bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid High-Resolution Prostate Diffusion MRI using Eddy Current-Nulled Convex Optimized Diffusion Encoding and Random Matrix Theory Denoising
Authors: Aygun Elif , Zhang Zhaohuan , Shih Shu-Fu , Raman Steven S. , Sung Kyunghyun , Wu Holden H.
Published: November 2024

Motivation: High-resolution (HR) DWI, although beneficial for prostate tissue characterization and cancer diagnosis, suffers from low signal-to-noise ratio (SNR). The common strategy of averaging to increase SNR prolongs the acquisition time (TA). Goal(s): Decreasing the number of repetitions for averaging while maintaining the SNR for HR prostate DWI. Approach: Eddy Current-Nulled Convex Optimized Diffusion Encoding (ENCODE) combined with random matrix theory (RMT)-based denoising to reduce the repetitions while maintaining SNR. Results: HR-ENCODE-RMT (TA=2 min 30 sec) improved SNR for rapid prostate HR-DWI, and achieved consistent and precise apparent diffusion coefficient (ADC) mapping compared to standard-resolution bipolar DWI (TA=5 min 50 sec). Impact: Eddy Current-Nulled Convex Optimized Diffusion Encoding combined with Random Matrix Theory-based denoising enables rapid high-resolution prostate DWI using fewer repetitions while maintaining the signal-to-noise ratio and robustness of apparent diffusion coefficient maps. This method could improve characterization of prostate cancer.
Is MR-visible Gleason 6 prostate cancer clinically insignificant?
Authors: Besser Alexandra , Raichandani Surbhi , Liang Tie , Franks Zachary , Fan Richard , Sonn Geoffrey , Ghanouni Pejman
Published: November 2024

Motivation: The clinical course of MR-visible Gleason 6 prostate cancer is unclear. Goal(s): We studied patients with MR-visible prostate lesions scored as Gleason 6 after MR-guided and systematic biopsy. Approach: We retrospectively analyzed the rate of Gleason grade upgrading in 95 men initially diagnosed with Gleason 6 prostate cancer in an MR-visible lesion who later underwent a second biopsy or prostatectomy. Results: Over half of MR-visible Gleason 6 lesions were upgraded to Gleason 7 with a median time to upgrade of ~2 years. Neither PSA density nor ADC value predicted subsequent upgrade. Impact: Gleason 6 prostate cancer that is MR-visible often harbors higher grade cancer. This argues for more aggressive monitoring or earlier intervention in these patients.
Fast 3D Neuro T2-FLAIR with Learned Sampling and fully 3D Model Based Deep learning
Authors: Tang Chenwei , Rivera-Rivera Leonardo , Eisenmenger Laura , Johnson Kevin
Published: November 2024

Motivation: T2-FLAIR is an essential contrast for clinical neuroimaging. However, the inherently long scan time limits its application in screening. Goal(s): We aim to accelerate 3D T2-FLAIR scan while maintaining sufficient image quality. Approach: We developed a framework that simultaneously learns a sampling pattern and a fully 3D deep learning reconstruction neural network. This allows exploiting the optimization space in both sampling and reconstruction. Results: Learned sampling pattern with MoDL reconstruction trained with added Gaussian noise was able to provide high quality T2-FLAIR scan with 1x1x1.6mm resolution in 1 min 39s. Impact: This work confirms the feasibility of a short 3D T2-FLAIR scan, provides insights for optimization strategies, and could lead to clinical implementation.
Navigator-gated Simultaneous Multi-Slice STEAM Diffusion Tensor Cardiac Magnetic Resonance
Authors: Wen Ke , Ferreira Pedro , Lim Eun Ji , Munoz-Escobar Camila , Neji Radhouene , Kunze Karl , Pennell Dudley , Scott Andrew , Nielles-Vallespin Sonia
Published: November 2024

Motivation: Low scanning efficiency of stimulated echo acquisition mode (STEAM) diffusion tensor cardiac magnetic resonance images (DT-CMR) limits its clinical translation. Goal(s): We aimed to improve the slice coverage efficiency of DT-CMR utilizing simultaneous multi-slice (SMS) techniques. Approach: The navigator-gated (Nav) SMS STEAM acquisition, integrated with a biofeedback system, was introduced, enabling controlled breathing and respiratory motion compensation. Results: Nav SMS STEAM acquisition reduced scanning time by 46±6% compared to single-band (SB) acquisition. No significant difference in global mean MD and FA was observed, but a lower median |E2A| was noted for the SMS basal slice. Impact: Our proposed Nav SMS STEAM acquisition would reduce the total breath-hold duration of STEAM DT-CMR acquisitions, enhancing clinical feasibility. Improved efficiency could also lead to whole heart coverage in DT-CMR scans, essential for example, in myocardial infarction.
Free-running 5D whole-heart MRI using a Gadolinium enhanced Fast-Interrupted Steady-State sequence to evaluate congenital heart disease
Authors: Roy Christopher , Rutz Tobias , Prša Milan , Romanin Ludovica , Yerly Jerome , Schwitter Juerg , Bastiaansen Jessica , Stuber Matthias
Published: November 2024

Motivation: There is a need for simplified and time-efficient dynamic whole-heart imaging in the evaluation of congenital heart disease patients. Goal(s): To demonstrate the feasibility of Gadolinium enhanced 5D FISS CMR and compare it to established 2D and 3D CMR methods in a cohort of congenital heart disease patients. Approach: Ejection fraction and vessel sharpness measurements derived from established 2D and 3D CMR are quantitatively compared to those obtained from the proposed 5D sequence. Results: 5D FISS CMR is feasible for the evaluation of cardiac function and anatomy building towards an easy-to-use and time-efficient method for evaluating congenital heart disease. Impact: Free-running 5D whole-heart MRI using a Gadolinium enhanced FISS sequence enables both measurements of cardiac function and evaluation of morphology in patients with congenital heart disease with a fixed six-minute scan time.
Ventilation and Gas Exchange Delay maps in Pre-clinical Thoracic Insufficiency Syndrome Models using Dynamic Hyperpolarized-Xenon MRI
Authors: Ismail Mostafa , Hamedani Hooman , Amzajerdian Faraz , Loza Luis , Boyes Madeleine , Hopster Klaus , Sinder Benjamin , Cahill Patrick , Snyder Brian , Schaer Thomas , Kadlecek Stephen , Ruppert Kai , Rizi Rahim
Published: November 2024

Motivation: The pressing need to develop and evaluate novel treatments of thoracic insufficiency syndrome (TIS), a condition with significant implications for compromised pulmonary growth and function. Goal(s): To gain deeper insights into the altered lung function associated with TIS, ultimately improving patient care and outcomes. Approach: Dynamic hyperpolarized-xenon-129 MRI was used to assess ventilation and gas exchange dynamics in pre-clinical models of TIS. Results: Significant ventilation and gas exchange delays were found in the TIS animals compared to the healthy ones. Impact: Our findings hold the promise to improve TIS assessment and treatment monitoring by providing crucial insights into altered lung function. This study offers a valuable tool for clinicians/researchers, fostering improved patient care and stimulating further TIS management and intervention research.
Establishing Magnetic Resonance Parkinsonism Index reference ranges to distinguish Progressive Supranuclear Palsy and Corticobasal Syndrome
Authors: Di Noto Tommaso , Venkategowda Punith , Corredor-Jerez Ricardo , Bodenmann Tobias , Swamy Madappa Shadakshari , Dunet Vincent , Lehericy Stephane , Sinha Neelam , Maréchal Bénédicte
Published: November 2024

Motivation: distinguishing Parkinsonian syndromes can be challenging since these diseases exhibit overlapping clinical manifestation. Goal(s): provide extended reference ranges of established biomarkers to distinguish Progressive Supranuclear Palsy (PSP) from Corticobasal Syndrome (CBS) in a fast, automated way. Approach: we build reference ranges of relevant brain measurements from a large cohort of healthy subjects; then, we compute corresponding Z-scores of these measurements to distinguish PSP and CBS patients. Results: the midbrain area is the most informative measurement to discern PSP and CBS. A logistic regressor that combines Z-scores of multiple brain measurements achieves mean AUC of .87 in 5-fold-cross-validation when distinguishing PSP and CBS. Impact: We release extended age-/sex-specific reference ranges built from healthy controls for several biomarkers used to differentiate Parkinsonian disorders. Our ranges show notable variation with age/sex and could be used by radiologists as benchmark to better differentiate Parkinsonian subtypes.
Subcortical structures alterations in Parkinson's disease patients with depression by Vertex-based morphological analysis
Authors: Qu Mingrui , Gao Bingbing , Jiang Yuhan , Li Yuan , Song Qingwei , Miao Yanwei
Published: November 2024

Motivation: Subcortical structure is critical to the pathogenesis of Parkinson's disease (PD) and depression. Goal(s): shape analysis can precisely localise regional shape deformations in the subcortical gray matter and detect changes that are not found in VBM and volumetric analyses. Approach: This study explored the shape change of subcortical gray matter nuclei in Parkinson's disease patients with depression (PDD). Results: PDD patients have multiple subcortical morphological atrophy, and local expansion areas were also found in several nuclei. The shape change of left nucleus accumbens was negatively correlated with Hamilton depression scale (HAMD) scores, and the shape change of right caudate nucleus was negatively correlated with disease duration. Impact: This study suggested that PDD patients have multiple subcortical morphological atrophy, and local expansion areas were also found in several nuclei. Partial correlation analysis showed that the shape change of left nucleus accumbens was negatively correlated with HAMD scores.
MRI meets economics: Balancing sample size and scan duration
Authors: Ooi Leon , Orban Csaba , Nichols Thomas , Zhang Shaoshi , Tan Trevor , Kong Ru , Marek Scott , Dosenbach Nico , Laumann Timothy , Gordon Evan , Zhou Juan Helen , Bzdok Danilo , Eickhoff Simon , Holmes Avram , Yeo B.T. Thomas
Published: November 2024

Motivation: Resting-state functional connectivity (RSFC) is widely used to predict behavioral traits in individuals. Goal(s): A pervasive dilemma when collecting functional MRI data is whether to prioritize sample size or scan duration given fixed resources. Approach: We systematically investigate the trade-off between sample size and scan time in the context of prediction accuracy and reliability of brain-behavior relationships using RSFC. Results: Increasing sample size (with fixed scan time) or scan time (with fixed sample size) leads to similar accuracy. Reliability of brain-behavior association can only be improved with bigger sample sizes but not scan time. Impact: Our findings establish an empirically informed reference for calibrating scan times and sample sizes to maximize prediction of behavioral performance and reliability of brain-behavior associations when using resting-state functional connectivity.
Resting-state functional connectivity with diffusion fMRI minimizes anti-correlations and captures white matter connectivity at 3T and 7T
Authors: de Riedmatten Inès , Olszowy Wiktor , Spencer Arthur , Jelescu Ileana
Published: November 2024

Motivation: Unlike BOLD (neurovascular contrast), dfMRI offers neuromorphological contrast that can detect white matter (WM) activity and attenuates anti-correlations in functional connectivity (FC) analysis. Goal(s): This work investigated resting-state gray and white matter connectivity and anti-correlations in BOLD and dfMRI, at 3T and 7T. Approach: FC matrices and graph metrics were computed. Results: Positive correlations were consistent among the contrasts whereas anti-correlations were attenuated with reduced hemodynamic contributions, suggesting a vascular origin to the latter. DfMRI FC displayed higher clustering than BOLD in WM. DfMRI provides unique insights into brain connectivity, particularly in WM, suggesting its value in enhancing our understanding of brain function. Impact: In functional connectivity analysis, diffusion fMRI exhibits comparable positive correlations to BOLD but reduces anti-correlations, indicating a potential vascular origin for the latter. Additionally, it uncovers previously overlooked white matter connectivity, traditionally treated as a nuisance variable.
Aggregation of Connectivity Gradient in Hippocampus Induced by Long-Term Cognitive Training with Development
Authors: Xu Tianyong , Chen Feiyan
Published: November 2024

Motivation: The hippocampus-cortical connections have shown rapid developmental-changed nature during childhood and learning-adapted plasticity with skill acquirement. Goal(s): However, little is known about the effect of development interacting with cognitive training on the hippocampal connectivity gradient during puberty. Approach: Here we employed longitudinal dataset (191 scans from training/control groups: n = 43/45) which collected neuroimaging data of school-age children across 0/3/5-year abacus mental calculation (AMC) training stages to explore this question. Results: By calculating connectivity gradient of hippocampus, we observed significantly development-induced gradient aggregation of hippocampus, and training promoted that effect, which were resulted from changes in functional connectivity between hippocampus with different cortices. Impact: These findings provide novel insights into development and training effects on function specialization of hippocampus during puberty from a largescale perspective of connectivity gradient, which may be helpful for better understanding of functionally atypical trait of hippocampal disorder for clinicians.
A framework for graph theory analyses of functional connectivity within resting state networks (RSNs) of neonates
Authors: Magondo Ndivhuwo , Warton Fleur , Fan Jia , Laughton Barbara , van der Kouwe Andre , Meintjes Ernesta
Published: November 2024

Motivation: The topological organisation of RSNs can be studied with graph theory. While graph nodes can be defined using atlases in adults, infant atlases are not readily available. Goal(s): To create a framework to define nodes and edges for graph theory analyses of infant RSNs. Approach: We resampled the original template voxel size and created evenly distributed nodes within RSNs. Results: We present a mask comprising 605 evenly-spaced spheres to discretize neonatal RSNs. Graph theory demonstrated lower global and/or nodal efficiency in 4 networks in HEU neonates compared to HUU, indicating decreased information transmission throughout and regionally within affected networks. Impact: The proposed method may enable more comprehensive analyses of the topological organisation of RSNs in infant cohorts. This will advance knowledge on how functional networks process and distribute information from birth.
Accelerated Acquisition and Cross-Platform Reconstruction of Diffusion Tensor-Derived Indices Using Convolutional Neural Networks
Authors: Tsai Chih-Chien , Chen Yao-Liang , Wang Jiun-Jie
Published: November 2024

Motivation: Diffusion-MRI faced limitations due to extended scan times and scanner/protocol variations. Goal(s): This study aims to assess its ability to accelerate imaging procedures and unify data from diverse sources. Approach: A convolutional neural network was employed to reconstruct diffusion-weighted images into diffusion tensor images. The effectiveness of reconstructed model was evaluated by normalized mean-square error (NMSE) and structural similarity index (SSIM). Results: The CNN showed significantly better SSIM and lower NMSE in FA and MD (p < 0.001) compared to conventional methods. Moreover, the CNN model maintained strong performance when applied to other Scanners for FA and MD. Impact: Through convolutional neural networks, images might be acquired fast and easily be harmonized across platforms . Subsequent research will further utilize deep/machine learning tools to investigate the impact of reconstructed image-segmented brain regions on the performance of classification models.
Zero-shot EPI Nyquist ghost correction with diffusion-based generative models and magnitude consistency regularization
Authors: Huang Shoujin , Li Jingyu , Wang Yuwan , Chen Ziran , Liu Shaojun , Liu Yilong , Xiong Yuhui , Wu Bing , Liu Jingzhe , Guo Hua , Wu Ed X. , Lyu Mengye
Published: November 2024

Motivation: To address EPI phase error correction caused by the problem of inconsistent positive and negative phases. Goal(s): We introduce an image prior-based method termed Phase Error Correction Diffusion-based Reconstruction with Echo Apart Magnitude-Consistency(PEC-DREAM). Approach: The method was trained on structural imaging data, and it performs robustly the inference on EPI phase error correction task without specific model finetune. Here, we introduce novel data consistency including k-space and magnitude consistency to enhance the performance of the SGM during reverse diffusion. Results: Experiments demonstrate the versatility of our approach across various scenarios, including human and rodent EPI, accelerated and non-accelerated imaging and SMS sampling. Impact: The method we have proposed effectively addresses EPI phase error correction. Prospective experiments demonstrate the versatility of our innovative approach across various scenarios, and our method holds promise as a potent tool.
LIPCON: Lipid Identification with Convolutional Neural Network for MR Spectroscopic Imaging
Authors: Weiser Paul , Langs Georg , Motyka Stanislav , Strasser Bern , Bogner Wolfgang , Courvoisier Sébastien , Hoffmann Malte , Andronesi* Ovidiu , Klauser* Antoine
Published: November 2024

Motivation: Magnetic Resonance Spectroscopic Imaging (MRSI) offers non-invasive metabolic concentration mapping, aiding early pathology detection like brain tumors. However, extracranial lipid signals can compromise neurochemical data. Goal(s): The potential of supervised neural networks remains unexplored, despite their success in other artifact removal and metabolite quantification tasks. We introduce a deep learning method for robust lipid removal. Approach: Our approach is compared to a state-of-the-art L2-lipid-regularization using simulated and in-vivo whole-brain MRSI data. Results: Our supervised deep learning method showed improved performance to the L2-lipid-regularization method by eliminating more lipid signal while preserving metabolic signals and spectral baseline. Impact: The LIPCON method achieves accurate lipid suppression across whole-brain MRSI datasets without the need for parameter tuning and within a few seconds. This should mark a step in enhancing the reproducibility and efficiency of MRSI pipelines.
Deep Learning-based MRS Reconstruction with Artificial Fourier Transform Network (AFT-Net)
Authors: Yang Yanting , Dagommer Matthieu , Guo Jia
Published: November 2024

Motivation: Complex-valued deep neural network has not been fully investigated in MRS reconstruction and preprocessing. Goal(s): We aim to solve the spectroscopy inverse problems in domain transform from FIDs to spectra, especially for accelerated MRS reconstruction. Approach: A complex-valued deep learning framework artificial Fourier transform network (AFT-Net) is proposed to directly reconstruct and process the complex-valued raw data in the sensor domain. Results: Evaluation of different acceleration rates was performed on the in vivo dataset. AFT-Net demonstrated the ability to reconstruct the data under up to 80 times acceleration rate. The proposed AFT-Net is an efficient and accurate approach for MEGA-PRESS MRS accelerated reconstruction. Impact: MRS reconstruction and preprocessing with AFT-Net should be able to determine the domain-manifold mapping and process FID data directly, which shows superior performance compared with numerical method and can be served as an efficient and accurate approach for MRS acquisition.
Improved MR Multitasking-based Dynamic Imaging for Cerebrovascular Evaluation (MT-DICE): Towards Multiparametric Brain Tumor Evaluation
Authors: Chen Yang , Xiao Jiayu , Christodoulou Anthony , Li Debiao , Chow Frances , Zada Gabriel , Chang Eric , Shiroishi Mark , Fan Zhaoyang
Published: November 2024

Motivation: The lack of pathophysiologically relevant quantitative information hinders the precision management of brain tumors. Specifically, the characterization of intra-tumor heterogeneity influencing treatment decision-making, remains a critical challenge. Goal(s): This work aims to optimize a recently developed technique MT-DICE, to provide multiparametric mapping information for more comprehensive brain tumor evaluation. Approach: The MT-DICE technique was further refined by including 3D flow compensation, dictionary-based mapping, and water exchange quantification. Assessments for the repeatability of MT-DICE parameters and their agreement with routine measurements were performed. Results: Excellent reproducibility and agreement were achieved. Spatially co-registered multiparametric maps from MT-DICE facilitated comprehensive brain tumor characterization. Impact: Individuals with brain tumors may benefit from more comprehensive brain tumor evaluation using multiparametric maps derived from our improved MT-DICE technique.
Personalized Radiotherapy Clinical Target Volume from a novel DTI-derived Tumour Spread Index (TSI) map
Authors: Abbasian Parandoush , Ryner Lawrence , McCurdy Boyd , Kakumanu Saranya , Venugopal Niranjan , Guan James , Pitz Marshall
Published: November 2024

Motivation: GBM patients suffer from poor treatment outcome. The current radiotherapy planning (i.e. GTV, CTV, PTV) does not adjust the CTV margin to account for the microscopic spread of the glioma tumour cells along white matter fiber tracts. Goal(s): Utilizing DTI MRI to tailor the CTV margin to each patient's unique tumour progression pathway. Approach: Pre-surgical DTI-based tractography was used to quantify tumor spread probability, producing the Tumour Spread Index (TSI) map, which was used to generate a personalized CTV. Results: This proof-of-concept study showed that using DTI-based tractography with a TSI map to personalize CTV improved coverage of recurrent regions in follow-up imaging. Impact: Utilizing tractography to map the probable path of tumour spread and use this to direct radiation is a new paradigm in targeted radiotherapy which may lead to improved progression free survival in GBM patients.
Whole-Tumor Histogram Analysis of Synthetic MRI Predicts IDH mutation status in gliomas
Authors: Ge Xin , shen Ying , Xiong Yuhui , Li Min , Wang Xiaodong , Zhang Jing
Published: November 2024

Motivation: There is an urgent need to identify a novel, cost-effective, and non-invasive method for determining the IDH mutation status in differentiating between astrocytoma and glioblastoma. Goal(s): To investigate the potential value of whole-tumor histogram metrics derived from synthetic MRI in distinguishing IDH mutation status. Approach: Histogram metrics were extracted from the quantitative maps. Variables with statistical significance in univariate analysis were included in multivariate logistic regression analysis to develop the combined model. The AUC were used to assess the diagnostic performance of metrics and models. Results: The combined model could be a valuable preoperative tool to distinguish IDH mutation status. Impact: The current study proposes a combined model that comprises T1-10th, cT1-10th, and age. This model demonstrates differentiation between IDH-M astrocytoma and IDH-W glioblastoma. Moreover, it has the potential to decrease genetic testing expenses while offering treatment decision support for clinicians.
Imaging tumor habitats with PET-MRI HYPERDIrect maps to decode the spatial heterogeneity of malignant gliomas
Authors: Nocera Gianluca , Pecco Nicolo , Rosa Pasquale Della , Scifo Paola , Callea Marcella , Neri Ilaria , Fallanca Federico , Picchio Maria , Gagliardi Filippo , Mortini Pietro , Falini Andrea , Bailo Michele , Castellano Antonella
Published: November 2024

Motivation: Malignant gliomas are characterized by considerable intra-tumor heterogeneity directly related to treatment failure. Habitat imaging allows to visualize in vivo such heterogeneity. Goal(s): Investigating a novel approach based on 3T PET/MRI acquisitions for assessing HYpoxia, PErfusion, DIffusion, and methionine-PET for tissue metabolism in malignant gliomas. Approach: Quantitative data from PET/MRI are combined in a unique HYPERDIrect map to identify discrete radiomic clusters or `habitats', possibly reflecting diverse genetic and biological features, to be proven by image-guided tissue sampling. Results: Preliminary analysis showed high habitat imaging reproducibility and a reliable correlation between the expected microenvironment of the different habitats and the actual histopathological characteristics. Impact: The HYPERDIrect map may represent a tool for decoding pattern of glioma heterogeneity and a new biomarker for stratifying prognosis and selecting patients for personalized approaches.
Accelerated MRI Reconstruction with Fourier-Constrained Diffusion Schrodinger Bridges
Authors: Mirza Muhammad Usama , Dalmaz Onat , Bedel Hasan , Elmas Gokberk , Gungor Alper , Cukur Tolga
Published: November 2024

Motivation: Diffusion probabilistic methods synthesize realistic images via a denoising transformation from Gaussian noise onto MRI data, but this normality assumption can yield suboptimal performance in accelerated MRI reconstruction tasks. Goal(s): Our goal was to devise a new diffusion-based method that generates high-quality images by capturing a task-relevant transformation for accelerated MRI. Approach: We introduced a novel reconstruction method based on a diffusion Schrodinger bridge (FDB) that learns to directly transform between undersampled and fully-sampled MRI data via a multi-step process. Results: Higher reconstruction performance was obtained with FDB over previous state-of-the-art at up-to 8-fold acceleration. Impact: The improvement in image quality and acquisition speed in accelerated MRI enabled through FDB may facilitate comprehensive MRI exams in many applications, particularly in assessments of pediatric and elderly individuals in need of fast exams due to limited motor control.
qDiMo: Domain-conditioned Diffusion Modeling for Accelerated qMRI Reconstruction
Authors: Bian Wanyu , Jang Albert , Liu Fang
Published: November 2024

Motivation: Quantitative MRI (qMRI) is time-consuming and requires substantial efforts for acceleration to cut down the acquisition time. Goal(s): This paper proposes a novel generative AI approach for image reconstruction based on diffusion modeling conditioned on the native data domain. Approach: Our method is applied to multi-coil quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the tissue parameter domain. Results: The proposed method demonstrates a significant promise for reconstructing quantitative maps at high acceleration factors. Notably, it maintains excellent reconstruction accuracy and efficiency for MR parameter maps across diverse anatomical structures. Impact: This work demonstrates the feasibility of a new generative AI method for rapid qMRI. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.
Navigator based prospective motion correction in short TR sequences with minimal scan time penalty
Authors: van Niekerk Adam , Rydén Henric , Shauman Sophie , Norbeck Ola , Sprenger Tim , Avventi Enrico , Skare Stefan
Published: November 2024

Motivation: Inserting navigators into short-TR gradient echo pulse sequences increases scan duration as the TR needs to be extended to maintain a steady-state. Goal(s): Periodically interleave navigators without disrupting stead-state. Approach: A packed pulse sequence encoding was implemented that divides the phase encoding table into partitions. Each partition begins with two blank-TRs that contain identical RF and spoiling to the parent sequence. The first blank-TR readout is replaced with a navigator acquisition. The second is replaced with computation time to perform a field of view update. Results: The packed sequences reduced the time penalty from 1-minute to 3-13 seconds, without impacting motion correction efficacy. Impact: The packed sequence structure allows researchers to augment the acquisition of short-TR GRE sequences with fast (<TR) navigators for almost no additional scan time without affecting motion correction efficacy, and is applicable to all sequences where the TR is minimised.
Versatile motion-corrected brain MRI leveraging ERIC-PT: Efficient, Robust and Instruction-free Calibrated Pilot Tone
Authors: Brackenier Yannick , Cordero-Grande Lucilio , McElroy Sarah , Tomi-Tricot Raphael , Bridgen Philippa , Malik Shaihan , Hajnal Joseph
Published: November 2024

Motivation: Robust motion correction relies on sequence modifications, either adding navigators or re-ordering the k-space sampling. These modifications might not be possible for every sequence. Goal(s): To leverage motion-sensitive Pilot Tone (PT) signals to guide motion correction for any standard 3D acquisition. Approach: We propose the ERIC calibration protocol, which distributes short self-navigated (DISORDER) acquisitions across the whole examination. Combined with data-driven motion correction reconstructions, we can achieve robust PT calibration. Results: We show the potential to correct standard MPRAGE acquisitions with a linear phase encoding scheme in 4 healthy volunteers (HV) even when using 54 seconds worth of calibration data. Impact: Correcting motion in any 3D acquisition is an unsolved problem. Combining pre-calibrated PT signals with data-driven optimizations explores a promising avenue. To this end, building a robust calibration model by acquiring ~1min worth of data would easily integrate into examinations.
Predicting IDH Mutation and MGMT Methylation Status in Glioma Patients at the Voxel Level using CEST-Based Deep Learning
Authors: Wang Siyu , Lu Jue , Zhang Xinli , Wang Jing , Chen Lin
Published: November 2024

Motivation: Predicting glioma subtypes based on molecular profiles is crucial for treatment decisions and predicting survival rates. Goal(s): We proposed a CEST-based deep learning method to predict IDH mutation and MGMT methylation status in glioma patients at the voxel level. Approach: 86 patients were recruited for CEST experiments on 3T MRI scanner. A CEST-based deep learning method, composed of a 1D convolutional neural network, was proposed for different types of status prediction at the voxel level. The confusion matrix and ROC were conducted to evaluate the performance of the proposed method. Results: Our method achieves higher accuracy compared to existing CEST-based prediction methods. Impact: The proposed method may facilitate the application of CEST MRI in the diagnosis of glioma.
Characterizing asymmetric deep medullary veins by 7.0T susceptibility-weighted MRI to predict glioma genotype and Ki-67 index
Authors: Li Chenxi , Lyu Jinhao , Ma Xiaoxiao , Duan Caohui , Qu Jianxun , Duan Qi , Lou Xin
Published: November 2024

Motivation: Knowing the genotype of gliomas is critical for prognostic assessment and treatment selection. 7.0T susceptibility-weighted imaging (SWI) allows visualizing the deep medullary veins and provides additional metabolic information. Goal(s): We used the asymmetric deep medullary vein (ADMV) sign on 7.0T SWI to predict glioma isocitrate dehydrogenase mutation status and Ki-67 expression level. Approach: We assessed the ADMV sign and conventional morphological and screening features (P<0.1) via multivariate logistic regression to evaluate the predicted performance. Results: The ADMV sign on 7.0T images was independently associated with isocitrate dehydrogenase mutation status and Ki-67 index and improved the images’ diagnostic efficacy. Impact: Discovery of the ADMV sign as an imaging biomarker and the advantages of 7.0T MRI may help markedly improve the diagnosis and management of gliomas and may have broader applications in medical imaging and biomarker development.
Application of accelerated quantitative magnetic resonance imaging in predicting drug resistance in pituitary prolactinomas
Authors: Lu Rong , Ji Lijin , Tang Weijun , Li Qing , Fu Caixia , Chu Ying-Hua , Wu Zheyuan , Kober Tobias , Hibert Tom , Shi Shangxuan , Hwang Tingfang
Published: November 2024

Motivation: Predicting and managing dopamine agonists (DA) resistance of prolactinomas remain a challenge. There is no reliable quantitative imaging marker. Goal(s): The goal is to use accelerated quantitative T2 mapping(GRAPPATNI) for early diagnosis of drug resistance in pituitary prolactinomas to guide treatment. Approach: This is a cross-sectional study. It will analyze the differences in T2 values between drug-resistant and sensitive groups and explore their diagnostic value in predicting drug sensitivity. Results: Quantitative T2 values have better sensitivity than T2 signal intensity (SI) in predicting drug resistance in pituitary prolactinoma. Impact: This is the first study to apply GRAPPATINI in prolactinoma. We found that T2 values of tumors were lower in drug-resistant prolactinoma than sensitive patients. T2 values might be a promising predictive imaging tool.
Classification of Breast Edema on T2-weighted imaging for predicting sentinel lymph node metastasis and biological behavior in breast cancer
Authors: Zhou Yunfeng , Xie Shijia , Liu Mengxiao , Hou Zhe
Published: November 2024

Motivation: Predicting sentinel lymph node (SLN) metastasis and biological behavior in patients with early-stage breast cancer is important. Goal(s): To determine whether preoperative classification of breast edema can predict SLN metastasis and biological behavior in patients with early-stage breast cancer. Approach: Breast edema was scored on a scale of 1 to 4 on T2WI to explore additional predictive value of the breast edema score (BES) model. Results: The combined BES model significantly improved the predictive performance of SLN metastasis. Impact: Breast edema on T2-weighted imaging can be used to predict SLN metastasis in breast cancer, helping clinicians to develop individualized treatment plans and evaluate prognosis.
Supine breast MRI with a wearable coil (BraCoil) improves lesion localization and clinical workflow for US-guided biopsy
Authors: Czerny Raphaela , Clauser Paola , Obermann Michael , Baltzer Pascal A. T. , Laistler Elmar , Nohava Lena
Published: November 2024

Motivation: Supine breast MRI might enable higher image correlation to supine ultrasound (US) and therefore facilitate the clinical workflow. Goal(s): The aim was to quantify lesion displacement between supine US, prone MRI, and supine MRI in Cartesian and panoramic views, and derive the impact on the clinical workflow. Approach: Using MRI and US patient data, the spatial lesion displacement was measured. For supine MRI a wearable breast coil (BraCoil) was used. Results: Supine MRI shows comparable lesion position compared to US, in contrast to prone MRI. 18% of the lesions could only be localized in 2nd look US or biopsy after supine BraCoil MRI. Impact: Supine breast MRI with a wearable coil could improve the clinical workflow by facilitating lesion localization for 2nd look US, biopsy, or surgery.
NORMAL APPEARING BREAST TISSUE ON BREAST MRI HAS ALTERED CHEMISTRY CONSISTENT WITH “SWITCHED-ON” STATES IN WOMEN WITH INVASIVE CARCINOMA
Authors: Mountford Carolyn , Lukas Darren , Naude Natali , Khoo Jeremy , Hormaechea Gorane Santamaria , Irvine John , Lloyd Thomas , Bennett Ian , Clark David , Brown Randell , Rich Lisa , Kear Laurie , Malycha Peter
Published: November 2024

Motivation: Adenoma-carcinoma cell models examined using 2D COSY recorded altered triglyceride, cholesterol and metabolites prior to malignant transformation. Goal(s): Investigate if such chemical profiles are recorded in vivo in MRI apparently normal tissue in women with invasive cancer. Approach: Nineteen women with invasive breast cancer were compared with healthy low risk controls. Results: Compared to controls, MRI normal tissue in cancer patients with low-density breasts recorded increases in cross peak F (68%), cholesterol (127%), and tumor promotor UDP-GlcNAc (81%). For dense breasts, increases recorded in cross peak F (47%), decreases in cholesterol (12%), triglyceride (56%) and double bonds (40%) in "switched-on" tissue. Impact: Altered chemistry states, consistent with mechanisms leading to development of invasive carcinoma, are recorded in vivo from breast tissue distant to the cancer compared to controls. These states are referred to as "switched-on" tissue and differ according to breast density.
Bridging Health Disparities: Accessible MRI in Underserved African Countries
Authors: Hissein Israa , Yao Jingting , Zhao Ming , Sakadi Foksouna , van der Kouwe André , Ackerman Jerome
Published: November 2024

Motivation: Neurological disorders, including cerebral malaria and HIV/AIDS-associated complications, are leading causes of death in underserved African nations, hindered by a lack of medical equipment, particularly MRI facilities. Goal(s): We seek to address two key questions: the need for MRI facilities in underserved African regions and the potential of accessible MRI in reducing healthcare disparities related to medical imaging. Approach: We employed a multifaceted approach, involving literature review, interviews, and evaluations of accessible scanner benefits and enhancements. Results: The marked disparities in MRI capabilities in Africa underscore the pressing need for investment in enhanced MRI infrastructure and customized imaging technologies, tailored to resource-limited settings. Impact: This research underscores the urgent need for MRI facilities to address neurological disorders in African countries, highlighting infrastructure gaps and the potential for innovative, compact MRI solutions to improve healthcare in resource-limited settings.
Parallel Imaging Reconstruction in Public Datasets Biases Downstream Analysis in Retrospective Sampling Studies
Authors: Frenklak Evan , Arefeen Yamin , Tamir Jonathan
Published: November 2024

Motivation: We explore the “Implicit Data Crime” of datasets whose subsampled k-space is filled using parallel imaging. These datasets are treated as fully-sampled, but their points derive from (1)prospective sampling, and (2)reconstruction of un-sampled points, creating artificial data correlations given low SNR or high acceleration. Goal(s): How will downstream tasks, including reconstruction algorithm comparison and optimal trajectory design, be biased by effects of parallel imaging on a prospectively undersampled dataset?Approach: Comparing reconstruction performance using data that are fully sampled with data that are completed using the SENSE algorithm. Results: Utilizing parallel imaging filled k-space results in biased downstream perception of algorithm performance. Impact: This study demonstrates evidence of overly-optimistic bias resulting from the use of k-space filled in with parallel imaging as ground truth data. Researchers should be aware of this possibility and carefully examine the computational pipeline behind datasets they use.
Research use of an ultra-low-field MRI to measure child neurodevelopment at 3 and 12 months of age in Southern Malawi, Sub-Saharan Africa
Authors: Vokhiwa Maclean , Khosa Able , Nthulula Blessings , Chetcuti Karen , Randall Louise , Greenstein Steven , Seal Marc , Beare Richard , Bourke Niall , Padormo Francesco , Rogers John , Torelli Pip , Deoni Sean , Pasricha Sant-Rayn , Phiri Kamija
Published: November 2024

Motivation: In Sub-Saharan Africa, limited MRI access and expertise can be addressed through international collaborations to enhance quality neuroimaging data collection in brain research. Goal(s): We describe research usability and reliability of an ultra-low-field (64mT) MRI data collection from Zomba, Malawi. Approach: We scanned ~481 children at 3 and 12 months of age, using hyperfine Swoop ULF-MRI (64T) for neuroimaging data to augment traditional randomized control trial outcome measures. We summarize procedures, participant responsiveness, and neuroimaging quality. Results: Full-scan success was in over 88% of participants within 55 weeks, with 87.4 to 99.6% completing all 5 scanning sequences. Full-brain quality scans were in >79%. Impact: International collaborations, such as UNITY project, utilizing ultra-low-field MRI improves research capacity and enables reliable measurement of brain development in Sub-Saharan Africa. This significantly promotes advancement of developmental neuroscience in the region.
Clinical Utility of 0.55T MRI System for the Surveillance of Intraductal Papillary Mucinous Neoplasm
Authors: Rajeev Radhika , Kelsey Lauren , Mervak Benjamin , Consul Nikita , Wells Shane , Stein Erica , Chahine Reve , Lin Tayson , Mendiratta-Lala Mishal , Gulani Vikas , Seiberlich Nicole , Hussain Hero
Published: November 2024

Motivation: Explore surveillance of Intraductal Papillary Mucinous Neoplasms (IPMN) at 0.55T, as an alternative to high field strength systems. Goal(s): To assess the image quality at 0.55T and its impact on radiologists' confidence in evaluating IPMN versus 1.5T/3TApproach: Images from 39 patients with IPMN who underwent 0.55T and 1.5/3T MRI were rated by 7 blinded radiologists for image quality and diagnostic certainty. Pearson correlation analysis performedResults: A strong positive correlation existed between image quality and rater’s confidence. Negligible differences in confidence observed between 0.55T and 1.5T/3T for images with quality scores≥5. Impact: 0.55T can be used for IPMN surveillance without compromising diagnostic effectiveness
3D Fat/Water-Separated Liver T1 Mapping: A Study on the Influence of Fat and Repeatability
Authors: Chen Jingjia , Huang Yuhui , Xia Ding , Chandarana Hersh , Feng Li
Published: November 2024

Motivation: Liver T1 estimation depends on the echo time when fat signal is not eliminated, implying that traditional T1 mapping techniques without considering the presence of fat may lack accuracy and reliability. Goal(s): This study investigated the correlation between liver T1 and fat fraction in 3D free-breathing Fat/Water-Separated T1 mapping and the repeatability of water-specific T1 measurement. Approach: Fifty subjects were recruited and four of them were scanned multiple times to assess the repeatability. Results: Difference between the first echo T1 and water-specific T1 shows strong correlation with the fat fraction, while water-specific T1 shows little correlation. Water-specific T1 mapping is repeatable. Impact: This study aims to investigate the influence of fat on liver T1 in 3D free-breathing T1 mapping. This can help understand how the fat affects liver T1 mapping and facilitate the development new techniques for estimating water-specific liver T1.
Distinct Neuroimaging Subtypes within ADHD Population Based on Semi-supervised Learning
Authors: Chen Yiwei , Li Mingyang , Zheng Tianshu , Xu Xinyi , Zhao Ruoke , Chen Ruike , Xu Haoan , Zhang Yuqi , Wang Guanghai , Wu Dan
Published: November 2024

Motivation: Attention deficit hyperactivity disorder (ADHD) is a childhood-onset disease whose diagnosis and subtyping methods are primarily based on clinical traits, which is prone to subjectivity and instability. Also, the patient outcome and neuroimaging signatures of these subtypes are not clear. Goal(s): We aimed to use a data-driven approach for subtyping. Approach: We used a semi-supervised learning method based on 929 ADHD patients selected from ABCD study. Results: We identified three distinct subtypes in ADHD based on cortical thickness (under-developed, over-developed, and mixed subtypes). Follow-up analysis found significant differences in cognitive and behavior outcomes, disease progression, and response to medication among the subtypes. Impact: We identified three distinct subtypes in ADHD based on cortical thickness (under-developed, over-developed, and mixed subtypes), with unique cognitive, behavioral, progression profiles, and treatment responses. These findings may shed insights into personalized treatment in ADHD.
Cross-species fMRI reveals transcriptomically and behaviorally-dissociable autism neurosubtypes
Authors: Pagani Marco , Zerbi Valerio , Galbusera Alberto , Alvino Filomena , Xu Ting , Lombardo Michael , Milham Michael , Di Martino Adriana , Gozzi Alessandro
Published: November 2024

Motivation: Resting-state fMRI (rsfMRI) studies have revealed atypical patterns of functional connectivity in autism. However, large heterogeneity in the manifestation of these alterations exists across samples and its etiopathological significance remains unclear. Goal(s): Here, we used cross-species rsfMRI to probe if distinct patterns of functional dysconnectivity observed across 20 genetic models can be identified in rsfMRI scans of individuals with idiopathic autism. Approach: We mapped whole brain functional connectivity in 20 genetic mouse models and in over 2000 individuals with and without autism. Results: Our work reveals two autism neurosubtypes characterized by divergent patterns of dysconnectivity, and dissociable transcriptomic and behavioral profiles. Impact: Connectivity alterations in idiopathic autism encode for etiologically-relevant information.
Altered Functional Connectivity of Resting State Networks in Children with Sensory Over-Responsivity But Not Autism
Authors: Choi Hannah , Powers Rachel , Lazerwitz Maia , Cai Lanya , Brandes-Aitken Annie , Chu Robyn , Trimarchi Kaitlyn , Garcia Rafael , Marco Elysa , Mukherjee Pratik
Published: November 2024

Motivation: Sensory Over-Responsivity (SOR) adversely impacts over 2.5% of children, prompting a study into its neural correlates in the absence of ASD to comprehend its unique effect on brain function. Goal(s): We test the hypothesis that SOR in non-ASD children is linked to impaired connectivity in sensory networks and alterations in higher-order, regulatory networks. Approach: Functional brain networks are constructed and analyzed using ICA, dual regression, fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo). Results: SOR children exhibit increased functional connectivity in default-mode, frontoparietal, and salience networks, alongside reduced connectivity in visual and cerebellar networks, confirming a distinctive neural profile of SOR. Impact: This study reveals distinct functional connectivity in SOR. It establishes a basis for novel interventions and tailored medical approaches for children, with or without ASD, encouraging further investigation into the neural basis and management of sensory processing disorders.
Using Inductively-Coupled Dipole Pairs as Array Elements for Improving Whole-Brain Coverage at 9.4T
Authors: Popova Kristina , Glybovski Stanislav , Scheffler Klaus , Avdievich Nikolai , Solomakha Georgiy
Published: November 2024

Motivation: At ultra-high fields, homogeneity of brain MRI is deteriorated by the subject-specific non-uniform distribution of RF magnetic field B1+. Goal(s): To design a 9.4T eight-channel transceiver dipole array with improved homogeneity of B1+ in the axial direction with better whole-brain coverage. Approach: We used an array consisting of paired passively coupled folded-end dipoles. We numerically optimized the B1+ homogeneity by adjusting the overlap between the folded ends of the active and passive dipoles and the load impedance of the passive one. Results: The proposed array demonstrated improved B1+ whole-brain homogeneity including the upper C-spine compared to several state-of-the-art dipole and loop arrays. Impact: The presented antenna element of coupled folded dipoles can be used in designing UHF array coils with improved longitudinal whole-brain coverage. Such coils can be beneficial for studies where imaging of the entire brain including the upper C-spine is required.