Marion Irene Menzel

Technische Hochschule Ingolstadt · Department of Electrical Engineering

Objective: In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. To eliminate CT from the workflow, it is necessary to generate the information provided by CT via an MR image. We propose a method to generate accurate synthetic CT (sCT) from MRI to suit the radiation t...

Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion–relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-st...

Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion-recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion-relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-st...

One of the most prominent methods for uncertainty quantification in high-dimen-sional statistics is the desparsified LASSO that relies on unconstrained $\ell_1$-minimization. The majority of initial works focused on real (sub-)Gaussian designs. However, in many applications, such as magnetic resonance imaging (MRI), the measurement process possesse...

Current state-of-the-art reconstruction for quantitative tissue maps from fast, compressive, Magnetic Resonance Fingerprinting (MRF), use supervised deep learning, with the drawback of requiring high-fidelity ground truth tissue map training data which is limited. This paper proposes NonLinear Equivariant Imaging (NLEI), a self-supervised learning...

In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness the ability of a multi-task n...

In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness the ability of a multi-task n...

The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imagi...

Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes duri...

Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here...

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to...

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to...

Purpose Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T...

We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage a...

This study aims to develop a silent, fast and 3D method for T1 and proton density (PD) mapping, while generating time series of T1-weighted (T1w) images with bias-filed correction. Undersampled T1w images at different effective inversion times (TIs) were acquired using the inversion recovery (IR) prepared RUFIS sequence with an interleaved K-space...

In brain tumor diagnosis, fully quantitative, multiparametric MRI offers great opportunities as it allows for comprehensive tissue and hence tumor characterization which is essential for treatment planning and monitoring the treatment response. With its highly accelerated acquisition, advanced rapid MR mapping techniques facilitate multiparametric...

We synchronize dimension reduction and parameter inference and propose a hybrid neural network with a signal-encoding layer followed by a dual-pathway structure, for parameter prediction and recovery of the artifact-free signal evolution. Complementing the fast acquisition of coupled multiparametric MR signals, multiple studies have dealt with impr...

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mappi...

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mappi...

Purpose The linear change of the water proton resonance frequency shift (PRFS) with temperature is used to monitor temperature change based on the temporal difference of image phase. Here, the effect of motion‐induced susceptibility artifacts on the phase difference was studied in the context of mild radio frequency hyperthermia in the pelvis. Met...

We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage a...

Purpose To introduce a robust methodology for fast ¹H MRSI of the brain at 3T with improved SNR and reduced phase‐related artifacts. Method An accelerated acquisition scheme using echo‐planar spectroscopic imaging (EPSI) was combined with the overdiscrete reconstruction framework. This approach enables the interleaved acquisition of a water refere...

Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. These methods suffer from heavy storage and computation requirements as the dictionary size grows. To address these issues, we proposed an end to end fully convolutional neural network for MRF reco...

Recent deep learning approaches demonstrated their ability to successfully circumvent computationally and memory expensive dictionary matching (DM) in MR Fingerprinting (MRF). However, none of them takes advantage of the temporal nature of the MRF signal. Here, we rely on Long Short-Term Memory (LSTM) recurrent neural networks (RNN)aiming for: 1) r...

The signature element of MR Fingerprinting (MRF), the matching of acquired signal evolutions to a precomputed dictionary, has proven to allow reliable and accurate parameter quantification. Its heavy memory and computational requirements, however, make it very inefficient. Compression algorithms and deep learning approaches have hence emerged to ac...

We study a deep learning approach to address the heavy storage and computation re-quirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprint-ing (MRF) reconstruction. The MRF-Net provides a piece-wise affine approximation to the (temporal) Bloch response manifold projection. Fed with non-iterated back-projected images, t...

This work proposes an end-to-end deep fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employs linear dimensionality reduction and then uses a neural network to project the data into the tissue parameters. The MRF dictionary is only used for training the network and not during image reconstruction. We show that MR...

Purpose: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. Methods: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simulta...

Magnetic resonance imaging (MRI) has evolved into an outstandingly versatile diagnostic modality, as it has the ability to non-invasively produce detailed information on a tissue’s structure and function. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as...

Purpose: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. Methods: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simu...

MR Fingerprinting enables the quantification of multiple tissue properties from a single, timeefficient scan. Here we present a novel Diffusion Tensor MR Fingerprinting acquisition scheme that is simultaneously sensitive to T1, T2 and the full diffusion tensor. We circumvent the long-standing issue of phase errors in diffusion encoding and expensiv...

Deep learning (DL) has recently emerged to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MRF) reconstruction. Fed with non-iterated back-projected images, the network is unable to fully resolve spatially-correlated corruptions caused from the undersampling arte...

Magnetic resonance imaging (MRI) is a remarkably powerful diagnostic technique: it generates wide-ranging information for the non-invasive study of tissue anatomy and physiology. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric m...

Objective Mild hyperthermia (HT) treatments are generally monitored by phase-referenced proton resonance frequency shift calculations. A novel phase and thus temperature-sensitive fast spin echo (TFSE) sequence is introduced and compared to the double echo gradient echo (DEGRE) sequence. Theory and methods For a proton resonance frequency shift (P...

Glioblastoma is the most common and aggressive brain tumor. In clinical practice, diffusion MRI (dMRI) enables tumor infiltration assessment, tumor recurrence prognosis, and identification of white-matter tracks close to the resection volume. However, the vasogenic edema (free-water) surrounding the tumor causes partial volume contamination, which...

The main purpose of this study is to show that a highly accelerated Cartesian MRF scheme using a multi-shot EPI readout (i.e. multi-shot EPI-MRF) can produce good quality multi-parametric maps such as T1, T2 and proton density (PD) in a sufficiently short scan duration that is similar to conventional MRF. This multi-shot approach allows considerabl...

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a matched-filtering step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. In this abstract we investigate and evaluate advantages of...

Purpose The compartmental nature of brain tissue microstructure is typically studied by diffusion MRI, MR relaxometry or their correlation. Diffusion MRI relies on signal representations or biophysical models, while MR relaxometry and correlation studies are based on regularized inverse Laplace transforms (ILTs). Here we introduce a general framewo...

Biophysical techniques are used in many key stages of the drug discovery process including in screening for new receptor ligands, in characterising drug mechanisms, and in validating data from biochemical and cellular assays. This book provides an overview of the biophysical methods applied in drug discovery today, including traditional techniques...

Background: Non-invasive tumor characterization and monitoring are among the key goals of medical imaging. Using hyperpolarized ¹³C-labelled metabolic probes fast metabolic pathways can be probed in real-time, providing new opportunities for tumor characterization. In this in vitro study, we investigated whether measurement of apparent diffusion co...

We propose a robust reconstruction model for dynamic per-fusion magnetic resonance imaging (MRI) from undersampled k-space data. Our method is based on a joint penalization of the pixel-wise incoherence on temporal differences and patch-wise dissimilarities between spatio-temporal neighborhoods of perfusion image series. We evaluate our method on d...

We present a new spatio-temporal denoising method for arterial spin labeling (ASL) MR image repetitions, and mainly aim to improve the quality of perfusion-weighted images and cerebral blood flow (CBF) maps obtained from a subset of all dynamics available. Our technique is based on a two-step 3D total variation regularization, which is applied to s...

Purpose: Diffusion MRI often suffers from low signal-to-noise ratio, especially for high b-values. This work proposes a model-based denoising technique to address this limitation. Methods: A generalization of the multi-shell spherical deconvolution model using a Richardson-Lucy algorithm is applied to noisy data. The reconstructed coefficients a...

In the past decades, new methods for tumor staging, restaging, treatment response monitoring, and recurrence detection of a variety of cancers have emerged in conjunction with the state-of-the-art positron emission tomography with ¹⁸F-fluorodeoxyglucose ([¹⁸F]-FDG PET). ¹³C magnetic resonance spectroscopic imaging (¹³CMRSI) is a minimally invasive...

Diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive imaging technique allowing to estimate the molecular self-diffusion tensors of water within surrounding tissue. Due to the low signal-to-noise ratio of magnetic resonance images, reconstructed tensor images usually require some sort of regularization in a post-processing step. P...

Dynamic perfusion magnetic resonance (MR) imaging is a commonly used imaging technique that allows to measure the tissue per-fusion in an organ of interest via assessment of various hemodynamic parameters such as blood flow, blood volume, and mean transit time. In this paper, we tackle the problem of recovering perfusion MR images from undersampled...

Magnetic resonance fingerprinting (MRF) quantifies various properties simultaneously by matching measurements to a dictionary of precomputed signals. We propose to extend the MRF framework by using a database to introduce additional parameters and spatial characteristics to the dictionary. We show that, with an adequate matching technique which inc...

Dear Editor, Indeed acetate trafficking matters, however, hyperpolarized 13C‐acetate‐to‐acetylcarnitine is unable to detect any significant alterations between healthy controls and type‐1 diabetic rat heart, liver, and kidney, respectively in the fed state, with the current clinical setting hyperpolarized methodology. One potential reason for thi...

Most tumours exhibit a high rate of glycolysis and predominantly produce energy by lactic acid fermentation. To maintain energy production and prevent toxicity, the lactate generated needs to be rapidly transported out of the cell. This is achieved by monocarboxylate transporters (MCTs), which therefore play an essential role in cancer metabolism a...

Individual tumor characterization and treatment response monitoring based on current medical imaging methods remain challenging. This work investigates hyperpolarized (13) C compounds in an orthotopic rat hepatocellular carcinoma (HCC) model system before and after transcatheter arterial embolization (TAE). HCC ranks amongst the top six most common...

Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have requ...

Previous studies suggest that isocitrate dehydrogenase 1 (IDH1) mutation plays a significant role in the cancerous metabolome. Among other alternations, expression of branched chain amino-acid transaminase 1 (BCAT1) is reduced, causing a decrease of α-ketoglutarate (αKG) to glutamic acid metabolic pathway. More importantly, the mutated IDH1 catalyz...

Purpose: Because of the intrinsic low signal-to-noise ratio in diffusion-weighted imaging (DWI), magnitude processing often causes an overestimation of the signal's amplitude. This results in low-estimation accuracy of diffusion models and reduced contrast because of a superposition of the image signal and the noise floor. We adopt a new phase cor...

Purpose: Diffusional kurtosis imaging (DKI) is an approach to characterizing the non-Gaussian fraction of water diffusion in biological tissue. However, DKI is highly susceptible to the low signal-to-noise ratio of diffusion-weighted images, causing low precision and a significant bias due to Rician noise distribution. Here, we evaluate precision...

Purpose: Diffusion spectrum imaging (DSI) is an imaging technique that has been successfully applied to resolve white matter crossings in the human brain. However, its accuracy in complex microstructure environments has not been well characterized. Theory and methods: Here we have simulated different tissue configurations, sampling schemes, and...

Objective: (13)C metabolic MRI using hyperpolarized (13)C-bicarbonate enables preclinical detection of pH. To improve signal-to-noise ratio, experimental procedures were refined, and the influence of pH, buffer capacity, temperature, and field strength were investigated. Materials and methods: Bicarbonate preparation was investigated. Bicarbonat...

Magnetic resonance fingerprinting (MRF) is a novel technique that allows for the fast and simultaneous quantification of multiple tissue properties, progressing from qualitative images, such as T1- or T2-weighted images commonly used in clinical routines, to quantitative parametric maps. MRF consists of two main elements: accelerated pseudorandom a...

Diffusion MRI uses a multi-step data processing pipeline. With certain steps being prone to instabilities, the pipeline relies on considerable amounts of partly redundant input data, which requires long acquisition time. This leads to high scan costs and makes advanced diffusion models such as diffusion kurtosis imaging (DKI) and neurite orientatio...

In the metabolism of acetate several enzymes are involved, which play an important role in free fatty acid oxidation. Fatty acid metabolism is altered in diabetes patients and therefore acetate might serve as a marker for pathological changes in the fuel selection of cells, as these changes occur in diabetes patients. Acetylcarnitine is a metabolic...

The aim of this study was to characterise and compare widely used acquisition strategies for hyperpolarised (13) C imaging. Free induction decay chemical shift imaging (FIDCSI), echo-planar spectroscopic imaging (EPSI), IDEAL spiral chemical shift imaging (ISPCSI) and spiral chemical shift imaging (SPCSI) sequences were designed for two different r...

A method of generating a magnetic resonance (MR) image of a tissue includes acquiring MR signals at undersampled q-space encoding locations for a plurality of q-space locations that is less than an entirety of the q-space locations sampled at the Nyquist rate, wherein the acquired signal at the q-space locations represents the three-dimensional dis...

Recently, super-resolution methods for diffusion MRI capable of retrieving high-resolution diffusion-weighted images were proposed, yielding a resolution beyond the scanner hardware limitations. These techniques rely on acquiring either one isotropic or several anisotropic low-resolution versions of each diffusion-weighted image. In the present wor...

A magnetic resonance (MR) imaging method includes acquiring MR signals having phase and magnitude at q-space locations using a diffusion sensitizing pulse sequence performed on a tissue of interest, wherein the acquired signals each include a set of complex Fourier encodings representing a three-dimensional displacement distribution of the spins in...

Hyperpolarized (13)C imaging allows real-time in vivo measurements of metabolite levels. Quantification of metabolite conversion between [1-(13)C]pyruvate and downstream metabolites [1-(13)C]alanine, [1-(13)C]lactate, and [(13)C]bicarbonate can be achieved through kinetic modeling. Since pyruvate interacts dynamically and simultaneously with its do...

PurposeThe metabolism of acetate in the heart resembles fatty acid metabolism, which is altered in several diseases like ischemia, diabetes mellitus, and heart failure. A signal-to-noise ratio (SNR) optimized imaging framework for in vivo measurements of hyperpolarized [1-13C]acetate and its metabolic product [1-13C]acetylcarnitine (ALCAR) in rats...

Hyperpolarization of [1-13C]pyruvate in solution allows real-time measurement of uptake and metabolism using MR spectroscopic methods. After injection and perfusion, pyruvate is taken up by the cells and enzymatically metabolized into downstream metabolites such as lactate, alanine, and bicarbonate. In this work, we present comprehensive methods fo...

PurposeBecause pH plays a crucial role in several diseases, it is desirable to measure pH in vivo noninvasively and in a spatially localized manner. Spatial maps of pH were quantified in vitro, with a focus on method-based errors, and applied in vivo.Methods In vitro and in vivo 13C mapping were performed for various flip angles for bicarbonate (Bi...

The combination of hyperpolarized MRS with diffusion weighting (dw) allows for determination of the apparent diffusion coefficient (ADC), which is indicative of the intra- or extracellular localization of the metabolite. Here, a slice-selective pulsed-gradient spin echo sequence was implemented to acquire a series of dw spectra from rat muscle in v...

Dynamic nuclear polarisation has enabled real-time metabolic imaging of pyruvate and its metabolites. Conventional imaging sequences rely on predefined settings and do not account for intersubject variations in biological parameters such as perfusion. We present a fully automatic real-time bolus tracking sequence for hyperpolarised substrates which...

PurposeTo evaluate a model-independent, multi-directional anisotropy (MDA) metric that is analytically and experimentally equivalent to fractional anisotropy (FA) in single-direction diffusivity, but potentially superior to FA in its sensitivity to the underlying anisotropy of multi-directional diffusivity. Materials and Methods An expression for M...

Within the last decade hyperpolarized [1-(13) C] pyruvate chemical-shift imaging has demonstrated impressive potential for metabolic MR imaging for a wide range of applications in oncology, cardiology, and neurology. In this work, a highly efficient pulse sequence is described for time-resolved, multislice chemical shift imaging of the injected sub...

The detection of tumors noninvasively, the characterization of their progression by defined markers and the monitoring of response to treatment are goals of medical imaging techniques. In this article, a method which measures the apparent diffusion coefficients (ADCs) of metabolites using hyperpolarized 13 C diffusion-weighted spectroscopy is prese...

Hyperpolarization quenching in 13 C nuclei bound to fast relaxing quadrupolar 14 N mediated by scalar coupling relaxation in amide groups exposed to Earth's magnetic field. The observed low field relaxation behavior for 14 N-13 C amides suggested that, in such conditions (relatively strong J coupling, short 14 N nucleus T 1 and weak magnetic field)...

Unlabelled: Abnormalities of tumor metabolism can be exploited for molecular imaging. PET imaging of (18)F-FDG is a well-established method using the avid glucose uptake of tumor cells. (13)C MR spectroscopic imaging (MRSI) of hyperpolarized [1-(13)C]pyruvate and its metabolites, meanwhile, represents a new method to study energy metabolism by vis...

Metabolic imaging with hyperpolarized [1-(13)C]pyruvate offers the unique opportunity for a minimally invasive detection of cellular metabolism. Efficient and robust acquisition and reconstruction techniques are required for capturing the wealth of information present for the limited duration of the hyperpolarized state (~1 min). In this study, the...

Real-time in vivo measurements of metabolites are performed by signal enhancement of [1-13C]pyruvate using dynamic nuclear polarization, rapid dissolution and intravenous injection, acquisition of free induction decay signals and subsequent quantification of spectra. The commonly injected dose of hyperpolarized pyruvate is larger than typical trace...

We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sen...