bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-delay pseudo-continuous arterial spin labeling in head and neck healthy tissues
Authors: Sijtsema Nienke , Petit Steven , Poot Dirk , Verduijn Gerda , Warnert Esther , Hoogeman Mischa , Hernandez-Tamames Juan
Published: August 2023

Arterial spin labeling (ASL) has potential for response prediction of tumor and healthy tissue in the head and neck region after radiotherapy. Since little is known about blood flow (BF) values in this region, we measured BF and the repeatability of BF in several tissues simultaneously using multi-delay pseudo-continuous ASL. This enables investigation of the correlation between perfusion and severity of radiotherapy side-effects in a future study. Of the tissues we assessed, we found the parotids have the highest and the tonsils have the lowest perfusion.
Three-fold accelerated MRI and MR angiography on Moyamoya disease examination using deep learning constrained Compressed SENSE reconstruction
Authors: Zhang Yajing , Zhang Jilei , Wang Maoxue , Wang Kun , Zhao Xiance , Wu Peng , Chen Weibo , Chan Queenie , Peeters Johannes M. , Van Cauteren Marc , Zhang Bing
Published: August 2023

MR angiography (MRA) in moyamoya disease has been uniquely important in the evaluation of changes in vasculature and brain parenchyma, and clinical follow-up. In this work, we investigate the performance of accelerated MRA under a deep learning constrained Compressed SENSE reconstruction framework. Results on both volunteer and patients demonstrate a three-fold scan acceleration against the routine MRA with comparable image quality.
Influence of group-oriented registration on functional ventilation parameters derived by 3D phase-resolved functional lung ventilation MRI
Authors: Klimeš Filip , Voskrebenzev Andreas , Gutberlet Marcel , Crisosto Cristian , Grimm Robert , Wacker Frank , Vogel-Claussen Jens
Published: August 2023

Demonstration of a high repeatability is a mandatory step prior to the usage of new methods in clinical routine. Good to moderate repeatability of 3D phase-resolved functional lung (PREFUL) MRI was shown before. In this study, group-oriented registration (GOREG) was compared to stepwise registration regarding the repeatability of 3D PREFUL ventilation parameters in 30 study participants. Furthermore, image sharpness and structural similarity (SSIM) were assessed. An increased repeatability of dynamic 3D PREFUL ventilation parameters was observed using the GOREG approach. Also, image sharpness and SSIM values were significantly higher for measurements assessed with GOREG registration.
Feasibility of phase resolved functional lung imaging (PREFUL) with ultrashort echo time sequence (PUTE)
Authors: Behrendt Lea , Gutberlet Marcel , Voskrebenzev Andreas , Klimeš Filip , Obert Arnd , Kern Agilo , Wacker Frank , Vogel-Claussen Jens
Published: August 2023

Phase-resolved Functional Lung imaging (PREFUL) allows the contrast-free quantification of pulmonary ventilation and perfusion dynamics in free breathing. Typically, it is used in conjunction with cartesian spoiled gradient echo sequence (SPGRE) with asymmetric echo. Due to the short T2* times in lung tissue, acquiring signal with echo times as low as possible is essential. Therefore, a 2D ultrashort echo time sequence with a TE of 0.07ms was developed and tested in the PREFUL context in one healthy volunteer. A similar visual impression of derived ventilation maps was found with UTE and SPGRE. However, ventilation defects were more pronounced for UTE.
The value of Modulated Flip Angle in Refocused Imaging with Extended Echo Trains with Compressed Sensing (MATRIX) in Knee MRI Imaging
Authors: Sui He , Gong Yu , Zhang Yunfei , Dai Yongming , Mo Zhanhao
Published: August 2023

Three-Dimensional (3D) sequence of magnetic resonance imaging (MRI) plays a critical role in musculoskeletal joints imaging; however, its long acquisition time limits the clinical application. In such condition, Compressed sensing (CS) is introduced to accelerate MRI during clinical practice. We aimed to investigate the feasibility of an isotropic 3D variable-flip-angle fast spin echo (FSE) sequence with CS technique (CS-MATRIX) compared to conventional 2D sequences on knee imaging.
Data-driven characterization of knee structures using non-negative matrix factorization of quantitative UTE Spiral VIBE MRI
Authors: Smekens Céline , Van Dyck Pieter , Janssens Thomas , Sijbers Jan , Jeurissen Ben
Published: August 2023

Model-driven methods for knee structure characterization, such as bi-exponential T2* mapping, may result in unreliable parameter estimation. In contrast, data-driven approaches, such as non-negative matrix factorization (NMF), allow to robustly identify multiple compartments. In this work, convexity-constrained NMF was used to decompose quantitative UTE MRI of three asymptomatic and one degenerative knees for structural characterization. Decomposition in four compartments led to minimal residual errors and low inter-subject differences in normalized mean compartment weights. Shifts in compartment weight distributions correlated to structural abnormalities, suggesting that the proposed approach may aid in the detection, grading and monitoring of internal knee derangements.
Automatic segmentation of the hip bony structures on 3D Dixon MRI datasets using transfer learning from a neural network developed for the shoulder
Authors: Montin Eros , Deniz Cem , Rodrigues Tatiane , Gyftopoulos Soterios , Kijowski Richard , Lattanzi Riccardo
Published: August 2023

We describe a network for automatic segmentation of acetabulum and femur on 3D-Dixon MRI data. Given the limited number of labeled 3D hip datasets publicly available, our network was trained using transfer learning from a network previously developed for the segmentation of the shoulder bony structures. Using only 5 hip datasets for training, our network achieved segmentation dice of 0.719 and 0.92 for acetabulum and femur, respectively. More training data is needed to improve results for the acetabulum. We show that transfer learning can enable automatic segmentation of the hip bones using a limited number of labeled training data.
GRL-MF: The first lego-brick of a cerebellar Mean-Field Model for BOLD signal simulations
Authors: Lorenzi Roberta , Geminiani Alice , Wheeler-Kingshott Claudia Gandini , Palesi Fulvia , Casellato Claudia , D'Angelo Egidio
Published: August 2023

Following MRI recordings, the connectome can be reconstructed and BOLD-signals simulated extracting relevant parameters on brain organization and dynamics. This requires mathematical models of neural activity in specific brain regions. However, models of the cerebellar circuit are still missing. We present here a biologically-driven mean-field (MF) model summarising the main statistical moments of cerebellar granular layer activity as the first “lego-brick” toward full cerebellar network reconstruction. Once integrated into brain simulators, like Dynamic Causal Modelling and The Virtual Brain, the cerebellar MF models will improve the investigation of neuronal functions at the origin of hemodynamic responses captured by BOLD fMRI.
Novel particle-based spatial stochastic Bloch simulation applied to LDH-mediated pyruvate conversion
Authors: Dingwell Dylan , Cunningham Charles
Published: August 2023

In order to model signal mechanisms relevant to HP 13C MRI of lactate, we developed a novel particle-based MR model which extends the Brownian dynamics simulator Smoldyn. The model performs concurrent calculation of a forward solution of the Bloch equations for simulated particles using quaternion rotations. Reaction kinetics of LDH-mediated conversion of pyruvate to lactate were simulated and compared to a benchtop experiment (LDH activity assay). Modelling of particle compartmentalization and motion were also tested in simulated structures.
MR-only staging of osteonecrosis of the femoral head (ONFH) using self-supervised learning for multiple MR protocols
Authors: Kim Bomin , Lee Geun Young , Park Sung-Hong
Published: August 2023

Diagnosing stages of osteonecrosis of the femoral head (ONFH) based on MR images can reduce additional cost and radiation exposure caused by CT scan. We propose a deep learning network that enables 4-way classification of ONFH stages, utilizing the information from MR images with different contrasts and planes. Given the limited number of available data, we enhanced the network performance by using self-supervised learning based on MR-to-CT translation task, which increased AUC significantly. We also investigated the diagnostic results from different MR protocols, and obtained more precise and robust results by combining them.
Improved assessment of fetal ocular pathologies in MRI using ocular ratios and machine-learning multi-parametric classification
Authors: Avisdris Netanell , Sourani Daphna Link , Ben Sira Liat , Joskowicz Leo , Malinger Gustavo , Yagel Simcha , Miller Elka , Ben Bashat Dafna
Published: August 2023

Brain MRI of 296 fetuses (22-40 weeks’ gestational-age, normal n=244, hypo/hyper-telorism n=52 were included. Binocular, interocular, and ocular diameters, and ocular volume were measured using automatic methods. Two new parameters, binocular-ratio and interocular-ratio, were defined. In normal fetuses, all four measurements increased with gestational-age. However, constant values were detected across all gestational-ages of binocular-ratio=1.56±0.05 and interocular-ratio=0.60±0.05. A random-forest classifier achieved the best results (out of eight classifiers) with AUC-ROC=0.90±0.03 for classification between normal and fetuses with hypo/hyper-telorism. mainly based on the two new ratios. Machine-learning multi-parametric classification and the new ratios are suggested to be used in routine clinical practice.
Deep Learning based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease
Authors: Raj Anish , Tollens Fabian , Strittmatter Anika , Hansen Laura , Noerenberg Dominik , Zöllner Frank
Published: August 2023

The total kidney volume (TKV) increases with ADPKD progression and hence, can be used to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes which is usually performed manually by an expert physician. However, this is time consuming and automated segmentation is warranted, e.g., using deep learning. The implementation of the latter is usually hindered due to a lack of large, annotated datasets.In this work, we address this problem by implementing the cosine loss function and a technique called Sharpness Aware Minimization (SAM) into the U-Net to improve TKV estimation in small sized datasets.
Quantification of Gadolinium Deposition in the Brain Using Quantitative MRI
Authors: Liu Angie Landgren , Blystad Ida , Tisell Anders
Published: August 2023

Gadolinium-based contrast agents (GBCAs) used for MRI are known to cause gadolinium depositions in the brain. In this study we show that the using quantitative MRI we can detect gadolinium depositions after fewer doses of GBCAs than generally seen on conventional MRI. We included 43 patients that were earlier enrolled in a prospective longitudinal cohort study of early Multiple Sclerosis. All patients had received the linear GBCA Magnevist. Measurements of the longitudinal relaxation rate R1 showed a significant increase in dentate nuclei, globi pallidi and frontal grey matter.
Development of a fluorescent, MRI contrast agent (Gd-DOTA-coumarin) to improve measurement of Gd concentration
Authors: Das Ayesha Bharadwaj , Zhang Jin , Tung Ching-Hsuan , Kelly James , Wadghiri Youssef , Kim Gene
Published: August 2023

Measurement of gadolinium concentration in DCE-MRI is a significant step but remains challenging. In this study, we developed a novel Gd-based contrast agent Gd-DOTA-coumarin that can directly quantify gadolinium concentration through florescence measurement using a plate reader. The preliminary results shows that the longitudinal relaxivity in plasma at 7T is 8.6 mM-1s-1 and the limit of detection for the fluorescence measurement is 100 µM in blood. This new agent has also been successfully used for in vivo MRI experiments.
A serial dilution study of four Gadolinium-based contrast agents for the detection of low-Gd concentration
Authors: Poirion Emilie , Marie Corentine , Fournier Dann , Carrion Remy , Savatovsky Julien
Published: August 2023

Gadolinium-enhanced imaging provides valuable information in clinical practice. Concerns raised by gadolinium depositions favors strategies reducing injected doses in patients requiring repeated scans. We designed four phantoms containing serial dilutions of four gadolinium-based contrast agents to compare their efficacy for low-Gd concentration detection in clinically used MR sequences, based on signal intensity and contrast-to-noise ratio. We performed a preliminary study comparing T1-TSE, FLAIR and FLAIR with optimized parameters on these phantoms. The optimized FLAIR sequence shows an efficient detection of low concentrations, in particular using gadobutrol or gadoteridol.
Folded-End Dipole Transceiver Array for Human Whole Brain Imaging at 7 T.
Authors: Avdievich Nikolai , Solomakha Georgiy , Ruhm Loreen , Nikulin Anton , Magill Arthur , Scheffler Klaus
Published: August 2023

In spite of great benefits of 7T MRI, its further clinical development is associated with difficulties in designing RF coils. Recently, we developed a novel type of dipole antennas, a folded-end dipole. In this work we evaluated an 8-element transceiver folded-end dipole array for 7T human head imaging. The array provided ~20% higher Tx-efficiency and significantly better whole-brain coverage than that of a widely-used commercial array. In addition, we evaluated passive dipoles for decoupling the proposed array. In contrast to the common unfolded dipole array, the passive dipoles produce practically no destructive interference with the RF field of the array.
A novel 1H-dipole/31P-birdcage transceiver coil for head imaging at 7T
Authors: Forner Ria , Voogt Ingmar , Steensma Bart , Nam Kyungmin , Gosselink Mark , Rivera Debra , Clarke William , Alihaghnejad Aidin , Hendriks Arjan , Klomp Dennis
Published: August 2023

A novel coil design with a 31P birdcage and a 1H dipole array is presented for the purpose of fMRS applications. This design allows for free line-of-sight for fMRI and fMRS visual stimulation via mirrors and despite a lower fill-factor performs comparably to a smaller dual-tuned birdcage. The matching of both frequencies is adequate for a range of human head circumferences from 52cm through 57cm. The 31P birdcage performance is not significantly degraded by presence of 1H dipoles when compared to a conventional birdcage dual-tuning strategy with Foster networks.
Evaluating acquisition and preprocessing methods for diffusion MRI in the cervical spinal cord
Authors: Schilling Kurt , Combes Anna , Prock Logan , O'Grady Kristin , Landman Bennett , Smith Seth
Published: August 2023

The aim of this study was to evaluate acquisition and preprocessing strategies for diffusion MRI in the cervical spinal cord. We tested 6 acquisition and 5 preprocessing strategies, and quantified evaluation criteria, and found that pipelines that include distortion correction significantly improve data quality. However, motion and lack of cardiac triggering significantly impact quality measures. Standard diffusion processing packages need to be adapted and modified for spinal cord microstructure to ensure accurate and robust diffusion quantification.
Thalamic Involvement in the Pediatric Chronic Spinal Cord Injured Population with Respect to the Severity of Injury
Authors: Kang KiChang , Fleming Kristen , Sathe Anish , Muller Jennifer , Middleton Devon , Mohamed Feroze , Krisa Laura , Alizadeh Mahdi
Published: August 2023

Little is known about microstructural alterations in the thalamus following spinal cord injury (SCI), especially in the pediatric population. In this study, we used diffusion tensor imaging (DTI) derived metrics to characterize microstructural changes in thalamic nuclei of pediatric subjects with chronic SCIs with respect to the severity of injury based on the American Spinal Injury Association Impairment Scale (AIS). We report significant differences in mean diffusion metrics in thalamic nuclei that are suggestive of microstructural alterations between different AIS classifications.
Detection of resting-state functional connectivity networks in the human lumbar spinal cord at 3T
Authors: Combes Anna , Sengupta Anirban , Rogers Baxter , Houston Delaney , Prock Logan , Bagnato Francesca , McKnight Colin , Gore John , Smith Seth , O'Grady Kristin
Published: August 2023

3D multishot GRE resting-state BOLD-sensitive images of the lumbar spinal cord were acquired in 10 healthy participants as part of a functional MRI (fMRI) protocol. Temporal SNR in the gray matter (GM) was 18.2±4.3 after post-processing. Average correlations at rest were 0.49±0.17 between ventral horns, and 0.55±0.13 between dorsal horns, and could be detected at the single-subject level using seed-based analysis. Independent Component Analysis in 6 subjects, each scanned on two different occasions, identified network nodes within GM that were consistent across sessions. FMRI of the lumbar spine is feasible and may reflect functional integrity of the lumbar cord.
Characterization of the spinal cord fMRI signal in the healthy subjects and in multiple sclerosis patients
Authors: Fratini Michela , Maugeri Laura , Guidi Maria , Di Nuzzo Mauro , Moraschi Marta , Mangini Fabio , Egidi Irene , Mascali Daniele , Pisani Valerio , Nocentini Ugo , Giove Federico
Published: August 2023

Functional Magnetic Resonance Imaging (fMRI) has become one of the most powerful tools in neuroscience research, with promising applications in clinical practice. Particularly, fMRI based on blood oxygenation level dependent (BOLD) contrast has gained a primary role in the study of human brain and spinal cord, for the characterization of normal and pathological brain/spinal cord (SC) activity. In this framework, we have studied and report the characterization of the functional response of the SC to a multilevel motor task, designed to assess the linearity of the hemodynamic response as a function of the intensity of a graded task.
Comparison of Physiological Noise Models for Thermal Stimulus fMRI in the Cervical Spinal Cord at 7T
Authors: Seifert Alan , Vannesjo S
Published: August 2023

A physiological noise model (PNM) is crucial for maximizing sensitivity and specificity to activation in 7T spinal cord fMRI. The PNM must balance exhaustive modeling of non-BOLD signal, preservation of degrees of freedom, and avoidance of nuisance regressors that may be correlated with the task. We compared four candidate PNMs. A 37-term PNM and a 35-term PNM excluding heart rate and respiratory volume per unit time, which may be correlated with the task, performed approximately equally well. A 32-term PNM containing only regressors derived from physiological recordings, and a 3-term PNM containing only image-derived regressors, were also evaluated.
B1 shimming for cervical spine 7T parallel transmission MRI: preliminary in vivo imaging and preparation of virtual observation points
Authors: Destruel Aurelien , Jomin Pierre , Wichmann Tobias , Guye Maxime , Abdeddaim Redha , Callot Virginie
Published: August 2023

Parallel transmission for 7T MRI of the spinal cord is a promising area of research, as high specific absorption rate and coil inefficiency in some levels may limit certain applications. In the absence of virtual observation points (VOP) provided by the coil manufacturer, preliminary testing and first in vivo applications on a healthy volunteer were done in a conservative SAR-restricted, showing potential in regions that suffered from signal drops with the default shim parameters. The workflow to ensure radiofrequency safety with virtual observation points, based on simulations of the specific absorption rate and their validation, is also described.
TNM and VALSG Stage Assessments in SCLC: Diagnostic Capability Comparison among PET/MRI, PET/CT, Whole-Body MRI and Conventional Examination
Authors: Ohno Yoshiharu , Yui Masao , Yamamoto Kaori , Takenaka Daisuke , Yoshikawa Takeshi , Takeda Saki , Iwase Akiyoshi , Oshima Yuka , Hamabuchi Nayu , Hanamatsu Satomu , Obama Yuki , Nagata Hiroyuki , Ueda Takahiro , Ikeda Hirotaka , Murayama Kazuhiro , Toyama Hiroshi
Published: August 2023

Whole-body MRI and co-registered or integrated PET combined with MRI (PET/MRI) using 1.5 T or 3T MR systems has been suggested as equal to or more accurate than PET/CT or conventional staging or recurrence surveillance methods in thoracic oncologic patients. We hypothesize that whole-body MRI and co-registered PET/MRI for SCLC patients have equal to or superior potential for VALSG and TNM stage assessments than PET/CT and conventional staging method. The purpose of this study was to prospectively compare the capabilities for assessing VALSG and TNM stages among whole-body MRI, PET/MRI, PET/CT and conventional staging method in SCLC patients.
CEST Imaging vs. DWI vs. FDG-PET/CT: Therapeutic Effect Prediction for Chemoradiotherapy in Non-Small Cell Lung Cancer Patients
Authors: Ohno Yoshiharu , Yui Masao , Takenaka Daisuke , Yoshikawa Takeshi , Yamamoto Kaori , Ikedo Masato , Takeda Saki , Iwase Akiyoshi , Hanamatsu Satomu , Obama Yuki , Nagata Hiroyuki , Ueda Takahiro , Ikeda Hirotaka , Murayama Kazuhiro , Toyama Hiroshi
Published: August 2023

We hypothesize that CEST imaging has a potential for therapeutic effect prediction in NSCLC patients treated with chemoradiotherapy and may be at least as valuable as DWI and FDG-PET/CT. Moreover, combined predictors may improve therapeutic effect prediction capability, when compared with single predictor from MRI or PET/CT. The purpose of this study was to compare the capability of therapeutic effect prediction for chemoradiotherapy among CEST imaging, DWI, FDG-PET/CT and combined predictors from MRI and PET/CT in NSCLC patients.
Improving reconstruction quality in non-contrast-enhanced functional lung imaging via LORAKS
Authors: Ilicak Efe , Ozdemir Safa , Schad Lothar , Zapp Jascha , Zöllner Frank
Published: August 2023

Functional lung imaging is of great importance for diagnosis and follow-up of prevalent lung diseases. To this end, novel pulse sequences and post-processing techniques have been previously proposed to assess pulmonary functions. However, one overlooked aspect has been the parallel imaging reconstruction. Here, we propose the use of an advanced reconstruction scheme based on Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS). In vivo results are provided to demonstrate the performance of LORAKS compared to commonly used GRAPPA reconstructions. Preliminary results indicate that LORAKS can be a viable option for improving reconstruction quality in pulmonary functional imaging.
Bi-Component Cartilage T2 Analysis in Subjects With Anterior Cruciate Ligament Reconstruction
Authors: Kijowski Richard , Moskwa Robert , Liu Fang
Published: August 2023

mcDESPOT was used to measure single-component T2 relaxation time (T2Single) and the bi-component T2 parameter fraction of the fast relaxing macromolecular bound water component (FF) of the cartilage of the knee at 3.0T in 10 subjects with ACL reconstruction and 10 control subjects. There were significant differences (p<0.05) between ACL reconstruction and control subjects on 5 of 6 articular surfaces of the knee for FF compared with 2 of 6 surfaces for T2Single with FF having higher absolute effect size than T2Single for all surfaces. The results demonstrate the superiority of bi-component over single-component T2 analysis for detecting cartilage degeneration.
Assessment of Achilles Tendon Changes After Long-Distance Running Using Ultrashort Echo Time Magnetization Transfer MR Imaging
Authors: Fang Yijie , Zhu Dantian , Yu Wenjun , Wu Wenhao , Li Wei , Qian Long , Ma Yajun , Li Shaolin
Published: August 2023

Long-distance running is one of the common causes of Achilles tendon injury. UTE-MT is a magic angle-insensitive MRI technique and an excellent fit for the assessment of Achilles tendon which has a highly anisotropic collagen structure.This study aims to explore the feasibility of quantitative UTE-MT imaging in the assessment of Achilles tendon changes for subjects before and after long-distance running, then compare the technique’s performance with that of UTE-T2*.
Application of Two Denoising Methods to the Diffusion-Weighted 129Xe MRI and Dynamic Ventilation 19F/129Xe MRI Data Obtained from Normal Rats
Authors: Woodward Elise , Lemberskiy Gregory , Fox Matthew , Ouriadov Alexei
Published: August 2023

In this study, we demonstrate the effectiveness of two de-noising methods on diffusion-weighted and dynamic lung imaging in healthy rats using 129Xe and 19F. While the results showed a insignificant difference for denoising method 1 (MP-PCA), de-noising method 2 (MP-PCA preceded by hard-thresholding over a nuclear norm in k-space) showed significant difference in the overall mean Lm and LmD values for ventilation and r (fractional ventilation estimate) for ventilation images.
Isometric Contractions of the Quadriceps muscle: Strain and Strain Tensor Mapping using Velocity Encoded Phase Contrast Imaging.
Authors: Sinha Shantanu , Hernandez Ryan , Malis Vadim , Oda T , Sinha Usha
Published: August 2023

The quadriceps muscle plays an extremely critical role in various aspects of human locomotion. Changes in its function from various disease such as aging, muscular disuse or modified gait can degrade mobility. VE-PC imaging provides a convenient way to study muscle motion and assess the relative contribution of the thigh muscles to force production. This study is focused on deriving the strain and strain rate tensors to explore patterns along the fiber direction and cross-section. We were able to successfully obtain strain tensor images as well as to follow the temporal variation of these indices in different muscle compartments.
A Hybrid IVIM-DTI Model with Optimized Acquisition Time
Authors: Javidi Sam , Salighehrad Hamidreza
Published: August 2023

IVIM-DTI imaging is capable of revealing both structural and functional maps. We conduct this study to address the long acquisition time of IVIM-DTI imaging and the inaccuracy of estimates of the model’s outputs. Diffusion-weighted images were acquired at 11 b-values and 64 orientations. We used the Kalman filter to reach higher accuracy in a lower number of images. The resulting maps indicated that diffusion maps and pseudodiffusion maps for a healthy case are of highly visual similarity. Our results also confirmed that achieving a good accuracy is possible with just half number of images.
Brain age prediction using fusion deep learning combining pre-engineered and convolution-derived features
Authors: Lim HeeJoo , Ha Eunji , Lee Suji , Yoon Sujung , Lyoo In Kyoon , Shin Taehoon
Published: August 2023

Prediction of biological brain age is important as its deviation from chronological age can serve as a biomarker for degenerative neurological disorders. In this study, we suggest novel fusion deep learning algorithms which combine pre-engineered features and convolutional neural net (CNN) extracted features of T1-weighted MR images. Over all backbone CNN architectures, fusion models improved prediction accuracy (mean absolute error (MAE) = 3.40–3.52) compared with feature-engineered regression (MAE = 4.58–5.15) and image-based CNN (MAE = 3.60–3.95) alone. These results indicate that using both features derived from convolution and pre-engineering can complement each other in predicting brain age.
Synthetic T2-weighted fat sat delivers valuable information on spine pathologies: multicenter validation of a Generative Adversarial Network
Authors: Schlaeger Sarah , Drummer Katharina , El Husseini Malek , Kofler Florian , Sollmann Nico , Schramm Severin , Zimmer Claus , Karampinos Dimitrios , Wiestler Benedikt , Kirschke Jan
Published: August 2023

Generative Adversarial Networks (GANs) can synthesize missing Magnetic Resonance (MR) contrasts from existing MR data. In spine imaging, sagittal T2-w fat sat (fs) sequences are an important additional MR contrast next to conventional T1-w and T2-w sequences. In this study, the diagnostic performance of a GAN-based, synthetic T2-w fs is evaluated in a multicenter dataset. By comparing the synthetic T2-w fs with its true counterpart regarding ability to detect spinal pathologies not seen on T1-w and non-fs T2-w, diagnostics agreement, and image and fs quality our work shows that a synthetic T2-w fs delivers valuable information on spine pathologies.
Prostate Cancer Risk Assessment using Fully Automatic Deep Learning in MRI: Integration with Clinical Data using Logistic Regression Models
Authors: Schrader Adrian , Netzer Nils , Görtz Magdalena , Schwab Constantin , Hohenfellner Markus , Schlemmer Heinz-Peter , Bonekamp David
Published: August 2023

For patients with clinical suspicion for significant prostate cancer, the decision to undergo prostate biopsy can be supported by calculating the individual risk profile using demographic and clinical information along with multiparametric MRI assessment. We could show that the prediction performance of an established risk calculator remained stable after substituting manual PI-RADS scores for assessments from a fully automated deep learning system. Combining deep learning and PI-RADS resulted in significant improvements over using only PI-RADS. Complementary information that deep learning models are able to extract enable synergies with radiologists to improve individual risk predictions.
Differentiation of IDH and TERTp mutations in Glioma Using Dynamic Susceptibility Contrast MRI with Machine Learning at 3T
Authors: Buz-Yalug Buse , Danyeli Ayca Ersen , Yakicier Cengiz , Pamir Necmettin , Dincer Alp , Ozduman Koray , Ozturk-Isik Esin
Published: August 2023

Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase promoter (TERTp) mutations highly affect the clinical outcome in gliomas. The aim of this study was to identify IDH and TERTp mutations in glioma patients using machine learning approaches on relative cerebral blood volume (rCBV) maps obtained from dynamic susceptibility contrast MRI (DSC-MRI). The highest classification accuracy was 87.2% (sensitivity = 85.7%, specificity = 88.9%) for the IDH subgroup, 81.8% accuracy (sensitivity = 77.5%, specificity = 86.4%) was obtained for classifying the TERTp subgroup. Additionally, a classification accuracy of 89.6% (sensitivity = 88.3%, specificity = 91.2%) was obtained for identifying the TERTp-only gliomas.
Machine learning based classification of major depressive disorder using clinical symptom scales and ultrahigh field MRI features
Authors: Verma Gaurav , Xing Xin , Jacob Yael , Delman Bradley , Murrough James , Lin Ai-Ling , Balchandani Priti
Published: August 2023

Major depression is highly-prevalent disorder with frustratingly-high rates of treatment resistance. Ultrahigh field imaging may provide objective quantitative biomarkers for characterizing depression, generating insight into clinical phenotypes of this heterogeneous disease. Forty-two major depressive disorder patients currently off anti-depressant treatment were recruited for scanning at ultrahigh field, and given batteries of clinical symptom measures. Machine-learning clustering analysis was performed to group patients by clinical symptoms and differences in imaging features observed. A separate analysis was performed in the reverse direction clustering on quantified imaging features and identifying clinical differences between clusters, including differences in ruminative response between the patient clusters.
Comparison of whole-prostate radiomics models of disease severity derived from expert and AI based prostate segmentations
Authors: Summers Paul , Isaksson Lars , Pepa Matteo , Zaffaroni Mattia , Vincini Maria , Corrao Giulia , Mazzola Giovanni , Rotondi Marco , Raimondi Sara , Gandini Sara , Volpe Stefania , Haron Zaharudin , Alessi Sarah , Pricolo Paola , Mistretta Francesco , Luzzago Stefano , Cattani Federico , Musi Gennaro , De Cobelli Ottavio , Cremonesi Marta , Orecchia Roberto , Marvaso Giulia , Jereczek-Fossa Barbara , Petralia Giuseppe
Published: August 2023

A persisting concern is that downstream models of clinical endpoints may depend on whether the contours were drawn by an expert or an AI. Prediction models for surgical margin status, and pathology-based lymph nodes, tumor stage and ISUP grade group were formed using clinical and radiological features along with whole-prostate radiomic features based on manual and AI segmentations of the prostate in 100 patients who proceeded to prostatectomy after multiparametric-MRI. The models based on AI segmented prostates differed from those based on manual segmentation, but with similar if not better performance. Further testing of generalizability of the models is required.
A Deep Neural Network for Detection of Glioblastomas in Spectroscopic MRI
Authors: Bjørkeli Erin Beate , Geitung Jonn Terje , Esmaeili Morteza
Published: August 2023

We have developed a MRSI spectra classifying convolutional neural network (CNN), building on the AUTOMAP model for image and spectra reconstruction. The model was trained to discern between non-water-suppressed spectra from healthy subjects and glioblastoma (GBM) patients. The trained CNN was able to classify the spectra correctly and seemed to recognize the healthy spectra based on the NAA-peak and the GBM based on the choline levels and possibly 2HG, indicating an IDH mutation.
Virtual Dynamic Contrast Enhanced MRI of the Breast using a U-Net
Authors: Schreiter Hannes , Sukumar Vishal , Kapsner Lorenz , Folle Lukas , Ohlmeyer Sabine , Laun Frederik , Wenkel Evelyn , Uder Michael , Maier Andreas , Bickelhaupt Sebastian , Liebert Andrzej
Published: August 2023

Magnetic resonance imaging (MRI) examinations of the breast require intravenous administration of gadolinium based contrast agents (GBCA) for comprehensive characterization of the tissue. Novel approaches reducing the need for GBCA might therefore be of value. Here a virtual dynamic contrast enhancement (vDCE) using a U-net architecture is investigated in a cohort of n=540 patients. The vDCE generates T1 subtraction images for five consecutive time points predicting the perfusion maps based on native T1-weighted, T2-weighted, and multi-b-value diffusion weighted acquisitions. A mean structural similarity index (SSIM) value over a test group of 82 patients of 0.848±0.025 was achieved.
Iron, myelin and cortical brain ageing: investigating correlations between QSM, R2, R2* and R2'
Authors: Oros-Peusquens Ana-Maria , Hau Luis , Shah N. Jon
Published: August 2023

Understanding MR contrast/relaxation in vivo based on myelin and iron content is an on-going endeavour. We study the age dependence and correlation of QSM, R2, R2’ and R2* in vivo with whole brain coverage, investigating whether the interplay between demyelination and iron increases explains changes in these four qMRI parameters. The observations suggest that age-related influences proceed mostly by the intra-cellular iron effect on QSM and demyelination effect on R2.
Simplified fast multislice T1app mapping for improved cerebral blood flow arterial spin labeling MRI
Authors: Ji Yang , Lu Dongshuang , Sun Phillip
Published: August 2023

Given the association between MT and longitudinal relaxation rate (1/T1), we hypothesized that T1app, under RF saturation, can be estimated from T1 to improve CBF quantification. Using Wistar rats, we showed that the T1app over T1 ratiometric map (i.e., T1app/T1) is homogeneous despite large regional variation in T1 and T1app maps. Therefore, we postulated that T1app map could be estimated from the routine T1 map instead of T1app, which is cumbersome and not straightforward. A fast T1app estimation provides satisfactory Cerebral blood flow (CBF) measurement across the brain, not affected by the concomitant MT effect.
On the Noise Correlation in Receive Phased Arrays
Authors: Gapais Paul-François , Luong Michel , Almokdad Saadou , Georget Elodie , Amadon Alexis
Published: August 2023

The noise covariance matrix is useful for SNR and g-map computation and, with the noise correlation matrix, commonly taken as metrics to characterize decoupling of denser and denser receive arrays. Roemer provided a resistance matrix accounting for noise correlation. In the literature, Bosma defined the noise covariance matrix from the scattering matrix, easing predictions based on electromagnetic simulations or VNA measurements. Here noise correlation is analyzed for 50 Ω power-matched and low-input impedance preamplifiers. As predicted by Bosma, strong coupling does not necessarily imply a high noise correlation, which also depends on the ports impedance matching.
Striatal tau deposition in mild cognitive impairment revealed by removal of iron-related off-target binding effects in 18F-AV1451 PET
Authors: Langley Jason , Dara Sumanth , Bennett Ilana , Hu Xiaoping
Published: August 2023

We use tissue susceptibility to control for iron-related off-target binding effects in 18F-AV1451 PET and examine the impact of APOE-ε4 carrier status on striatal tau-PET signal in mild cognitive impairment. We found significant increases in tau-PET SUVR in the putamen (p=0.01) and caudate nucleus (p=0.046) of APOE-ε4 positive participants compared to APOE-ε4 negative participants. Controlling for striatal iron, significant correlations were seen between striatal tau-PET SUVR memory measures in the control (putamen: r=0.435; caudate: r=0.623) and APOE-ε4 positive MCI (putamen: r=0.403; caudate: r=0.648) groups with greater tau burden correlated with greater memory impairment.
Adapting and applying the brain age paradigm for clinical imaging in multiple sclerosis (MS)
Authors: Colman Jordan , Goodkin Olivia , Foster Michael , Mahmoudi Nima , Wattjes Mike , Gonzalez-Escamilla Gabriel , Groppa Sergiu , Pontillo Giuseppe , Høgestøl Einar , Westlye Lars , Messina Silvia , Palace Jacqueline , Cortese Rosa , De Stefano Nicola , Rovira Alex , Sastre-Garriga Jaume , Ropele Stefan , Rocca Mara , Filippi Massimo , Toosy Ahmed , Ciccarelli Olga , Yousry Tarek , Prados Ferran , Barkhof Frederik , Cole James
Published: August 2023

Brain-predicted age difference (brain-PAD; brain-predicted age – chronological age) is a potential biomarker for neurodegenerative diseases, including multiple sclerosis (MS). Previous models generally rely on T1-weighted (T1w) MRI brain scans. Here, we developed a deep-learning brain-age prediction model on FLAIR MRI. Our Inception-ResNet-V2 model was more accurate than a current state-of-the-art architecture and the FLAIR based model is comparable to a T1w MRI model. We used saliency maps, showing that areas such as the thalamus and ventricles are salient for brain-age prediction. We applied the FLAIR model to patients with MS, finding significantly higher brain-PAD compared to healthy controls.
Comparison of Blip-Up and Blip-Down EPI Distortion Correction Methods for Cardiac Diffusion Tensor Imaging
Authors: Cork Tyler , Middione Matthew , Loecher Michael , Liao Congyu , Moulin Kévin , Setsompop Kawin , Ennis Daniel
Published: August 2023

To maintain minimal scan times, cardiac Diffusion Tensor Imaging (cDTI) uses an echo-planar imaging (EPI) readout. Off-resonance, that causes geometric distortion in EPI, remains an obstacle that degrades image quality and can affect the underlying quantitative information. In cDTI, the lung/liver/heart interface amplifies the effect of geometric distortion. Distortion correction algorithms, such as TOPUP and DR-BUDDI, have proved to adequately correct distortion in neuroimaging, but limited work has been done for the heart. A first look at comparing these two correction strategies head-to-head was evaluated and resulting in TOPUP as a slightly better tool addressing distortion correction in the heart.
In-vivo comparison of Active Tracking based cardiac triggering and ECG-triggering for cardiac MR-thermometry
Authors: Mooiweer Ronald , Schneider Rainer , Krafft Axel , Empanger Katy , Stroup Jason , Neofytou Alexander , Mukherjee Rahul , Williams Steven , Lloyd Tom , O'Neill Mark , Razavi Reza , Schaeffter Tobias , Neji Radhouene , Roujol Sébastien
Published: August 2023

Continuous AT was used for cardiac synchronization of MR-thermometry with on-line signal filtering, calibration, and triggering. Experiments in a porcine animal model, where AT-trig was compared to ECG triggering, showed that AT-trig could potentially serve as alternative cardiac triggering strategy in situations where ECG triggering is not effective.
5D Free-Running-LIBRE MRI for 3D whole-heart Cine: A pilot study in patients at 3T
Authors: Eyre Katerina , Chetrit Michael , Leroi Lisa , Yerly Jérôme , Benovoy Mitchel , Piccini Davide , Stuber Matthias , Friedrich Matthias , Bastiaansen Jessica
Published: August 2023

Cardiac magnetic resonance imaging is often limited by long scan times and the need for multiple breath holds. A Free-Running sequence may overcome this limitation by providing 3-D cine data in a free-breathing non-ECG-triggered acquisition of 7 minutes. The clinical applicability of this method was tested in a cohort of 9 patients with varying pathologies. Myocardial blood-pool volumes and ejection fractions were compared between standard 2-D protocols and 5-D Free-Running-LIBRE. Free-Running-LIBRE successfully quantified volumes and ejection fractions with results correlating strongly to reference measurements. The method demonstrated additional potential to visualize irreversible myocardial injury in these patients.
Wideband Pulse Sequences for Suppressing Image Artifacts Induced by an ICD: A study in a Pediatric Anthropomorphic Phantom
Authors: Aboyewa Oluyemi , Hong KyungPyo , Jiang Fuchang , Bhusal Bhumi , Bonmassar Giorgio , Popescu Andrada , Webster Gregory , Golestanirad Laleh , Kim Daniel
Published: August 2023

This study investigated whether wideband late-gadolinium enhancement (LGE) and T1 mapping pulse sequences can suppress image artifacts induced by an ICD positioned 12cm inferior to the heart in a pediatric anthropomorphic phantom. The ICD induced a peak-to-peak center frequency shift of 970Hz across the heart. The normalized mean signal intensity within the heart was 1.91±1.20 and 0.98±0.39, for standard and wideband LGE with ICD, respectively. The mean myocardial T1 was 1486.1±607.7 and 1728.3±332.3msec for standard and wideband T1 mapping, respectively. Our results demonstrate that wideband pulse sequences can suppress image artifacts induced by an ICD in a pediatric anthropomorphic phantom.
Scalable and Modular 8-Channel Transmit and 8-Channel Flexible Receive Coil Array for 19F-MRI of Myocardial Infarction Studies in Large Animals
Authors: Özen Ali , Fischer Johannes , Reiss Simon , Lottner Thomas , Maier Alexander , Schimpf Waldemar , Heidt Timo , von zur Mühlen Constantin , Bock Michael
Published: August 2023

In this study, we have developed an 8-channel transmit/ 8-channel receive coil array system for cardiac and thoracic MRI in pigs. Transmit array is scalable in size to increase transmit efficiency in pigs with smaller abdomen size. Receive array is semi-flexible to fit tightly on the dome-shaped thorax of pigs. We present a detailed transmit efficiency analysis of the Tx array including tolerance to mismatches at the input ports. We also present 19F MRI results from a cadaver measurement.
Reproducibility of Left Ventricular CINE DENSE Strain in Pediatric Subjects with Duchenne Muscular Dystrophy
Authors: Liu Zhan-Qiu , Maforo Nyasha , Renella Pierangelo , Halnon Nancy , Wu Holden , Ennis Daniel
Published: August 2023

Left ventricular (LV) peak mid-wall circumferential strain (Ecc) and twist are early biomarkers for evaluating the subtle and highly variable onset and progression of cardiomyopathy in pediatric subjects with Duchenne muscular dystrophy (DMD). Cine Displacement Encoding with Stimulated Echoes (DENSE) has proven sensitive to changes in Ecc and twist, but has not been reported in a DMD cohort. We show that free-breathing DENSE CMR at 3T provides highly reproducible middle-ventricular Ecc, radial strain (Err), and twist measurements in healthy boys and boys with DMD without evidence of late gadolinium enhancement.
Biophysical-Model Based Synthesis of Cine CMR of Healthy and Pathological Left-Ventricular Function
Authors: Buoso Stefano , Joyce Thomas , Kozerke Sebastian
Published: August 2023

We propose to augment MRXCAT with left-ventricular anatomies and function, representing realistic population statistics, to enable design and validation of CMR imaging and inference approaches. A variational autoencoder is deployed to identify parametric anatomical representations from a cohort of healthy and diseased anatomies to allow for the generation of new synthetic anatomies. Cardiac function is simulated using a biophysical model also incorporating local tissue defects. The resulting ventricular tissue masks are enriched with background information from XCAT. Synthetic MR images are generated using MRXCAT, making available paired data of CMR images and (patho)physiological parameters including ground-truth ventricular strains and displacements.
The left atrial function in hypertension patients with left ventricular diastolic dysfunction：A myocardial MR strain study
Authors: Wen Jinyang , Zhang Xin , Gong Lianggeng , Dai Jiankun , Qian Long
Published: August 2023

Heart failure with preserved ejection fraction（HFpEF）was evaluated by echocardiography commonly. In this study, we try to use Cardiovascular Magnetic Resonance Tissue-Tracking (CMR-TT) to assess left atrial (LA) function in hypertension patients (HP) with left ventricular diastolic dysfunction (LVDD). The results demonstrated that LA myocardial function decreased in HP with LVDD. LA myocardial strain parameters could detect sensitively the myocardial deformation of LVDD in HP. Total strain(εs) parameter was an excellent parameter for diagnose HFpEF and evaluating the degree of LVDD.
Hybrid PET/MRI Dual-Condition Framework for Response Monitoring in Myocardial Infarction
Authors: Habach Fayez , Barry Jennifer , Larsen Melissa , Jamieson Marne , Singnurkar Amit , Laflamme Michael , Ghugre Nilesh
Published: August 2023

Hybrid PET/MRI is a promising tool that can characterize both metabolic and structural information and has been used extensively in the context of MI-related heart failure. We propose a novel dual-condition protocol that can be used to characterize metabolic state of cardiac tissue in vivo following MI, under different substrate conditions. Our results show significant metabolic changes under fasting and glucose loading conditions, and further longitudinal differences in MI-related metabolic remodeling within both the infarct zone and surrounding myocardium. This framework has potential to be used in testing therapeutic efficacy following MI in both preclinical and clinical contexts.
Towards Automated Scan Volume Placement for Breast MRI Using a Deep Neural Network
Authors: Allen Timothy , Bancroft Leah Henze , Bayram Ersin , Estkowski Lloyd , Wang Pingni , Cashen Ty , Holmes James
Published: August 2023

We investigate the use of a deep neural network to automatically position imaging volumes for breast MR exams. The axial localizer images and scan volume information from a variety of MR scanners were used to train a deep neural network to replicate the clinical technologists’ placement. The average intersection over union between clinical placement and neural network predicted placement was 0.46 ± 0.21. The distance between volume centers was 7.4 cm on average and as low as 1.1 cm. These results show promise for improving consistency of imaging volume placement in breast MRI.