Michael Vaeggemose

Purpose: Pulmonary hypertension is related to a 1‐year mortality ranging from < 5% to > 20% in low‐ and high‐risk groups. Right heart catheterization is the diagnostic gold standard, associated with a 1.1% rate of severe adverse events, posing the need for a non‐invasive diagnostic tool. This study aims to delineate the effects of controlled changes on pulmonary hemodynamics by quantifying corresponding changes in hyperpolarized ¹²⁹ Xe MRSI dissolved‐phase parameters, including RBC oscillations. Methods: Seven Danish Landrace pigs underwent hyperpolarized ¹²⁹ Xe dissolved‐phase 3D MRSI with interleaved MRS under baseline conditions and three vasoactive interventions, monitored with invasive catheters or phase‐contrast MRI. Results were evaluated using one‐way ANOVA or mixed‐effects analysis followed by Dunnett's multiple comparisons test. Results: Hypoxia increased mPAP ( p = 0.038), reduced RBC:M (−31%, p = 0.007) and RBC:G (−34%, p = 0.009), while increasing RBC chemical shift (0.57%, p = 0.020). Adenosine and dobutamine increased RBC:M (22%, p = 0.003) and RBC:G (30%, p = 0.009), while decreasing RBC chemical shift (−0.38%, p = 0.11). Linear regression revealed an inverse correlation between mPAP and RBC oscillation amplitude ( R ² = 0.158, p = 0.04), RBC:M ( R ² = 0.25, p = 0.05), and RBC:G ( R ² = 0.22, p = 0.01). Conclusion: This study demonstrates that dissolved‐phase hyperpolarized ¹²⁹ Xe MRSI parameters are sensitive to hemodynamic changes, exhibiting consistent inverse relationships with invasively measured mPAP. By leveraging concurrent invasive reference measurements, these findings provide physiological validation that dissolved‐phase ¹²⁹ Xe metrics reflect pulmonary vascular pressure, supporting their further development as a non‐invasive adjunct to right heart catheterization.

Purpose: Deuterium metabolic imaging (DMI) offers noninvasive magnetic resonance imaging (MRI)–based assessment of metabolism in vivo, making it a relevant paraclinical tool for diseases with neurological metabolic alterations. This study aimed to establish a normative reference atlas of brain glucose metabolism accounting for age and sex. Methods: DMI were obtained for 30 healthy adults (aged 51–84 years, 15 female) with a 3 T MRI scanner after ingestion of deuterated [6,6´‐ ² H ₂ ]glucose. The images were parcellated to determine the regional distribution of deuterated water, glucose, lactate, and glutamate plus glutamine (Glx). Linear models were applied to investigate the effects of age, sex, and other exploratory adjustments. As a proof‐of‐concept example of atlas application, the normative atlas was compared with patients with Alzheimer's disease and healthy subjects from a previous study. Results: Regional differences were significant for all metabolites ( p < 10 ⁻¹⁵ ), with the highest values in the occipital lobes, except for lactate, whose regional distribution pattern was less consistent. While lactate production showed no overall age‐dependency, global Glx production decreased 13% ± 4% per decade. Lactate production tended to be higher in males than females ( p = 0.042), but this was not significant after regional adjustment ( p = 0.084). Discriminating between health and Alzheimer's disease required additional adjustments for weight, blood glucose, and timing. Conclusions: While regional and age effects explained a substantial part of the variability in Glx, reliable intersubject comparisons required additional adjustments. The normative atlas presented here provides a reference for future DMI studies of brain metabolism.

Axonal demyelination is a key feature in multiple sclerosis (MS), that is, increasingly linked to a state of energetic failure, however current non-invasive methods to probe downstream metabolic changes are lacking. This study aimed to investigate Deuterium Metabolic Imaging (DMI) to measure alterations in glucose metabolism in MS compared to healthy controls (HC). In this prospective study DMI was performed on eight patients with relapsing-remitting MS (RRMS) together with eight age- and sex-matched HC. Following oral administration of [6,6′- ² H ₂ ] glucose, DMI was acquired on 3 T MRI to quantify the metabolic conversion of glucose (Glc) into glutamate and glutamine (Glx) and lactate. White matter (WM) oxidative metabolism ( ² H-Glx/ ² H-Water), showed a negative correlation with the Expanded Disability Status Scale score (EDSS) (Pearson r = −0.71, p = 0.049). Glucose ( ² H-Glc/ ² H-Water), remained unchanged across EDSS (Pearson r = −0.13, p = 0.76). Impaired glucose levels and oxidative metabolism in WM correlated negatively with increased number of T2-FLAIR lesions (Pearson r = −0.86 to −0.83, respectively). These in vivo findings provide evidence for a relative failure of oxidative energy production within the WM of RRMS compared to HC offering proof of concept that DMI is a feasible tool for measuring neurodegeneration related metabolic changes.

Lung MRI with hyperpolarized xenon (¹²⁹Xe) gas reveals key characteristics of pulmonary physiology such as ventilation and alveolar‐capillary gas transfer. Magnetic resonance spectroscopic imaging (MRSI) offers insights into regional oxygenation saturation (sO₂) through chemical shift changes related to xenon–hemoglobin binding. The similarity between porcine and human anatomy and physiology, particularly in terms of lung volume, airway structure, and alveolar‐capillary microstructure, offers the opportunity to investigate physiological effects linked to oxygen supply using ¹²⁹Xe MRSI. We hypothesize that ¹²⁹Xe MRSI can detect regional chemical shift changes related to red blood cell oxygenation and arterial oxygen partial pressure (p_aO₂) in a porcine model. Imaging was performed on a 3‐T clinical MRI scanner on four healthy pigs mechanically ventilated at fractional inspired oxygen levels (FiO₂) of 40% and 100%. Dissolved‐phase images were acquired using a 3D Cartesian MRSI sequence with a spherical sampling pattern in a matrix size of 28 × 28 × 6. A spectrally tailored RF pulse excited the dissolved and gaseous phases with flip angles of 10° and 0.1°, respectively. Repetition time was 7.4 ms resulting in a total acquisition time of 18 s. In addition, ¹²⁹Xe ventilation, pulmonary anatomical scans, dynamic contrast‐enhanced perfusion, and arterial blood gas were measured at each FiO₂. Pair‐wise comparisons were performed between inspired oxygen levels, along with linear regression analysis of p_aO₂ and dissolved‐phase chemical shift imaging. Porcine lung lobes were segmented, and two‐way ANOVA were performed to evaluate regional effects of oxygen concentrations. Arterial blood gas and cardiopulmonary measures showed an increase in p_aO₂ with the increase in FiO₂. Ventilation defect percentage and perfusion metrics did not significantly change with higher oxygen concentration. Dissolved‐phase ratios of red blood cells (RBC) to membrane increased with higher oxygen concentration. Increasing inspired oxygen resulted in a lower RBC chemical shift and increased linewidth, indicating RBC measures are sensitive to p_aO₂. Simple linear regression analysis of RBC chemical shift and a multiple linear regression model including linewidth were applied for regional p_aO₂ maps. Regional effects of oxygen were confirmed in the segmented lung lobes. Dissolved‐phase ¹²⁹Xe chemical shift of RBC decreased linearly with p_aO₂ in healthy porcine lungs. Regional chemical shift, linewidth, and signal ratio changes were determined in dissolved‐phase imaging of RBC at 40% and 100% FiO₂. Our data suggest that regional p_aO₂ prediction is possible with a multiple linear regression model including RBC chemical shift and linewidth as combined effect of oxygen across animal lung lobes affects regions differently.

Deuterium metabolic imaging was highly aligned with fluorodeoxyglucose PET in participants with Alzheimer disease (AD) but was unable to help differentiate participants with AD from age-matched controls.

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications.

Commercial human MR scanners are optimised for proton imaging, containing sophisticated prescan algorithms with setting parameters such as RF transmit gain and power. These are not optimal for X-nuclear application and are challenging to apply to hyperpolarised experiments, where the non-renewable magnetisation signal changes during the experiment. We hypothesised that, despite the complex and inherently nonlinear electrodynamic physics underlying coil loading and spatial variation, simple linear regression would be sufficient to accurately predict X-nuclear transmit gain based on concomitantly acquired data from the proton body coil. We collected data across 156 scan visits at two sites as part of ongoing studies investigating sodium, hyperpolarised carbon, and hyperpolarised xenon. We demonstrate that simple linear regression is able to accurately predict sodium, carbon, or xenon transmit gain as a function of position and proton gain, with variation that is less than the intrasubject variability. In conclusion, sites running multinuclear studies may be able to remove the time-consuming need to separately acquire X-nuclear reference power calibration, inferring it from the proton instead.

Purpose: X‐nuclei (also called non‐proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x‐nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state‐of‐the‐art denoising method.Methods: Denoising approaches were compared on human acquisitions of sodium (²³Na) brain, deuterium (²H) brain, carbon (¹³C) heart and brain, and simulated dynamic hyperpolarized ¹³C brain scans, with and without additional noise. The current state‐of‐the‐art denoising method Global‐local higher order singular value decomposition (GL‐HOSVD) was compared to the few‐input method tensor Marchenko‐Pastur principal component analysis (tMPPCA). Noise‐removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean‐square‐error and Bland–Altman analysis to quantify agreement between original and denoised results of noise‐added data.Results: GL‐HOSVD and tMPPCA showed similar performance for the variety of x‐nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL‐HOSVD. The mean ratio between noise‐added and denoising reproducibility coefficients of the Bland–Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL‐HOSVD and tMPPCA, respectively.Conclusion: The strength of tMPPCA lies in the few‐input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x‐nuclei imaging improvements and the preferred denoising method.

Sodium MRI allows the non‐invasive quantification of intra‐organ sodium concentration. RF inhomogeneity introduces uncertainty in this estimated concentration. B₁ field corrections can be used to overcome some of these limitations. However, the low signal‐to‐noise ratio in sodium MRI makes accurate B₁ mapping in reasonable scan times challenging. The study aims to evaluate Bloch–Siegert off‐resonance (BLOSI) B₁ field correction for sodium MRI using a 3D Fermat looped, orthogonally encoded trajectories (FLORET) read‐out trajectory.We propose a clinically feasible B₁ field map correction method for sodium imaging at 3 T, evaluating five healthy subjects' brain, heart blood, kidneys, and thigh muscle. We scanned the subjects twice for repeatability measures and used sodium phantoms to determine organ total sodium concentration. Conventional proton scans were compared with sodium images for organ structural integrity. The BLOSI approach based on the 3D FLORET read‐out trajectory was used in B₁ field correction and 3D density‐adapted radial acquisition for sodium imaging.Results indicate improvements in sodium imaging based on B₁ field correction in a clinically feasible protocol. Improvements are determined in all organs by enhanced anatomical representation, organ homogeneity, and an increase in the total sodium concentration after applying a B₁ field correction.The proposed BLOSI‐based B₁ field correction using a 3D FLORET read‐out trajectory is clinically feasible for sodium imaging, which is shown in the brain, heart, kidney, and thigh muscle. This supports using fast B₁ field mapping in the clinical setting.