Quantification of porcine myocardial perfusion with modified dual bolus MRI : a prospective study with a PET reference

Abstract Background The reliable quantification of myocardial blood flow (MBF) with MRI, necessitates the correction of errors in arterial input function (AIF) caused by the T1 saturation effect. The aim of this study was to compare MBF determined by a traditional dual bolus method against a modified dual bolus approach and to evaluate both methods against PET in a porcine model of myocardial ischemia. Methods Local myocardial ischemia was induced in five pigs, which were subsequently examined with contrast enhanced MRI (gadoteric acid) and PET (O-15 water). In the determination of MBF, the initial high concentration AIF was corrected using the ratio of low and high contrast AIF areas, normalized according to the corresponding heart rates. MBF was determined from the MRI, during stress and at rest, using the dual bolus and the modified dual bolus methods in 24 segments of the myocardium (total of 240 segments, five pigs in stress and rest). Due to image artifacts and technical problems 53% of the segments had to be rejected from further analyses. These two estimates were later compared against respective rest and stress PET-based MBF measurements. Results Values of MBF were determined for 112/240 regions. Correlations for MBF between the modified dual bolus method and PET was rs = 0.84, and between the traditional dual bolus method and PET rs = 0.79. The intraclass correlation was very good (ICC = 0.85) between the modified dual bolus method and PET, but poor between the traditional dual bolus method and PET (ICC = 0.07). Conclusions The modified dual bolus method showed a better agreement with PET than the traditional dual bolus method. The modified dual bolus method was found to be more reliable than the traditional dual bolus method, especially when there was variation in the heart rate. However, the difference between the MBF values estimated with either of the two MRI-based dual-bolus methods and those estimated with the gold-standard PET method were statistically significant

Quantification of porcine myocardial perfusion with modified dual bolus MRI-A prospective study with a PET reference

BackgroundThe reliable quantification of myocardial blood flow (MBF) with MRI, necessitates the correction of errors in arterial input function (AIF) caused by the T1 saturation effect. The aim of this study was to compare MBF determined by a traditional dual bolus method against a modified dual bolus approach and to evaluate both methods against PET in a porcine model of myocardial ischemia.MethodsLocal myocardial ischemia was induced in five pigs, which were subsequently examined with contrast enhanced MRI (gadoteric acid) and PET (O-15 water). In the determination of MBF, the initial high concentration AIF was corrected using the ratio of low and high contrast AIF areas, normalized according to the corresponding heart rates. MBF was determined from the MRI, during stress and at rest, using the dual bolus and the modified dual bolus methods in 24 segments of the myocardium (total of 240 segments, five pigs in stress and rest). Due to image artifacts and technical problems 53% of the segments had to be rejected from further analyses. These two estimates were later compared against respective rest and stress PET-based MBF measurements.ResultsValues of MBF were determined for 112/240 regions. Correlations for MBF between the modified dual bolus method and PET was rs = 0.84, and between the traditional dual bolus method and PET rs = 0.79. The intraclass correlation was very good (ICC = 0.85) between the modified dual bolus method and PET, but poor between the traditional dual bolus method and PET (ICC = 0.07).ConclusionsThe modified dual bolus method showed a better agreement with PET than the traditional dual bolus method. The modified dual bolus method was found to be more reliable than the traditional dual bolus method, especially when there was variation in the heart rate. However, the difference between the MBF values estimated with either of the two MRI-based dual-bolus methods and those estimated with the gold-standard PET method were statistically significant.</div

Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination

Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF). However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited. In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. Domestic pigs (n = 5) were subjected to contrast enhanced first pass MRI (MRI-FP) and the impulse response at different regions of the myocardium (n = 24/pig) were evaluated at rest (n = 120) and stress (n = 96). Reference MBF was then measured using positron emission tomography (PET). Since the impulse response may include artefacts, classification models based on SVM and RF were developed to discriminate noisy signal. In addition, regression models based on SVM, RF and linear regression (for comparison) were developed for estimating MBF from the impulse response at rest and stress. The classification and regression models were trained on data from 4 pigs (n = 168) and tested on 1 pig (n = 48). Models based on SVM and RF outperformed linear regression, with higher correlation (R2SVM  = 0.81, R2RF  = 0.74, R2linear_regression  = 0.60; ρSVM = 0.76, ρRF = 0.76, ρlinear_regression = 0.71) and lower error (RMSESVM = 0.67 mL/g/min, RMSERF = 0.77 mL/g/min, RMSElinear_regression = 0.96 mL/g/min) for predicting MBF from MRI impulse response signal. Classifier based on SVM was optimal for detecting impulse response signals with artefacts (accuracy = 92%). Modified dual bolus MRI signal, combined with machine learning, has potential for accurately estimating MBF at rest and stress states, even from signals with dark rim artefacts. This could provide a protocol for reliable and easy estimation of MBF, although further research is needed to clinically validate the approach.</p

European Planetary Science Congress 2021

The Space environment is known to be populated by highly energetic particles. These particles originate from three main sources: (1) Galactic Cosmic Rays (GCRs), a low flux of protons (90%), heavy ions, and to some extent electrons, with energies up to 1021 eV, arriving from outside of the Solar System; (2) Solar Energetic Particles (SEPs), sporadic and unpredictable bursts of electrons, protons, and heavy ions, travelling much faster than the Space plasma, accelerated in Solar Flares and Coronal Mass Ejections; and (3) planetary trapped particles, a dynamic population of protons and electrons trapped around planetary magnetospheres first discovered at Earth by Van Allen. Solar activity is responsible for transient and long-term variation of the radiation environment. During periods of low activity, the GCR flux increases as a result of the lower heliospheric modulation exerted on charged particle from outside the solar system and the probability of SEP events decreases; vice-versa, during high activity, GCR fluxes decrease, and the probability of SEP events increases. Extreme Solar Events also affect the Earth’s magnetosphere and the radiation belts which can lead to ground-level enhancements. These three components of radiation in space combine into a hazardous environment for both manned and unmanned missions and are responsible for several processes in planetary bodies. Therefore, it is important to monitor and comprehend the dynamics of energetic particles in space. BepiColombo is the first mission of the European Space Agency to the Hermean System. It was launched in 2018 and will enter Mercury’s orbit in 2025 with the first flyby to Mercury planned for 2021. It is composed of two Spacecraft, ESA’s Mercury Planetary Orbiter (MPO) and JAXA’s Mercury Magnetospheric Orbiter (MMO). Both Spacecraft carry a rich suite of scientific instruments to study the planet geology, exosphere, and magnetosphere. In particular, the MPO spacecraft carries the BepiColombo Radiation Monitor (BERM), which is capable of measuring electrons with energies from ~100 keV to ~10 MeV, protons with energies from 1 MeV to ~200 MeV, and heavy ions with a Linear Energy Transfer from 1 to 50 MeV/mg/cm2. While BERM is part of the mission housekeeping, it will provide valuable scientific data of the energetic particle population in interplanetary space and at Mercury. Because BERM is in operation during most of the cruise phase, it is able to detect and characterize SEP events. In fact, two events were already registered and will be included in a multi-spacecraft analysis.  BERM is based on standard silicon stack detectors such as the SREM and the MFS. It consists of a single telescope stack with 11 Silicon detectors interleaved by aluminum and tantalum absorbers. Particle species and energies are determined by  charged particle track signals registered in the Si stack. Because of the limited bandwidth, particle events are processed in-flight before being sent to Earth. Particles are then assigned to 18 channels, five corresponding to electrons, eight to protons, and five to heavy ions. In this work, we will present the response of the 18 detector channels obtained by comparing Geant4 simulations with the BERM beam calibration data. The response functions are validated using measurements made during of the BepiColombo Earth flyby and during the cruise phase. Special focus is given  to the synergies between BERM and the Solar intensity X-ray and particle Spectrometer (SIXS) instrument signals. The latter measures electrons from ~50 keV to ~3 MeV and protons from ~1 to ~30 MeV. The availability of two instruments with overlapping energy ranges allows to validate and cross-calibrate their data, namely during Earth flyby at the radiation belts, and to maximize the scientific output of the mission. In fact, lessons learned during this joint analysis are expected to set the basis for a similar collaboration between the RADiation hard Electron Monitor (RADEM) and the Particle Environment Package (PEP) instruments aboard the future JUICE mission.</p

Quantification of myocardial blood flow by machine learning analysis of modified dual bolus MRI examination

Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF). However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited. In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. Domestic pigs (n = 5) were subjected to contrast enhanced first pass MRI (MRI-FP) and the impulse response at different regions of the myocardium (n = 24/pig) were evaluated at rest (n = 120) and stress (n = 96). Reference MBF was then measured using positron emission tomography (PET). Since the impulse response may include artefacts, classification models based on SVM and RF were developed to discriminate noisy signal. In addition, regression models based on SVM, RF and linear regression (for comparison) were developed for estimating MBF from the impulse response at rest and stress. The classification and regression models were trained on data from 4 pigs (n = 168) and tested on 1 pig (n = 48). Models based on SVM and RF outperformed linear regression, with higher correlation (R = 0.81, R = 0.74, R = 0.60; ρ = 0.76, ρ = 0.76, ρ = 0.71) and lower error (RMSE = 0.67\ua0mL/g/min, RMSE = 0.77\ua0mL/g/min, RMSE = 0.96\ua0mL/g/min) for predicting MBF from MRI impulse response signal. Classifier based on SVM was optimal for detecting impulse response signals with artefacts (accuracy = 92%). Modified dual bolus MRI signal, combined with machine learning, has potential for accurately estimating MBF at rest and stress states, even from signals with dark rim artefacts. This could provide a protocol for reliable and easy estimation of MBF, although further research is needed to clinically validate the approach

Reaction Time and Visual Memory in Connection to Hazardous Drinking Polygenic Scores in Schizophrenia, Schizoaffective Disorder and Bipolar Disorder

The purpose of this study was to explore the association of cognition with hazardous drinking Polygenic Scores (PGS) in 2649 schizophrenia, 558 schizoaffective disorder, and 1125 bipolar disorder patients in Finland. Hazardous drinking PGS was computed using the LDPred program. Participants performed two computerized tasks from the Cambridge Automated Neuropsychological Test Battery (CANTAB) on a tablet computer: the 5-choice serial reaction time task, or Reaction Time (RT) test, and the Paired Associative Learning (PAL) test. The association between hazardous drinking PGS and cognition was measured using four cognition variables. Log-linear regression was used in Reaction Time (RT) assessment, and logistic regression was used in PAL assessment. All analyses were conducted separately for males and females. After adjustment of age, age of onset, education, household pattern, and depressive symptoms, hazardous drinking PGS was not associated with reaction time or visual memory in male or female patients with schizophrenia, schizoaffective, and bipolar disorder.Peer reviewe