bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting artifacts in maximum intensity projections of high b-value DWI of the breast using neural networks.
Authors: Liebert Andrzej , Kapsner Lorenz , Folle Lukas , Schreiter Hannes , Das Badhan , Ohlmeyer Sabine , Maier Andreas , Wenkel Evelyn , Laun Frederik , Uder Michael , Bickelhaupt Sebastian
Published: August 2023

DWI acquisitions in MRI are prone to artifacts caused e.g. by insufficient fat saturation and can significantly impede the diagnostic assessment. We investigated the ability to predict the occurrence of these artifacts in maximum intensity projections (MIPs) of high b-value DWI using a neuronal network that analyses T2w images, which were acquired prior to the DWI sequence. An AuRoC of 0.83 with sensitivity of 0.82 and specificity of 0.61 was achieved. Investigation of the regions of the T2w images most important for the decision of the neural network showed a good correlation with artifact-affected areas in the DWI MIP images.
Convolutional Neuronal Network Inception-v3 detects Partial Volume Artifacts on 2D MR-Images of the Lung for Automated Quality Control
Authors: Voskrebenzev Andreas , Crisosto Cristian , Zubke Maximilian , Wacker Frank , Vogel-Claussen Jens
Published: August 2023

The partial volume effect (PVE) is an often-observed artifact in MR imaging. Especially images with a low spatial resolution, will show an averaged voxel signal of multiple tissue components. These artifacts can be so substantial that a further image analysis can be omitted. This is e.g. the case for phase-resolved functional lung imaging (PREFUL), which is based on the 2D acquisition of coronal image-time-series to assess ventilation and perfusion dynamics. In this study the pretrained convolutional neural network Inception-v3 was trained via transfer-learning to detect images, which show substantial PVE with a classification accuracy of 91%.
Resting-state functional connectivity predicts subsequent pain-related threat learning
Authors: Kincses Balint , Forkmann Katarina , Schmidt Katharina , Bingel Ulrike , Spisak Tamas
Published: August 2023

Fear conditioning has a role in anxiety-disorders and the neurobiological correlates of it are not yet well understood. Therefore, we trained a machine learning predictive model on individual functional resting state connectivity data to predict the emotional aspects of fear conditioning. The model was found to predict individual pain-related threat learning measured by the change of valence with an explained variance of 24%-41%. These results highlight the potential of machine learning to enhance our understanding of fear conditioning.
Multi-band echo-planar spectroscopic imaging of hyperpolarized 13C probes in a compact preclinical PET/MR scanner
Authors: von Morze Cornelius , Blazey Tyler , Baeza Richard , Garipov Ruslan , Whitehead Timothy , Reed Galen , Garbow Joel , Shoghi Kooresh
Published: August 2023

Hyperpolarized (HP) 13C MRI requires advanced pulse sequences to capture the dynamic, localized metabolic information. We developed an echo planar spectroscopic imaging (EPSI) pulse sequence incorporating multi-band spectral-spatial radiofrequency (SSRF) pulses for rapid and efficient HP 13C MRI on a new cryogen-free simultaneous PET/MR molecular imaging platform with compact footprint. Excitation profiles were measured in phantoms, and the SSRF-EPSI sequence was tested in rats using two HP 13C probes. We also obtained simultaneous 18F-FDG-PET data for comparison. In conclusion, advanced 13C SSRF imaging approaches are feasible on the new PET/MR platform, facilitating direct comparison with PET.
Sensitivity analysis of the MOXE model of gas exchange for lung hyperpolarised 129Xe MRS and MRI
Authors: Taylor Yohn , Wilson Frederick , Parker Geoff
Published: August 2023

Univariate sensitivity analysis was used to determine parameter importance within the model of gas exchange (MOXE) for lung hyperpolarised 129Xe MRS/MRI. Sensitivity of the model to parameter variation across a range of saturation recovery delay times within the chemical shift saturation recovery (CSSR) technique was assessed. Simulated parameter values spanned those in healthy and patient groups. MOXE is found to be most sensitive to changes in surface-area-to-volume ratio and in septal wall thickness. MOXE is negligibly sensitive to alveoli-capillary barrier thickness. CSSR measurements taken within delay periods between 0 - 200 ms provide optimum parameter sensitivity.
Refining selectivity of XTC saturation pulse parameters through spectroscopic analysis
Authors: Achekzai Tahmina , Loza Luis , Kadlecek Stephen , Ruppert Kai , Amzajerdian Faraz , Rizi Rahim
Published: August 2023

In Xenon Polarization Transfer Constant (XTC) imaging, saturation pulses are applied at DP Xe frequencies for red blood cell (RBC) and tissue plasma (TP), but, due to their high power, are not perfectly selective, often depolarizing both DP frequencies instead of only the one intended. We used XTC spectroscopy to explore how various saturation pulse parameters, primarily pulse power, affects saturation selectivity. The generated graphs plot depolarization as a function of saturation frequency. They demonstrate that as pulse power increases, the contribution of gas-phase depolarization due to off-resonance saturation increases, compromising selectivity.
The Feasibility of 3D MR Fingerprinting using Cartesian sampling for isotropic 1mm3 resolution at 0.55T
Authors: Jiang Yun , Zhu Ruogu , Zhao Bo , Seiberlich Nicole
Published: August 2023

The purpose of this work is to assess the feasibility of performing MR Fingperprinting with 3D pseudorandom Cartesian sampling for an isotropic spatial resolution of 1mm3 at 0.55T in the brain.
T1 and T2 Mapping of the Infant Brain with a Low-Field, Portable MRI System
Authors: Artz Nathan , Sien Maura , Robinson Amie , Hu Houchun , O'Halloran Rafael , Poorman Megan , Chan Sherwin
Published: August 2023

Low-field, portable MRI offers great potential to improve diagnostic ability in the neonatal intensive care unit. Brain relaxometry will be critical to tune scan parameters at a low magnetic field for infants, whose brain tissue composition is very different than adults. In this study, ten infants were scanned at bedside on a 64mT system. Whole brain T1 and T2 mapping was performed for five infants each. Regions of interest were drawn in the white matter and thalamus for all subjects. Mean T1 and T2 values were 702ms and 294ms for white matter and 364ms and 139ms for the thalamus.
Quantitative MRI measurements for T1 and T2 at 0.064T
Authors: Jordanova Kalina , Martin Michele , Stupic Karl , By Samantha , Poorman Megan , O'Halloran Rafael , Keenan Kathryn
Published: August 2023

Increased use of low-field MRI and quantitative MRI suggests a need for accurate quantitative measurements for fields <0.55T. We measure T1 and T2 for normal brain tissues at 0.064T using a Hyperfine scanner. T1 measurements gave a mean of 266±8.4ms for white matter, 358±6.6ms for grey matter; T2 measurements gave a mean of 95.8±2.4ms for combined white matter and grey matter, 794±357ms for CSF. Validation samples are measured on the Hyperfine system and compared to measurements taken on an electromagnet NMR at 0.064T.
Postmortem Multiple Sclerosis Brain Lesion Identification at Low Field
Authors: Balaji Sharada , Dvorak Adam , Vavasour Irene , Poorman Megan , Liu Hanwen , Ljungberg Emil , Williams Steve , Deoni Sean , Laule Cornelia , Li David , Moore G.R. Wayne , MacKay Alex , Kolind Shannon
Published: August 2023

A single hemisphere of postmortem brain tissue from a subject with multiple sclerosis (MS) was scanned at high (Philips Elition 3T) and low (Hyperfine Swoop 64mT) field strength to determine whether lesions could be detected using a portable low field MRI system. T2-weighted scans were acquired at both field strengths with matching resolutions to assess the impact of low field. Comparisons with a high resolution 3T scan showed that of 17 visible lesions, 11 were seen on the lower resolution 3T and 10 were seen on the 64mT scan, demonstrating the feasibility of lesion detection at low field.
Physics-Informed Deep Learning for Image Distortion Correction from B0-inhomogeneities in Low-Field MRI
Authors: Schote David , Winter Lukas , Kolbitsch Christoph , Zimmermann Felix , O'Reilly Thomas , Webb Andrew , Seifert Frank , Kofler Andreas
Published: August 2023

A physics-informed deep neural network using a U-Net in combination with a multifrequency interpolation is presented to correct for B0-field image distortions in low-field MRI. Training data is based on 1.5T and 3.0T knee images and realistic measurement-based assumption of SNR and B0-field inhomogeneities from previously constructed Halbach magnets. Significant (p<0.05) improvements are demonstrated applying the suggested methodology to uncorrected images.
Evaluation of dynamic 2D-EPI acquisitions for fetal brain tracking with neural networks
Authors: Silva Sara Neves , Grigorescu Irina , Uus Alena , Steinweg Johannes , Deprez Maria , Hajnal Jo , Pushparajah Kuberan , Hutter Jana , De Vita Enrico
Published: August 2023

Fetal MRI and MRS are often compromised due to unpredictable fetal motion and commonly require multiple repetitions for diagnostic studies. To overcome these limitations, we are working towards developing a deep-learning based automatic MRI fetal motion tracking method. Our method uses, as input, rapid dynamic multi-echo 2D-EPI acquisitions and is based on a 3D U-Net for brain localisation and translational motion parameters estimation for brain tracking. The results show rapid low-resolution acquisitions contain sufficient information to allow automated fetal brain localisation. Our technique can be used to assess fetal movements and to build navigation systems for fetal MRI/MRS.
Resolving activity suppression in the rat visual pathway using fMRI combined with lesions
Authors: Gil Rita , Valente Mafalda , Renart Alfonso , Shemesh Noam
Published: August 2023

Monocular visual stimulation with short inter-stimulus intervals (ISIs) evokes (i) negative BOLD responses (NBRs) in the contralateral superior colliculus (cSC), and (ii) positive BOLD responses (PBRs) in the ipsilateral superior colliculus (iSC). This pattern suggests a potential "push-pull" mechanism between SCs possibly evoked by (mostly inhibitory) tectotectal projections. Here, we mapped activity in the entire visual pathway using fMRI, and modulated cSC inputs through lesions in visual cortex and SC to dissect collicular communication mechanisms. While, cortical context potentiated a putative “push-pull” mechanism, further silencing of iSC resulted in PBRs in cSC, suggesting that iSC exerts suppression on cSC.
Impact of day-night cycle on functional brain connectome in mice
Authors: Lokossou Houefa , Rabuffo Giovanni , Bernard Monique , Perles-Barbacaru Teodora-Adriana , Viola Angèle , Bernard Christophe
Published: August 2023

Controlled by the suprachiasmatic nucleus of the hypothalamus, circadian rhythm defines the day-night cycle in most animal species. We evaluated the effect of night and day cycle on the functional connectome by exploring a group of mice under light condition (LC) and another one under night condition (NC) using BOLD rs-fMRI. Our results show a significant increase in functional connectivity in NC group compared to LC group at the pineal gland level. This result is in line with this gland’s function. Furthers studies are needed to explore the effects of the circadian rhythm on both genders at different ages.
A1-diameter asymmetry of the Circle of Willis induces blood-flow changes directed towards the anterior communicating artery
Authors: van Tuijl Rick , Ophelders Maud , Ruigrok Ynte , Vos Iris , van der Schaaf Irene , Zwanenburg Jaco , Velthuis Birgitta
Published: August 2023

Asymmetry in diameter of the pre-communicating part (A1-segment) of both anterior cerebral arteries is common and is related to anterior communicating artery aneurysm formation. We used TOF-MRA and 4D phase-contrast 3T-MRI in 122 subjects without intracranial aneurysms, and found that blood-flow asymmetry between A1-segments increased linearly with increasing A1 diameter asymmetry. Asymmetry of >30% in A1-segment diameters resulted in statistical significant blood-flow differences compared to persons with symmetric A1-segments. Whether this >30% A1-diameter asymmetry is a good cut-off to define a risk factor for anterior communicating artery aneurysm formation requires further research.
Non-Contrast-Enhanced Whole Neck MR Angiography using Velocity Selective Saturation and Slab Selective Inversion
Authors: Park Chanjoo , Zun Zungho , Park Jaeseok , Choi Seung Hong , Shin Taehoon
Published: August 2023

Velocity-selective (VS) MRA is a promising non-contrast-enhanced angiography method allowing for high 3D spatial resolution and high angiographic contrast. This study proposes a VS-MRA protocol which includes field error compensation and slab-selective inversion for improved visualization of neck arteries. Initial in-vivo test shows that the proposed VS-MRA better depicts small vessels (such as facial and occipital arteries) and yields higher contrast enhancement ratio than clinical 3D TOF.
MRI Measures of Non-atherosclerotic Brain Arterial Remodeling in a Population-based Study
Authors: Zhou Xinwei , Caughey Melissa , Du Arther , Zhou Shang , Liu Yi-Pei , Ghosh Shatorupa , Wasserman Bruce , Qiao Ye
Published: August 2023

We have reported reliable MRI measures of non-atherosclerotic brain arterial remodeling (NABAR), as assessed by outward and tortuous remodeling of the large intracranial arteries in older individuals. This will allow us to elucidate the predictors of prevalent and progressing NABAR, and its correlations with brain hypoperfusion and cognition function in an older community-based population.
Semantic CMR Synthesis: Generating Coherent Short- and Long-Axis Images with Corresponding Multi-Class Anatomical Labels
Authors: Joyce Thomas , Schulthess Nico , Wolkerstorfer Gloria , Buoso Stefano , Kozerke Sebastian
Published: August 2023

We propose to use a combination of the StyleGAN2, ADA and DatasetGAN methods to produce synthetic short- and long-axis view cardiac magnetic resonance (CMR) images accompanied with corresponding 11-class tissue masks. The image generator networks are trained on datasets of approximately 1850 and 5000 unlabelled images, for short- and long-axis images respectively. The segmentation networks are trained on only 30 manually annotated synthetic images in total. We further demonstrate a proof-of-concept method for generating coherent long- and short-axis images of the same synthetic patient.
Intra- and intersubject synthesis of cardiac MR images using a VAE and GAN
Authors: Amirrajab Sina , Lorenz Cristian , Weese Jürgen , Pluim Josien , Breeuwer Marcel
Published: August 2023

We propose a method for synthesizing cardiac MR images with plausible heart shape and realistic appearance. It breaks down the synthesis into labels deformation and label-to-image translation. The former is achieved via latent space interpolation in a VAE model, while the latter is accomplished via a conditional GAN model. We synthesize 32 short-axis slices within each subject (intrasubject), as well as eight intermediary generated subjects between two dissimilar real subjects (intersubject) that have different anatomies. Such method could provide a solution to enrich a database and to pave the way for the development of generalizable DL based algorithms.
Quantifying Cardiac Hypertrophy using Biophysical Models of Diffusion MRI
Authors: Farzi Mohsen , McClymont Darryl , Whittington Hannah , Zdora Marie-Christine , Khazin Leah , Lygate Craig A. , Rau Christoph , Dall’Armellina Erica , Teh Irvin , Schneider Jürgen E.
Published: August 2023

Biophysical modelling is a valuable technique for interrogating cardiac microstructure. We utilise a two-compartment model where intra- and extra-cellular space were represented by impermeable cylinders and an oblate diffusion tensor, respectively. Data was acquired ex vivo using a bespoke diffusion scheme with five diffusion times, six b-values and ten directions. Cardiomyocyte radius (r = 7.6 versus 8.9 μm) and volume fraction (ICV = 0.56 versus 0.67) were sensitive markers of cardiomyopathy validated in one healthy and one disease heart with transverse aortic constriction (TAC).
Retrospective motion correction in multiple average, free-breathing cardiac cine imaging
Authors: Neofytou Alexander , Neji Radhouene , Wong James , Fotaki Anastasia , Ferreira Joana , Evans Carl , Bosio Filippo , Mughal Nabila , Razavi Reza , Pushparajah Kuberan , Roujol Sébastien
Published: August 2023

A novel reconstruction technique based on iterative rejection of segmented k-space was developed for retrospective correction of respiratory motion in multiple average, free-breathing cine images. In comparison to standard signal averaging reconstruction, it provides higher sharpness, SNR, CNR, image quality, and rate of diagnostic quality images.
Anti-angiogenic therapy in recurrent glioblastoma uncouples tumor hyperperfusion measures from spectroscopic measures of Lac/c-Cr
Authors: Talati Pratik , Chun Benjamin , Clark Patrick , Ranasinghe Anthony , El-Abtah Mohamed , Fu Melanie , He Julian , Rapalino Otto , Gonzalez R. Gilberto , Dietrich Jorg , Gerstner Elizabeth , Ratai Eva-Maria
Published: August 2023

Patients with recurrent glioblastoma (rGBM) are often started on anti-angiogenic therapy, making it challenging to determine disease progression with standard MR methods. We performed a prospective analysis on 14 patients with rGBM utilizing MR spectroscopy and DSC-perfusion imaging to investigate metabolic changes before and 4 weeks after anti-angiogenic therapy. We found that anti-angiogenic therapy significantly decreases relative tumor hyperperfusion values of CBV and CBF and uncouples a correlation between CBV and CBF with Lac/c-Cr. Baseline hyperperfusion values can stratify longer-term from shorter-term survivors at nine months. Further investigation into the early stages of anti-angiogenic therapy are warranted.
Radial Echo-Planar Imaging with Subspace Reconstruction for Brain MRI
Authors: Tan Zhengguo , Uecker Martin
Published: August 2023

While echo planar imaging (EPI) is successful in many MRI applications, this study demonstrates that radial EPI is applicable to brain MRI as well. Initial volumetric brain experiments at 1 mm isotropic resolution show that radial EPI is capable of rendering high-quality T1- and T2*-weighted images at 2.5 and 1.3 minutes scan time, respectively. Further, radial EPI requires neither magnetization preparation nor fat saturation.
Reduction of Acquisition time using RAPID-SI technique: Preliminary in-vitro results and comparison with CSI at Ultra-High-Field.
Authors: Serrai Hacene
Published: August 2023

It has been shown that RAPID-SI, implemented at 3 Tesla magnet, allows for reduction of acquisition time while providing accurate metabolite information as compared to CSI. To benefit from the higher sensitivity of the Ultra-High-Field (UHF), RAPID-SI was implemented on a Siemens Terra 7T scanner, and phantom MRSI data were acquired, with less averaging, and compared in terms of acquisition time, signal-to-noise ratio (SNR), and data analysis to those collected using CSI method. As expected, and in addition to the acquisition time reduction, proportional to the speed factor R, RAPID-SI provides similar quantification results with similar SNR values, afterward data reconstruction, as compared to CSI.
Single-Shot Multi-b-value (SSMb) Diffusion-weighted MRI Using Spin Echo and Stimulated Echoes with Variable Flip Angles
Authors: Dan Guangyu , Sun Kaibao , Luo Qingfei , Zhou Xiaohong Joe
Published: August 2023

In diffusion-weighted imaging with multiple b-values, the acquisition times using a spin-echo EPI sequence can be long because each b-value requires a new shot. The lengthy acquisition time imposes issues with patient compliance and increases venerability to motion-induced errors in quantitative analysis. We herein report a novel pulse sequence that acquires multiple diffusion-weighted images with difference b-values in a single shot by utilizing the combination of a spin echo and a train of stimulated echoes with variable flip angles. This sequence has been demonstrated in a quantitative diffusion phantom and the human brain to realize a 4-fold scan time reduction.
Diffusivity-limited q-space trajectory imaging
Authors: Boito Deneb , Herberthson Magnus , Özarslan Evren
Published: August 2023

Q-space trajectory imaging (QTI) characterizes microstructures through the statistical moments of the diffusion tensor distribution. A constrained estimation framework named QTI+ was recently proposed to achieve mathematically and physically acceptable estimates of these moments. Here we consider expanding QTI+ with a new set of conditions based on the theoretical maximum value of water diffusivity. We show where these conditions are violated for different bulk diffusivity values, and how the new constraints affect the QTI scalar maps. The results show violations occurring almost exclusively in voxels containing gray matter and CSF. Imposing the constraints produces metrics closer to the expected values.
Pressure Pain Activation Revealed in Simultaneous Brain, High-Res Brainstem, and High-Res Spinal Cord fMRI.
Authors: Law Christine , Weber Ken , Mackey Sean , Glover Gary
Published: August 2023

We present a novel fMRI acquisition that mitigates susceptibility-induced off-resonance and simultaneously captures brainstem, spinal cord, and brain. Brainstem and spinal cord are imaged in high spatial resolution whereas brain resolution is standard. This fMRI acquisition is applied in a noxious pressure-pain experiment from which we observe activation in brain, spinal cord, and brainstem pons & medulla.
Impact Of MR Intensity Normalization For Different MR Sequences In MRI Based Radiomics Studies
Authors: Salome Patrick , Sforazzini Francesco , Kudak Andreas , Dostal Matthias , Bougatf Nina , Debus Jürgen , Knoll Maximilian , Abdollahi Amir
Published: August 2023

MR-based radiomics prognostic signatures have great clinical potential but are currently hindered due to a lack of standardisation in the radiomics workflow. This study focuses on a crucial step of these workflows, i.e. intensity normalisation, while presenting a methodology that allows for determining the most suitable MR intensity normalization method for a specific entity.
Cortical thickness predicts pain sensitivity in a multi-centre cohort: a machine learning approach
Authors: Kotikalapudi Raviteja , Kincses Balint , Hoffschlag Kevin , Zunhammer Matthias , Schmidt-Wilcke Tobias , Kincses Zsigmond , Bingel Ulrike , Spisak Tamas
Published: August 2023

Individual sensitivity to pain is both a precursor and a symptom of many clinical pain conditions. A pain predictive model would have potential applications in objectively characterizing pain in acute and chronic pain individuals. Here, we developed a cortical thickness-based predictive model of pain sensitivity using a machine learning approach and multi-centre T1-weighted MRI and quantitative pain threshold data. We found that our model significantly predicts pain sensitivity, that was measured through heat, cold and mechanical stimuli. Furthermore, the predictions were exclusively driven by cortical thickness and not confounded by variables of demographic and psychological value.
Benchmarking Accelerated MRI: A Head-to-Head Comparison of Deep Learning Reconstruction and Super-Resolution Techniques
Authors: Gibbons Eric , Fang Zhongnan , Desai Arjun , Sandino Christopher , Gold Garry , Hargreaves Brian , Chaudhari Akshay
Published: August 2023

Deep-learning (DL) can be used to extend compressed sensing (CS) to learn the regularization function in a data-driven manner. In contrast, super resolution (SR) algorithms have been used to transform rapidly-acquired low-resolution images into higher-resolution images. This work compares DL-CS with DL-SR for accelerated MRI on a test dataset of 50 patients with conventional image quality metrics and clinically-relevant quantitative T2 relaxation measurements. We demonstrate that DLCS approaches outperform DLSR approaches for accelerated MRI.
Uncertainty quantification for ground-truth free evaluation of deep learning reconstructions
Authors: Chatterjee Soumick , Sciarra Alessandro , Dünnwald Max , Ashoka Anitha Bhat Talagini , Vasudeva Mayura Gurjar Cheepinahalli , Saravanan Shudarsan , Sambandham Venkatesh , Oeltze-Jafra Steffen , Speck Oliver , Nürnberger Andreas
Published: August 2023

Many deep learning-based techniques have been proposed in recent years to reconstruct undersampled MRI – showing their potential for shortening the acquisition time. Before using them in actual practice, they are usually evaluated by comparing their results against the available ground-truth – which is not available during real applications. This research shows the potential of using uncertainty estimation to evaluate the reconstructions without using any ground-truth images. The method has been evaluated for the task of super-resolution MRI, for acceleration factors ranging from two to four – in all three dimensions.
On the importance of fetal brain numerical models for domain adaptation strategies in fetal brain MRI tissue segmentation
Authors: de Dumast Priscille , Kebiri Hamza , Cuadra Meritxell Bach , Lajous Hélène
Published: August 2023

Manual fetal brain tissue segmentation is needed for training machine learning methods but is a tedious and error-prone task. The generation of synthetic magnetic resonance images can overcome the lack of clinical annotations by supplementing scarce clinical fetal datasets. However, we highlight that the choice of the numerical model from which additional data are derived is key to maximize the segmentation accuracy of clinical data via domain adaptation strategies. We demonstrate that the resort to high-resolution segmented images from real neurotypical and pathological cases enhances the morphological variability compared to an atlas, resulting in improved fetal brain tissue delineation overall.
Integrating hybrid CSI/EPSI acquisitions with L2 regularization for lipid removal of MRSI
Authors: Liu Yiling , Bao Jianfeng , Chen Hao , Liu Guiqin , Chen Jingliang , Zhang Zhiyong
Published: August 2023

Placements of lipid saturation bands not only demand a skillful MRSI scanning operation but also restrict the full brain MRSI coverage. Therefore, MRSI acquisition without lipid suppression while doing post-removal of lipid becomes clinically attractive and valuable. In this work, we propose to integrate hybrid CSI/EPSI acquisitions with L2 regularization for fast lipid removal of MRSI. The hybrid CSI/EPSI acquisitions rely on the same excitation and refocusing evolution with the modified CSI and EPSI sequences. Phantom and in-vivo brain studies demonstrate the effectiveness and low computational cost of the proposed method.
Investigating metabolite regional dependencies in the frontal lobe: overlapping small and large voxels
Authors: DeMayo Marilena , McGirr Alexander , Selby Ben , MacMaster Frank , Debert Chantel , Harris Ashley
Published: August 2023

Small anatomically specific voxels in cerebral cortex require a large number of averages to quantify metabolites by magnetic resonance spectroscopy. It is unclear whether cortical regional variability in metabolite concentration requires small voxels, or whether a larger voxel which incorporates the region of interest can provide adequately representative measures with fewer averages. Concentrations of glutamate, glx, creatine, choline, myo­-inositol and N-acetyl-aspartate (NAA) were correlated within subjects between an overlapping small (15x15x15mm) and large (30x30x30mm) voxel. There was a significant correlation between creatine measures, while choline showed a positive trend. There was no correlation between glutamate, glx, NAA, and myo-inositol.
A comparison of human brain GABA levels measured at 3T and 7T
Authors: Bell Tiffany , Goerzen Dana , Dehghani Masoumeh , Near Jamie , Harris Ashley
Published: August 2023

Measuring GABA in the human brain in vivo using magnetic resonance spectroscopy (MRS) is not trivial due to its low abundance in the brain and the overlap of signal from higher concentration metabolites. At 3T, an editing sequence is typically used to isolate the GABA signal. Data acquired at 7T has higher SNR and better spectral resolution, therefore editing is often not used to measure GABA at this field strength. Here we compare GABA levels acquired at 7T using STEAM to GABA levels acquired at 3T using GABA-edited MEGA-PRESS and find little agreement between the two measures.
Application of Litz Wires in MRI Coil Design up to 15 MHz
Authors: Kowal Robert , Fomin Ivan , Prier Marcus , Pannicke Enrico , Rose Georg , Speck Oliver
Published: August 2023

The use of litz wire in their optimized frequency range can provide increased efficiency of high-frequency coils and lead to higher SNR in MR measurements. This work experimentally explores the added value of such a litz wire using equivalent solenoid coils in the range of 1 to 61 MHz. An improvement could be measured up to a frequency of 15 MHz, whereas the maximum effectiveness of the investigated wire was at 3 MHz. In addition, the SNR gain was validated in agreement on an 0.26 T MR system by comparing 1H FID signals.
A flexible, 4-channel head array for 0.2T readout FCI (Field-Cycling Imaging).
Authors: Stormont Robert , Ross James , Davies Gareth , Mallikourti Vasiliki , Alamri Amnah , Broche Lionel , Lurie David
Published: August 2023

A 4-Channel Head Coil RX Array has been created supporting Field-Cycling Imaging studies at an 8.5 MHz readout frequency. Head imaging to date, has been accomplished with a quadrature TX/RX birdcage coil.

The array goals include improved SNR, enhanced patient comfort, peripheral compatibility, with fast application to various sized heads.

Evaluation with healthy volunteers is now underway. The array appears well tolerated by individuals when compared to the 8-rung birdcage, accommodating a communication headset, and leaving the face quite open.

SNR is comparable to the birdcage at the image center and improved as you move towards the array elements.
Optimized radio frequency coil for low-field magnetic resonance relaxometry in human finger
Authors: Wang Junnan , Lu Rongsheng , Bazzi Layale , Jiang Xiaowen , Chen Yi , Wu Zhengxiu , Yang Qing , Ni Zhonghua , Yi Hong , Xiao Dan
Published: August 2023

The permanent magnet based low-field and low-cost MR systems increase the accessibility and potential of point-of-care diagnosis, where relaxometry measurement is usually preformed due to the low requirement of B0 field homogeneity. However, inhomogeneous B1 field results in errors in the T2 relaxation times, especially in in-vivo localized human tissue measurements. A novel RF coil termed T coil has been developed to provide a better B1 field homogeneity and more accurate in-vivo relaxation measurements than the conventional coils. This may enable the noninvasive diagnosis of diseases through the human finger measurements with portable and affordable MR systems.
Design and Optimization of Asymmetrical Tapered RF Solenoid Tx / Rx Coil for Halbach-Array based Low-field MRI Scanner
Authors: Rajendran Meena , Huang Shao Ying
Published: August 2023

The optimization of an asymmetrical tapered solenoid coil using multi-objective Genetic Algorithm is presented to achieve high sensitivity, high homogeneity, low inductance simultaneously at 2.84MHz within a cylindrical volume(95×95х10mm). The asymmetrical solenoid coil has a lower half that consists of turns with variable pitches and a constant radius and an upper half that consist of turns with variable pitches and profile curvature(variable radius). Two coil winding patterns are included: single winding and multiple windings at each groove. The optimal design shows a sensitivity of 138 μT/√W (29.7%increase), 3.8% homogeneity reduction, comparable inductance compared to a traditional solenoid of similar dimensions.
Using a Spherically Distributed Spirals Trajectory to Collect Coil Sensitivity Maps for Iterative SENSE Reconstruction
Authors: Zhang Le , Chao Tzucheng , Wang Dinghui , Krishnamoorthy Guruprasad , Pipe James
Published: August 2023

Conventionally, Cartesian pre-scans are collected over several seconds to create accurate coil sensitivity maps (CSM) for SENSE reconstruction of under-sampled MRI data, with body-coil images used for signal normalization. Since body coils have a larger sensitivity region than phased-array coils, these pre-scans typically employ a very large field-of-view (FOV) to avoid aliasing, leading to low-resolution CSM and potential small errors in subsequent reconstructions. This abstract proposes a new CSM pre-scan sequence using a spherically-distributed-spirals trajectory. The acquisition efficiency and incoherent aliasing offered by this trajectory allow for smaller FOV and higher resolution while maintaining similar scanning time as conventional pre-scans.
Exploring Ultra-High Resolution Imaging of the Ex Vivo Whole Brain: Initial Results with Balanced Steady State Free Precession Sequences at 3T
Authors: Weigel Matthias , Dechent Peter , Galbusera Riccardo , Bahn Erik , Kappos Ludwig , Brück Wolfgang , Stadelmann Christine , Granziera Cristina
Published: August 2023

Balanced steady state free precession (bSSFP) sequences provide the highest signal intensity per unti time, which makes them virtually predestined for ultra-high resolution MR imaging at 3T. Their sensitivity to susceptibility effects and demand for high performance, however, represent two major drawbacks. It will be shown that a carefully chosen protocol, which also includes common phase cycling techniques, will enable artifact-free 200-microns isotropic bSSFP acquisitions of the entire human fixed brain within approximately 27h.
High Resolution Postmortem Brain Imaging at 7 Tesla using Parallel Transmission and a Simple Set-Up
Authors: Pracht Eberhard , Cremer Markus , Loewen Daniel , Mauconduit Franck , Massire Aurelien , Bludau Sebastian , Boulant Nicolas , Amunts Katrin , Stoecker Tony
Published: August 2023

In this work we present a simple set-up for high resolution postmortem imaging at ultra high field strength. The processed images have high, as well as homogeneous gray/white matter contrast over the whole brain. Additionally, during post-processing, the background signal of the fixation liquid is almost completely removed, allowing for an optimal image registration process after histological sectioning.
The Feasibility of Meningeal Imaging using multi-echo gradient echo sequence mFFE with optimized scan parameters
Authors: Dou Han , Wang Xiaoming , Zheng Yang , Shen Zhiwei
Published: August 2023

At present, the detection of meningeal diseases is mainly based on MRI enhanced scan sequences, however, the poor image quality of affected the degree of meninges visualization due to the limited concentration of contrast agent staying in the meningeal after administration. In this study, the feasibility of meningeal imaging with multi-echo gradient echo MR sequence (Mffe) was explored, and optimized the main scan parameters. The optimal imaging parameters were obtained by analyzing the meninge-skull signal intensity ratio (SIR) and contrast-noise ratio (CNR) of healthy volunteers.
A proposed method to objectively quantify hippocamapal dentation through midlayer AUC analysis
Authors: Hoef Lawrence Ver , Ramaniharan Anandh Kilpattu
Published: August 2023

We present a novel method to objectively quantify a morphological feature known as hippocampal dentation from ultra high resolution 3D T2w images.
Frailty assessment using synthetic MRI
Authors: Wang Di , Li Chunmei , Chen Yuhui , Wu Pu-Yeh , Chen Min
Published: August 2023

Synthetic MRI is a novel method which can provides quantitative relaxation mapping and synthetic contrast-weighted images at the same time. In this study, we adopted this technique to assessed the frailty. We found T2 relaxation of right insular lobe and right limbic subcortical nuclei had significant difference between frailty and prefrailty patients. T1 and T2 relaxation of right limbic subcortical nuclei and right basal ganglia significantly correlated with the short physical performance battery total scores. Overall, our findings suggested the synthetic MR can be considered an effective tool for assessing frailty.
7T metabolic MRI in focal epilepsy
Authors: Jacobs Sarah , Shams Zahra , Wiegers Evita , Wijnen Jannie , Klomp Dennis , Versteeg Edwin , Siero Jeroen , Mühlebner Angelika , Van Hecke Wim , van Eijsden Pieter , Robe Pierre , Zijlmans Maeike , van der Kolk Anja
Published: August 2023

In this clinical study, we combined different (metabolic) MRI sequences at 7T to characterize focal epileptogenic lesions, and uncover potential metabolic markers that could help identifying the culprit lesion in MRI-negative epilepsy patients. Using QSM, we observed increased iron deposition in the affected hippocampus of two HS patients that was not found in the contralateral hippocampus, neither in a suspected HS patient with no abnormal tissue, nor in matched healthy volunteers. No increased iron deposition was found in patients with FCD or matched healthy volunteers. No significantly different metabolite ratios between patients and healthy volunteers were found using SV 1H-MRS.
Structural Connectivity in Default Mode Network May Lateralize Temporal Lobe Epilepsy: A Graph-Theory Approach
Authors: Salimi Fatemeh , Masoudnia Saeed , Fallahi Alireza , Tabatabaei Narges Hoseini , Ay Mohammadreza , Nazem-Zadeh Mohammad-Reza
Published: August 2023

Difficulties in lateralizing the epileptogenic side of the brain challenge the temporal lobe epilepsy (TLE) surgery. We examined structural connectivity in default mode network (DMN) based on graph network analysis for specifying the epileptogenic side of the brain in TLE patients. The results showed significantly different connectivity in DMN nodes among the TLE patients compared to the control cohort. DMN regions with abnormal structural connectivity in TLE subjects corresponded with the epileptogenic brain sides.
Static and Dynamic Resting-State fMRI Features to Localize Brain Areas Generating Epileptic Spikes
Authors: Tang Siu Lung , Sabaroedin Kristina , Wilson Will , Pittman Daniel , Federico Paolo , LeVan Pierre
Published: August 2023

The potential of using features derived from resting-state fMRI time series to localize epileptic brain areas was studied. Static and dynamic correlations between local spike rates measured with intracranial EEG and regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and functional connectivity (FC), respectively, were analyzed based on data collected from 13 subjects with refractory epilepsy. While static measures of ReHo and ALFF were not statistically correlated with spike rates measured during long-term monitoring, static correlation with FC was apparent. However, in dynamic analysis, temporal variations in instantaneous spike rates were associated with synchronous fluctuations of both ALFF and FC.
Magnetic Resonance Spectroscopy in Children and Adolescents with Functional Neurological Disorder
Authors: Shukla Vishwa , Charney Molly , Foster Sheryl , Zhao Wufan , Jiang Han , Kozlowska Kasia , Lin Alexander
Published: August 2023

Functional Neurological Disorder (FND) is characterized by a broad array of neurological symptoms that are thought to reflect aberrant activity in neural networks. This study aims to compare the neurochemical levels in the posterior default mode network (pDMN) in children and adolescents with FND and healthy controls using magnetic resonance spectroscopy (MRS). FND patients exhibited lower levels of tNAA/tCr in the pDMN compared to controls, indicating potential neuronal alterations or modified neuronal-glial signaling.
SENSE and Compressed SENSE chemical shift encoded multi-gradient echo for T2* quantification in the liver
Authors: Pickles Elisabeth , Connell John , Kin Stella , Telford Alison , Kazimianec Arina , Brady Michael , Bulte Daniel , Chong Oi Fong , Choo Han Jun , Ithnin Sulaiha Binte , Chang Jason Pik Eu , Lee Guan Huei , De Roza Marianne Anastasia , Ong Kok Kiong , Wee Sabrina Yi-Mei , Tan Kee Tung , Tan Ngiap Chuan , Toh Han Chong , Wong Yu Jun , Yang Wei Lyn , Yeap Xin Yi , Chua Cheryl Min En , Chua Jacelyn Siou Sze , Goh Jade Shu Qi , Lai Lynette Soh Han , Sim Yu Ki , Chow Pierce K.H.
Published: August 2023

T2*, measured using 3D breath-hold chemical shift encoded multi-gradient echo can be used to estimate liver iron concentration. The breath-hold duration (typically 10-20 s) with traditional parallel imaging acceleration techniques such as Sensitivity Encoding (SENSE) can be difficult for patients. Compressed SENSE (C-SENSE) can be used to decrease acquisition time. We compare T2* obtained with SENSE and C-SENSE in 21 patients. We found that C-SENSE acquisition was faster (9 s vs 18 s), with strong correlation and agreement with SENSE. Our findings suggest C-SENSE may potentially improve patient tolerance and turnover speed for T2* acquisitions when compared to SENSE.
The value of diffusion kurtosis imaging texture analysis in predicting treatment response to transcatheter arterial chemoembolization in HCC
Authors: Qiao Chen , Liu Ailian , Zhao Ying , Song Qingwei , Li Xin , Guo Yan , Wu Tingfan
Published: August 2023

This work aimed at texture analysis based on mean apparent diffusion coefficient (MD) of diffusion kurtosis imaging (DKI) to evaluate the treatment efficacy of transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC). The results showed that MD texture features can differentiate objective response and non-response groups. When using the single parameter, the GreyLevelNonuniformity_angle135_offset1 achieved the best result (AUC: 0.714, sensitivity: 68.2%, specificity: 81%). In the combined diagnosis of multiple parameters, FrequencySize,MaxIntensity, GreyLevelNonuniformity_angle0_offset1 and GreyLevelNonuniformity_angle135_offset1 were combined to obtain the best combined diagnosis（AUC：0.760，sensitivity: 81.8%, specificity: 66.7%).
Radiomics assessment of liver fibrosis in patients with chronic liver disease using MRI: a machine learning approach
Authors: Mobayyyen Farzin , Taghvainia Behrooz , Homayoun Hassan , Ardakani Ali Abbasian , Kazerooni Anahita Fathi , Salari Hanieh Mobarak , Rakhshani Nasser , Salighehrad Hamidreza , haghighatkhah Hamid Reza
Published: August 2023

Neither biochemical markers nor a qualitative assessment of medical images are reliable to differentiate mild from moderate stages of liver fibrosis. The main purpose of this study is to develop a machine learning model to classify mild and moderate liver fibrosis based on radiomic features extracted from MRI images (T1-w, T2*-map & ADC-map). Nu-SVC classifier was employed as the classification technique, trained by the extracted data from image series of 29 patients with histopathology-confirmed mild and moderate liver fibrosis. Results demonstrate that radiomic analysis of T2*-map and ADC-map has high potential in classifying different stages of liver fibrosis.
Motion tracking verification with a 3D printed compliant mechanisms and laser interferometry
Authors: Schildknecht Christoph , Prüssmann Klaas
Published: August 2023

Precisely characterizing motion tracking modalities in the MRI bore is quite a challenge. Especially when not only static positions ought to be evaluated, but also dynamic characterization is desired. In this work, we show how a compliant mechanism in the form of a folded-beam suspension can be utilized for accurate dynamic positioning. A piezoelectric actuator drives the system, and position feedback is acquired with a laser interferometer. Testing the system with short-wave motion tracking revealed a sub-micrometre agreement.
Evaluation of markerless prospective motion correction for neuroanatomical MRI
Authors: Zariry Zakaria , Frost Robert , Lamberton Franck , Troalen Thomas , Richard Nathalie , van der Kouwe Andre , Hiba Bassem
Published: August 2023

Head-motion is a main cause of artifacts in MRI. The performance of a markerless optical system, which records the subject's face, estimates head-motion, and allows real-time repositioning of the FOV, is evaluated for neuroanatomical MRI. Sets of T1W/T2W-images were collected from 2 subjects instructed to perform different head-motion protocols during the acquisitions. The System performance is evaluated by comparing images with/without motion-correction. The optical system ensures good corrections of head-motions. However, the correction quality depends on the amplitude of the movement, its location in k-space and its nature. Residual effects of large amplitudes/amounts movements may persist on corrected images.
Joint Neural Network for Fast Retrospective Rigid Motion Correction of Accelerated Segmented Multislice MRI
Authors: Singh Nalini , Hoffmann Malte , Moyer Daniel , Jang Ikbeom , Chen Lina , Rockenbach Marcio , Guidon Arnaud , Aganj Iman , Kalpathy-Cramer Jayashree , Adalsteinsson Elfar , Fischl Bruce , Dalca Adrian , Golland* Polina , Frost* Robert
Published: August 2023

We demonstrate a deep learning approach for fast retrospective intraslice rigid motion correction in segmented multislice MRI. The proposed neural network architecture combines convolutions on frequency and image space representations of the acquired data to produce high quality reconstructions. Unlike many prior techniques, our method does not require auxiliary information on the subject head motion during the scan. The resulting reconstruction procedure is more accurate and is an order of magnitude faster than GRAPPA. Our work offers the first step toward fast motion correction in any setting with substantial, unpredictable, challenging to track motion.
Self-Assisted Priors with Cascaded Refinement Network for Reduction of Rigid Motion Artifacts in Brain MRI
Authors: Al-masni Mohammed , Lee Seul , Yi Jaeuk , Kim Sewook , Gho Sung-Min , Choi Young Hun , Kim Dong-Hyun
Published: August 2023

MRI is sensitive to motion caused by patient movement. It may cause severe degradation of image quality. We develop an efficient retrospective deep learning method called stacked U-Nets with self-assisted priors to reduce the rigid motion artifacts in MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. We further design a refinement stacked U-Nets that facilitates preserving of the image spatial details and hence improves the pixel-to-pixel dependency. The experimental results prove the feasibility of self-assisted priors since it does not require any further data scans.
Automatic 3D to 2D reformatting in 4D flow MRI using continuous reinforced learning
Authors: Bisbal Javier , Sotelo Julio , Arrieta Cristobal , Irarrázabal Pablo , Andia Marcelo , Tejos Cristian , Uribe Sergio
Published: August 2023

One major limitation on 4D flow MRI is the time-consuming and user-dependent post-processing. We developed an automated reinforced deep learning framework for plane planning in 4D flow data. This method sequentially updates plane parameters towards a target plane based on a continuous policy. A total of 83 4D flow MRI scans were considered, 41 for training, 14 for validation and 28 for test. Our method achieves good results in terms of angulation and distance error (9.21 ± 3.85 degrees and 3.72 ± 2.19 mm).
Learning motion correction from YouTube for real-time MRI reconstruction with AUTOMAP
Authors: Waddington David , Chiu Christopher , Hindley Nicholas , Koonjoo Neha , Reynolds Tess , Liu Paul , Zhu Bo , Paganelli Chiara , Rosen Matthew , Keall Paul
Published: August 2023

Today’s MRI does not have the spatio-temporal resolution to image the anatomy of a patient in real-time. Therefore, novel solutions are required in MRI-guided radiotherapy to enable real-time adaptation of the treatment beam to optimally target the cancer and spare surrounding healthy tissue. Neural networks could solve this problem, however, there is a dearth of sufficiently large training data required to accurately model patient motion. Here, we use the YouTube-8M database to train the AUTOMAP network. We use a virtual dynamic lung tumour phantom to show that the generalized motion properties learned from YouTube lead to improved target tracking accuracy.