconstruction with improved image quality, compared to deterministic approaches. Deterministic approaches are based on energy-windowing of the detector response, and on the cumulative estimate and subtraction of the scattering component. In this paper, we review the main strategies and algorithms to correct the scattering effect in SPECT and focus on Monte Carlo developments, which nowadays allow the threedimensional reconstruction of SPECT cardiac images in a few seconds.

Artificial intelligence applied to non-contrast-enhanced cardiac computed tomography for the prediction of cardiovascular events

Background

Non-contrast-enhanced cardiac computed tomography (CT) may provide two measures that are emerging as independent predictors of cardiovascular events: coronary calcium score (CCS) and the volume of epicardial fat, a metabolically and immunologically active tissue surrounding the coronary arteries. The quantification of epicardial fat volume (EFV) is not routinely performed in clinical practice for the long time required for image reconstruction and the intra- and inter-observer variability.

Purpose

We evaluated if artificial intelligence (AI) might prove a valuable tool to interpret the CT data-set, and to better understand the relative prognostic value of CCS and EFV compared to “traditional” cardiovascular risk factors.

Methods

The Montignoso HEart and Lung Project is a community-based study carried out in a small town of Northern Tuscany (Italy). Starting from 2009, asymptomatic individuals from the general population underwent a baseline screening including a non-contrast cardiac CT, and were followed-up. For the present study, CCS and EFV were automatically measured from CT scans through a deep learning (DL) strategy based on convolutional neural networks. Because of the low incidence of the primary endpoint (myocardial infarction [MI]), the observed cardiac events were predicted with a random forest model built using a subsampling approach.

Results

Study participants (n=1528; 48% males, age 40 to 77 years) experienced 47 MI events (3%) over 5.5±1.5 years. CCS and EFV independently predicted this endpoint (p values <0.001 and 0.005, respectively) in a model including other predictors, namely weight, age, male gender, and hypertension. The model displayed a good prognostic performance, with an out-of-bag accuracy of 80.43% (accuracy on non-event prediction: 81.17%; performance on event prediction: 57,45%). The CCS emerged as the most important predictor, followed by EFV, weight and age. Interestingly, the incidence of cardiovascular events linked with CCS levels was associated with elevated EFV and the subjects with elevated CCS values but low EFV had no events (figure 1).

Conclusions

The tools of AI allow to perform an automated analysis of non-contrast-enhanced CT scans, with rapid and accurate measurement of CCS and EFV through a DL approach. In asymptomatic individuals from the general population, these features are more predictive of non-fatal MI than other variables related to the cardiovascular risk, as we can be demonstrated through an application of AI.

Figure 1

Funding Acknowledgement

Type of funding source: None

Deep learning to diagnose cardiac amyloidosis from CMR findings

Background

Cardiac magnetic resonance (CMR) is an important diagnostic technique for cardiac amyloidosis (CA). A deep learning (DL) approach to define the likelihood of CA based on automated interpretation of CMR images has never been attempted so far.

Methods

187 subjects underwent standard 1.5 T CMR examination (GE-Healthcare, Milwaukee, USA) as part of a diagnostic workup for either unexplained left ventricular hypertrophy or blood dyscrasia with suspected light-chain (AL) amyloidosis. Patients were randomly assigned to 3 subgroups, which were used for training (n=121, 65%), internal validation (n=28, 15%), and model testing (n=38, 20%). LGE images in different orientations (short-axis, 2- and 4-chambers) were selected as the most informative CMR features. A deep convolutional neural network was trained to classify CMR examinations as “amyloidosis” (probability ≥50%) or “no amyloidosis” (probability <50%) based on these features. Different learning strategies (data augmentation, batch normalization in convolutional layers, dropout before dense layers) were adopted to prevent model overfitting. Binary cross entropy was used as loss function. For comparison, a machine learning (ML) model based on gradient boosting trees was built for the binary classification of patients (amyloidosis vs no amyloidosis) based on clinical and imaging features extracted from the CMR exam.

Results

CA was diagnosed in 101 subjects (54%; 45 AL, 56 transthyretin amyloidosis). A model including 2C, 4C and SA LGE images was created. In the test cohort, it allowed to diagnose CA with good diagnostic accuracy (84.2%), and an area under the curve (AUC) of 0.96 (Figure). The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 0.78, 0.94, and 0.86, respectively. An ML algorithm considering all available parameters (LV volumes and function, LGE presence and pattern, early darkening, pericardial and pleural effusion, etc.) displayed a similar diagnostic performance than the DL method (AUC 0.93 vs. 0.96; p=0.45).

Conclusions

The deep learning technique allowed to create an accurate diagnostic tool for CA based on LGE patterns, which could be easily converted into an online platform for automated image analysis.

Funding Acknowledgement

Type of funding source: None

Associations between common carotid artery diameter, Framingham risk score and cardiovascular events

BACKGROUND AND AIMS

Vascular biomarkers are associated with risk burden and are capable to predict the development of future cardiovascular (CV) events; yet, their additive predictive value over and above established risk algorithms seems to be only modest. The present study evaluated the cross-sectional associations between vascular biomarkers, 10-year Framingham risk (FR) and prevalent CV events in a population with a high prevalence of hypertension and diabetes.

METHODS AND RESULTS

As many as 681 subjects (419 men, age = 60 ± 10 years, 282 diabetics, 335 hypertensives, mean FR score = 22.5 ± 16.5%) underwent an integrated vascular examination including: radiofrequency-based ultrasound of common carotid artery (cca) to measure intima-media thickness (IMT), inter-adventitial diameter (IAD) and local pulse wave velocity (PWV); applanation tonometry to assess carotid pulse pressure (PP) and augmentation index (AIx); carotid-femoral PWV (cfPWV) measurement. One hundred and thirty-five subjects (19.8%) had history of CV events, and CV events were independently associated with male sex, age, antihypertensive treatment, current smoking, HDL-cholesterol and ccaIAD. In logistic regression model, only ccaIAD was associated with prevalence of CV events after adjustment for FR score, with the OR of 1.71 [1.34-2.19] (P < 0.0001) that remained unchanged when ccaIMT was included into the model (OR = 1.76 [1.36-2.27]; P < 0.0001). The association between prevalent CV events and ccaIAD was significant (OR of 1.65 [1.24-2.20]; P = 0.0005) also in a subgroup of subjects being at a high 10-year risk of CV disease (N = 330).

CONCLUSIONS

In a population with a high prevalence of diabetes and hypertension, ccaIAD was the only vascular measure associated with prevalent CV events, independently of FR score.

[Mother and child sharing the same room and breast feeding]

The author analyzes the existing relationship among breast feeding and socioeconomical level and degree of instruction of the mother. He also describes the causes for the interruption of breast feeding in a maternity where rooming-in existed. He stressed the importance of the motivation to the mother for breast feeding and the independence between levels of instruction and frequency of breast feeding.