bluuebunny/crossref_metadata_2025 huggingface dataset:
Peri-tumoural spatial distribution of lipid composition and tubule formation in breast cancer
Authors: Cheung Sai Man , Chan Kwok-Shing , Senn Nicholas , Husain Ehab , Masannat Yazan , Heys Steven , He Jiabao
Published: August 2023

The spatial distribution of lipid composition in breast has a major role in breast cancer prevention, with deregulation of lipid metabolism identified in BRCA1/2 genetic mutation carriers. Neoplastic tubule formation, a key component of grading score, is directly associated with cellularity, with significant implications on prognosis. Lipid composition measurement through biochemical extraction is invasive, while conventional spectroscopic imaging demands long acquisition time. Novel chemical shift-encoded imaging (CSEI) allows lipid composition mapping of the whole breast in a clinically acceptable timeframe. We set out to examine the relationship between peri-tumoural lipid composition and tubule formation using CSEI in breast tumours.
DTI quantitative parameter texture features for differentiating solid pseudopapillary neoplasm from pancreatic neuroendocrine tumors
Authors: Wang Yi , Wang Xinqi , Wang Lu , Xie Lizhi , Zhang Qinhe , Liu Ailian
Published: August 2023

This work aimed for diffusion tensor imaging (DTI) quantitative parameter texture features based strategy to identify solid pseudopapillary neoplasm（SPN）and pancreatic neuroendocrine tumors（PNET），which may represent a diagnostic challenge due to many overlapping MRI features. The results showed that Large Area Emphasis (AUC: 0.737, sensitivity: 64.7%, specificity: 80%on FA signal intensity) was the optimal strategy to identify SPN and PNET.
Evaluation of Multiparametric Magnetic Resonance Imaging to assist Adaptive Radiotherapy in Prostate Cancer – an interim report
Authors: Turnbull Angela , Taylor N. Jane , Lakhani Amish , McGuire William , Bowie Rachael , Alonzi Roberto , Mcwilliam Alan
Published: August 2023

Initial results of a prostate cancer study investigating whether multiparametric MRI (MP-MRI), involving diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI, can predict or assess tumour response to radiotherapy (RT) and potentially support adaptive radiotherapy for high-risk patients. Adaptive radiotherapy is a treatment technique utilised to minimise radiation related toxicity. For some cancers it is possible to adapt radiotherapy according to physical changes that occur during treatment. This is not appropriate for prostate cancer treatment since observable changes are generally due to rectal movement, however it may be possible to use multiparametric MRI (MP-MRI) to measure treatment response.
Deep Learning based MR reconstruction for accelerated 3D-PREFUL ventilation assessment of post-COVID-19 patients from undersampled MR-images
Authors: Zubke Maximilian , Klimeš Filip , Voskrebenzev Andreas , Gutberlet Marcel , Kern Agilo , Müller Robin , Obert Arnd , Wacker Frank , Vogel-Claussen Jens
Published: August 2023

3D phase-resolved functional lung (3D-PREFUL) proton MRI enables a radiation-free and non-contrast-enhanced ventilation assessment of human lungs. However, generating high-quality images usually requires a long acquisition time. Acceleration can be achieved by undersampling k-space data, but the resulting violation of the Nyquist theorem leads to image artifacts. Deep learning (DL)-based reconstruction approaches are proposed as a solution for this dilemma. Two novel loss functions are introduced to create a deep learning based reconstruction, optimized for lung MRI. The feasibility of ventilation assessment, including ventilation defect identification, from 8x undersampled MR-images of post-COVID-19 patients, reconstructed by a neural network is demonstrated.
Coil-sketched unrolled networks for computationally-efficient deep MRI reconstruction
Authors: Oscanoa Julio , Ozturkler Batu , Iyer Siddharth , Li Zhitao , Sandino Christopher , Pilanci Mert , Ennis Daniel , Vasanawala Shreyas
Published: August 2023

Deep unrolled networks can outperform conventional compressed sensing reconstruction. However, training unrolled networks has intensive memory and computational requirements, and is limited by GPU-memory constraints. We propose to use our previously developed “coil-sketching” algorithm to lower the computational burden of the data consistency step. Our method reduced memory usage and training time by 18% and 15% respectively with virtually no penalty on reconstruction accuracy when compared to a state-of-the-art unrolled network.
Near Metal MRI with Compressed Sensing PETRA
Authors: Ilbey Serhat , Bock Michael , Jung Matthias , Konstantinidis Lukas , Özen Ali
Published: August 2023

MRI near metallic implants require high bandwidth excitation and acquisition to minimize signal void and pile-up artefacts. In this work, compressed sensing PETRA (csPETRA) was modified to have intentionally a longer TE, so that the phase-encoding part of the k-space is extended. To prevent extremely long scan times, SPI points in csPETRA sequence are pseudo-randomly undersampled to reduce the scan time below 6 minutes. csPETRA reduced artifacts near metal significantly while preserving the anatomical details. Isotropic 3D MRI of two volunteers with mouth and knee prostheses was performed with csPETRA and the artifacts are significantly reduced compared to T1w-WARP sequence.
Imaging near metal at 0.55 T using gradient-echo based sequences: Feasibility and Opportunities
Authors: Keskin Kübra , Hargreaves Brian , Nayak Krishna
Published: August 2023

The large magnetic susceptibility difference between metallic implants and surrounding tissues causes severe MRI artifacts that scale with the B0 field strength. At conventional field strengths, spin-echo based multispectral imaging is used to mitigate this artifact and produce diagnostic images. At lower field strengths, such as 0.55 T, other strategies may be feasible. Here, we investigate gradient-echo based sequences for imaging near metal at 0.55 T, which provide high SNR efficiency and different contrast.
A Deep Forward-Distortion Model for Unsupervised Correction of Susceptibility Artifacts in EPI
Authors: Alkilani Abdallah Zaid , Çukur Tolga , Saritas Emine Ulku
Published: August 2023

Echo planar imaging (EPI) requires the correction of susceptibility artifacts for further quantitative analyses. Images acquired in reversed phase-encode (PE) directions are typically used to estimate the susceptibility-induced field from EPI data. In this work, an unsupervised deep Forward-Distortion Network (FD-Net) is proposed for the correction of susceptibility artifacts in EPI: The field and underlying anatomically-correct image are predicted, subject to the constraint that forward-distortion of this image with the field explains the input warped images. This approach provides rapid correction of susceptibility artifacts, with superior performance over deep learning methods that unwarp input images based on a predicted field.
Time-segmented and contrast-neutral motion correction using an end-to-end deep learning approach
Authors: Gabr Refaat , Edalati Masoud , Hu Lingzhi , Chandler Adam , Zhang Weiguo , Narayana Ponnada
Published: August 2023

We developed an end-to-end deep learning-based motion correction technique that utilized the time-segmented nature of MRI data acquisition. Results of computer simulations and in vivo studies show the network to be highly effective in correcting motion artifacts.
Creating a multi-centre harmonised surface-based MRI dataset for the Multi-centre Epilepsy Lesion Detection Project
Authors: Ripart Mathilde , Spitzer Hannah , Shinohara Russell , Consortium MELD , Baldeweg Torsten , Adler Sophie , Wagstyl Konrad
Published: August 2023

The Multi-centre Epilepsy Lesion Detection (MELD) project presents a methodology to harmonise a large heterogenous cohort of surface-based MRI data. Structural features were extracted from T1w and FLAIR images and pre-processed to reduce systematic site, scanner, and age-specific biases. The harmonised dataset enabled the characterisation of subtle radiological markers of focal cortical dysplasia (FCD), a cortical abnormality causing drug-resistant epilepsy. Machine-learning algorithms trained on the harmonised dataset improved the classification of FCD histopathologies. With open-source protocols and code, the MELD preprocessing pipeline offers a reproducible method to prepare large heterogeneous datasets for statistical analysis and machine-learning tasks.
The relationship between structural asymmetry and shear stiffness asymmetry in the human brain
Authors: Xiang Lily , Hiscox Lucy , Johnson Curtis , Roberts Neil
Published: August 2023

On average the human brain has a global structural asymmetry comprising a counter-clockwise twist in the transverse plane and which is referred to as the cerebral torque. We have investigated whether there is a relationship between the magnitude of the torque and measures of brain shear stiffness obtained using Magnetic Resonance Elastography (MRE) in a group of Older Adults (AD) and a group of patients with Alzheimers DIsease (AD).
High Resolution Ex Vivo Diffusion Tensor Distribution MRI of Neural Tissue
Authors: Magdoom Kulam Najmudeen , Komlosh Michal , Kadharbatcha Saleem , Gasbarra Dario , Basser Peter
Published: August 2023

Neural tissue microstructure plays a key role in developmental, physiological, and pathophysiological processes. Diffusion tensor distribution (DTD) MRI is a promising approach to resolve sub-voxel microstructural features using multiple diffusion encodings. In this study, we have applied a novel DTD framework and pulse sequence to investigate its capabilities in revealing neural tissue microstructure. We present high resolution data acquired using an excised visual cortex and cervical spinal cord of a macaque monkey. The results show DTD MRI untangles size, shape and orientation heterogeneity within a voxel and parcellates tissue consistent with histological findings.
Time-dependent diffusion and kurtosis in the extra-axonal space from 3D electron microscopy substrates of injured rat brain white matter
Authors: Coronado-Leija Ricardo , Lee Hong-Hsi , Abdollahzadeh Ali , Tohka Jussi , Sierra Alejandra , Fieremans Els , Novikov Dmitry
Published: August 2023

In this work, we perform Monte Carlo simulations of diffusion in the extra-axonal space segmented from realistic 3D electron microscopy substrates. Simulations in sham and TBI rat brains confirm the universality of the power-law functional form of the axial and radial time-dependent diffusion $$$D^{\parallel,\perp}(t)$$$ and kurtosis $$$K^{\parallel,\perp}(t)$$$. We characterize the changes caused by TBI, finding that the dependence of long-time asysmptote $$$D^{\perp}_\infty$$$ on the extra-axonal volume fraction follows Archie's law. We also validate the theoretically predicted relationship between the power-law tails of $$$D(t)$$$ and $$$K(t)$$$.
Probing complex morphologies at decreasing diffusion times using Diffusion-weighted MR Spectroscopy
Authors: Just Nathalie , Lasič Samo , Christensen Ditte Bentsen , Valette Julien , Dyrby Tim , Siebner Hartwig , Lundell Henrik
Published: August 2023

Metabolite diffusion provide the unique ability to study the intracellular environment of specific cell types of the brain. Multiple studies suggest that the diffusivity along dendrites and axons is time dependent due to variations in diameter. We explore the signal due to such effects in Monte Carlo simulations in settings feasible for preclinical PGSE measurements and find that time dependent kurtosis, but not diffusivity provide the most potent source of contrast to this effect. We observe similar effects of intraneuronal NAA diffusion in rat.
Test-retest reliability of whole-brain and bundle-level microstructural properties
Authors: Roine Timo , Genc Sila
Published: August 2023

We investigated the test-retest reliability of bundle- and voxel-/fixel-level microstructural metrics with fixel-based analysis, neurite orientation dispersion and density imaging, and diffusion tensor imaging. For the bundle-level analyses, TractSeg was used to automatically segment 72 fiber bundles of the brain. Our results indicate that most of the metrics, especially those measured with fixel-based analysis, are highly robust and show excellent test-retest reliability both in local and bundle-level analyses. In general, the reproducibility was higher in the white matter in contrast to gray matter and for acquisitions with multi-shell compared to single-shell data.
Predictive modelling and center effects: towards a robust functional connectivity-based neuromarker of pain sensitivity
Authors: Spisak Tamas , Kincses Balint , Kotikalapudi Raviteja , Zunhammer Matthias , Schlitt Frederik , Schmidt-Wilcke Tobias , Kincses Zsigmond Tamas , Bingel Ulrike
Published: August 2023

Center effects significantly limit the generalizability of brain imaging-based biomarker candidates. Although our previously published resting state functional connectivity-based predictive signature for pain sensitivity (the RPN-signature) showed remarkable out-of-center generalizability, it remained unclear which connectivity features are the most generalizable across study centers.

Here, we re-trained the RPN-signature on multi-center data and found that it outperforms the single-center model in all three centers (explained variance: 26-38% vs. 16%-19%). Our results highlight that neurobiological interpretation of feature importance in predictive modelling is constrained both by center-specific artifacts and by certain characteristics (e.g. regularization) of the applied machine learning algorithm.
A novel method for characterizing dynamic resting state functional connectivity in Alzheimer's Disease
Authors: Yue Kun , Webster Jason , Grabowski Thomas , Shojaei Ali , Jahanian Hesamoddin
Published: August 2023

With advances in experimental therapeutics for Alzheimer's Disease (AD) the need for an accurate, non-invasive and widely available AD biomarker is more pressing than ever. Resting-state functional connectivity in default mode network is a candidate biomarker that is gaining traction in the field. However, the traditional stationary measurement of the default mode network connectivity cannot capture complicated dynamic patterns of functional connectivity that exist in the brain. Here, we have proposed a novel, reliable technique, based on Dynamic Condtional Correlation model, to quantify the dynamic functional connectivity in the brain and evaluated its sensitivity to cerebrospinal fluid biomarkers in AD.
Temporal resolution impacts in-vivo human brain dynamic functional MRI connectivity
Authors: Saviola Francesca , Tambalo Stefano , Van De Ville Dimitri , Jovicich Jorge
Published: August 2023

The advent of fast fMRI acquisition techniques has enabled whole-brain acquisitions with sub-second temporal resolution enriching the information available throughout the time course. However, there is no consensus about how the application of frame-wise analysis, performed to better understand functional brain fluctuations, could benefit from high temporal resolution fMRI. In this work, we demonstrate the potential need to: (i) gain a finer understanding of signal and noise signatures peculiar to fast acquisition and (ii) extend the models to better estimate dynamic functional connectivity in rapidly sampled fMRI time series.
3D joint T1/T1ρ mapping and water-fat imaging for contrast-agent free myocardial tissue characterization
Authors: Crabb Michael , Kunze Karl , Velasco Carlos , Fotaki Anastasia , Munoz Camila , Hua Alina , Neji Radhouene , Prieto Claudia , Botnar Rene
Published: August 2023

In patients with suspected myocardial infarction, late-gadolinium enhancement (LGE) images are often acquired to detect focal and diffuse scarring. T1ρ is a sensitive marker for assessment of macromolecular-water interaction and has shown potential for non-contrast enhanced imaging of fibrosis. Here we propose a novel free-breathing, 3D whole-heart joint T1/T1ρ mapping sequence with Dixon encoding to provide 3D T1 and T1ρ maps and co-registered water and fat volumes with isotropic resolution for comprehensive contrast-agent free myocardial tissue characterization. Preliminary 3D joint T1/T1ρ mapping and water/fat imaging results demonstrate good agreement in phantoms and promising results in-vivo in healthy volunteers and patients.
Free-running 3D T1 and T2 myocardial mapping and cine MRI in 3 mins using low-rank non-rigid motion-corrected reconstruction
Authors: Phair Andrew , Cruz Gastao , Qi Haikun , Botnar René , Prieto Claudia
Published: August 2023

Recent work has enabled the simultaneous acquisition of 3D myocardial T1 and T2 maps with isotropic spatial resolution and cardiac cine images from a ~10-minute scan. Herein, we propose to incorporate non-rigid cardiac motion correction into a dictionary-based low-rank reconstruction scheme, allowing k-space data from all cardiac phases to be included in the reconstruction of any given phase. Reconstructed cine images and T1 and T2 maps with and without motion correction are presented and demonstrate that a reduction to 30% of the acquired k-space data (~3-minute scan) can be achieved while image quality is maintained.
Chemical Shift Artifact Correction for 19F MRI using Spectral Hadamard Encoding
Authors: Bock Michael , Ilbey Serhat , Maier Alexander , Özen Ali
Published: August 2023

Chemical shift artifact correction is achieved via spectral Hadamard encoding of the individual resonances using composite RF pulses. During decoding individual images of the resonances are created that can be combined to a final composite image. The method is demonstrated in 19F MRI of perfluorooctyl bromide that can be used to label monocytes for inflammation. Compared to other correction methods Hadamard decoding can be implemented directly in the image reconstruction, and the full SNR advantage of the image combination can be realized.
Improving the Sensitivity to Myocardial Scar in Automatic Segmentations of Left Ventricular Myocardium on LGE Images
Authors: Vornehm Marc , Fenski Maximilian , Preuhs Elisabeth , Maier Andreas , Schulz-Menger Jeanette , Wetzl Jens , Giese Daniel
Published: August 2023

Automatic segmentation of left ventricular myocardium on LGE images of patients with chronic myocardial infarction is challenging due to inhomogeneous signal intensities in the presence of myocardial scar. However, the inclusion of scar in automatically generated myocardium segmentations is critical for further LGE analysis. We propose a Deep Learning-based method for myocardial segmentation on cardiac LGE images, achieving average Dice scores of 0.78 and 0.80 on test images with and without scar, respectively. We furthermore evaluate the network’s sensitivity to scar and show that it can be improved by incorporating synthetic images with diverse enhancement patterns in the training data.
Association between lesion locations and depressive symptoms in acute stroke patients using voxel-based lesion-symptom mapping
Authors: Wang Peng , Wang Jinjing , Zhang Yong , Gu Mengmeng , Jiang Shiyi , Yin Dawei , Sun Wen , Liu Xinfeng
Published: August 2023

Post-stroke depression (PSD) is a common neuropsychiatric symptom after stroke. The purpose of this study was to investigate the association between lesion locations of brain stroke and PSD occurrence using Voxel-based Lesion-Symptom Mapping (VLSM). VLSM analysis identified clusters within anterior cingulate gyrus, left hippocampus, left lingual lobe where lesions were significantly associated with PSD. The lesion location showed the promise to be used as an independent risk factor of PSD in patients with acute ischemic stroke.
Contraction-Induced Changes in Acetylcarnitine in the Human Vastus Lateralis Muscle Detected by 1H MRS at 3T
Authors: Nagarajan Rajakumar , Hayden Christopher , Gilmore Samantha , Kent Jane
Published: August 2023

Using 1H-MRS at 3T, vastus lateralis muscle acetylcarnitine was measured in 8 young male subjects at rest and in response to incremental, dynamic knee extension contractions. Intramyocellular [acetylcarnitine] increased >3-fold in response to the contraction protocol, thus buffering mitochondrial acetyl coenzyme A and thereby supporting oxidative metabolism of glucose and fat in the citric acid cycle. Measurement of acetylcarnitine has the potential to be a useful tool for the investigation of muscle substrate use and metabolic flexibility. This work is the first demonstration of dynamic changes in muscle [acetylcarnitine] at 3T.
Capability of Diffusion-Weighted Stimulated-Echo Imaging to Visualize Spontaneous Muscular Activities in Resting Musculature
Authors: Schwartz Martin , Martirosian Petros , Steidle Guenter , Feiweier Thorsten , Yang Bin , Schick Fritz
Published: August 2023

Visualization of spontaneous mechanical activities in resting musculature using diffusion-weighted stimulated-echo imaging is highly depending on the sequence parameter settings as well as on the physiological parameters of the muscular contraction. Therefore, a concept for estimation of the visualization probability by using two contraction models is given in this work. Furthermore, the proposed concept is validated using diffusion-weighted measurements with varying diffusion-sensitizing times. The determined visualization probability is in good accordance to the relative number of visible spontaneous mechanical activities in the resting lower leg musculature.
Multi-modal MR in the upper arm muscles of patients with Spinal Muscular Atrophy
Authors: Hooijmans Melissa , Habets Laura , van den Berg-Faay Sandra , Froeling Martijn , Strijkers Gustav , Asselman Fay-lynn , Nederveen Aart , Jeneson Jeroen , Bartels Bart , van der Pol W. Ludo
Published: August 2023

With upcoming therapeutic developments, quantitative MR is increasingly used to assess disease state and progression in neuromuscular disorders, including SMA. Yet, little is known about the disease state in the arm muscles in SMA. A multi-modal MR approach was used to examine fat fraction (FF), T2-relaxation time and diffusion properties, in the arm muscles of SMA patients and controls. Our results showed higher FF in the arm muscles of SMA patients which were negatively associated with the level of muscle strength. No clear changes were observed for qT2 while diffusion indices showed potential for mapping changes in muscle tissue itself.
Paraspinal muscles in low back pain: Comparison between standard parameters and chemical shift encoding-based water-fat MRI
Authors: Sollmann Nico , Bonnheim Noah , Joseph Gabby , Chachad Ravi , Zhou Jiamin , Bailey Jeannie , Guo Xiaojie , Lazar Ann , Link Thomas , Fields Aaron , Krug Roland
Published: August 2023

Low back pain (LBP) is a global health burden, and up to 90% of affected patients are diagnosed with non-specific LBP. A structure increasingly recognized as a potential contributor to LBP is paraspinal musculature (PSM). In this study we revealed that the fat fraction (FF) of PSM derived from chemical shift encoding-based water-fat MRI (CSE-MRI) is associated with the Goutallier classification (GC) at each lumbar level as well as with self-reported pain. In contrast, other parameters (muscle volume, lumbar indentation value [LIV], muscle-fat index [MFI]) do not significantly correlate to the FF, despite being frequently used to evaluate PSM quality.
Highly Asymmetric Acquisitions for Diffusion MRI Tractography
Authors: Shrestha Manoj , Hok Pavel , Nöth Ulrike , Deichmann Ralf
Published: August 2023

Frequently, diffusion-weighting modules such as schemes based on the Stejskal-Tanner concept or the STEAM are combined with EPI readout for fast k-space sampling. In contrast to standard sequences, highly asymmetric spin-echo and highly asymmetric STEAM sequences (HASE, HASTEAM) offer shorter TE, maintaining constant diffusion preparation duration, independent of the matrix-size. In this study, HASE and HASTEAM sequences were implemented for neuronal fibre tracking, and results were compared with standard sequences. Both asymmetric sequences HASE and HASTEAM allow for more accurate estimations of the subsidiary fibre orientations, which may considerably improve the quality and quantity of the detected fibre tracts.
Pulmonary imaging Using 3D Dual-Echo FID Ultra-short Echo Time MRI with Rosette k-space Pattern: Introduction and Feasibility.
Authors: Ooms Nathan , Shen Xin , Ozen Ali , Ilbey Serhat , Chiew Mark , Emir Uzay
Published: August 2023

Pulmonary imaging traditionally uses techniques utilizing ionizing radiation such as x-rays and CT. Pediatrics, pregnant patients, and patients who will be exposed multiple times over long periods of time would benefit from a non-ionizing modality. MRI does not use ionizing radiation but suffers from low signal from the lungs and breathing motion artifact. We are proposing a novel MRI technique to combat these issues: a 3-dimensional Dual-Echo FID Ultra-short Echo Time (3D DE UTE) sequence utilizing Rosette k-space acquisition. Preliminary results demonstrated better pulmonary artery segmental branches and pleural wall definition compared to other commercially available MRI sequences.
Simultaneous ADC mapping and water-fat separation with B0 correction using a rosette acquisition
Authors: Yan Kang , She Huajun , Du Yiping
Published: August 2023

In diffusion-weighted imaging (DWI) using echo-planar acquisition, fat signals are commonly suppressed by using chemical saturation or by spectral-spatial RF excitation. In this study, we present a technique using a rosette trajectory for simultaneous mapping of apparent diffusion coefficient (ADC) and water-fat separation with inherent B0 correction. The feasibility of using this technique is demonstrated in phantom, brain, and head-neck scans.
Outlier Rejection for Fetal Brain MRS
Authors: Highton Jack , Lopez Maria , Price Anthony , Kyriakopoulou Vanessa , Story Lisa , Rutherford Mary , Hajnal Jo , De Vita Enrico
Published: August 2023

Magnetic resonance spectroscopy (MRS) can measure the concentration of a range of metabolites to understand pathophysiological and normal development in the fetus, as well as provide diagnostic and prognostic markers [1]. However, MRS applications in the fetus are technically very challenging due to the inherently low SNR (fetal size, distance from the coil, the use of 1.5T scans) and signal disruption caused by unrestricted fetal motion during data collection. Given the limited available SNR, outlier rejection to remove motion corrupted averages and improve spectral quality is particularly important. Spectral phase shifts are associated with motion, both during signal acquisition and between MRS repeats [2]. We propose a threshold-free method for optimal outlier rejection of fetal MRS, “Ranked residual-water Phase Shift”: the phase shift in the residual water after water suppression is used as a metric linked to potential motion in individual transients/repeats.
Protocol harmonization using a generative adversarial network decreases morphometry variability
Authors: Ravano Veronica , Démonet Jean-François , Damian Daniel , Meuli Reto , Piredda Gian Franco , Huelnhagen Till , Maréchal Bénédicte , Thiran Jean-Philippe , Kober Tobias , Richiardi Jonas
Published: August 2023

In radiology, the deployment of automated clinical decision support tools to new institutions is often hindered by inter-site data variability. In MRI, data heterogeneity often arises from differences in acquisition protocols. To overcome this issue, we propose a post-hoc harmonization technique based on generative adversarial networks (GAN). Seventy-seven patients suffering from dementia were scanned with two distinct T1-weighted MP-RAGE protocols. We show that cross-protocol harmonization of brain images using a conditional GAN improves image similarity and reduces the variability of brain morphometry.
Data scarcity mitigation approaches in deep learning reconstruction of undersampled low field MR images
Authors: Senft Tobias , Ayde Reina , Salameh Najat , Sarracanie Mathieu
Published: August 2023

Low magnetic field (LF) MRI is gaining popularity as a flexible and cost-effective complement to conventional MRI. However, LF-MRI suffers from a low signal-to-noise ratio per unit time which calls for signal averaging and hence prolonged acquisition times, challenging the clinical value of LF MRI.

In this study, we show that Deep Learning (DL) can reconstruct artifact-free heavily undersampled 2D LF MR images (34% sampling) with great success, both retrospectively and prospectively. Our results also highlight that a transfer learning approach combined with data augmentation improves the overall reconstruction performances, even when only small LF training datasets are available.
Brain MR image super resolution using simulated data to perform in real-world MRI
Authors: Ayaz Aymen , Lukassen Kirsten , Lorenz Cristian , Weese Juergen , Breeuwer Marcel
Published: August 2023

We propose to simulate a large set of anatomically variable voxel-aligned and artifact-free brain MRI data at different resolutions to be used for training deep-learning based Super Resolution (SR) networks. To the best of our knowledge, no such efforts have been made in past regarding use of simulated data to train a SR network. We trained a SR network using such simulated data and tested the performance on real-world MRI data. The trained network could significantly sharpen low-resolution input MR images and clearly outperformed classic image interpolation methods.
Complex graph convolutional neural networks compared to GRAPPA for reconstruction of undersampled non-Cartesian MRSI
Authors: Weiser Paul , Motyka Stanislav , Bogner Wolfgang , Langs Georg
Published: August 2023

In this work a Graph Neural Network for the reconstruction of undersampled k-space data is proposed. The results were evaluated and compared against a state-of-the-art method. Overall, the results suggest that this Deep Learning-based approach for reconstruction is promising.
Single-Shot Adaptation using Score-Based Models for MRI Reconstruction
Authors: Arvinte Marius , Jalal Ajil , Daras Giannis , Price Eric , Dimakis Alex , Tamir Jonathan
Published: August 2023

This work deals with the problem of few-shot adaptation in data-driven MRI reconstruction, where models must efficiently adapt to new distributions. We introduce score-based models for MRI reconstruction and an algorithm for adjusting inference parameters (step size, noise level and stopping point), investigate the impact of these parameters on reconstruction performance, and demonstrate average gains of at least 2 dB in PSNR across a range of acceleration values, all while using a pretrained model that was trained for brain MRI and fine-tuned using only a single fully-sampled 2D knee scan from the fastMRI database.
Lactate excitation limits bicarbonate detection in hyperpolarized pyruvate MRI of the brain
Authors: Bøgh Nikolaj , Rasmussen Camilla , Bertelsen Lotte , Hansen Esben , Laustsen Christoffer
Published: August 2023

In hyperpolarized [1-13C]pyruvate MRI of the brain, pyruvate-to-bicarbonate conversion, representing oxidative metabolism, is detected at low levels, despite the highly oxidative metabolism of the brain. We sought to determine the effect of lactate excitation on bicarbonate detection in healthy rats imaged in a cross-over fashion (lactate flip angle = 90° or 0°). In the brain, the bicarbonate signal-to-noise ratio increased 65 % when lactate was not excited. This effect was not observed in the heart, kidneys or liver. Collectively, our data show that lactate saturation limits bicarbonate detection in the brain solely, which has implications for study designs.
Modelling the kinetics of cerebral lactate production after hyperpolarized [2H7, U-13C6]-D-glucose bolus
Authors: Flatt Emmanuelle , Lanz Bernard , Lê Thanh Phong , Gruetter Rolf , Mishkovsky Mor
Published: August 2023

The present work describes a quantitative analysis of lactate 13C-labeling pattern following the metabolism of hyperpolarized [2H7,U-13C6]-D-glucose. This lactate production results from 12 biochemical steps including glucose transport, 10 enzymatic steps of glycolysis, and LDH mediated pyruvate-to-lactate conversion. The 3-compartment model description was found a good compromise to interpret the hyperpolarized metabolic curves. This demonstrates the potential of hyperpolarized [2H7, U-13C6]-D-glucose as a new biochemical probe for brain energy metabolism.
Liver and heart metabolism of [1-13C]-pyruvate in awake and anesthetized rats
Authors: Hyppönen Viivi , Rosa Jessica , Kettunen Mikko
Published: August 2023

Anesthesia has profound effects on overall metabolism, leading to significant signal changes in metabolic MR brain experiments using hyperpolarized [1-13C]pyruvate. In the current study, we studied the possible origins of the signals from rat liver and heart under isoflurane anesthesia and while the animals were awake. Increased metabolite signals, especially lactate, were observed from both organs in awake animals.
Investigating liver fibrosis using water T1 estimated by flip angle corrected multi-parameter MRS
Authors: Louie Ashley , Hamilton Gavin , Schlein Alexandra , Henderson Walter , Batakis Danielle , Ceriani Lael , Covarrubias Yesenia , Wolfson Tanya , Panagiotopolous Nikolaos , Harris David , Alan McMillan , Tamada Daiki , Fowler Kathryn , Reeder Scott , Sirlin Claude
Published: August 2023

The purpose of this study was to examine the relationship between water T1 and T2 estimated by Flip Angle Corrected Multi-Parameter (CMP) MRS and biopsy-determined fibrosis. The sample of twenty-eight subjects with biopsy and MRS data is part of a dual-center, weight loss surgery study. Subjects without fibrosis were found to have significantly higher CMP MRS T1 water values than patients with fibrosis.
Reproducibility of localized 31P-MRS methods using different surface coils for assessment of hepatic metabolism in vivo at 3T
Authors: Jonuscheit Marc , Wierichs Stefan , Kupriyanova Yuliya , Roden Michael , Schrauwen-Hinderling Vera
Published: August 2023

We determined day-to-day variation and intra-session reproducibility of 31P-MRS-based hepatic phosphorus metabolite quantification. To this end, we compared a standard single loop with a custom-created quadrature surface coil. Absolute concentrations of γ-adenosine triphosphate and inorganic phosphate were assessed in 7 volunteers with both coils. Both coils showed comparable day-to-day and intra-session CVs < 10 %. Metabolite concentrations determined by the two coils were similar. Therefore, both coils yield robust absolute concentrations of hepatic phosphorous metabolites. The quadrature coil allows measurements at a higher distance from the coil which can be important for the application in obese patients.
New Endo Coil and Novel Methods for Improving mpMR-TRUS Guided Fusion Prostate Biopsies with Hyperpolarized C-13 Pyruvate Molecular Imaging
Authors: Gebrezgiabbhier Daniel , Chen Hsin-Yu , Bok Robert , Carvajal Lucas , Cooperberg Matthew , Nguyen Hao , Shinohara Katsuto , Okamoto Kimberly , Frost Mary , Wang Zhen , Ohliger Michael , Gordon Jeremy , Larson Peder , Aggarwal Rahul , Vigneron Daniel
Published: August 2023

A new 13C/1H endorectal coil was designed, 3D printed, and manufactured with optimal dimensions to improve the comfort and tolerability for patients and provide higher SNR over the original endorectal coil used for over a decade. After bench and phantom tests, the new endorectal coil was applied in patient studies for mpMRI-TRUS guided fusion prostate biopsies with hyperpolarized C-13 pyruvate molecular imaging in three patients on active surveillance. The results of this novel approach with the new 13C/1H coil provides an increase in sensitivity, image quality and ultimately supports better detection of lesions.
Robust Fiducial-based Registration for Mechatronics-Assisted MRI-guided Focal Laser Ablation of Localized Prostate Cancer
Authors: Knull Eric , Park Claire , Bax Jeffrey , Tessier David , Fenster Aaron
Published: August 2023

MRI-guided focal laser ablation (FLA) is a promising minimally-invasive therapy method for men with localized prostate cancer. We previously developed an MR-compatible mechatronic system capable of transperineal needle delivery within an in-bore 3T MRI environment. This work presents an improved multi-fiducial structure for robust registration of the mechatronic system for MRI-guided FLA needle delivery. Real-time MRI-guided needle delivery was performed in a tissue-mimicking prostate phantom to virtual targets simulating focal ablation zones. With the implementation of the improved multi-fiducial structure, mechatronics-assisted MRI-guided needle delivery enables a 1.44 mm ablation radius, showing potential utility for accurate FLA to small prostate lesions.
16-Channel Hybrid Dipole+Loop RF Applicator for Thermal MR at 7.0 Tesla
Authors: Saha Nandita , Kuehne Andre , Eigentler Thomas , Niendorf Thoralf
Published: August 2023

This work proposes the concept of a loop and SGBT dipole antenna array for diagnostic proton (1H) imaging and thermal intervention at 7.0T MRI. Dipole antennas are the general choice for RF induced heating application as they can be made to operate over a broadband frequency range, have symmetric B1+ field and provide a central E-field distribution. Adding the inherently orthogonal and thus well-decoupled electromagnetic (EM) fields of loops to the dipoles provides additional degrees of freedom for RF shimming and thermal intervention not afforded by only using dipole antennas.
3D stack-of-stars MR thermometry sequence during hepatic microwave ablation: a phantom study with a commercially available microwave generator
Authors: Horstmann Dominik , Hensen Bennet , Hartlage Karen Meyer zu , Reimert Daniel , Caballero Josef Joaquin Löning , Wacker Frank , Gutberlet Marcel
Published: August 2023

A 3D stack-of-stars MR-thermometry sequence was developed and evaluated during hepatic microwave ablation in a chicken phantom. A commercially available microwave generator (MWG) working in pulsed mode controlled by a temperature sensor was used. Results show a spatially dependent phase-offset in the MR-image only during active ablation mode of the pulsed MWG impairing thermometry. Consequently, 73% of thermometry data were discarded for the fixed phantom yielding a temperature precision of 0.72°C±0.60°C whereas 94% were discarded for the moving phantom with an accuracy of 1.08°C±1.25°C. For clinical MR-thermometry, an MWG with less EMI is required to improve resolution and temperature precision.
Application of Haralick Texture Analysis to Differentiate Suspicious Prostate Lesions from Normative Tissue on Low-field MRI
Authors: Le Dang Bich Thuy , Narayanan Ram , Sadinski Meredith , Nicholas Kathryn , Nacev Aleksandar , Kumar Dinesh , Venkataraman Srirama
Published: August 2023

Haralick texture features extract frequencies of local spatial variations in signal intensity, quantifying pixel relationships within regions of interest. In this study, Haralick texture features were used to differentiate biopsy-proven, cancerous lesions from non-suspicious regions in low-field MR images. The results demonstrate consistency in texture measures for cancerous regions compared to non-suspicious, where Energy and Homogeneity are elevated while Contrast and Correlation are reduced within cancerous regions compared to non-suspicious regions.
Acoustically-transparent passive RF field repeater elements for the mitigation of the low-signal artifact and improved sensitivity in MRgFUS
Authors: Saniour Isabellle , Taracila Victor , Robb Fraser , Fukuda Rena , Vincent Jana , Voss Henning , Kaplitt Michael , Chazen J. Levi , Winkler Simone
Published: August 2023

MRgFUS image quality remains poor and suffers from a low-signal band artifact, preventing efficient image acquisition. We propose to place overlapped acoustically transparent loop elements in “passive mode” between the transducer and the head that act as a transmit field repeater/concentrator. Simulation results not only show mitigation of the low-signal band artifact in the thalamus region, but also demonstrate improvement of the RF transmit signal by a factor of 5 and 2 in the upper brain and the thalamus region, respectively.
Field-Mapping-Embedded EPI for Geometric Distortion Correction
Authors: Chi Didi , Blunck Yasmin , Glarin Rebecca , Davey Catherine , Staeb Daniel , Johnston Leigh
Published: August 2023

A Field-Mapping-Embedded (FME) EPI sequence is proposed in which the map is acquired during EPI acquisition without increasing scan time, using phase-encoded phase correction navigators. Results from in vivo experiment demonstrate accurate measurement and robust geometric distortion that performs favourably in comparison with existing techniques that require additional scans.
T-Hex spirals for whole-brain fMRI at 5 frames per second: Image quality and BOLD sensitivity
Authors: Bianchi Samuel , Engel Maria , Heinzle Jakob , Frässle Stefan , Stephan Klaas , Pruessmann Klaas
Published: August 2023

fMRI is one of the driving technologies of neuroscience. During the past years, acquisition speeds below one second per whole-brain volume have been achieved. The recently proposed 3D T-Hex Spiral trajectories provide a powerful approach to enable even faster fMRI. Here, we demonstrate the feasibility of fMRI at a sampling rate of 5Hz with these trajectories at 7T. We acquired whole-brain images of one subject performing a visuo-motor task in the scanner. Our results indicate high BOLD sensitivity and fidelity of voxel timeseries. In total, we analyzed 9000 volumes obtained during 30min of scanning.
3D whole-heart free-breathing T1⍴ quantification: a preliminary clinical evaluation.
Authors: Velasco Carlos , Fotaki Anastasia , Qi Haikun , Botnar Rene , Prieto Claudia
Published: August 2023

T1⍴-CMR allows the detection of scarred myocardial tissue without the need of an exogenous contrast. However, the generation of a single-slice T1ρ map requires sequential acquisitions under several breath-holds to acquire the T1⍴-weighted images at different spin-lock times, leading to long, inefficient scan times. We recently proposed a free-breathing motion-compensated 3D whole heart T1ρ mapping technique with near-isotropic spatial resolution (1.7×1.7×2 mm3) and predictable and clinically feasible scan In this work time (~8min). Here, we extend this technique to incorporate non-rigid motion corrected reconstruction and validate the proposed technique on subjects with suspected myocardial infarction.
Investigation of Spatial-Spectral Selective Pulses for B1 Compensation and Improved Saturation Homogeneity in Cardiac CEST MRI
Authors: Ayala Cindy , Luo Huiwen , Godines Kevin , Grissom William , Vandsburger Moriel
Published: August 2023

Chemical Exchange Saturation Transfer (CEST) MRI is highly susceptible to B1 inhomogeneities, resulting in inconsistent contrast for tissues with equal metabolite concentrations. To enhance B1-uniformity of saturation across the myocardium, a spectral-spatially selective pulse for CEST preparation was tailored for each subject. Simulations showed the tailored pulse reduced B1 variation from 6.85⁰ to 2.9⁰. These results were investigated on a Siemens 3T Trio scanner by performing full cardiac CEST exams using a conventional Gaussian or a tailored pulse for saturation. The tailored pulse resulted in reduced water and MT contrast variation across the myocardium when compared to the conventional pulse.
Derivation of clinically significant low-frequency oscillations in resting-state BOLD fMRI data as a novel marker of endothelial dysfunction
Authors: Thomas James , Jezzard Peter , Webb Alastair
Published: August 2023

We tested whether clinically-relevant, vascular-origin low frequency oscillations (LFOs) in 0.05–0.15 Hz could be derived on routinely-acquired resting-state BOLD-fMRI to determine their potential as a marker of vascular dysfunction in cerebral small vessel disease. Our case control study in 840 UK Biobank participants showed a statistically significant increase in LFOs in the arterial vessels of participants with high white matter hyperintensities, consistent with greater transmission of arterial blood pressure to the cerebral circulation in subjects with increased arterial stiffness. LFOs could provide a practical measure, applicable to large datasets and clinical practice, as an index of intracerebral vascular dysfunction.
Signal-to-noise requirements for accurate and precise perfusion parameter estimation with pulsed arterial spin labeling MRI in skeletal muscle
Authors: Cameron Donnie , Veeger Thom , Schrama Esther , Baligand Celine , Hirschler Lydiane , van Osch Matthias , Kan Hermien
Published: August 2023

To develop perfusion applications in neuromuscular diseases, we examined the SNR and noise sensitivity of pulsed ASL for estimating microvascular reactivity and perfusion in skeletal muscle. We determined muscle blood flow and arterial transit time in volunteers after exercise and used these to inform simulations to establish the minimum ASL SNR required for accurate and precise perfusion parameter estimation. We observed a peak SNR of 10 in the tibialis anterior muscle. Simulations indicated that SNR>6 is sufficient for accurate parameter estimation, while SNR>12 is preferred for good precision. These findings will inform future exercise ASL protocol development in patient cohorts.
Muscle stiffness quantified by multifrequency MR elastography in patients with Achilles tendinopathy
Authors: Majd Mahsa Salimi , Lerchbaumer Markus , Kim Suchung , Hees Tilman , Yang Yang , Görner Steffen , Fischer Thomas , Sack Ingolf , Guo Jing
Published: August 2023

Achilles tendinopathy is a common injury among athletes. It has been reported that the calf muscles are involved and affected by Achilles tendinopathy. In this study, tomoelastography, a multifrequency MR-elastography (MRE) technique, was applied to patients with Achilles tendinopathy for the characterization of the biomechanical properties of gastrocnemius muscle. Preliminary findings showed the stiffness of gastrocnemius muscle increased significantly as a result of tendinopathy.
Impact of the B0 and B1+-adjustments on the in vivo metabolite quantification accuracy of simultaneous 2voxel 1H brain MRS at 7T
Authors: Riemann Layla Tabea , Aigner Christoph Stefan , Mekle Ralf , Schmitter Sebastian , Ittermann Bernd , Fillmer Ariane
Published: August 2023

For simultaneous multi-voxel spectroscopy (sMVS), it is necessary to optimize the B0 shimming and the B1+ adjustment simultaneously for two voxels. The impact of these adjustments on the smallest possible distance for simultaneous two-voxel MRS acquisitions is determined by Bloch simulations, and the influence on the spectral quality is assessed for three different brain regions. To this end, the previously introduced 2 spin-echo full-intensity acquired localization (2SPECIAL) sequence and the voxel-GeneRalized Autocalibrating Partial Parallel Acquisition (vGRAPPA) decomposition algorithm are utilized to simultaneously acquire and retrospectively decompose in vivo brain 1H-MRS data from two voxels at short echo times at 7T.
Quantitative measurements of GABA in mouse brain using MR Spectroscopy at 7T
Authors: Tachrount Mohamed , Lerch Jason , Clare Stuart
Published: August 2023

The goals of this study were to implement a MEGA-sLASER sequence with prospective field drift correction on a preclinical scanner, develop the processing tools and validate them on a group of wild type mice.
Multi-Shot Liver Diffusion MRI Using Variable Auto-Calibrating (vARC) Sampling Across Averages
Authors: Reddy Ashok , Agarwal Harsh , Das Rajdeep , Sundaresan Rajagopalan , Ahmed Shaik , Rajamani Sajith , Mehta Bhairav , Wu Gaohong , Reddy M Ramasubba , Venkatesan Ramesh
Published: August 2023

Obesity is a key biomarker of liver pathology. Diffusion MRI in liver is key protocol in the absence on intravenous contrast agent and also provides additional insights on diffuse liver diseases and lesions However, its usage is limited by the inability to use multi-channel surface coil for obese liver patients in non-wide-bore MRI scanners. A variable k-space sampling scheme and auto-calibrating image reconstruction technique, vARC, is proposed in this abstract to reduce the amount of distortion and improve the image quality of Liver DWI acquired with single channel volume coil.