bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved 3D DESS MR Neurography of the Lumbosacral Plexus with Deep Learning and Geometric Image Combination
Authors: Lin Yenpo , Tan Ek T. , Campbell Gracyn , Colucci Philip G. , Singh Sumedha , Wen Yan , Li Qian , Sneag Darryl B.
Published: August 2024

Geometric image combination (GIC) and deep learning (DL) reconstructions were together applied to improve nerve visualization in lumbosacral plexus MR neurography (MRN) using the 3D dual echo steady-state free precession (DESS) sequence. Qualitative comparisons were made against standard image reconstruction of the 2nd echo of the DESS sequence. While standard-of-care (SOC) reconstructions of 3D DESS images provided effective vascular suppression and good nerve conspicuity, the DL-GIC reconstructed images demonstrated similar or improved nerve conspicuity.
Quantitative 3D DESS T2 mapping with Deep Learning Reconstruction for Magnetic Resonance Neurography
Authors: Campbell Gracyn , Sneag Darryl , Li Qian , Tan Ek Tsoon
Published: August 2024

Conventional, quantitative T2 mapping for MR neurography may depict peripheral neuropathy related changes but has insufficient spatial resolution within acceptable acquisition times (<6 min.) to mitigate motion. Alternatively, dual-echo steady-state (DESS) can simultaneously provide high resolution 3D qualitative anatomical data and quantitative T2 maps for characterizing both nerve and muscle within this targeted acquisition window. Analysis of subjects with peripheral neuropathy in the elbow/forearm region showed that DESS-T2 was higher in involved nerves and muscles, and that DL-reconstruction slightly decreased DESS-T2. Additionally, DESS enabled analysis of the magic angle effect, demonstrating a positive correlation between nerve orientation and DESS-T2 values.
Development of a novel sequence for T2* quantification of slow-relaxing water pools in the brain
Authors: Shin* Seonyeong , Oros-Peusquens* Ana-Maria , Yun Seong Dae , Farrher Ezequiel , Shah N. Jon
Published: August 2024

T2* relaxation in the brain covers a broad range of values, which can be grouped in three, roughly logarithmically spaced, intervals (short, intermediate, very long). Using a multi-echo GRE sequence to quantify T2* is time-efficient for brain parenchyma, but accurate quantification of slow-relaxing water pools, such as CSF, lengthens the acquisition time. In this work, we propose a novel sequence (ES-QUTE) that combines multi-echo acquisition with echo shifting techniques to effectively quantify the whole range of T2* relaxation times in the brain without increasing the scan time. In addition, ES-QUTE simultaneously characterises fast diffusion.
Accurate B1 Mapping with Actual Flip Angle Imaging (AFI) in the Presence of Fat
Authors: Samsonov Alexey , Velikina Julia , Yarnykh Vasily
Published: August 2024

Actual Flip angle Imaging (AFI) is an efficient B1 mapping method requiring the proper spoiling of transverse magnetization. Optimal spoiling can be achieved using large spoiling gradients enabling water diffusion-based signal decay. However, spoiling the non-aqueous signal like from fat is typically ignored in AFI optimizations. We demonstrate that infinitesimal diffusion in the fat signal makes fat spoiling in AFI unachievable in the reasonable scan time and that incomplete fat spoiling is a major source of previously unexplained AFI errors. We propose method to minimize them using a superposition model of the spoiling artifacts and chemical shift encoded fat/water separation.
Fully Automated Segmentation of Brain and Scalp Blood Vessels on Multi-Parametric Magnetic Resonance Imaging Using Multi-view Cascaded Network
Authors: Yang Yang , wu songxiong , Huang Bingsheng , Zeng Ping , Wang Mingyu , Huang Zilong
Published: August 2024

Accurate segmentation of blood vessels allow neurosurgical navigation and can help neurosurgeons accurate surgical and treatment plans. However, traditional blood vessel segmentation methods based on thresholds have limited performance. To solve problem, we proposed a cascaded DL network (MVPC-Net) that combines three refinements: multi-view learning, multi-parameter input, and a multi-view ensemble module-based strategy. The results of ablation experiments showed that, by adding all the refinements proposed, the performance of the baseline model improved from Dice similarity coefficient 0.865 to 0.922. Thus, our method can provide better segmentation of the brain, and scalp blood vessels and has potential for clinical application.
High Resolution TOF-MRA Using SmartSpeed-AI for the Visualization of Lenticulostriate Arteries at 3.0 T: a Preliminary Study
Authors: Hirano Yuya , Fujima Noriyuki , Ishizaka Kinya , Aoike Takuya , Kwon Jihun , Yoneyama Masami , Kudo Kohsuke
Published: August 2024

SmartSpeed-AI is recently introduced as a physics driven type deep learning-based novel image reconstruction technique. We investigated the utility of SmartSpeed-AI for the better visibility of lenticulostriate artery (LSA) in high spatial resolution TOF-MRA by comparing the compressed-sensing sensitivity-encoding (compressed SENSE) algorithm. Both quantitative and qualitative assessment revealed that the visibility of LSAs were significantly higher under the SmartSpeed-AI reconstruction than compressed SENSE. SmartSpeed-AI can be helpful to provide superb image quality for the depiction of small arteries like LSA in high resolution TOF-MRA.
Combined IVIM and DKI MRI in pre-operative assessment of bladder cancer with different stages and grades
Authors: Wang Fang , Xu Xinghua , Wu Guangtai , Chang Lingyu , Li Dumin , Dou Weiqiang , Pylypenko Dmytro , Yu Dexin , Wang Qing
Published: August 2024

This study aimed to determine the feasibility of intravoxel-incoherent-motion(IVIM) and diffusion-kurtosis-imaging (DKI) parameters in differentiating tumor stages and grades of bladder cancers preoperatively. The IVIM-related parameter (true-diffusion-coefficient) and DKI-related parameters (mean-diffusivity; mean-kurtosis) were significantly higher in non–muscle-invasive than muscle-invasive bladder cancers, and in low-grade than high-grade ones (all p <0.05). Robust diagnostic efficacies were separately confirmed with high AUCs for IVIM and DKI in staging and grading bladder cancers, and the optimal efficacy was obtained with combined IVIM and DKI models. Therefore, combined IVIM and DKI might be considered effective in differentiating the stages and grades of bladder cancers.
MR elastography-based slip interface imaging (SII) to assess the mobility of the myofascial interface in extremities: A feasibility study
Authors: Yin Ziying , Sui Yi , Zheng Keni , Shan Xiang , Rossman Philips , Manduca Armando , Bauer Brent , Ehman Richard
Published: August 2024

Myofascial pain syndrome (MPS) is a common chronic pain disorder that can cause disability. Efforts to understand the MPS pathology have focused on myofascial connective tissue and the function of fascial plane mobility. Slip interface imaging (SII) offers a unique opportunity to assess fascial plane mobility noninvasively. Here, we investigated the feasibility of SII to visualize the mobility of the intermuscular myofascial interface in the upper leg and the functional intramuscular interface in the forearm flexor muscles in healthy volunteers. This creates a foundation for using MRE/SII to distinguish between a healthy and a dysfunctional fascial plane in MPS patients.
Motorized template for MRI-guided focal cryoablation of prostate cancer
Authors: Moreira Pedro , Grimble John , Bernardes Mariana , Iftimia Nick , Tuncali Kemal , Tokuda Junichi , Park Jesung
Published: August 2024

MR-guided focal cryoablation of prostate cancer has often been selected as a minimally-invasive treatment option. Placing multiple cryo-needles accurately to form an ablation volume that adequately covers the target volume is crucial for a better oncological/functional outcome. This work presents an MRI-compatible system combining a motorized template grid and insertion depth sensing, which allows the physician to precisely place the cryo-needles into the desired location. Our preliminary results demonstrate the advantages of adding additional degrees of freedom to the template and providing the physician with real-time feedback on the insertion depth.
Experimental and numerical calibration procedure for RF safety evaluation of implant-embedded sensors
Authors: Petzold Johannes , Silemek Berk , Winter Lukas , Ittermann Bernd , Seifert Frank
Published: August 2024

Embedded sensors in implants that communicate with an MRI system can improve patient safety substantially, in particular when parallel transmission is applied to mitigate the RF-heating risk. Calibration procedures and virtual models are presented in this work to support a rigorous safety assessment of this novel approach to implant safety. Experiments show a good correlation (r>0.96) between sensor signal (voltage at the tip) and field-probe measurements (E-field and temperature) for a custom-built reference implant. These results are further supported by simulations that can be extended to investigate more complex implant heating scenarios in human voxel models.
Motion Monitoring using a Wireless Ultrasound-Based Sensor and an Integrated RF/Wireless Coil Array
Authors: Willey Devin , Lynch Spencer , Dickinson Olivia , Truong Trong-Kha , Robb Fraser , Song Allen , Madore Bruno , Darnell Dean
Published: August 2024

A wireless, battery-powered, MR-compatible ultrasound device consisting of an integrated RF/wireless coil, an organ-configuration motion sensor and its associated electronics was used to acquire OCM sensor signals on a healthy volunteer. Signals were obtained that characterized internal motion, which were wirelessly transmitted to the console room. Validation was performed against a real-time MR acquisition.
Doppler radar signal integrity investigation for in-bore vital sensing applications
Authors: Lee Wonje , Pauly John , Vasanawala Shreyas , Scott Greig
Published: August 2024

This study investigates complex baseband doppler radar signal integrity with linear and phase demodulations to confirm feasibility for in-bore MRI vital sensing applications.
Wireless In-Bore Ballistocardiography with 2.4GHz Beat Pilot Tone (BPT)
Authors: Anand Suma , Lustig Michael
Published: August 2024

We previously proposed the Beat Pilot Tone (BPT), a motion sensing method that uses microwave frequencies and preamplifier intermodulation to obtain increased motion sensitivity compared to Pilot Tone (PT). Here, we show that cardiac signals acquired with BPT correlate strongly with displacement ballistocardiograms (dBCG). We compare the BPT signals to dBCG acquired with an accelerometer. Additionally, we show that BPT has greater signal modulation compared to PT in sensing cardiac motion. BPT could be a novel, non-contact, and wireless method for acquiring dBCG signals, offering robustness in setup for patients and rich physiological information complementary to the MRI exam.
Synthetic data shows the potential of unsupervised and supervised learning for incorporating spatial information in IVIM fitting
Authors: Kaandorp Misha , While Peter , Zijlstra Frank
Published: August 2024

DWI data is spatially homogeneous, yet microstructures are irregular, where neighboring voxels do not always share information. Therefore, we need to utilize neighboring correlations only when they are present. In simulations, we show that by training on synthetic data with all plausible combinations of neighboring correlations, the accuracy of supervised deep learning IVIM model fitting can be improved. Conversely, unsupervised learning did not benefit from incorporating spatial information. In in-vivo data from a glioma patient, supervised training on this synthetic data improved the performance of IVIM fitting by effectively denoising the DWI data while preserving edge-like structures.
Propagator-sensitive diffusion measurements hear the percussion ensemble
Authors: Özarslan Evren , Boito Deneb , Ordinola Alfredo
Published: August 2024

In 1966, Kac asked the question ‘Can one hear the shape of a drum?’ which refers to linking the density of states function to the shape of the pores. We illustrate how the density of eigenstates as well as the pore shape can be obtained through diffusion measurements performed via a recently introduced arrangement of long and narrow gradient pulses. In the presence of dispersity, maps derived from the signal provide exquisite details about the ensemble of pore shapes within the voxel.
Multi-TE SANDI: Quantifying compartmental T2 relaxation times in the grey matter
Authors: Gong Ting , Tax Chantal MW , Mancini Matteo , Jones Derek K , Zhang Hui , Palombo Marco
Published: August 2024

This study models the echo time (TE) dependence of the apparent soma and neurite signal fractions derived from the soma and neurite density imaging (SANDI) and quantifies the apparent compartmental T2 relaxation times in the grey matter (GM). The SANDI model divides the intra-cellular space into intra-neurite and intra-soma compartments to account for contribution of water diffusion in cell bodies. By collecting multi-TE SANDI datasets, we provide the first ever estimates of apparent intra-neurite and intra-soma T2 relaxation times in the human brain and characterise their distribution in cortical and subcortical GM regions.
A practical methodology to study the relationship between arterial blood pressure fluctuations and the BOLD signal
Authors: Dagenais Rémi , Mitsis Georgios
Published: August 2024

The relationship between arterial blood pressure (ABP) fluctuations and the BOLD-fMRI signal is mostly unknown due to the limited availability of reliable MR-compatible ABP devices. Here, we propose a combined experimental and mathematical modeling methodology that uses two calibration recordings outside the scanner to estimate the ABP during the MR scan. Significant correlations between BOLD and ABP were observed in all tested subjects, which also revealed that the global BOLD signal is intrinsically correlated with the underlying ABP fluctuations. Our method is expected to improve our understanding of ABP regulation in the brain and allow for improved physiological fMRI denoising.
MPc-RAGE: Simultaneous MP-RAGE and Phase Contrast Angiography Acquisition
Authors: Johnson Brian , Ganji Sandeep , Pinter Nandor
Published: August 2024

The 3D magnetization-prepared rapid gradient-echo (MP-RAGE) and 3D phase contrast angiography (PCA) are widely used sequences in both clinical and research settings, due to high spatial resolution, excellent contrast, and clinically feasible scan time. Recent image acceleration techniques, such as compressed sensing and artificial intelligence reconstruction can drastically reduce scan times, allowing to create multi-domain imaging approaches. Here we introduce MPc-RAGE, a simultaneous MP-RAGE and PCA acquisition that combines the benefits of these two sequences in a single scan. Furthermore, we explore the possibility of the MPc-RAGE as a potential black blood technique for use in vessel wall imaging.
Application of 3D Cones Trajectory Acquisition in Hepatobiliary MR Imaging for Detection of Hepatic Lesions
Authors: Iranpour Negaur , Sheth Vipul , Syed Ali , Brunsing Ryan
Published: August 2024

Conventional cartesian T1 weighted 3D spoiled gradient recalled echo imaging with respiratory navigation (3D-SPGR-NAV) can help mitigate motion artifacts in post contrast liver imaging but suffer from respiratory rate dependence. 3D cones trajectories with golden angle ordering (T1g) allow continuous sampling during free-breathing liver imaging. We compared the diagnostic performance of an accelerated T1g (T1gER) versus 3D-SPGR-NAV in liver lesion detection. Two readers reviewed imaging from 35 patients containing 127 hepatic lesions. T1gER was non-inferior to 3D-SPGR-NAV for <5 mm and >=10 mm lesions. 3D-SPGR-NAV was superior for lesions 5-9 mm, possibly due to susceptibility artifact on the T1gER images.
Prediction of microvascular invasion in solitary hepatocellular carcinoma using bi-regional quantitative dynamic contrast-enhanced MRI
Authors: Zhu Yongjian , Feng Bing , Cai Wei , Wang Shuang , Xie Lizhi , Ma Xiaohong , Zhao Xinming
Published: August 2024

The peri-tumoral region (PTR) of liver is the main-site of microvascular invasion (MVI) taken place, and contains perfusion information which could reflected the hemodynamic change during MVI. This study investigated the value of quantitative dynamic contrast-enhanced MRI (DCE-MRI) to evaluate the MVI status of hepatocellular carcinoma (HCC) in both intra-tumoral region (ITR) and PTR. The result showed quantitative DCE-MRI perfusion parameters could predict the MVI status in both ITR and PTR. Combining parameters from ITR and PTR could improve the prediction performance. Our study suggest quantitative DCE-MRI perfusion parameters could be employed as an efficient approach to predicting MVI status.
Differentiating Benign from Malignant Gallbladder Wall Thickening in Non-Contrast MRI Imaging: Preliminary Study of a Combined Indicator
Authors: He Wen-Wen , Li Hai-Ge , Zhu Jian-Guo , Pylypenko Dmytro , Liu Fei , Wang Mei , Wu Yue-Fei , Tian Jun
Published: August 2024

This study evaluated the diagnostic accuracy of image features for differentiating benign from malignant gallbladder wall thickening disease with non-contrast MRI and constructed the optimal diagnostic indicator. 23 patients with wall thickening type gallbladder carcinoma and 61 patients with benign wall thickening disease were included. Six image indicators (the layered pattern on T2WI and DWI images, T2WI signal intensity, papillary growth, ADC value, and ratio of the ADC value of the lesion to that of liver parenchyma) were shown to have high diagnostic accuracy. The layered pattern on DWI combined with papillary growth was demonstrated as the optimal indicator.
InnerEye as a Tool for Accurate Hippocampal Segmentation
Authors: Schroder Anna , Moggridge James , Wu Jiaming , Salhab Hamza , Vos Sjoerd , Bristow Melissa , Pérez-García Fernando , Alvarez-Valle Javier , Yousry Tarek , Thornton John , Barkhof Frederik , Grech-Sollars Matthew , Alexander Daniel
Published: August 2024

Accurate hippocampal segmentation tools are critical for monitoring neurodegenerative disease progression on MRI and assessing the impact of interventional treatment. Here we present the InnerEye hippocampal segmentation model and evaluate this new model against three standard segmentation tools in an Alzheimer’s disease dataset. We found InnerEye performed best for Dice score, precision and Hausdorff distance. InnerEye performs consistently well across the different cognitive diagnoses, while performance for other methods decreased with cognitive decline.
Exploiting the inter-rater disagreement to improve probabilistic segmentation
Authors: Chatterjee Soumick , Gaidzik Franziska , Sciarra Alessandro , Mattern Hendrik , Janiga Gabor , Speck Oliver , Nürnberger Andreas , Pathiraja Sahani
Published: August 2024

Disagreements among the experts while segmenting a certain region can be observed for complex segmentation tasks. Deep learning based solution Probabilistic UNet is one of the possible solutions that can learn from a given set of labels for each individual input image and then can produce multiple segmentations for each. But, this does not incorporate the knowledge about the segmentation distribution explicitly. This research extends the idea by incorporating the distribution of the plausible labels as a loss term. The proposed method could reduce the GED by 47% and 63% for multiple sclerosis and vessel segmentation tasks.
Segmentation of Brain Structures MR Images via Semi-Supervised Learning
Authors: Li Rui , Xu Ziming , Yang Runyu , Dou Jiaqi , Yan Tianyi , Chen Huijun
Published: August 2024

The precise segmentation of brain structures is of great importance in quantitatively analyzing brain medical resonance images. In recent years, more and more experts attempt to apply semi-supervised learning to medical image segmentation tasks, since it could make use of the rich unlabeled data. Based on this, we modified a segmentation model based on semi-supervised learning for automatically segmenting brain structures. We tested model on two hippocampus public datasets and results show that our model has considerable segmentation performance compared with that of model trained in a supervised manner, which illustrates the effectiveness and potential of our model.
IMJENSE: scan-specific IMplicit representation for Joint coil sENSitivity and image Estimation in parallel MRI
Authors: Feng Ruimin , Wu Qing , Zhang Yuyao , Wei Hongjiang
Published: August 2024

Current parallel imaging techniques for MRI acceleration can still not reliably reconstruct a high-quality image from highly reduced k-space measurements with fewer calibration data. In this study, we applied the new insight of implicit neural representation (INR) to parallel MRI reconstruction. The underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates. These functions were simultaneously learned from the sparsely-acquired k-space itself without additional training data. Thanks to the continuous representation and joint estimation scheme, the proposed method outperforms the existing scan-specific methods, demonstrating its potential for further accelerating the MRI acquisition.
Architecture-agnostic Deep Image Prior for Accelerated MRI reconstruction
Authors: Liu Yilin , Chen Yong , Yap Pew-thian
Published: August 2024

This work aims to simplify deep image prior (DIP) architectural design decisions in the context of unsupervised accelerated MRI reconstruction, facilitating the deployment of MRI-DIP in real-world settings. We first show that architectures inappropriate for specific MRI datasets (knee, brain) can lead to severe reconstruction artifacts, and then demonstrate that proper network regularization can dramatically improve image quality irrespective of network architectures. The proposed plug-and-play regularization is applicable to any network form without architectural modifications.
MERLIN: In-depth investigation on complex-valued image reconstruction in PyTorch and Tensorflow
Authors: Terpstra Maarten , Hammernik Kerstin , Küstner Thomas , Maspero Matteo , van den Berg Cornelis , Rueckert Daniel
Published: August 2024

Machine learning (ML) has become a powerful technique for reconstructing undersampled MRI. However, applying ML to MRI reconstruction requires several essential building blocks, consisting of general operations and MRI-specific operators in the context of reconstruction. While the former are available in generic frameworks such as Keras/TensorFlow or PyTorch, the MR-specific operators are generally custom-implemented. MERLIN was proposed as a generic toolkit for ML-based medical imaging to harmonize the machine learning landscape and to provide complex-valued interfaces for commonly used back-ends. Here, we evaluate MERLIN as a cross-platform toolkit for complex-valued image reconstruction.
Using Noisier2Noise to choose the sampling mask partition of Self-Supervised Learning via Data Undersampling (SSDU)
Authors: Millard Charles , Chiew Mark
Published: August 2024

To train a neural network to recover images with sub-sampled examples only, Self-Supervised Learning via Data Undersampling (SSDU) proposes partitioning the sampling mask into two subsets and training a network to recover one from the other. Recent work on the connection between SSDU and the self-supervised denoising method Noisier2Noise has shown that superior reconstruction quality is possible when the distribution of the partition matches the sampling mask. We test this principle on three types of sampling schemes and find that the test set loss is indeed closest to a fully supervised benchmark when the partition distribution is matched.
NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data
Authors: Khawaled Samah , Freiman Moti
Published: August 2024

Deep learning (DL) models are currently employed to reconstruct high quality MRI image from undersampled k-space measurements. Yet, uncertainty quantification in images reconstructed by such models is critical for reliable clinical decision making. We propose NPBREC, a non-parametric Bayesian approach for uncertainty estimation in DL-based MRI reconstruction. We demonstrated the added-value of our Bayesian registration framework on the fastmri multi-coil brain MRI dataset, compared to the baseline E2E-VarNet trained with and without inference-time dropout for uncertainty quantification. Our NPBREC approach demonstrated both improved reconstruction accuracy and a better correlation between reconstruction errors and uncertainty measures.
Deconvolution for Hyperpolarized Magnetic Resonance Imaging
Authors: Miller Jack , Bøgh Nikolaj , Tyler Andrew , Lau Justin , Hansen Esben , Væggemose Michael , Rider Oliver , Tyler Damian , Laustsen Christoffer
Published: August 2024

Hyperpolarized metabolic imaging is translating into clinical practice. However, the desire to obtain chemical, spatial and temporal information combined with the finite and non-renewable exogenous magnetisation provided by hyperpolarized techniques often results in imaging sequences with a poor point-spread function. Here we compare multiple deconvolution algorithms to improve apparent reconstructed resolution, as is commonly performed in PET imaging. We show that deconvolution can significantly improve the reconstructed resolution of hyperpolarized images.
Hyperpolarized δ-[1-13C]-gluconolactone reports on TERT expression and response to therapy in brain tumors
Authors: Batsios Georgios , Taglang Celine , Gillespie Anne Marie , Viswanath Pavithra
Published: August 2024

TERT expression is essential for telomere maintenance and uncontrolled tumor proliferation in oligodendrogliomas. TERT is an attractive therapeutic target and the drug 6-thio-2’-deoxyguanosine that disrupts telomere maintenance is in clinical trials for cancer. We previously showed that TERT expression is associated with upregulation of the pentose phosphate pathway in oligodendrogliomas. Here, we have established the ability of hyperpolarized δ-[1-13C]-gluconolactone, a probe of the pentose phosphate pathway, to assess TERT expression and response to 6-thio-2’-deoxyguanosine in oligodendrogliomas in vivo. Our findings provide a non-invasive method of imaging a hallmark of cancer and have the potential to improve management of oligodendroglioma patients.
Novel Structure-guided Design of Amino Acid based Hyperpolarized 13C Probes for Metabolic Imaging
Authors: Yamamoto Kazutoshi , Saito Yutaro , Yatabe Hiroyuki , Koyasu Norikazu , Tamura Iori , Kondo Yohei , Ishida Ryo , Seki Tomohiro , Eguchi Akihiro , Takakusagi Yoichi , Oshima Nobu , Krishna Murali , Sando Shinsuke
Published: August 2024

Profiling the metabolic phenotypes of tumors by molecular imaging is a promising approach in treatment planning and response monitoring. Here, dissolution Dynamic Nuclear Polarization (dDNP) is an emerging technique to detect site-specific enzymatic activities noninvasively. While drastic dDNP sensitivity enhancements are advantageous for real-time measurements, the limited number of applicable probes with longer T1 relaxation times continues to be a major drawback particularly for in vivo use. In this presentation, we will demonstrate a framework to design novel dDNP probes, detecting APN enzymatic activities which is a cancer therapeutic target, and its applications to in vivo pancreatic cancer.
Multi-Voxel PRESS of Hyperpolarized 13C-labelled Agents In Vivo
Authors: Gottwald Wolfgang , Heid Irina , Nagel Luca , Grashei Martin , Bauer Sebastian , Semina Mariia , Setzer Nadine , Suehnel Sandra , Topping Geoffrey , Braren Rickmer , Schilling Franz
Published: August 2024

A newly developed multi-voxel point resolved spectroscopy (MV-PRESS) sequence was evaluated for the study of murine pancreatic ductal adenocarcinoma (PDAC) using hyperpolarized (HP) 13C-labelled molecules. First, the sequence was compared to standard 2D free-induction decay chemical shift imaging (2D FID-CSI) in vivo using HP [1-13C]pyruvate (PA). Then, multiple ROIs in mice with endogenous PDAC were selected based on anatomical 1H imaging. From those voxels, high resolution spectra using the MV-PRESS sequence were acquired to evaluate differences in pyruvate-to-lactate conversion. Finally, the sequence was used to determine the extracellular pH of tumor lesions and kidneys in the same animal model.
High-throughput kinetic analysis in a microfluidic multiwell platform by dissolution dynamic nuclear polarization-magnetic resonance
Authors: Azagra Marc , Lozano Jose Yeste , Ortega Maria , Portela Alejandro , Matajsz Gergö , Gómez Alba Herrero , Yaewon Kim , Sriram Renuka , Kurhanewicz John , Vigneron Daniel , Rius Irene Marco
Published: August 2024

High throughput dissolution Dynamic Nuclear Polarization (dDNP) magnetic resonance spectroscopic imaging (MRSI) was achieved by developing a multiwell microfluidic platform. This platform allows for multiple experiments to be performed with a single injection of a dDNP substrate, enabling faster and more reproducible data gathering, reducing experimental costs and time by a factor of 8. Here, we present a proof of concept of the methodology with an oxidation reaction combining [1-13C]pyruvic acid with hydrogen peroxide, testing 2 different conditions with 3 replicates each.
Compressed sense reconstructions improve non-Cartesian hyperpolarized 129Xe MRI
Authors: Plummer Joseph , Bdaiwi Abdullah , Soderlund Stephanie , Willmering Matthew , Woods Jason , Cleveland Zackary , Walkup Laura
Published: August 2024

Hyperpolarized 129Xe MRI is a powerful pulmonary imaging modality to assess regional ventilation, gas exchange, and lung microstructure. However, non-equilibrium magnetization decay and relatively long breath-hold durations remain major limitations to routine clinical dissemination. While non-Cartesian sampling methods can improve sampling efficiency, these methods are yet to be combined with non-linear reconstruction methods like compressed sensing to further increase speed and reduce image noise. Here, we implement compressed-sense reconstructions across a range of non-Cartesian 129Xe MRI techniques and show that SNR and image quality can all be improved while reducing scan time.
Navigator-free water/fat separation for multi-shot diffusion-weighted EPI using structured low-rank reconstruction
Authors: Dong Yiming , Koolstra Kirsten , Li Ziyu , van Osch Matthias J.P. , Börnert Peter
Published: August 2024

Multi-shot EPI readout-approaches provide high spatial resolution at reduced geometric distortions and improved SNR in diffusion weighted imaging (DWI). As a specific challenge, physiological motion induces shot-to-shot phase variations and needs specific handling, e.g., using additionally measured phase navigators, data-driven phase estimation and/or low-rank regularizations. Furthermore, good fat-suppression is also needed in DWI, making the use of chemical-shift encoding interesting. In this work, a structured low-rank-based water/fat separation pipeline is proposed to jointly estimate water/fat images while correcting motion-induced phase variations with improved time efficiency. In-vivo examples from different anatomies demonstrate improved water/fat separation compared to conventional approaches.
Microstructural and microvascular alterations in small vessel diseases using multi-shell DTI inside and outside white matter hyperintensities
Authors: Voorter Paulien , van Dinther Maud , Stringer Michael , Kerkhofs Danielle , Dewenter Anna , Blair Gordon , Garcia Daniela Jaime , Chappell Francesca , Kopczak Anna , Staals Julie , Ingrisch Michael , Duering Marco , Thrippleton Michael , Doubal Fergus , Dichgans Martin , Wardlaw Joanna , van Oostenbrugge Robert , Jansen Jacobus , Backes Walter
Published: August 2024

The pathophysiology underlying white matter hyperintensities (WMHs) and changes in perilesional white matter are not fully understood in cerebral small vessel disease (SVD). Using multi-shell diffusion tensor imaging (DTI), we studied the mean (parenchymal) diffusivity (MD) and microvascular perfusion (f) inside and outside WMHs in sporadic and monogenetic (CADASIL) SVDs. The microstructure (expressed by MD) was most damaged inside WMHs and extended beyond the WMHs. Furthermore, f was highest at the WMH border, decreased towards the WMH center, and decreased outside WMHs, possibly reflecting multiple SVD-related alterations in the microvasculature, such as dilated microvessels, capillary rarefaction, and reduced microvascular integrity.
Hemodynamic imaging parameters in brain metastases patients – Agreement between multi-delay ASL and hypercapnic BOLD
Authors: van Grinsven Eva , Philippens Marielle , Siero Jeroen , Bhogal Alex
Published: August 2024

This study compared baseline ASL with BOLD cerebrovascular reactivity (BOLD-CVR) parameters in the brain under different hemodynamic circumstances in patients with brain metastases. There was a strong relationship between baseline cerebral blood flow and BOLD-CVR measurements and the temporal metrics of ASL and BOLD-CVR. However, the relationship between baseline ASL and BOLD-CVR does not hold in tissue with exhausted cerebral autoregulation (i.e. vascular steal regions). Thereby, BOLD-CVR may be able to flag at-risk areas with depleted vascular reserve capacity before they become visible on ASL MRI.
Multivariate analysis of perfusion and oxygenation patterns in asymptomatic unilateral carotid artery stenosis
Authors: Kufer Jan , Göttler Jens , Hoffmann Gabriel , Schmitzer Lena , Zimmer Claus , Hyder Fahmeed , Preibisch Christine , Kaczmarz Stephan
Published: August 2024

Asymptomatic internal carotid artery stenosis (ICAS) is linked with increased stroke risk and cognitive decline. Physiological MRI of cerebral hemodynamic changes in ICAS patients is promising to inform treatment decisions. Here, we investigated the complex pathophysiology of ICAS using principal component analysis of cerebral perfusion and oxygenation changes to establish disease-specific patterns of CBF, OEF, CMRO2 and the effective oxygen diffusivity of the capillary bed. Regression models revealed the association of pattern expression with sonography-based degree of stenosis and flow velocity in the carotid artery, as well as systemic blood pressure.
Simultaneous PET/MRI can measure extracellular pH in the tumor microenvironment
Authors: Pollard Alyssa , de la Cerda Jorge , Schuler F William , Pollard Tyler , Kotrotsou Aikaterini , Palagi Loreno , Dhakan Chetan , Pisaneschi Federica , Pagel Mark
Published: August 2024

Acidosis is a useful biomarker for tumor diagnoses and for evaluating early response to anti-cancer treatments. T1-based MRI contrast agents have been developed with a r1 relaxivity that is responsive to extracellular pH (pHe) in the tumor microenvironment. However, the concentration of these agents in the tumor must also be known to determine r1 relaxivity. We have developed PET/MRI co-agents that can be evaluated with simultaneous PET/MRI during pre-clinical studies with mouse models of human cancers. The PET agent reports on the concentration of the agents, which can be used to determine r1 relaxivity and estimate pHe in tumors.
Relaxation-compensated CEST-MRI at 3 T exhibits regional differences in gray matter and white matter contrasts of glioma patients
Authors: Kroh Florian , Boyd Philip , von Knebel Doeberitz Nikolaus , Schlemmer Heinz-Peter , Ladd Mark , Bachert Peter , Paech Daniel , Korzowski Andreas
Published: August 2024

In this study, the APT-weighted (APTwasym) and relaxation-compensated CEST-MRI contrasts of 9 postoperative glioma patients, were analyzed for potential regional differences in gray and white matter. The purpose of this analysis was to identify the influence of the location within the brain on CEST contrasts of tumorous and healthy control tissue, respectively. For relaxation-compensated CEST (MTRRex), (i) differences between gray and white matter and also (ii) regional differences with a decreased contrast inside the frontal lobe were found. On the contrary, the APTwasym was found to be independent of (i) location and (ii) comparable for gray and white matter.
Unraveling contributions to the saturated signal at 3.5 ppm in the Z-spectrum of human brain tumors
Authors: Heo Hye-Young , Singh Munendra , Jiang Shanshan , Zhou Jinyuan
Published: August 2024

Increased cytosolic mobile protein content in gliomas causes amide proton transfer (APT) hyperintensity. However, most current APT imaging protocols acquire APT-weighted images that reflect multiple contributions, including residual direct water saturation (or relaxation), semisolid macromolecular magnetization transfer contrast (MTC) asymmetry, and nuclear Overhauser enhancement (NOE) effects, thus limiting the assessment of clean APT. Herein, we separated water, MTC, and APT signal components from RF saturated signals using an MR fingerprinting sequence and evaluated the contributions to the saturation signal at 3.5 ppm in the Z-spectrum of brain tumors.
Can APT-weighted CEST MRI provide robust measurement at 3T? A reproducibility study in healthy brain and tumor across sessions and scanners
Authors: Wu Yulun , Wood Tobias , Derks Sophie , Pruis Ilanah , van der Voort Sebastian , van Zanten Sophie Veldhuijzen , Smits Marion , Warnert Esther
Published: August 2024

The goal of this study was to investigate whether APT-weighted CEST imaging can provide reproducible measurements across scan sessions and scanners. Reproducibility of APT-weighted imaging in healthy brain tissue and tumors was evaluated for three CEST metrics: Lorentzian Difference (LD), magnetization transfer ratio asymmetry (MTRasym), and relaxation-compensated inverse magnetization transfer ratio (MTRREX).
A deep learning framework for cardiac self-gating in free-running radial 4D flow MRI
Authors: Falcão Mariana , Rossi Giulia , Richiardi Jonas , Sieber Xavier , Monney Pierre , Rutz Tobias , Prša Milan , Tenisch Estelle , Pavon Anna Giulia , Antiochos Panagiotis , Stuber Matthias , Roy Christopher
Published: August 2024

Self-gating (SG) techniques improve the ease-of-use of cardiac MR by deriving cardiac signals from the data itself, obviating the need for ECG lead placement. Nonetheless, unpredictable shifts between the features of SG signals and the conventionally used R-wave peaks from ECG might hamper a direct link of reconstructed image frames with physiology. In this work, we developed a fully convolutional neural network to predict R-wave peak timepoints from SG imaging readouts in free-running radial 4D flow data, and provided a proof-of-concept of the usability of such learned R-wave peak timepoints for reconstructing cardiac-resolved 4D flow images.
Repeatability of whole aorta 4D flow pulse wave velocity in healthy volunteers and comparison with Marfan Syndrome patients
Authors: Bosshardt Daan , van Andel Mitzi , Merton Renske , de Waard Vivian , van Kimmenade Roland , Scholte Arthur , Cox Moniek , Zwinderman Aeilko , Nederveen Aart , Groenink Maarten , van Ooij Pim , Schrauben Eric
Published: August 2024

Pulse wave velocity (PWV) is a well-established measure to evaluate vessel wall stiffness and increases in the aorta of patients with Marfan Syndrome (MFS). With sufficient temporal resolution, 4D-flow MRI can be used for PWV calculations. In this study, we assess the repeatability of aortic PWV calculations in healthy volunteers using an open-source software tool and apply it in a MFS cohort. Our results show high repeatability of global aortic PWV, elevated PWV in MFS patients, and an association between elevated PWV and biomarkers for advanced disease.
Phase-Contrast MRI with Hybrid One- and Two-sided Simultaneous Three Directional Flow-Encoding and Velocity SPectrum SepAration (HOTSPA+)
Authors: Liu Wenjian , Hu Junpu , Zhu Jiayu , Xu Jian , Zhou Zijian , Qi Haikun , Hu Peng
Published: August 2024

Phase contrast MRI (PC MRI) has been widely used to quantify blood flow and velocity. Four-dimensional (4D) flow PC MRI needs to acquire the FC data and three-directional (3D) FE data interleaved within each cardiac k-space segment. In this work, we propose a more efficient flow encoding strategy for PC MRI using a temporal modulation technique and we showed the preliminary feasibility of quadrupling the temporal resolution or reducing the scan time by 70% compared with conventional 4D flow by redesigning and adjusting the temporal modulation strategy for under-sampled M1 space.
Intracranial Arterial Flow Velocimetry in Quantitative Time-of-Flight MR Angiography using Deep Machine Learning
Authors: Koktzoglou Ioannis , Huang Rong
Published: August 2024

Quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) is a recently introduced technique that provides for simultaneous luminal and hemodynamic imaging of the intracranial arteries. We hypothesized that the application of a deep machine learning (DML) image analysis strategy to qTOF MRA data would improve agreement of intracranial arterial velocity measures with respect to phase contrast MRI. Compared to a more conventional image analysis procedure, we found that the application of DML image analysis to qTOF data improved agreement of component, total, and peak intracranial arterial flow velocity measures with respect to phase contrast MRI, and reduced calculation times by 35-fold.
MRI-based Direct PET Attenuation Correction Using Cross-modality Attention Network for Combined PET/MRI
Authors: Yang Xiaofeng , Lei Yang , Wang Tonghe , Mandava Sagar , Liu Tian , Nye Jonathon , Mao Hui
Published: August 2024

This study aims to develop an efficient and clinically applicable deep-learning method using routine T1-weighted MRI and non-AC PET to directly synthesize high-quality attenuation-corrected PET without the need of an attenuation coefficient map for simultaneous PET/MRI.
Prediction of MGMT Methylation Status of Gliomas Using Pre-operative MR Images: a Fully Automatic Convolutional Neural Networks Based Approach
Authors: Chen Xiaohua , Chen Zhiqiang , Wang Zhuo , Zhang Shaoru , Zhou Yunshu , Liu Shili , Zhang Ruodi , Xiong Yuhui , Wang Aijun
Published: August 2024

This study aims to propose a fully automatic approach based on convolutional neural networks (CNNs) to predict the O6-Methylguanine-DNA-methyltransferase (MGMT) methylation status of gliomas using conventional pre-operative MR images. It was shown that the Markov Random Field-U-Net network can accurately segment the tumor region, and the improved 34-layer Resnet network can predict the MGMT methylation status effectively. This model has the potential to be a practical tool for the non-invasive characterization of gliomas to help the individualized treatment planning.
Physics-Informed Deep Learning Approach to Quantifying Magnetic Resonance Spectroscopy Data with Simultaneous Uncertainty Estimation
Authors: Shamaei Amirmohammad , Rizzo Rudy
Published: August 2024

While deep learning (DL)-based approaches have been employed to quantify MRS signals, the interpretability of deep-model results and assessing what a DL model knows is a crucial component of DL-based approaches. We present a physics-informed DL-based algorithm for MRS data quantification with simultaneous uncertainty estimation, which uses the advantages of linear combination model fitting and the capabilities of ensembles of variational autoencoders. We acknowledge the need for further investigation with in-vivo datasets and other approaches. Furthermore, a more thorough analysis that includes scores that take estimation variation and uncertainty for both the proposed DL technique and traditional model fitting is required.
Deep-Learning Enhancement of 3D-MSI using Conventional Images as Training Targets
Authors: Koch Kevin , Mickevicius Nikolai , Ausman Robin , Nencka Andrew
Published: August 2024

We present a feasibility study exploring the use deep learning (DL) methods to enhance the image quality of isotropic 3D-multi-spectral metal artifact suppressed images. Conventional high resolution 2D fast/turbo spin echo images are used as network training labels by masking regions corrupted by metal artifacts. A pilot set of 3 cases deploying the presented concept to T2-weighted imaging of the instrumented spine are analyzed. The quantitative results of this analysis demonstrate improved spinal cord contrast, image resolution, and general agreement with 2D-FSE images when the DL enhancement model is inferred on the complete 3D-MSI datasets.
Characterization of normal appearing white matter in Multiple Sclerosis with diffusion MRI using signal representations and model parameters
Authors: Liao Ying , Coelho Santiago , Chen Jenny , Novikov Dmitry , Fieremans Els
Published: August 2024

Multiple sclerosis (MS) is a neurodegenerative and inflammatory disease characterized by focal lesions and damages to normal appearing white matter (NAWM). Diffusion MRI (dMRI) is known for its sensitivity to the microstructural changes in white matter. In this work, we study the sensitivity of dMRI metrics to MS pathology in NAWM by distinguishing MS patients from healthy controls. We found that beyond-spherical-mean rotational invariants of high-b shells contribute mostly to the classification, indicating nontrivial information content of high-b diffusion signal beyond DTI.
Zero-shell diffusion MRI: Focus on microstructure by decoupling fiber orientations
Authors: Coelho Santiago , Fieremans Els , Novikov Dmitry
Published: August 2024

In brain dMRI, microstructure parameters of fiber fascicles, such as compartment fractions, exchange, diffusivities, relaxation rates, and structural disorder, are highly sought after. They can be accessed by sampling multiple diffusion weightings, b-tensor shapes, diffusion and/or echo times. Yet most scan time is spent oversampling the fiber orientation distribution function – because factoring it out nominally requires rotational invariants like the spherical mean. We show how to measure multiple inequivalent combinations of $$$(b,\,\Delta)$$$, while spending single gradient directions per unique combination. We recover signal’s rotational invariants for each one and use them for biophysical modeling of white and gray matter.
Incorporating susceptibility effects into the Standard Model of diffusion in white matter
Authors: Sandgaard Anders , Kiselev Valerij , Shemesh Noam , Jespersen Sune
Published: August 2024

Estimating parameters of the Standard Model (SM) of white matter (WM) is an ill-posed problem (WM). To overcome this degeneracy, models extensions have been proposed adding echo-time dependent information. Here we investigate the feasibility of incorporating our analytical solution for the frequency shift from white matter axons accounting for magneto-structural anisotropy along with an empirical orientation dependence of relaxation into SM parameter estimation. This may also help achieving rotation-free mapping of susceptibility-related parameters using diffusion MRI instead of the impractical sample rotation in the scanner.
Quantitative MRI Interpretable 100D Feature Space of Knee Osteoarthritis
Authors: Hoyer Gabrielle , Gao Kenneth , Lee Jinhee , Luitjens Johanna , Gassert Felix , Majumdar Sharmila , Pedoia Valentina
Published: August 2024

While the Osteoarthritis Initiative data has been explored in independent studies, to our knowledge, never has such a comprehensive analysis been completed, investigating morphology of femur, patella, tibia, menisci, for biomarkers: cartilage thickness, cartilage T2, bone shape, and meniscus shape. With 4,796 subjects from the OAI, this study utilizes an automatic statistical shape modeling technique to create a 100D interpretable space of biomarker-tissue PC modes, which can be disseminated using an innovative opensource app. In combination with Image Twin and Clinical Twin analyses, variations in morphology, as well as clinical metadata, were identified as being significantly associated with osteoarthritis incidence.