bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of multiband variable-rate selective excitation (MB-VERSE) diffusion-weighted imaging (DWI) of the liver MRI
Authors: Yoon Ja Kyung , Chung Yong Eun , Shin Jaeseung , Choi Jin-Young , Park Mi-Suk , Kim Myeong-Jin
Published: August 2023

In liver MRI, diffusion-weighted imaging (DWI) allows better detection and characterization of focal lesions, but requires a relatively long scan time. The multiband variable-rate selective excitation (MB-VERSE) echoplanar imaging for DWI provides accelerated acquisition time with some expected trade-off in image quality. Qualitative and quantitative image quality of MB-VERSE images as well as focal liver lesion detectability was evaluated by three readers. The MB-VERSE sequence as well as focal liver lesion detectability showed significant sacrifice in quantitative and qualitative overall image quality, but comparable focal lesion detectability.
Fully Connected Deep Neural Network combined with Segmented Least Square Fitting for improved extraction of IVIM Parameters
Authors: Mastropietro Alfonso , Scalco Elisa , Procissi Daniele , Bertolino Nicola , Rizzo Giovanna
Published: August 2023

Voxel-by-voxel fitting of intravoxel incoherent motion (IVIM) MRI data using a bi-exponential model is challenging especially with low signal-to-noise ratio (SNR) diffusion-MR images. We propose to combine and use a supervised Deep Neural Network (DNN) approach to increase SNR of the acquired images and thus improve extraction of reliable parameter estimation using a segmented least square fitting algorithm. The effectiveness of the proposed method was demonstrated in both simulated and acquired in vivo data. The proposed approach is promising and can increase performance of the fitting algorithms especially for the case of images with high background noise.
Revised-NODDI with conventional dMRI data enabled by deep learning
Authors: Guerreri Michele , Azadbakht Hojjat , Zhang Hui
Published: August 2023

This work shows that deep learning (DL) enables revised-NODDI parameter estimation from conventional dMRI data. Revised-NODDI is a recently proposed model which overcomes some limitations of NODDI. With conventional fitting methods, revised-NODDI parameters can be robustly estimated only in the presence of data acquired with multiple tensor-valued diffusion encodings. However, this new generation of acquisitions is not yet routinely available in clinical research. We show that revised-NODDI parameters estimated using conventional dMRI data via a DL framework are comparable with the parameters estimated fitting the model to data acquired using multiple tensor-valued diffusion encoding.
New “restricted SAR mode” definition based on a thermal conservative model for relaxed unconditional safe in vivo experiments
Authors: Dudysheva Natalia , Boulant Nicolas , Vignaud Alexandre , Mauconduit Franck
Published: August 2023

In this work, we propose to extend the capacities of ultra-low SAR protocol used in coil design. A developed restricted SAR protocol holds the worst-case SAR below the threshold by constraining the total transmitted RF power. Since SAR reflects tissue heating, we exploit a conservative thermal model to derive relaxed SAR constraints depending on acquisition time. This enables to extend the options available for sequences played under this mode, including fat suppression, higher image resolution, or decreased TR.
Classification of Gulf War Illness Patients vs Control Veterans Using fMRI Dynamic Functional Connectivity
Authors: Sakoglu Unal , Mishra Amaresh , Gopinath Kaundinya , Crosson Bruce , Haley Robert
Published: August 2023

Around 200,000 veterans suffer from Gulf War Illness (GWI). GWI is characterized by multiple deficits in cognitive, emotion, somatosensory and pain domains. In this study we studied 23 GWI patients and 30 age-matched control veterans with resting-state fMRI in order to classify patients versus controls using dynamic functional connectivity among brain networks. Results show that different brain networks have discriminating power, pointing to widespread impairments in functional connectivity of visual, semantic, multi-sensory, and sensory-motor processing networks in GWI, consistent with multi-symptom nature of GWI.
Quantitative Radiomic Features of Deep Learning Image Reconstruction in MRI
Authors: Peake Edward , Priest Andy , Graves Martin
Published: August 2023

Radiomic features are sensitive to changes in imaging parameters in MRI. This makes it challenging to develop robust machine learning models using imaging features. We explore the effect of clinically available deep learning image reconstruction on the performance of radiomic features. Correlation coefficient values varied (0.56 - 1.00) when comparing radiomic features of deep learning reconstructed images and ‘conventional’ MRI scans. The noise reduction level had a large impact on correlation coefficients, but variations were also significant between different types of imaging feature. Identification of highly correlated features may help identify more stable sets of radiomic features for machine learning.
Improving Lipid Suppression for 1H-MRSI Using Region-Optimized Virtual Coils
Authors: Lam Fan , Li Yahang , Zhao Yibo , Haldar Justin
Published: August 2023

One major challenge for 1H-MRSI with localized excitation is minimizing interference from undesired regions, particularly the subcutaneous lipids. In this work, we exploit the complementariness of a new technique called region-optimized virtual coils (ROVir) that is capable of generating a set of virtual channels sensitized to specific regions and spatiospectral processing for lipid removal. Using brain 1H-MRSI data, we demonstrated improved lipid removal by combining ROVir and a state-of-the-art union-of-subspaces method. We expect this integration to be useful for MRSI applications where VOIs are often lateralized (e.g., stroke and tumor) and/or cerebral lipids are of interest.
What you see is not what you get: localization of Deep Brain Stimulation leads from metal artifacts
Authors: Nuzov Noa , Bhusal Bhumi , Jiang Fuchang , Rosenow Joshua , Golestanirad Laleh
Published: August 2023

Deep Brain Stimulation (DBS) implants are implanted into target regions of the brain to treat symptoms of many neurological disorders. Postoperative Magnetic Resonance Imaging (MRI) scans are used to localize DBS leads, however, there are large, distorted artifacts surrounding the leads in the images. This study uses MRI phantoms to investigate the true location of a DBS lead relative to its artifact. The results will help researchers and clinicians know the actual locations of the lead tip and contacts from postoperative MRI scans to confirm the implant is correctly targeting the desired brain area.
On the basis of sex: Preclinical 23Na MRI at 21.1 T Delineates Sodium Distribution Differences in Females Following Central Sensitization
Authors: Holder Samuel , Richter Dayna , Hike David , Harrington Michael , Grant Samuel
Published: August 2023

23Na FID-based 3D CSI was implemented in female rats at 21.1 T to examine sex differences in sodium response to NTG-triggered central sensitization. The female brainstem was resilient to NTG-triggered increases in sodium seen with males, despite sodium increases in the female cisterna magna and fourth ventricle. Lateral ventricles and thalamus sodium in females decreased with NTG trigger. Female resilience in brainstem and thalamus, both connected to trigeminal first order neuron, holds interesting implications for migraine.
A model for fat-suppressed variable flip angle T1-mapping and dynamic contrast enhanced MRI
Authors: Wennen Myrte , Moll Manon , Marcus Tim , Kuijer Joost , Heunks Leo , Lavini Christina , Strijkers Gustav , Nederveen Aart , Gurney-Champion Oliver
Published: August 2023

Although fat suppression can substantially improve image quality, the pulses corrupt the steady-state in a spoiled GRE sequence used for DCE and T1-relaxometry. Consequently, the conventional signal equations for quantifying tissue properties (T1, Ktrans, etc) do not hold. We have now included the effect of fat-suppression pulses in the signal equation that forms the basis for T1-relaxometry and DCE modelling. We have validated our equation with T1-mapping in a phantom and show its performance in healthy subjects and patients. Our equation enables accurate T1-mapping and pharmakinetic modelling for sequences that use fat suppression, including GRASP.
Measuring lactate metabolism in vivo in at physiological level using [1-13C]lactate hyperpolarised with an endogenous labile radical precursor
Authors: Gaunt Adam , Lewis Jennifer , Hesse Friederike , Marco-Rius Irene , Brindle Kevin , Comment Arnaud
Published: August 2023

A hyperpolarised [1-13C]lactate solution prepared by dynamic nuclear polarization at 7T and 1.4K using photo-irradiated alpha-ketoglutarate as the polarizing agent was injected at low dose in healthy rodents to allow measuring in vivo metabolism in the rodent brain and liver at physiological concentration. As a result of the large 13C polarisation, it was possible to detect metabolic products of [1-13C]lactate in vivo, including [1-13C]pyruvate and 13C-bicarbonate. The observed pyruvate-to-lactate ratios were commensurate with in vitro values previously reported in the literature. The purely endogenous composition of the solution offers the possibility of performing similar measurements in humans.
TensorFlow MRI: A Library for Modern Computational MRI on Heterogenous Systems
Authors: Montalt-Tordera Javier , Steeden Jennifer , Muthurangu Vivek
Published: August 2023

We present TensorFlow MRI, a new open-source library of TensorFlow operators for MR image reconstruction and processing. Its goal is to enable fast prototyping of modern MRI applications within a single computing framework. It is intended for researchers working in MR image reconstruction and/or processing, especially those interested in ML applications. The library is primarily Python-based and is easy to use, understand and extend. It has a single-command installation procedure and extensive documentation. Thanks to the use of a TensorFlow backend, it has excellent performance and runs on heterogenous and distributed systems.
An open-source repository of minimum echo time sequences
Authors: Tong Gehua , Vaughan John , Geethanath Sairam
Published: August 2023

Ultrashort Echo Time (UTE) sequences are clinically useful in short T2 tissues such as bone, cartilage, and lung parenchyma. To help harmonize UTE protocols across clinical sites, we present an open-source repository implementing three basic radial minimal TE sequences using PyPulseq: 2D UTE, 3D UTE, and COncurrent Dephasing and Excitation (CODE). Functions for customized sequences and reconstruction scripts were provided for a complete pipeline. Echo Times (TEs) from 0.12 to 0.82 ms were achieved, and 27.6% more signal was captured in the bone and connective tissue in 2D ex vivo images compared to the GRE reference.
Head Position Related SAR Uncertainty Depends on Slice Orientation and Pulse Complexity
Authors: Kopanoglu Emre
Published: August 2023

Safety models on scanners are unaware of the actual patient position while excitation pulses are inherently position dependent. This study investigates the effect of this positional mismatch on SAR estimation for axial, coronal and sagittal slice orientations. The positional mismatch yields up to 5.2-fold underestimation of peak local SAR. RF shimming and 2-spoke parallel-transmit pulses for axial slice orientation have reduced SAR-sensitivity to positional mismatch with the worst-case underestimation being <2.0-fold whereas a reduced SAR-sensitivity was not observed for coronal and sagittal slices. For extreme head positions not represented in safety-models, axial RF shimming / 2-spokes parallel-transmit pulses maybe beneficial.
Understanding the vasodilatory effects of oxygen in oxygen enhanced functional lung MRI at 0.55T
Authors: Wieslander Björn , Seemann Felicia , Javed Ahsan , Bruce Christopher , Ramasawmy Rajiv , Jaimes Andi , Lucas Katherine , Frasier Victoria , Potersnak Amanda , Lederman Robert , Campbell-Washburn Adrienne
Published: August 2023

Oxygen-enhanced lung MRI is promising for assessing regional lung function (ventilation and perfusion) but the physiological contributions to signal enhancement are poorly characterized. We sought to assess how oxygen-induced pulmonary vasodilation may contribute to signal enhancement by altering pulmonary blood volume and therefore lung proton density. We acquired repeated PDw and T1w images using a 0.55 T prototype MRI system in anesthetized pigs exposed to room air, 100% O2, 50% O2, hypoxia and experimental pulmonary hypovolemia. Our findings suggest that T1w signal intensity is affected by oxygen through both T1 shortening and vascular activity.
A preliminary study of hemodynamic assessments of cerebral venous sinus stenosis using 4D flow MRI
Authors: zhou hongwei , zhao kexin , kong derui , zheng chong , jiang yueluan , wang xiaoye
Published: August 2023

4D flow MRI provides detailed insight into the alterations of hemodynamic，including quantitative assessment of flow parameters for the venous system. This technique overcomes the missing hemodynamic information of standard magnetic resonance imaging and the invasive disadvantage of digital subtraction angiography (DSA). Through data visualization and quantification, the particle tracking map is obtained and the peak velocities, wall shear stress (WSS) is calculated. The dynamics of venous sinus stenosis will change especially in severe area.
Use of Arterial Blood Contrast (ABC) to detect resting state networks at short TE
Authors: Taheriyan Fatemeh , Fazal Zahrah , Gunnewiek Kevin Klein , Schulz Jenni , Marques Jose , Norris David
Published: August 2023

Arterial blood contrast increase the cerebral blood volume contribution to brain activation studies by suppressing grey matter signal using on-resonance magnetisation transfer. An increase in CBV will lead to an increase in signal, which is then complementary to BOLD. We performed multi-echo resting state fMRI in a group of 16 subjects and compared the results at TE=28ms with MT-off (standard BOLD) and TE=6.9 MT-on (ABC). Z-scores were higher for standard BOLD at all TEs but with higher variance in the z-score value. Dual regression results never the less showed high similarity activation maps for standard resting state networks.
Test-retest repeatability of MRI radiomics features in head and neck region using 3D-T1w-TSE and 3D-T2w-TSE: a prospective multi-scan study
Authors: xue Cindy , Yuan Jing , Zhou Yihang , Wong Oilei , Cheung Kin Yin , Yu Siu Ki
Published: August 2023

Radiomics has been increasingly used as potential quantitative imaging biomarkers for head and neck cancer diagnosis and treatment. However, the test-retest repeatability of radiomics features in the head and neck (HN) region is rarely investigated. This study aims to investigate the test-retest repeatability of MRI radiomics features in HN. Radiomics features were extracted from 15 volunteers receiving 4 re-positioned MRI scans with thermoplastic mask immobilization and flexible coils using 3D-T1w-TSE and 3D-T2w-TSE. The results found that test-retest repeatability of MRI radiomics features varied dependent on tissues and pulse sequences. Only a small percentage of radiomics features showed excellent test-retest repeatability.
Clinical Feasibility Study of Accelerated 2D Magnetic Resonance shoulder Imaging Using Deep Learning-based Algorithm
Authors: Liu Jing , Xue Ke , Dai Yongming , Wu Peng , Qiu Jianxing
Published: August 2023

Deep Learning-based magnetic resonance imaging (DL-MRI) could accelerate 3D MRI scanning time. In this study, we investigate the feasibility of DL-MRI in 2D shoulder MRI. Totally 20 consecutive patients were enrolled for both conventional MRI and DL-MRI. Both qualitative and quantitative analyses were conducted to compare the image quality and lesion diagnosis on conventional MRI and DL-MRI. And our results revealed that DL-MRI was valuable for improving the overall workflow of shoulder MRI with scanning time saved and image quality improved.
Monitoring changes of Achilles Tendon in amateur Marathon athletes by using component Analysis UTE-T2* technology: a preliminary study
Authors: Zhu Dantian , Fang Yijie , Wu Wenhao , Yu Wenjun , Li Wei , Ma Yajun , Li Shaolin
Published: August 2023

Long-distance running is a common cause of Achilles tendinopathy. A fast, reliable, and non-invasive magnetic resonance imaging (MRI) technique to track the early changes in tendon is of critical importance for effective clinical intervention and evaluation that can prevent the progression of Achilles tendinopathy. This study aims to evaluate UTE-T2* in the detection of changes in the Achilles tendons of amateur marathon runners before and after long-distance running.
XCloud-HyperLRF: Fast Hypercomplex NMR Spectroscopy with Cloud-based Low Rank Hankel Matrix Reconstruction
Authors: Guo Di , Zhan Jiaying , Tu Zhangren , Guo Yi , Zhou Yirong , Wu Jianfan , Hong Qing , Orekhov Vladislav , Qu Xiaobo
Published: August 2023

Nuclear magnetic resonance (NMR) serves as an indispensable tool in revealing physical, chemical and structural information about molecules. We present a hypercomplex low rank approach to reconstruct hypercomplex NMR spectrum reconstruction. We first introduce an adjoint matrix operation to convert the hypercomplex signal into complex matrix and then propose a low-rank model and algorithm to reconstruct hypercomplex signal. The experiment results demonstrate that the proposed method provides a fast and high-fidelity reconstruction of hypercomplex NMR data. Furthermore, we made the method available at an open-access and easy-to-use cloud computing platform.
Role of quantification of relaxation time from synthetic MRI in diagnosis and grading of prostate cancer
Authors: Zhong Suixing , Zhang Ya , Ding Yingying , Nie Lisha , Tan Jing , Ai Conghui , Jin Yan , Wang Hongbo , Zhang Huimei , Li Miaomiao , Zhu Rong , Gu Shangwei
Published: August 2023

This study aims to investigate the value of quantitative relaxation time derived from synthetic MRI (SyMRI) in the diagnosis and grading of prostate cancer (PCa). The results of this study showed that the diagnostic efficiencies of SyMRI in distinguishing PCa from most benign prostate lesions and different Gleason grades of PCa were similar to that of DWI, and the combined index of SyMRI and DWI can obtain higher diagnostic efficiencies. In addition, SyMRI has the advantages of short scanning time, unified parameters and simple operation, so it has a good prospect of clinical application.
Alternating Joint Learning Approach for Variational Networks and Sampling Pattern in Parallel MRI
Authors: Zibetti Marcelo , Knoll Florian , Regatte Ravinder
Published: August 2023

We propose a new alternating learning approach to jointly learn the sampling pattern (SP) and the parameters of a variational network (VN) for acquisition and reconstruction on 3D Cartesian parallel MRI problems. This approach is composed of alternating short training with BASS algorithm to learn the SP, and ADAM algorithm to learn the parameters of the VN, both with forced monotonicity. The results illustrate that this approach provides reduced error when compared to other joint learning approaches, and surpasses VN trained with recently developed fixed SPs.
Mapping cortical-depth dependent responses in human motor cortex using Spin-echo Echo Planar Time-resolved Imaging (SE-EPTI)
Authors: Wang Fuyixue , Dong Zijing , Wald Lawrence , Polimeni Jonathan , Setsompop Kawin
Published: August 2023

This work utilizes our recently developed spin-echo EPTI technique with minimal T2’-contamination and high specificity to map the layer-dependent responses in primary motor cortex at 7T and further investigate the impact of large vessel bias from T2’ contamination. EPTI resolves multi-contrast images across the readout that are free from distortion and blurring, and simultaneously obtains a SE image with pure T2-weighting and multiple asymmetric SE images with various T2’-weightings. The activation of finger-tapping task was studied using SE-EPTI at 0.9-mm isotropic resolution. Improved specificity of layer-dependent responses was observed using pure SE images.
Rectal Cancer MRI Motion Quality Assessment using a Convolutional Neural Network
Authors: Sharma Avishkar , Lei Ke , Vasanawala Shreyas , Sheth Vipul
Published: August 2023

Magnetic resonance (MR) imaging plays a pivotal role in the staging and treatment planning of rectal cancer. Accurate staging depends on good-quality high-resolution axial T2-weighted images orthogonal to the rectal tumor. Rectal MRI is often confounded by motion artifacts secondary to bowel peristalsis and patient movement. We propose a CNN model that automatically assesses image quality instantaneously after a scan is finished to reduce the frequency of patient recalls and non-diagnostic images. Our model achieves high accuracy in identifying motion degradation on an individual slice basis and perfect accuracy when classifying the entire sequence.
Multimodal MRI of cervical spinal cord injury before and after surgical myelotomy
Authors: Lee Seung-Yi , Meyer Briana , Kurpad Shekar , Budde Matthew
Published: August 2023

Surgical myelotomy, a procedure intended to relieve pressure on an acutely-injured spinal cord, improves long-term functional and pathologic outcomes in animal models. However, the direct consequences of myelotomy on the cord pathophysiology have not been evaluated in the acute setting. In this work, we used multimodal MRI tailored for the spinal cord to monitor changes in edema, hemorrhage, axonal injury, and perfusion immediately before and 24 hours after surgical myelotomy. The results demonstrate spatial and temporal changes and provide unique insight into the pathophysiology of acute injury and its intervention.
Using Low Rank Plus Sparse (L+S) Reconstruction to Accelerate Dynamic Hyperpolarized 13C Spiral Chemical Shift Imaging In Vivo
Authors: Zhu Minjie , Jhajharia Aditya , Rogers Joshua , Dirk Mayer
Published: August 2023

The goal of this study is to validate the Low Rank Plus Sparse Reconstruction algorithm in in-vivo spectroscopic imaging applications. The proposed method can be used to increase temporal and/or spatial resolution of dynamic hyperpolarized 13C imaging without compromising image quality.
Contrastive Learning of Inter-domain Similarity for Unsupervised Multi-modality Deformable Registration
Authors: Dey Neel , Schlemper Jo , Salehi Seyed , Zhou Bo , Sofka Michal
Published: August 2023

We propose an unsupervised contrastive representation learning framework for deformable and diffeomorphic multi-modality MR image registration. The proposed deep network and data-driven objective function yield improved registration performance in terms of anatomical volume overlap over several previous hand-crafted objectives such as Mutual Information and others. For fair comparison, our experiments train all methods over the entire range of a key registration hyperparameter controlling deformation smoothness using conditional registration hypernetworks. T1w and T2w brain MRI registration improvements are presented across a large cohort of 1041 high-field 3T research-grade acquisitions while maintaining comparable deformation smoothness and invertibility characteristics to previous methods.
Preserving Privacy While Maintaining Consistent Postprocessing Results: Fast and Effective Anonymous Refacing using a 3D cGAN
Authors: Molchanova Nataliia , Maréchal Bénédicte , Thiran Jean-Philippe , Kober Tobias , Richiardi Jonas , Huelnhagen Till
Published: August 2023

We propose a novel refacing technique employing a 3D conditional generative adversarial network to allow protecting subject privacy while maintaining consistent post-processing results. We evaluate the method compared to current refacing techniques using brain morphometry as an example. Results show that the proposed method compromises brain morphometry results to a lesser extent than existing methods while showing lower similarity of the final image to the original one, hence suggesting an improved privacy protection. We conclude that the proposed method represents a fast and viable alternative for image data de-identification compared to currently existing methods.
Feasibility of the Ventilatory ADC Approach Using Hyperpolarized 129Xe Pulmonary MRI
Authors: Parniyany Elnaz , Woodward Elise , Wu Tingting , Fox Matthew , Ouriadov Alexei
Published: August 2023

Hyperpolarized 129Xe lung MRI is an efficient technique used to investigate and assess pulmonary diseases. However, the longitudinal observation of the emphysema progression using hyperpolarized gas MRI-based ADC can be problematic, as the disease-progression can lead to increasing unventilated-lung areas, which likely excludes the largest ADC estimates. One solution to this problem is to combine static-ventilation and ADC measurements following the idea of 3He MRI ventilatory ADC (vADC). We have demonstrated this method adapted for 129Xe MRI to help overcome the above-mentioned shortcomings and provide an accurate assessment of the emphysema progression.
Brain Microstructure Imaging with Ultrahigh B-Encoding using MAGNUS High Performance Gradients
Authors: Abad Nastaren , Madhavan Radhika , Sprenger Tim , Bhushan Chitresh , Zhu Ante , Marinelli Luca , Fiveland Eric , Lee Seung-Kyun , DeMarco J. Kevin , Shih Robert , Hood Maureen , Kohls Gail , Morris H Doug , Kenny Kimbra , Ho Vincent , Foo Thomas
Published: August 2023

An ultra-high diffusion b-value protocol (b=7,000-30,000s/mm2) was acquired in a high-performance 3.0 T MAGNUS head gradient system (max gradient amplitude=200 mT/m and max slew rate=500 T/m/s) for white matter microstructure imaging.With shorter TE enabled by MAGNUS, measurements of intra-axonal diffusivities are demonstrated at ultra-high b-values. At these ultra-high b-values, signal from extra-axonal water is effectively suppressed but SNR is also reduced. We show that real-valued image reconstruction maintains Gaussian noise properties and reduces Rician bias, allowing better quantification of intra-axonal diffusivities and effective axonal radii in healthy volunteers and in ongoing human subject research studies of mild traumatic brain injury.
Hyperpolarized [1-13C]Pyruvate MRI Using a Metabolite-Specific Rotated Echo-Planar Imaging Sequence with Half Scan
Authors: Liu Xiaoxi , Cui Di , Tang Shuyu , Bok Robert , Mu Changhua , Chen Hsin-Yu , Santos Romelyn Delos , Sinha Avantika , Gordon Jeremy , Larson Peder
Published: August 2023

Hyperpolarized 13C-labeled compound is an emerging technique in biomedical imaging to non-invasively detect metabolic processes and has been translated in clinical for cancer and cardiac metabolism studies. However, hyperpolarized MRI signal is limited by high spatial and temporal resolution and high SNR. This work describes a novel rotated Echo-planar imaging (EPI) trajectory with a half scan acquisition that shortens echo time to reduce the T2* effect in metabolic imaging. Compared to the standard full sampled EPI trajectory, the proposed trajectory has the advantage of gaining higher signal intensity, and is less sensitive to motion artifacts, distortion artifacts and aliasing artifacts.
Quantifying Ventilation Using Dynamic Xenon MRI During Free Breathing
Authors: Hamedani Hooman , Amzajerdian Faraz , Kadlecek Stephen , Baron Ryan , Ruppert Kai , Duncan Ian , Ismail Mostafa , Xin Yi , Achekzai Tahmina , Loza Luis , Rizi Rahim
Published: August 2023

In order to image function in the free breathing lung with high temporal and special resolution, we have developed a passive, mechanically controlled MR-friendly gas delivery device capable of real time dosimetry and end-tidal gas measurements that can be used in combination with a non-rigid image registration technique. These dynamic images can thus be translated into comprehensible quantitative maps of lung ventilation and gas exchange. In order to image the lung continuously during normal breathing, we have also developed a fast-acquisition pulse sequence that uses diaphragm position to facilitate image reconstruction.
Postnatal experience promotes the thickening but not myelination of the neonatal ventral visual cortex
Authors: Li Mingyang , Liu Tingting , Xu Xinyi , Wen Qingqing , Zhao Zhiyong , Zhang Yi , Hsu Yi-Cheng , Sun Yi , Wu Dan
Published: August 2023

Postnatal experience is important for the development of the visual cortex in animal newborns, but its influence in human infants remains unclear. We collected a dataset from the developing human connectome project, including multi-modal MRI of 783 newborns. We found the cortical thickness (CT) and myelination of ventral visual cortex were significantly increased between 37 and 45 weeks of postmenstrual menstrual age (PMA). Interestingly, the CT but not myelination showed significant correlation with postnatal experience in both the V1 and higher-level visual cortex. Our result suggested that experience plays an important role in early cortical thickening
The Role of Electric Properties Tomography in Biochemical Analysis of the Cerebrospinal Fluid and Parameter Optimization for Improved Accuracy
Authors: Tha Khin , Katscher Ulrich , Hamaguchi Hiroyuki , Li Xinnan , Kawasaki Tomohiro , Yamaguchi Shigeru , Yabe Ichiro , Hyodoh Hideki
Published: August 2023

This study was aimed to evaluate if electrical conductivity (σ) by electric properties tomography (EPT) could detect variations in the cerebrospinal fluid (CSF) biochemical composition and if optimization of scan and analysis parameters improved the accuracy of in vivo σ measurements. σ values varied among patients and CSF samples with varying CSF biochemical composition, and showed significant correlation with CSF albumin concentration and total cell count. σ may be sensitive to CSF abnormalities. Optimization of scan and analysis parameters is important for the accuracy of in vivo σ measurements.
Quantification of fine CSF dynamics using Intra-Voxel Incoherent Motion (IVIM) MRI
Authors: Yamada Shigeki , Watanabe Yoshiyuki , Otani Tomohito , Hiratsuka Shinnosuke , Yoshimura Masahiro , Takeishi Naoki , Wada Shigeo , Oshima Marie , Nozaki Kazuhiko
Published: August 2023

The f value calculated by the Intra-Voxel Incoherent Motion (IVIM) MRI was was high in the areas where the CSF flow was reported to be fast, specifically in the cerebral aqueduct and subarachnoid spaces around the major intracranial arteries. Therefore, f value was considered to reflect flow velocity of CSF, regardless of its direction. The mean f values in the whole lateral ventricles, anterior part of the third ventricle, ambient cisterns, central sulci, and marginal sulci were significantly lower and that in the foramina of Luschka was significantly higher among 28 iNPH patients, compared with 57 healthy volunteers.
SNR boost in whole-body DWIBS utilizing deep learning constrained Compressed SENSE reconstruction
Authors: Yoneyama Masami , Yoshida Takashige , Peeters Johannes , Kwon Jihun , Katsumata Yasutomo , Zhang Shuo , Van Cauteren Marc
Published: August 2023

Diffusion weighted whole body imaging with background body signal suppression (DWIBS) is an important tool for whole body imaging to e.g. visualize malignant tumor and abnormal lymph nodes. Next to the low intrinsic SNR of DWIBS, direct coronal DWIBS can suffer from another SNR penalty due to the high acceleration factors to minimize distortion. In this work, we compare SENSE, C-SENSE and C-SENSE-AI (CS-AI) accelerated direct coronal DWIBS scans. Results indicate that CS-AI clearly reduces the noise artifacts, boosts the overall image SNR, and improves lesion conspicuity.
Compressed Sensing and Deep Learning Reconstruction: Efficacy for Women’s Pelvic MRI at 1.5 T MR System in Routine Clinical Practice
Authors: Ueda Takahiro , Ohno Yoshiharu , Yamamoto Kaori , Yazawa Natsuka , Tozawa Ikki , Sato Masayuki , Katagiri Motohiro , Ikedo Masato , Yui Masao , Nagata Hiroyuki , Murayama Kazuhiro , Toyama Hiroshi
Published: August 2023

We hypothesized that compressed sensing (CS) with deep learning reconstruction (DLR) can improve image quality and shorten examination time on not only T2-weighted imaging (T2WI), but also T1-weighted imaging (T1WI) on women’s pelvic MRI, when compared with PI at 1.5T MR system. The purpose of this study was to compare the utility of CS and DLR for shortening examination time and improving image quality on MRI at 1.5T system in patients with various female pelvic diseases.
Prostate cancer Diagnosis and risk stratification with combination of DISCO and MUSE: a feasibility study.
Authors: Zhang Dan , Chen Zhi , Song Na , Wang Zhuo , Zhang Shao , Chen Xiao , Wei Xiao , Chen Bing
Published: August 2023

In this study, we aim to investigate the value of DISCO quantitative parameters combined with MUSE in distinguishing benign and malignant prostate lesions. It was concluded that Ktrans, Kep, Ve and ADC can be used as imaging biomarkers to distinguish benign and malignant prostate lesions; Ktrans and ADC are helpful to predict aggressiveness of the tumor，and can be used as independent predictors of prostate cancer.
Static-fluid MR Urography with three-dimensional Gradient and Spin-echo and Respiratory-trigger Fast Spin Echo sequences: A Comparison Study
Authors: Wang Wei , Liu Jing , Li Wei , Xue Ke , Dai Yongming , Qiu Jianxing
Published: August 2023

This study is targeted to compare the performance of three-dimensional (3D) breath-hold gradient and spin-echo sequence (GRASE) and conventional respiratory-trigger fast spin-echo sequence (FSE) for magnetic resonance urography (MRU). Image quality and diagnostic performance of urinary tract dilation from two data sets were evaluated by three radiologists. The results showed that 3D GRASE MRU had a better performance compared with the FSE MRU, indicating 3D GRASE technique could be a feasible option for 3D MRU.
An automatic pipeline combining deep learning and radiomics to predict placenta accrete spectrum disorders from T2W MR images
Authors: Wang Haijie , Wang Yida , Ling Lei , Wang Jue , Li Xiaotian , Zhu Hao , Zhang He , Yang Guang
Published: August 2023

Placenta accrete spectrum (PAS) disorders may lead to common complication like catastrophic perinatal hemorrhage. T2W is the most useful MRI sequence for identification of PAS disorders, but the diagnosis of PAS is often difficult and highly subjective. To overcome this problem, we automatically segmented the placental regions by nnU-Net and used radiomics features extracted from the segmented region to build a radiomics-clinical model for identification of placenta invasion. 512 pregnant women were enrolled in this study. Our segmentation model achieved a mean Dice coefficient of 0.890 and the classification model achieved an AUC of 0.849 on the independent validation cohort.
Fetal brain T1-weighted imaging with a deep learning constrained compressed SENSE reconstruction
Authors: Wang Jing , Wang Zhuo , Zhu Yi , Jiang Ke
Published: August 2023

The quality of commonly used fetal T1-weighed inversion recovery(IR) images is relatively poor. Compressed SENSE(CS) technique allows shortening of scan time, but the overall image quality has not been significantly improved. In this study, Compressed-SENSE Artificial intelligence(CS-AI) framework was applied to reduce the scan time and increase the spatial resolution. This study aims at acquiring high-resolution fetal brain T1-weighted image with reduced scan time and compare the image quality among images reconstructed with CS-AI, CS and conventional SENSE.
Distinguishing sonic hedgehog from Group 4 medulloblastoma using amide proton transfer–weighted MR Imaging
Authors: Ma AnDong , Qu YaoMing , Lu MingJun , Zou Xia , Liu XinZi , Zhao Chen , Wen ZhiBo
Published: August 2023

Preoperative prediction of molecular subgroup based on magnetic resonance imaging (MRI) remains a challenge. The objective of this study was to assess amide proton transfer-weighted (APTw) method in medulloblastoma (MB). Preliminary results showed that the mean value of APT value in sonic hedgehog (SHH) -activated MB tended to be higher than that of Group 4 MB.
Value of Qualitative and Quantitative DCE-MRI Analysis based on GRASP Acquisition in Diagnosing of breast lesions
Authors: Zhou Xin-zhu , Lai Hua , Liu Lian-hua , He Shuang , Yao Hui-fang , Chen Li-ping , Deng Chen , Yang Ya , Lei Qiang , Wu Yun-zhu
Published: August 2023

This study investigated the diagnostic capabilities of qualitative and quantitative parameters derived from high-resolution DCE-MRI with GRASP sequence. 31 patients (33 lesions, 13 benign and 20 malignant lesions) were prospectively included. The results showed that there were prominent discrepancies of quantitative and quantitative parameters between malignant and benign breast lesions. Ktrans, Kep, and iAUC(60) had excellent diagnostic efficiency for breast cancer.
Evaluation of Blood Flow and Plaque Vulnerability in Carotid Artery Stenosis Focusing on Morphological and Component Characteristics
Authors: Matsumoto Yuki , Kanazawa Yuki , Kinjo Yuki , Harada Masafumi , Miyati Tosiaki , Hayashi Hiroaki , Miyoshi Mitsuharu , Maeda Naoki , Kanematsu Yasuhisa , Takagi Yasushi , Haga Akihiro
Published: August 2023

Our purpose was to assess plaque vulnerability under the influence of blood flow in terms of morphological and component characteristics using MRI.Twenty-three patients with symptomatic and asymptomatic carotid stenosis, all of whom provided informed consent, underwent 3T MRI scan (GE Healthcare). We evaluated the consistency between the flow patterns and plaque features in the carotid artery stenosis, using 4D-flow MRI and 3D-FSE-MSDE sequences. During the vulnerability analysis, it was observed that there was a negative correlation between 3D-FSE-MSDE and 4D-flow parameter (R = -0.43, P < 0.001).
Axon radii estimation from dMRI data acquired from a non-fancy MRI scanner
Authors: Mandal Debdut , Ning Lipeng , Rathi Yogesh
Published: August 2023

Noninvasive measurement of axon radii has always been challenging using in vivo diffusion MRI (dMRI) data. Although dMRI allows estimation of the effective radius, a very high gradient strength scanner is required to acquire the data. In our proposed method, we overcome this limitation by using analytical methods to accurately predict the signal at high b-values from reasonably lower b-value data that can be obtained from a Prisma-like scanner. Our findings, both in synthetic data and in vivo dMRI data, show that estimating axon radii reliably is possible using dMRI data with a certain SNR level.
Optimized Ultrashort Echo Time – Magnetic Resonance Fingerprinting (UTE-MRF) for Myelin-Proton fraction Imaging
Authors: Zhou Zihan , Li Qing , Liao Congyu , Cao Xiaozhi , Ye Huihui , Zhong Jianhui , He Hongjian
Published: August 2023

Optimization of MRF (MR fingerprinting) sequence is important for encoding MR tissue parameters with the maximal SNR for better quantification of representative tissues of WM, GM and CSF. Here, we targeted ultrashort T2 tissues and proposed an optimized acquisition parameter patterns for 3D ultrashort echo time MRF sequence with the goal of achieving higher quantification accuracy for myelin-proton based on Cramér‐Rao Lower Bound. Our results show that the optimized UTE-MRF sequence achieved high accuracy in myelin-proton quantification and whole-brain myelin-proton imaging in 15 min with 1mm isotropic resolution. The optimization also opens the door to further reduce scan time.
Quasi-steady-state reconstruction provides fast equilibrium APT imaging of brain tumor patients at 3T
Authors: Wu Yin , Liu Zhou , Yang Qian , Zou Liyan , Zhang Fan , Qian Long , Liu Xin , Zheng Hairong , Luo Dehong , Sun Phillip
Published: August 2023

APT MRI scans are often performed under non-equilibrium conditions, confounding imaging quantification and tissue characterization. This study proposed a quasi-steady-state (QUASS) algorithm for fast and accurate tumor APT imaging. Seven healthy volunteers and nineteen tumor patients were scanned at 3T, with two representative RF saturation times (Ts) and relaxation delays (Td). The routine APT measures significantly varied with Ts and Td (P<.001) and were significantly smaller than the corresponding QUASS indices (P<.001). In contrast, QUASS reconstruction results showed little dependence on scan protocols (P>.05). The QUASS algorithm enables robust APT imaging, promising to expedite and standardize clinical APT tumor MRI.
Can a sub 2-minute MRI exam compete with CT for imaging sinus anatomy?
Authors: Reeve Sarah , Parker Mark , Rioux James , Cora Elena , Bowen Chris , Beyea Steven , Volders David
Published: August 2023

MRI of the paranasal sinuses with bSSFP sequences is not typically performed due to prohibitive exam times and the prevalence of artifacts caused by off-resonance effects. We have shown that bSSFP images acquired in under 2 minutes using high-performance gradients at low field resist banding artifacts and demonstrate SNR and resolution sufficient to delineate relevant structures. This is supported by high SNR values in tissues and fluid-filled spaces, and low SNR in the air-filled maxillary sinus. 2-minute scan times compete with CT for clinical work flow and demonstrate the potential of MRI for high throughput sinus pathology evaluation.
dMRIQCpy: a python based toolbox for diffusion MRI quality control and beyond
Authors: Theaud Guillaume , Descoteaux Maxime
Published: August 2023

Diffusion MRI data suffers from many artifacts and parameters such as Echo and Repetition Time. From raw data to bundle segmentation, acquisition/processing issues in processing can happen and bias analyses. That is why quality control (QC) at each step of a study is important. We present a python-based library: dMRIQCpy that supports QC of raw data, intermediate DWI and T1 preprocessing, metrics from DWI, tractogram and bundles screenshots. dMRIQCpy enables users to save/load QC reports to permit collaboration between users. dMRIQCpy was tested on 3 datasets and highlighted a faster QC (50 % faster) than reviewing screenshots in a folder.
Improved Padding in CNNs for Quantitative Susceptibility Mapping
Authors: Liu Juan
Published: August 2023

Recently, deep learning approaches have been proposed for QSM processing - background field removal, field-to-source inversion, and single-step QSM reconstruction. In these tasks, the networks usually take local fields or total fields as inputs, which have valid voxels within volume of interests (VOIs) and invalid voxels outside of VOIs. CNNs fail to consider this spatial information when using spatial invariant filters and conventional padding mechanism, which could introduce spatial artifacts in the QSM results. Here, we propose an improved padding technique utilizing neighboring valid voxels of invalid voxels to estimate the invalid voxels in feature maps of CNNs.
Quantitative MRI biomarkers for cortical pathology in multiple sclerosis at 7 Tesla
Authors: Straub Sina , El-Sanosy Edris , Emmerich Julian , Sandig Frederik , Ladd Mark , Schlemmer Heinz-Peter
Published: August 2023

Although substantial cortical gray matter tissue damage has been revealed by advanced MRI methods and in histopathology studies, clinical assessment of MS still mainly focuses on white matter lesions. When cortical pathology is evaluated, predominantly structural markers are used. In this ultra-high field study, data for $$$T_1$$$, $$$T_2$$$, $$$R_2^*$$$, $$$T_1w-T_2w$$$-ratio, and susceptibility mapping were acquired in 21 patients and 17 healthy controls. $$$T_1$$$–weighted data were post-processed to obtain cortical gray matter and deep gray matter segmentations. Statistically significant differences were found in 31 out of 34 investigated cortical and in three out of four deep gray matter regions (p<0.05).
Association between SFCI, a robust pathway-combined connectivity metric and neurological scores in multiple sclerosis
Authors: Bhattacharyya Pallab , Fox Robert , Lin Jian , Sakaie Ken , Lowe Mark
Published: August 2023

Structural and functional connectivity index (SFCI) is a composite metric that combines structural and functional connectivity along different functionally specific pathways in multiple sclerosis. Performing functional connectivity and diffusion tensor imaging scans, we show that motor and cognitive pathway-combined SFCI is closely associated with multiple sclerosis functional composite, a composite neurological metric. Pathway-combined SFCI shows stronger association with MSFC than motor or cognitive pathway-specific composite metrics. Results from this study support the use of SFCI and composite pathway-based imaging measures as surrogate biomarkers for MS disease status and progression.
Assessment of Placental Perfusion in Normal and Hypertensive Pregnancies using pCASL at 3T: Preliminary Findings
Authors: Wang Yiming , Herrera Christina , Owen David , Do Quyen , Xi Yin , Lewis Matthew , Fei Baowei , Spong Catherine , Twickler Diane , Madhuranthakam Ananth
Published: August 2023

Measuring placental perfusion can provide important information about its function. Arterial Spin Labeled (ASL) MRI is a non-contrast perfusion imaging method particularly suitable in pregnancy as it does not require an exogenous contrast. We performed a preliminary assessment of placental perfusion in pregnant subjects, both normal and with CHTN, at 16-20 week and 24-28 week gestational ages. Perfusion reduction was observed in all 5 normal subjects and in 75% (12 of 16) of the subjects with CHTN. This could provide important information about perfusion changes during pregnancy and can serve as a precursor for a larger-scale longitudinal study in CHTN.
Quantification of bilateral whole-organ renal metabolic rate of O2 by exploiting conservation of flow and mass principle: a preliminary study
Authors: Deshpande Rajiv , Langham Michael , Wehrli Felix
Published: August 2023

Type 2 diabetes mellitus is the leading cause of chronic kidney disease. Clinical measures of kidney function often detect dysfunction after irreversible damage has occurred. This highlights an unmet need for a biomarker that can enable earlier detection of disease. Quantification of renal metabolic rate of oxygen is a promising metric because it increases by 40-65% during the early stages of diabetic kidney disease. Here, we propose a “difference method” to quantify bilateral renal metabolic rate of oxygen by imaging above and below the renal vessels and exploiting conservation of blood flow rate and mass.
Evaluation of the Combined Deep Learning Networks Using Mask R-CNN and ResNet50 Classification for Detection and Diagnosis of Breast Cancer on MRI
Authors: Zhang Yang , Liu Yan-Lin , Nie Ke , Zhou Jiejie , Chen Zhongwei , Chen Jeon-Hor , Wang Meihao , Su Min-Ying
Published: August 2023

We developed two deep learning methods for breast MRI evaluation, first using Mask R-CNN for detection of suspicious areas, and then using ResNet50 for estimating the malignancy probability. These two networks were combined to test its diagnostic validity in two datasets. In Dataset-1, sensitivity=96.1% and specificity=78.1%. In Dataset-2, sensitivity=81.1% and specificity= 80.6%. We further characterized all false positives (FPs), and found other than confirmed benign lesions, FPs may come from vessels and asymmetric parenchymal enhancements, which can be further eliminated by other algorithms. The results suggest the potential of combined deep learning networks as a fully-automatic breast MRI CAD.
Whole-Abdomen Metabolic Imaging in Healthy Volunteers Using Hyperpolarized [1-13C]pyruvate MRI
Authors: Lee Philip , Chen Hsin-Yu , Gordon Jeremy , Wang Zhen , Bok Robert , Cheung Kiersten , De Las Alas Francesca , Daniel Heather , Larson Peder , von Morze Cornelius , Vigneron Daniel , Ohliger Michael
Published: August 2023

Whole-abdomen imaging with hyperpolarized 13C is challenging due to B0 and B1 inhomogeneities, respiratory motion, and broad spatial coverage. There is also little baseline data about healthy metabolism in abdominal organs. We develop and describe a reliable imaging method to overcome these challenges, enabling metabolic imaging of the entire abdomen in a series of healthy volunteers. We establish normal values for conversion of HP [1-13C]pyruvate to lactate and alanine in key organs such as the liver, kidneys, pancreas, and spleen. Methods established here set a firm foundation for investigating a broad spectrum of metabolic and neoplastic abnormalities in the liver.