bluuebunny/crossref_metadata_2025 huggingface dataset:
chi-separation using multi-orientation data in invivo and exvivo brains: Visualization of histology up to the resolution of 350 μm
Authors: Shin Hyeong-Geol , Seo Jincheol , Lee Youngjeon , Jeong Hwihun , Ji Sooyeon , Kim Minjun , Park Jang Woo , Kim Byeong C. , Lee Kyung-Hwa , Kim Seong Heon , Jang Jaewon , Woo Myung Kyun , Lee Jongho
Published: August 2023

Recently, the susceptibility source separation method, χ-separation, was suggested to separate paramagnetic and diamagnetic susceptibility distributions in the brain, potentially providing quantitative information of paramagnetic iron and diamagnetic myelin. However, the ill-posed nature of dipole-inversion has hindered accurate estimation and direct comparison with histology. Here, we extended the model for multi-orientation GRE, resolving the ill-posedness. The new model is applied to in-vivo and ex-vivo, revealing exquisite details of susceptibility distribution. When the results are compared to iron and myelin histology, great similarities are observed, suggesting the potentials of χ-separation for non-invasively acquiring three-dimensional histological information of iron and myelin.
“Better together”: a combination of machine learning and radiomics to predict long-term disability in Multiple Sclerosis
Authors: Cocozza Sirio , Cuocolo Renato , Pontillo Giuseppe , Ugga Lorenzo , Petracca Maria , Costabile Teresa , Lanzillo Roberta , Morra Vincenzo Brescia , Quarantelli Mario , Brunetti Arturo
Published: August 2023

The identification of early biomarkers to predict the disability accumulation is crucial in Multiple Sclerosis (MS). We performed a combined radiomics and Machine Learning (ML) study to predict long-term clinical changes in MS. Radiomics data were extracted from data of 177 patients with a 10-years clinical follow-up available. The model based on the recursive elimination of the features combined with the Extra Trees classifier was able to obtain a maximum precision for each endpoint of 0.71 and 0.69 for cognitive and motor disability, respectively.This combined radiomics-ML approach seems to be a feasible tool for long-term clinical prediction in MS.
Physics-Constrained Neural Network for Synthesis of MR Parameter Maps and Clinical Contrast
Authors: Lee Gawon , Son Ji Wan , SaKaie Ken , Nakamura Kunio , Zheng Yufan , Ontaneda Daniel , Trapp Bruce , Lowe Mark , Ye Dong Hye , Oh Se-hong
Published: August 2023

When a clinical MR scan is acquired, there might be missing tissue contrasts due to the corruption by patient’s motion during long scan time. In this study, we propose a method to synthesize the missing T2-weighted or FLAIR contrasts from a T1-weighted image using physics-constrained neural network. We incorporate the Bloch equations that generate MR contrast images from tissue parameter maps based on MR physical models into a synthesis neural network and show the improved performance compared with the existing U-Net directly synthesizing from a T1-weighted image.
Multimodal lesion characterization in multiple sclerosis: a pilot study
Authors: Schiavi Simona , Piredda Gian Franco , Lapucci Caterina , Tazza Francesco , Zacà Domenico , Roccatagliata Luca , Hilbert Tom , Kober Tobias , Inglese Matilde , Costagli Mauro
Published: August 2023

Using a comprehensive multimodal quantitative MRI protocol, we study the relationships between different microstructural metrics inside lesional tissue and investigate the heterogeneous pathological processes underlying alterations visible as hyperintense plaques in FLAIR images. Our preliminary results suggest that, although all the metrics can detect differences between lesions and normal appearing white matter, not all are directly associated with clinical status. Moreover, many metrics are intercorrelated and should not be considered as independent information when analyzing clinical outcomes. Understanding the clinical value of these parameters can advance the understanding of complex microstructural processes in neurological diseases and informs MRI protocol designs.
Improving the accuracy of multiple sclerosis classification via robust feature selection based on quantitative tractography
Authors: Petracca Maria , Azzari Alberto , Mensi Antonella , Graziano Nicole , Daducci Alessandro , Bicego Manuele , Inglese Matilde , Schiavi Simona
Published: August 2023

Improving image-based classification accuracy in multiple sclerosis while characterizing biological relevant features remains an open challenge. To this aim we applied a robust feature selection (FS) procedure based on a leave-one-out cross-validation scheme on structural connectivity features derived from connectomes computed with convex optimization modelling for microstructure informed tractography. We computed classification accuracy for different classifiers (NN, KNN, SVM-LIN, SVM-RBF, RF) before and after the application of the FS procedure. The highest overall accuracy (91%) was obtained using the FS procedure on the whole connectome. The biological meaningfulness of the selected features is supported by their correlations with clinical scores.
Multimodal MRI Investigation of Basal Forebrain Damage in Patients with Mild Cognitive Impairment
Authors: Chen Qian , Zhang Jilei , Zhang Bing
Published: August 2023

Cholinergic degeneration in the basal forebrain (BF) plays a crucial role in Alzheimer’s disease. In the present study, reduced BF volumes and damaged BF-associated tracts were observed in patients with mild cognitive impairment (MCI) compared to those with normal aging. Correlation and mediation analyses highlighted that volume reductions in the BF mainly accounted for deficits in global cognition, memory, and visuospatial ability, while disruption in BF-cortical tracts mainly contributed to executive and processing speed dysfunction. Collectively, this study may provide a better understanding of BF damage underlying cognitive impairment and provide guidance for intervention targets in MCI patients.
Effects of Respiration on Aortic Pulse Wave Velocity using Retrospective Respiratory Gated Radial 2D Phase Contrast MRI
Authors: Roberts Grant , Bernhardt Zach , Lose Sarah , Pandos Alyssa , Johnson Kevin , Eisenmenger Laura , Okonkwo Ozioma , Wieben Oliver
Published: August 2023

Pulse wave velocity (PWV) is a non-invasive biomarker related to vascular stiffness and cardiovascular risk. Cine 2D phase contrast (2DPC) MRI can be used to assess aortic PWV. However, the effects of respiration on PWV measures remain unexplored. Here, we assessed the effects of respiration by retrospectively gating free breathing radially sampled 2DPC MRI data in both inspiration and expiration. Two axial slices were obtained in the aortic arch and abdominal aorta and time shift methods were used to calculate PWV between the ascending and abdominal aorta. PWV measurements from inspiration (7.58m/s) were significantly decreased relative to expiration (8.42m/s).
Reproducibility of 4D flow MR angiography in peripheral vessels: A combined phantom and in vivo study
Authors: Sieben Martin , Deistung Andreas , Rothe Maik , Brill Richard , Wohlgemuth Walter , Gussew Alexander
Published: August 2023

Time-resolved 3D phase contrast MRA (4D flow MRA) is a technique providing morphological and quantitative 3D hemodynamic information of the blood vessel network without contrast agents and with a high temporal resolution. This study was performed to systematically validate flow measurements in peripheral vessels with high resolution, based on examinations in 4D flow phantom and in femoral vessels of healthy subjects. For this purpose, exams were run with varying spatial resolution and velocity encoding factors. It was shown, that the applied approach enables accurate and reproducible flow measurements by using venc factors of 120cm/s and isotrop spatial resolution of 1mm.
Dual-module velocity-selective arterial spin labeling (dm-VSASL) dramatically improves the temporal signal-to-noise ratio of VSASL
Authors: Guo Jia
Published: August 2023

High temporal signal-to-noise ratio (tSNR) is desired for arterial spin labeling (ASL) methods. The tSNR of velocity-selective arterial spin labeling (VSASL) was limited in practice due to its sensitivity to error sources such as motion, eddy currents and diffusion effects. A novel VSASL strategy was invented to enable dual-module (dm-) VS saturation (VSS) and VS inversion (VSI) labeling for further improved SNR efficiency with VSASL. This study focused on the tSNR performance analysis and found the tSNR was doubled in gray matter using the new dm-VSASL. The explanation of such significant improvement was explored and validated by in vivo experiments.
Open Science Initiative for Perfusion Imaging (OSIPI): DCE/DSC lexicon for reporting perfusion analysis pipelines
Authors: Dickie Ben , Sourbron Steven , van Houdt Petra , Bell Laura , van der Heijden Rianne , Fedorov Andrey , Arvidsson Jonathan , Debus Charlotte , Gutmann Ingomar , Quarles Chad , Floca Ralf , ahmed Zaki , Buckley David , Kompan Ina
Published: August 2023

The Open Science Initiative for Perfusion Imaging (OSIPI) aims to improve the reproducibility of perfusion MRI research through creation of acquisition and analysis standards. Specifically, Task Force 4.2 was established to develop a lexicon of variables and processes to facilitate standardised reporting of dynamic contrast-enhanced (DCE-) and dynamic susceptibility contrast (DSC-) MRI analysis pipelines. Here we report progress towards these objectives by presenting the current DCE/DSC lexicon structure, and provide a use-case to encode a simple analysis pipeline.
Feasibility of Flow-related Enhancement Brain Perfusion MRI
Authors: Glandorf Julian , Klimes Filip , Voskrebenzev Andreas , Gutberlet Marcel , Kern Agilo , Kornemann Norman , Mahmoudi Nima , Wattjes Mike , Wacker Frank , Vogel-Claussen Jens
Published: August 2023

To test the feasibility of brain perfusion imaging exploiting flow-related enhancement during fast gradient echo sequences. Imaging was performed using Fast Low-Angle Shot (FLASH) and balanced steady-state free precession (bSSFP) sequences of a single axial slice with various imaging parameters at 1.5T and 3T. Perfusion-weighted maps were generated based on the Fourier decomposition method “Phase-REsolved FUnctional Lung” (PREFUL) MRI. The derived imaging protocol was evaluated in patients with metastases and a stroke showing high spatial overlap to post-contrast MRI and CT-perfusion. A cohort of 19 healthy individuals presented moderate, but significant regional correlation and high spatial Dice overlap to pCASL-MRI.
Coherent flows detected in placental diffusion weighted data
Authors: Hutchinson George , Dellschaft Neele , Jones Nia , Leach Lopa , Gowland Penny
Published: August 2023

Diffusion weighted imaging is commonly used in the placenta to characterise regions of differing structure and flow, including differences between the healthy and compromised placenta. Typically placental diffusion data is described by a combination of exponential decays with increasing diffusion weighting, but we have observed signals that show oscillations in signal or even increasing initial signal and are likely to relate to refocusing caused by coherent flows. By altering our diffusion fitting we can access information about flow patterns and provide more information regarding placental haemodynamics.
Effects of Life-Long Maternal Western Diet on Guinea Pig Placental Metabolism Across Gestation using [1-13C]pyruvate MRI
Authors: Morris Lindsay , Friesen-Waldner Lanette , Wade Trevor , Smith Lauren , Empey Mary-Ellen , Regnault Timothy , McKenzie Charles
Published: August 2023

Effects of a life-long maternal Western Diet (WD: high fat and high sugar diet) on placental metabolism in 33- and 60-day gestation guinea pigs were investigated. Pregnant WD or control diet (CD) fed sows underwent T1-weighted and 13C hyperpolarized metabolic MRI at 3T. Metabolite mean signal intensities were measured in the placental volumes as a function of time and area under the curve (AUC) ratios were calculated for pyruvate and its metabolites: lactate, bicarbonate & alanine. The AUC ratios for lactate and bicarbonate were significantly higher in WD than CD groups, suggesting placental metabolic dysfunction.
Multiparametric Modeling of Fetal MRI to Compare Lobular and Whole-Placental Segmentations for Fetal Growth Restriction
Authors: Schellenberg Janina , Aughwane Rosalind , Flouri Dimitra , Mufti Nada , Maksym Katarzyna , David Anna , Melbourne Andrew
Published: August 2023

Babies with fetal growth restriction fail to grow properly in the womb and become hypoxic. They must be delivered prematurely to prevent stillbirth. DECIDE model fitting can determine properties which give information about the placental function and fetal blood saturation. This can be used to identify fetal growth restriction and manage the pregnancy effectively.

The aim is to compare DECIDE model fitting results for different mask segmentations. The placenta is segmented into lobules and used as a mask itself. Statistically significant differences of the fetal and placental properties between appropriately developing and growth-restricted pregnancies are determined.
Impact of motion correction on in-vivo assessment of Placenta Adhesion Abnormality (PAA) disorders using IVIM method
Authors: Ngremmadji Mbaimou Auxence , Draveny Robin , Krisch Audrey , Ambarki Khalid , NICKEL Dominik , Bertholdt Charline , Morel Olivier , Beaumont Marine , Chen Bailiang
Published: August 2023

Placenta adhesion abnormality (PAA) is the consequence of an excessive invasion of the placenta within the myometrium. Due to its severe maternal pregnancy outcomes, screening of subjects with suspicions of PAA are very important. The utilisation of intra voxel incoherent motion (IVIM) technique may help to improve the diagnosis. But its stability and robustness need to be improved and analysed. Here we aim to evaluate the impact and address the importance of motion correction technique to assess PAA disorders in-vivo using IVIM method in order to establish a robust and stable pipeline for PAA patient screening.
Enhanced T2-weighted images using Luminal Water Imaging and U-Net based segmentation for prostate cancer diagnosis.
Authors: Ip Candice , Chang Silvia , Sabouri Shirin , Yung Andrew , Reinsberg Stefan , Kozlowski Piotr
Published: August 2023

Clinical prostate carcinoma (PCa) detection with MRI has focused on qualitative assessment. However, novel Luminal Water Imaging (LWI) provides quantitative information with promising results for detecting PCa. To overcome shortcomings of clinical prostate MRI protocol, we propose using LWI to augment T2-weighted (T2W) images to improve image contrast for PCa detection while maintaining anatomical details needed for radiological diagnosis. Here, we investigate automatic segmentation models and various weighting functions of LWI parameter maps to generate semi-quantitative T2W images that also preserves anatomical detail. Our results show that a combined T2W and LWI parameter image provides enhanced detection of PCa.
Radiological Evaluation of Model-Based Reconstruction for Through-Plane Resolution Enhancement in T2-Weighted Spin-Echo Prostate MRI
Authors: Borisch Eric , Froemming Adam , Grimm Roger , Kawashima Akira , Trzasko Joshua , Riederer Stephen
Published: August 2023

Results of a radiological review of a previously described Model Based Iterative Reconstruction (MBIR) compensating for slice profile arising in a thick and overlapping-slice acquisition of the prostate are detailed. Blinded review by two radiologists indicated improvements in SNR, contrast fidelity, and diagnostic quality.
Incorporating compartmental T2 measurements into Restriction Spectrum Imaging of the prostate
Authors: Conlin Christopher , Karunamuni Roshan , Feng Christine , Rodriguez-Soto Ana , Kuperman Joshua , Holland Dominic , Rakow-Penner Rebecca , Hahn Michael , Dale Anders , Seibert Tyler
Published: August 2023

Restriction Spectrum Imaging (RSI) examines diffusion in discrete tissue compartments to better detect and characterize prostate cancer. T2 information from these compartments may further improve prostate cancer evaluation. In this study, RSI data was acquired using multiple echo times to measure both compartmental T2 and diffusion in patients with suspected prostate cancer. A multivariable model was then developed to identify cancer from compartmental T2 and diffusion measurements. Significant differences in compartmental T2 were observed between normal and cancerous prostatic tissue. However, the multivariable model did not significantly improve cancer detection performance over diffusion measurements alone.
1.5T Prostate MRI Utilizing Deep Learning Reconstruction in Patients with Hip Prosthesis
Authors: Atkins Melany , Guidon Arnaud , Vinski Michael , Schrack Thomas , Harris Heidi , Bayram Ersin
Published: August 2023

While 3T offers SNR benefits for prostate imaging, patients with hip prostheses are problematic due to susceptibility artifacts and implant heating concerns. These issues are better managed at 1.5T but 1.5T lacks the SNR versus 3T. Deep learning reconstruction (DLR) may provide the SNR boost to clear this hurdle. Feasibility of 1.5T prostate MRI with DLR in patients with hip prosthesis is explored in up to 20 subjects focusing on two key contrasts in the PI-RADS guideline: T2Wand DWI. Preliminary results suggest that 1.5T with DLR might be the preferred option for patients with hip implants.
Fast Fitting of Luminal Water Fraction in Prostate Cancer
Authors: Atkinson David , Brembilla Giorgio , Gong Fiona , Punwani Shonit
Published: August 2023

The use of a fast, direct, Luminal Water Fraction fitting technique is compared with a more commonly used iterative method on data from 76 prostate cancer patients with biopsy Gleason grade available. The fast method shows a similar Receiver Operating Characteristic Area Under the Curve (ROC-AUC) when used to classify an image region as clinically significant or non-significant cancer.
Real-time Motion Compensated ∆B0 Shimming with an AC/DC Shim Coil and Dual-Echo vNavs
Authors: Arango Nicolas , Frost Robert , Wighton Paul , Stockmann Jason , Andronesi Ovidiu , van der Kouwe Andre
Published: August 2023

Changes in subject position move susceptibility interfaces and therefore ∆B0 field patterns in the brain. We apply a prospective real time (TR-to-TR) shim updating scheme using dual echo EPI volume navigators to correct motion-induced changes in ∆B0 fields to reduce distortion in 2D EPI. Shim fields were produced by a 32 channel AC/DC head shim array. TR-to-TR shimming reduced EPI distortion in all head positions in in vivo experiments.
On the limits of model-based reconstruction for retrospective motion correction in the presence of spin history effects
Authors: Nurdinova Aizada , Ruschke Stefan , Karampinos Dimitrios
Published: August 2023

MRI reconstruction methods comprising model-based motion correction modules often rely on the assumption of absence or piecewise constant motion patterns during signal readout and on the insignificance of spin history effects. Furthermore, the success of such methods also depends on the characteristics and motion sensitivity of the employed sequence and measured tissue properties and may therefor limit the general utility of such approaches in a clinical context. The present study demonstrates the limitations of Fourier-model-based motion correction reconstruction schemes by subtle violations of the piecewise constant motion assumption using Bloch simulations a model-based motion-corrected reconstruction approach.
Deep Learning-Based Respiratory Motion Correction in Free-Breathing Abdominal Diffusion-Weighted Imaging
Authors: Kim Jinho , Gadjimuradov Fasil , Benkert Thomas , Vahle Thomas , Maier Andreas
Published: August 2023

Since a single diffusion-weighted image can suffer from low SNR, multiple DWI repetitions can be averaged to improve SNR, which, however, can introduce blurring due to respiratory motion between different repetitions. Consequently, retrospective gating can be performed to overcome this problem. However, conventional retrospective gating has low SNR efficiency as it discards parts of the data and may result in certain slices to be missing for the desired motion state. This work proposes an efficient Deep Learning-based motion-correction method to improve conventional retrospective gating in free-breathing DWI, resulting in sharper images while maintaining image information from all acquired repetitions.
VERDICT MRI for tumor tissue characterization – separating viable tumor from necrosis
Authors: Montelius Mikael , Lundholm Lukas , Jalnefjord Oscar , Forssell-Aronsson Eva , Ljungberg Maria
Published: August 2023

The biological meaning of MRI-based biomarkers of cancer can be studied using spatially matched histologically stained tumor sections. However, correlations are often weak or contradictory, possibly due to confounding necrosis that models fail to describe. In this work, we demonstrate the potential of using VERDICT MRI model parameters to separate necrosis from viable tumor tissue non-invasively. Radiation treated tumors demonstrating large necrotic and viable regions are imaged, excised and stained using immunohistochemical methods. Correlations between spatially matched VERDICT and immunohistochemical maps reveal that VERDICT clearly separates necrosis from viable tumor.
Efficient Mapping of Diffusion Tensor Distribution in a Live Human Brain
Authors: Magdoom Kulam Najmudeen , Avram Alexandru , Gasbarra Dario , Witzel Thomas , Huang Susie , Basser Peter
Published: August 2023

Electron microscopy of nervous tissue reveals a multitude of compartments separated by plasma membranes where the diffusion of water may be hindered. Diffusion tensor distribution (DTD) MRI assumes each voxel consists of an ensemble of diffusion tensors described by a tensor variate probability distribution function in line with the EM observed microstructure. In this study, we show in vivo results obtained in the living human brain using a new DTD framework on the Connectome scanner using 300 mT/m gradients and a novel time efficient and concomitant field free pulse sequence which allowed shorter echo time for a given b-value.
Resolving heterogeneous crossing fibers with Adaptive modelling and Generalized Richardson Lucy spherical deconvolution (AGRL)
Authors: De Luca Alberto , Leemans Alexander , Tax Chantal , Schilling Kurt , Biessels Geert-Jan
Published: August 2023

We evaluate the benefits of shifting from a global white matter (WM) model to an adaptive (voxel-wise) model in the Generalized Richardson Lucy (GRL) framework. Using simulations, we show that GRL with an adaptive model (AGRL) could resolve crossing fiber configurations with heterogeneous properties, whereas conventional GRL did not. In in-vivo data, AGRL simultaneously used different deconvolution models. Compared to GRL, fiber orientation distributions of AGRL showed remarkable angular differences, especially for the second and third peak. Tractography with AGRL resulted in a more extensive reconstruction of the arcuate fasciculus, suggesting adaptive modelling as a promising future direction.
BundleAtlasing: unbiased population-specific atlasing of bundles in streamline space
Authors: Romero-Bascones David , Chandio Bramsh , Fadnavis Shreyas , Park Jong , Koudoro Serge , Ayala Unai , Barrenechea Maitane , Garyfallidis Eleftherios
Published: August 2023

White matter bundle atlases play a crucial role in the segmentation of bundles and the understanding of brain connectomes. However, the construction of streamline atlases that accurately represent the underlying population anatomy is challenging. In this work, we present BundleAtlasing, a new method to compute population-specific bundle atlases in the space of streamlines. The proposed approach is based on two key aspects: an iterative groupwise unbiased bundle registration, and a pairwise bundle combination strategy. We show that our method is able to correctly generate unbiased atlases that represent the average group anatomy of a population.
Quantitative MRI measures of thalamic microstructural integrity in patients with multiple sclerosis
Authors: Cagol Alessandro , Rahmanzadeh Reza , Barakovic Muhamed , Lu Po-Jui , Weigel Matthias , Melie-Garcia Lester , Lutti Antoine , Nguyen Than , Wang Yi , Kuhle Jens , Kappos Ludwig , Granziera Cristina
Published: August 2023

Thalamus represents a pivotal structure to study MS-associated neurodegeneration. In this study we investigated the alterations in thalamic microstructure of MS patients by using magnetization transfer saturation (MTsat), T1-relaxometry, and myelin water fraction (MWF). Compared to healthy controls (HCs), MS patients presented significant modifications in the thalamic quantitative MRI metrics, suggesting ongoing microstructural and myelin loss. The thalamic quantitative MRI metrics explored showed variable degrees of association with MS lesion burden, brain atrophic changes, as well as with clinical and cognitive disability.
Magnetic Resonance Fingerprinting enables measuring T1-shortening in multiple sclerosis lesions after contrast media administration
Authors: Donatelli Graziella , Cecchi Paolo , Migaleddu Gianmichele , Cencini Matteo , D'Amelio Claudio , Buonincontri Guido , Peretti Luca , Pasquali Livia , Tosetti Michela , Cosottini Mirco , Costagli Mauro
Published: August 2023

Quantitative maps of T1-shortening (ΔT1) related to intra-venous contrast media administration are obtained in patients with Multiple Sclerosis by using 3D Quantitative Transient-state Imaging. Statistically significant ΔT1 is observed not only in enhancing lesions, but also in some non-enhancing lesions, suggesting a potential role of this method as a quantitative imaging biomarker to detect subtle blood-brain barrier damage.
Diffusion Tensor Imaging Helps to Differentiate Neuromyelitis Optica Spectrum Disorders and Multiple Sclerosis-Related Optic Neuritis
Authors: Xie Yan , Zhang Yan , Liu Weiyin , Zhu Wenzhen
Published: August 2023

The purpose of this study was to assess the differences in the patterns of NMOSD and MS-related optic nerve damage by DTI. In this study, the intraorbital optic nerve was divided anatomically in three equal parts. We found that NMOSD-related optic neuritis exhibited extensive optic nerve damage, particularly in the posterior segment of the optic nerve, whereas MS-related optic neuritis tended to be more anterior-middle optic nerve damage. In addition, the combination of FA with conventional MRI showed better differential diagnostic efficacy for NMOSD and MS-related optic neuritis.
Automated 4D flow-based blood vortex detection for estimation of mean pulmonary arterial pressure
Authors: Kräuter Corina , Reiter Ursula , Kovacs Gabor , Reiter Clemens , Masana Marc , Olschewski Horst , Fuchsjäger Michael , Stollberger Rudolf , Reiter Gert
Published: August 2023

Pulmonary hypertension is characterized by elevated mean pulmonary arterial pressure. Visual vortex analysis revealed a strong relation between elevated mean pulmonary arterial pressure and the duration of vortical blood flow along the pulmonary artery, however, automated pulmonary hypertension-related vortex detection methods are lacking. We propose a method for automated detection and tracking of the pulmonary hypertension-related vortex from 4D flow data and aim to compare it with visual analysis and to validate automatically estimated mean pulmonary arterial pressure against invasively measured results. The automated method agreed very well with visual vortex detection and accurately estimated elevated mean pulmonary arterial pressure.
Combined optimisation of delays and echo times for blood-brain barrier permeability measurements using multi-TE pCASL MRI
Authors: Zhang Logan X , Chappell Michael
Published: August 2023

Blood-brain barrier permeability to water has been measured as the exchange time (Texch) between intra- and extra-vascular spaces using multi-TE arterial spin labelling (ASL). This relies on small signal changes associated with labelled blood water delivery, making it challenging to achieve high accuracy in short yet clinically-desirable scan durations. In this study, we used an optimal sampling framework to generate pseudo-continuous ASL protocols that were jointly optimised for delays and echo times. Simulation showed that while optimised protocols had overall improvements in parameter estimation compared to conventionally even-sampled protocols, accurately estimating Texch could not be achieved without sacrificing CBF accuracy.
Correction of artefacts in simultaneous multi-slice multi-PLD arterial spin labelling data using Gaussian Process regression
Authors: Toner Jack , McConnell Flora Kennedy , Suzuki Yuriko , Coalson Timothy , Harms Michael , Glasser Matthew , Chappell Michael
Published: August 2023

Simultaneous multi-slice (SMS) acquisitions enable higher resolutions to be achieved for arterial spin labelling (ASL) images. However, SMS acquisitions can introduce a banded pattern of intensity within the images. This reduces the quality of motion estimation as the algorithm aligns the bands in preference to the brain structures. We introduce a Gaussian Process model that can be used to correct the banding in SMS multiple post-labelling delay ASL data, which should improve motion correction. We demonstrate its effectiveness on 10 subjects from the Human Connectome Project Aging dataset. We anticipate that the model will generalise to other ASL datasets.
A vortical phantom for ASL perfusion MRI
Authors: Oliver-Taylor Aaron , Golay Xavier
Published: August 2023

Most perfusion phantoms that have been created make use of porous media to simulate the microvasculature, however their characterisation introduces additional uncertainties into a perfusion measurement. We take inspiration from a recently published impinging jet perfusion phantom and presenting a phantom that makes use of vortices to mix and disperse the perfusion signal. Multi post labelling delay pCASL data was acquired, and fit to the general kinetic model. By assuming the measured perfusion equates to the equivalent perfusion from the systemic flow rate the labelling efficiency can be estimated, and the resultant measured and fitted signal curves closely match.
Repeatability of Apparent Diffusion Coefficient Estimates of Healthy Male and Female Pelvic Tissues
Authors: Fang Lauren , Rodríguez-Soto Ana , Dale Anders , Conlin Christopher , Seibert Tyler , Hahn Michael , Rakow-Penner Rebecca
Published: August 2023

Establishing ADC repeatability is important for clinical translation of quantitative DWI. Pelvic ADC repeatability in healthy men and premenopausal women are not well-defined. Four men were scanned twice and six women were scanned each week of their normal 28-day menstrual cycle using a 3.0T scanner. Prostate, cervix, and uterine tissue ROIs were drawn to calculate median ADC values. Minimum reliably detectable ADC change (RC) was estimated at 16% and 11% within the prostatic peripheral and transition zones, respectively. Female reproductive tissue ADCs and repeatability varied across the menstrual cycle, with the greatest RCs displayed by endometrium (40%) and cervix (36%).
Multiple quantitative parameters diagnosis of cellular and degeneration uterine leiomyoma with non-enhancement MRI
Authors: Tian Shifeng , Liu Ailian
Published: August 2023

Results shown that several quantitative parameters of diffusion kurtosis imaging (DKI) and enhanced T2 star weighted angiography (ESWAN) sequence had different between cellular uterine leiomyoma (CUL) and degeneration uterine leiomyoma (DUL). Therefore, it is feasible to use DKI and ESWAN to distinguish CUL and DUL.
Evaluation of microsatellite instability in endometrial carcinoma by amide proton transfer imaging and diffusion kurtosis imaging
Authors: Tian Shifeng , Liu Ailian , Meng Xing , Chen Lihua , Wang Nan , Lin Liangjie , Hu Geli , Wang Jiazheng
Published: August 2023

Microsatellite instability (MSI) is one of the four types of endometrial carcinoma (EC) molecular classification based on The Cancer Genome Atlas (TCGA) [1], which refers to the high mutation load caused by the failure of EC to correct certain errors in small sequence replication due to the abnormal function of DNA mismatch repair system [2]. The purpose of this study is to explore the value of multiple quantitative parameters of APT and DKI sequences in evaluating EC MSI status.
VERDICT-MRI Analysis of False Positives in Prostate Mp-MRI
Authors: Sen Snigdha , Valindria Vanya , Pye Hayley , Grey Alistair , Haider Aiman , Freeman Alex , Moore Caroline , Whitaker Hayley , Punwani Shonit , Singh Saurabh , Panagiotaki Eleftheria
Published: August 2023

Mp-MRI has moderate specificity for the diagnosis of prostate cancer, resulting in many false positive results and unnecessary biopsies for patients with other prostatic diseases. This work presents an approach using VERDICT-MRI to investigate differences in microstructural parameter estimates between normal prostate tissue, false positive lesions and clinically-significant cancer. The VERDICT intracellular volume fraction and intrinsic diffusivity in the extracellular-extravascular space can discriminate false positive lesions with greater accuracy than the clinical ADC from mp-MRI. These results show VERDICT has the potential to identify false positives and reduce unnecessary biopsies in prostate patients.
Targeted Metabolomic Profiling of Blood Plasma in Aggressive Prostate Cancer Patients using NMR Spectroscopy
Authors: Kumar Virendra , Kumar Pradeep , Kumar Rajeev , Gupta S. Datta , Thulkar Sanjay , Jagannathan N. R. , Jayasundar Rama
Published: August 2023

Gleason score (GS) is currently clinical predictor of prostate cancer (PCa) mortality. The present study investigates the metabolic profile of blood plasma for distinguishing aggressive PCa patients from non aggressive using targeted 1H-NMR metabolomics. A significantly higher concentration of lactate, pyruvate, choline, dimethylamine, glucose, betaine and lower concentration of leucine, isoleucine, valine, histidine, phenylalanine, and tyrosine were observed in patients with GS 8-10 as compared to patients with GS 7 and 6. Our results suggested that pathway alterations, amino acids, phospholipids and glucose related to PCa progression. Identifying biomarker/s associated with GS can be improved diagnosis and treatment.
Integrative Hyperpolarized 13C Metabolic and Standard-of-Care Abdominopelvic Staging/Restaging MRI for Patients with Advanced Solid Tumors
Authors: Chen Hsin-Yu , Ohliger Michael , Wang Zhen , Frost Mary , Carvajal Lucas , Lee Philip , Gordon Jeremy , Kurhanewicz John , Larson Peder , Bok Robert , Aggarwal Rahul , Vigneron Daniel
Published: August 2023

Harnessing unique metabolic insight from hyperpolarized (HP) 13C MRI, this study describes an integrated approach toward routine cancer staging and restaging using combined HP 13C plus standard of care (SOC) MRI with commercially-available 13C and 1H array coils and a time-efficient adaptive protocol. A series of patients with osseous, liver, nodal metastases, and locally advanced primary tumors suggested HP 13C not only detected high metabolic activity in progressive tumors, but it also revealed inter- and intratumoral metabolic heterogeneity. Results indicated HP 13C-MRI can greatly complement SOC-MRI in assessing therapeutic response and guiding clinical decisions.
B0 inhomogeneity in the abdominal subcutaneous fat at 3T
Authors: Tollefson Aidan , Roberts Nate , Pirasteh Ali , Hernando Diego
Published: August 2023

B0 inhomogeneities often lead to failures in chemical shift-based fat suppression, particularly in complex susceptibility environments such as the abdomen. To optimize the application of fat suppression, characterization of B0 inhomogeneities in fat regions is needed. However, such characterization has not been reported for abdominal fat. Therefore, the purpose of this work was to measure the magnitude and variability of B0 inhomogeneity at 3T in abdominal subcutaneous fat. This study shows that several subcutaneous fat regions exhibit B0 inhomogeneities large enough to induce failures in common fat suppression methods. These measurements may guide future optimization of fat suppression methods.
Changes in cortical blood flow >1 year after radiation for glioma using arterial spin labelling MRI
Authors: Sokolska Magdalena , Gupta Meetakshi , Eiben Björn , Markus Julia , Hyare Harpreet , Kosmin Michael
Published: August 2023

This work proposes a framework for monitoring brain perfusion after radiotherapy treatment of glioma in relation to the radiotherapy dose.
Gradual RAKI reconstruction merged with intermittent GRAPPA blurring moderation with limited scan-specific training samples
Authors: Homolya István , Kemenczky Péter , Dawood Peter , Vidnyánszky Zoltán , Blaimer Martin
Published: August 2023

RAKI is a scan-specific k-space interpolation technique based on deep convolutional networks, which bears superior noise resilience compared to GRAPPA. However, RAKI may introduce severe blurring in image reconstruction due to reduced number of autocalibration signal lines at higher acceleration factors. We propose Gradual RAKI, which exhibits the benefit of mixing RAKI and GRAPPA in a preparatory block for data augmentation purposes prior to a conventional RAKI reconstruction. Data augmentation provides an effective way to create synthetic ACS lines out of 8 original ACS lines at 4-fold acceleration, while valuable features are retained from both RAKI and GRAPPA reconstruction methods.
IMPULSED model-based brain tumor microstructural parameter estimation with deep neural network
Authors: Wu Jian , Kang Taishan , Chen Xinran , Xu Lina , Lin Jianzhong , Wu Zhigang , Yang Tianhe , Cai Congbo , Cai Shuhui
Published: August 2023

This study assesses the feasibility of training a convolutional neural network (CNN) for IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting to diffusion-weighted (DW) data and evaluates its performance on a brain tumor (poorly differentiated adenocarcinoma) patient data directly acquired from clinical MR scanner. Comparisons were made with the results calculated from the non-linear least squares (NLLS) algorithm. More accurate and robust results were obtained by our CNN method, with processing speed several orders of magnitude faster than the reference method (from 5 min to 1 s).
Improvements of Image Quality of 1H and 129Xe MRI by Using an Advanced Acquisition and Reconstruction Method Coupled with Deep Learning
Authors: Perron Samuel , Fox Matthew , Ouriadov Alexei
Published: August 2023

Accelerated MRI has the potential to significantly improve image quality without increasing costs, especially for low field strengths. A series of undersampled images are averaged for every unique permutation, and their SNR dependency is fitted to the Stretched-Exponential-Model. The proposed method was implemented in proactively undersampled phantom images at low field (0.074T) and in retroactively undersampled human lung images at high field (3T) using the FGRE pulse sequence; in all cases, SNR was significantly improved within the same scan duration compared to a fully-sampled image. Reconstruction artefacts were minimized or completely removed using a convolutional neural network.
Low Rank Off-resonance Correction for Double Half-Echo k-Space Acquisitions
Authors: Bydder Mark , Ali Fadil , Ghodrati Vahid , Wang Chencai , Hagiwara Akifumi , Ellingson Ben
Published: August 2023

The present study describes a model-based approach for correcting off-resonance in the context of short echo-time imaging using a double half-echo k-space acquisition. This technique employs center-out readouts in forward and reverse directions and is sensitive to off-resonance. The correction is simple and results in a marked reduction in blurring for a negligble cost in pre-processing.
Metal Artifact Corrected and Chemical Shift Encoded MRI
Authors: Buelo Collin , Colgan Timothy , Hernando Diego
Published: August 2023

Fat-suppressed MRI near metal remains a challenging technical problem. Chemical shift encoded (CSE) water/fat separation enables excellent SNR efficiency compared to short tau inversion recovery (STIR), but the large B0 field offsets and gradients complicate conventional CSE methods near metal. To address these challenges, we propose metal artifact correction and chemical shift encoding using optimized echo spacings, and with deep learning processing for reconstruction of water/fat separated imaging near metal. This method is able to correctly separate water and fat closer to a metallic surgical breast clip than conventional methods, without the drawbacks associated with STIR imaging.
Multi-domain Motion Correction Network for Turbo Spin-echo MRI
Authors: Lee Jongyeon , Lee Wonil , Jeong Namho , Park HyunWook
Published: August 2023

Motion correction in MRI has been successfully performed by adopting deep learning techniques to correct motion artifacts with data-driven algorithms. However, many of these studies have narrowly utilized MR physics. In addition to the efficient motion correction method proposed for multi-contrast MRI, we expand our previous method to utilize data in k-space domain for a single-contrast MRI. Using the motion simulation based on MR physics, we propose a multi-domain motion correction method in both k-space and image domains. Our proposed method finely reduces motion artifacts using the multi-domain network.
Female patients with atopic dermatitis have reduced functional connectivity in the precuneus and inferior parietal lobe
Authors: Li Tie-Qiang , Nordlind Klas , Theodorsson Elvar , Jonsson Tomas
Published: August 2023

A cohort of 32 atopic dermatitis (AD) patients and 32 matched healthy controls (HC) were recruited to study the neurological abnormalities which might be associated with this pruritic skin condition. The main findings are 1) reduced general functional connectivity in the precuneus, inferior parietal lobe and visual cortex was detected in females AD patients; 2) Severity of the symptoms was negatively correlated with functional connectivity in inferior parietal lobe, while salivary cortisol was positively associated. Resting-state fMRI can provide useful insight into the altered neurophysiology and clinically relevant assessment for the AD disorder.
Enzymatic activity monitoring through Dynamic Nuclear Polarization in Earth magnetic field
Authors: Boudries Dahmane , Franconi Jean-Michel , Marque Sylvain , Massot Philippe , Mellet Philippe , Parzy Elodie , Thiaudiere Eric
Published: August 2023

Earth-field MRI can provide new contrasts leading to the observation of pathologies at the biochemical level. However detection sensitivity is poor at low-field. In a preliminary spectroscopic approach, it is proposed here to detect protease-driven hydrolysis of a nitroxide probe thanks to electron-nucleus Overhauser enhancement in a homemade double resonance system. The nitroxide probe is a six-line nitroxide whose lines are shifted according to its substrate/product state. The Overhauser enhancement frequency dependence was in agreement with theoretical calculations. Enzymatic conversion of the nitroxide substrate was observed which opens the way for the design of new low-field DNP-MRI systems.
Quantifiable study of magnetic resonance super resolution reconstruction in Placenta Accreta Spectrum using Image Quality Metrics
Authors: Chappell Joanna , Mufti Nada , O'Brien Patrick , Attilakos George , Sokolska Magda , Narayanan Priya , Aughwane Rosalind , Kendall Giles , Atkinson David , Ourselin Sebastien , David Anna , Melbourne Andrew
Published: August 2023

Magnetic Resonance Imaging is increasingly used for assessment of placental complications1. Here we apply MRI to examine Placenta Accreta Spectrum (PAS) a condition where the placenta is abnormally adherent or invasive. Super-Resolution Reconstruction (SRR) provides a high-resolution 3D reconstruction from 2D MRI slices to improve the visibility of structures for future clinical use2. Image Quality metrics allows quantitative evaluation of the SRR images to compare with the original 2D images. These metrics are found to be statistical significant, providing an objective assessment of the SRR images.
Effects of exchange on spherical vs. linear diffusion tensor encodings: A simulation study
Authors: Nielsen Jonathan , Aggarwal Manisha
Published: August 2023

To investigate the influence of exchange on microscopic anisotropy estimates derived from kurtosis differences between linear-tensor-encoding (LTE) and spherical-tensor-encoding (STE) diffusion weighting waveforms, random walk simulations were performed in a substrate of packed spheres of varying permeability. A pronounced μFA bias was observed with spectrally detuned LTE due to restricted diffusion and kurtosis time dependence, decreasing with rate of exchange, but also with tuned LTE due to inherent differences in exchange sensitivity, increasing with exchange. Our results suggest a need for caution in interpreting MDE-derived microscopic anisotropy estimates in the presence of exchange.
Optimised Temporal Diffusion Ratio for Imaging Restricted Diffusion
Authors: Warner William , Ianus Andrada , Shemesh Noam , Molendowska Malwina , Jones Derek , Dell'Acqua Flavio , Palombo Marco , Drobnjak Ivana
Published: August 2023

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique, with potential for mapping areas of restricted diffusion. We optimise the TDR diffusion sequences in simulation to provide maximum contrast for a range of restricted structures, finding sequence shapes and the optimal HARDI subset size for calculating TDR. We then demonstrate the results experimentally in-vivo on the human brain, and ex-vivo using a pre-clinical scanner on the mouse brain.
How to understand diffusion MRI changes in the white matter of Multiple Sclerosis patients?
Authors: Liao Ying , Coelho Santiago , Sweidan Wafaa , Chen Jenny , Novikov Dmitry , Fieremans Els
Published: August 2023

Conventional diffusion MRI (dMRI) techniques, such as DTI and DKI, are sensitive to pathology but lack specificity. In brain white matter, the “Standard Model” framework of dMRI may provide specificity to microstructural changes. Generally, clinical dMRI is noisy and limited, making SM estimation challenging. Thus, different constraints and techniques have been introduced to robustly extract SM parametric maps. Here, we employ a large clinical dataset of Multiple Sclerosis patient data (N = 134) and noise propagation experiments to study the sensitivity and specificity of these techniques.
Violation of axial-symmetric assumption in DKI and consequences for biophysical parameter estimates across white matter
Authors: Oeschger Jan Malte , Tabelow Karsten , Mohammadi Siawoosh
Published: August 2023

The recently introduced axial-symmetric DKI framework is less noise sensitive but unable to capture more complex fiber configurations causing an AxDKI inherent bias in those voxels. We found that the parallel and perpendicular kurtosis are most often affected by this inherent bias and that it translates to the AxDKI based biophysical parameters. Bias-free estimation of the biophysical parameters with the AxDKI framework, therefore, is difficult if not impossible at this point. However, the parallel and perpendicular diffusivity and the mean kurtosis were largely bias-free, encouraging use of the AxDKI framework in studies where these parameters are focused on.
Diffusion dispersion and microscopic fractional anisotropy reveal acute sensitivity to mild traumatic brain injury in a mouse model
Authors: Rahman Naila , Xu Kathy , Brown Arthur , Baron Corey
Published: August 2023

Imaging markers of mild to moderate concussion are notoriously difficult to detect in vivo. Advanced diffusion MRI (dMRI) techniques have shown increased sensitivity and specificity to microstructural changes in various disease and injury models. Oscillating gradient spin-echo (OGSE) dMRI is sensitive to structural disorder and microscopic anisotropy (µA) dMRI is sensitive to water diffusion anisotropy independent of neuron fiber orientation. In this work, we demonstrate that both microscopic fractional anisotropy and diffusion dispersion show acute sensitivity to concussion, while traditional diffusion MRI markers do not.