bluuebunny/crossref_metadata_2025 huggingface dataset:
Anatomy of neo-aorta can cause abnormal flow patterns
Authors: Gabbert Dominik , Langnaese Lucas , Neidlin Michael , Schaffarczyk Alois , Voges Inga
Published: November 2024

Motivation: The descending aorta (DAo) in patients with hypoplastic left heart syndrome (HLHS) after Norwood procedure is frequently enlarged although this region is not subject to surgical procedures. Goal(s): The aim of this study is to investigate whether the aortic anatomy can cause abnormal flow patterns which are related to vascular alterations. Approach: Based on anatomic models and flow boundaries from 4D flow MRI, we studied fluid-dynamics using CFD simulations. Results: We found increased vorticity and wall-shear stress in the HLHS descending aorta. Increased wall shear stress has been associated with the development of dilation and may explain vascular alterations in the descending aorta. Impact: This study gives motivation for further investigations and may ultimately lead to future Norwood procedure modifications.
Reliability of fetal 4D flow in healthy volunteers and patients with suspected congenital cardiovascular anomalies
Authors: Englund Erin , Fujiwara Takashi , Smith Sarah , Meyers Mariana , Friesen Richard , Browne Lorna , Barker Alex
Published: November 2024

Motivation: Fetal 4D flow provides useful information related to cardiovascular structure and function, but its reliability is not yet defined. Goal(s): Here, we seek to define the accuracy, precision, internal consistency, and repeatability of fetal 4D flow. Approach: Pregnant women in their third trimester with healthy developing fetuses (N=22) and fetuses with suspected cardiovascular anomalies (N=12) were scanned with 2D phase contrast and 4D flow sequences. Quantitative flow metrics were compared. Results: 4D flow underestimated net flow in the great vessels relative to 2D-PC, but had reasonable precision, internal consistency, and repeatability. Fetal 4D flow can reliably measure percentage contributions from various vascular territories. Impact: Fetal 4D flow allows for quantitative hemodynamic evaluation of the fetal cardiovascular system with good precision, internal consistency, and repeatability. Flow was underestimated relative to 2D-PC, therefore presently percent contribution from various vascular territories is the most appropriate clinical endpoint.
Time-resolved functional PET-MRI fusion: temporally coupled metabolic and BOLD dynamics across task and naturalistic arousal
Authors: Coursey Sean , Feng Shirley , Chen Jingyuan
Published: November 2024

Motivation: Latest advances in functional PET (fPET)-FDG and PET-MRI have enabled us to map stimulus-driven metabolic and hemodynamic changes simultaneously within a single scan. However, existing PET-MRI studies have focused on the static, time-averaged (de)couplings of fPET-fMRI signals, neglecting the rich information embedded in their temporal dependence. Goal(s): The goal of this study is to propose and validate an analytical framework linking dynamic, concurrent variations in fPET and fMRI signals. Approach: The efficacy of the framework was tested on visual task and naturalistic arousal fPET-fMRI datasets. Results: Our results demonstrated that metabolic changes modeled by concurrent fMRI signals could successfully predict instantaneous fPET-FDG dynamics. Impact: The statistical framework proposed by our study will enable broad functional PET-MRI studies to elucidate the dynamic interplays amongst metabolic and hemodynamic processes that are otherwise obscured in the conventional, time-averaged analysis.
Inverted BOLD signal changes in the transient phase of passband balanced SSFP
Authors: Heule Rahel , Valsala Praveen , Engelmann Jörn , Charyasz Edyta , Scheffler Klaus
Published: November 2024

Motivation: Efficient, distortion-free, and selective to microvessels: these attributes make bSSFP an attractive candidate for fMRI. 2D multi-slice scans in the transient state are increasingly employed as an alternative to 3D steady-state imaging to improve localization. Goal(s): To evaluate the BOLD sensitivity of bSSFP during the transient phase as compared to the steady state. Approach: bSSFP acquisitions in transient and steady state are utilized for task-based fMRI. Monte Carlo simulations and in vitro microsphere experiments are conducted to verify the in vivo results. Results: An inversion of the BOLD contrast with a trend towards negative BOLD in the early transient phase can be observed. Impact: The bSSFP BOLD contrast undergoes an inversion during the transient phase. Consequently, the BOLD response appears suppressed in transient-state imaging with a tendency to become negative, which has important implications when designing 2D multi-slice fMRI experiments with high temporal resolution.
Frequency-selective brain state regulation with optogenetic stimulation of central thalamus in unconscious rats
Authors: Gómez-Cid Lidia , Liu Xiaochen , Jiang Yuanyuan , Tasich Ksenija , Man Weitao , Hike David , Zhou Alice , Choi Sangcheon , Yu Xin
Published: November 2024

Motivation: There is a lack of effective treatments and understanding of the mechanisms behind disorders of consciousness, including coma. Goal(s): Explore the mechanisms behind thalamus-mediated subcortical reactivation to promote effective therapies for disorders of consciousness. Approach: Study brain changes induced by optogenetic stimulation of the central thalamus (CThal) at different frequencies in unconscious rats with fMRI and electrophysiology. Results: CThal stimulation can trigger brain state changes and electrophysiologically activate the cingulate cortex and other areas of the cortex similar to a more conscious state. BOLD signal activation in the cortex seems to be dependent on the CThal stimulation frequency. Impact: This study provides useful insights into frequency-dependent central thalamus-mediated brain state changes and offers opportunities to investigate the potential of CThal stimulation in promoting subcortical reactivation and acute coma recovery in a rodent model.
Resting-state hypothalamic dysconnectivity associates with symptom severity in schizophrenia
Authors: Li Xing , Zeng Jiaxing , Liu Naici , Yang Chengmin , Tao Bo , Sun Hui , Zhang* Wenjing , Li* Chiang-Shan , Lui* Su
Published: November 2024

Motivation: The Hypothalamic-Pituitary-Adrenal (HPA) axis dysfunction is crucial in stress response in schizophrenia. The hypothalamus is an essential part of the HPA axis. The hypothalamic connections with brain regions in schizophrenia. Goal(s): This study examined hypothalamic resting-state functional connectivity (FC) in schizophrenia and how hypothalamic FCs related to symptom severity. Approach: Resting-state functional magnetic resonance imaging data were obtained from 235 patients with schizophrenia and 222 healthy controls. We processed the imaging data with published routines and evaluated the results at a corrected threshold. Results: Relative to healthy controls, schizophrenia showed lower hypothalamic FCs with mesocorticolimbic regions, which were connected with symptom severity. Impact: Decreased hypothalamic functional connectivities, which are associated with symptomology, extend the understanding of functional connectivity alterations and suggest the potential pathophysiology of stress response dysfunction in schizophrenia, which needs further study.
A Deep Learning Prediction Model for Deep Brain Stimulation Optimization by fMRI
Authors: Ajala Afis , Qiu Jianwei , Santyr Brendan , Germann Jürgen , Boutet Alexandre , Bhushan Chitresh , Marinelli Luca , Madhavan Radhika , Yeo Desmond , Lozano Andres
Published: November 2024

Motivation: Maximization of clinical benefits in the treatment of Parkinson’s disease (PD) using deep brain stimulation (DBS) requires clinical parameter optimization with a time-to-optimization per patient of ~1year. Goal(s): To build a deep-learning-based model for the prediction of optimal DBS parameters from a single functional MRI response map obtained during DBS. Approach: Multilayer perceptron based optimal DBS parameter prediction model was trained and tested (five-fold cross-validation) using features extracted by an autoencoder model from DBS-fMRI responses. Results: Accuracies of 79.1%, 84.5%, 81.7%, 83.3% and 70.2% (at 10% deviation from ground truth) were achieved in the prediction of voltage, frequency, and x-y-z contact locations respectively. Impact: This study gives an initial evaluation of a prediction model for DBS parameter optimization, which has the potential to reduce the time-to-optimization per patient from ~1 year to few hours during a single clinical visit, thereby reducing patient’s financial burden.
Macroscale angioarchitectural properties of functional and structural networks
Authors: Moia Stefano , Gulban Omer Faruk , Amico Enrico , Preti Maria Giulia , Poser Benedikt , Ivanov Dimo
Published: November 2024

Motivation: Previous findings from fMRI and from structural microvascular observations link vascular activity or structural organisation with neuronal activity. Goal(s): To verify whether macrovascular properties are related to functional (FC) and structural connectivity (SC), as well as function-structure coupling (SDI). Approach: FC and SC node strength, SDI, and vascular density and distance were compared, using a venous atlas, the Human Connectome Project data, and one subject of the Natural Scene Dataset for which we obtained an arteries-dominated vascular map. Results: Correlations between veins-dominated vascular properties, FC, and SDI, suggest a link between density and proximity of venous structures and connectivity strength of an area. Impact: Cerebral angioarchitecture is often overlooked in integrated multimodal MRI modelling, but its properties can help understanding the nature of BOLD-fMRI-based “vascular” or “physiological” networks.
Autonomic Regression in fMRI during Alertness: Insights from Respiration, Pupil Size, and PPG Amplitude
Authors: Eren Kübra , Tavashi Belal , Yıldırım Kadir , Can Elif , Karakuzu Cem , Alqam Lina , Dinçer Alp , Ozbay Pinar
Published: November 2024

Motivation: Understanding autonomic regulation during a cognitive task is vital for unraveling its neural mechanisms. This study delves into autonomic regression in fMRI, examining the roles of respiration, pupil size, and PPG amplitude during alertness. Goal(s): Our goal is to enhance our understanding of the autonomic nervous system's response to alert conditions by analyzing fMRI data and modeling with various physiological signals. Approach: Through the examination of respiration, pupil size, and PPG amplitude and fMRI, we aim to reveal patterns of autonomic regression. Results: The results uncover significant associations between autonomic parameters and fMRI, shedding light on the neuro-physiological correlates of cognitive stress. Impact: Our findings advance strategies for managing systemic variations during alert conditions, providing valuable insights for fMRI researchers.
Shining Light on Degeneracies and Uncertainties in the NEXI and SANDIX Models with µGUIDE
Authors: Jallais Maëliss , Palombo Marco , Jelescu Ileana , Uhl Quentin
Published: November 2024

Motivation: New biophysical models of gray matter microstructure have been introduced, with a particular focus on exchange time and soma size estimations. However, the fitting quality of these models has not been studied. Goal(s): Our goal is to study the feasibility of estimating both exchange time and soma size in a clinical setting. Approach: We applied µGUIDE, a Bayesian inference framework, to quantify the quality of the fitting of two models from the literature, NEXI and SANDIX, using an extensive protocol, and a clinical one. Results: Estimations of both exchange time and soma size in clinical setting shows high uncertainty. Impact: For the first time, we are using µGUiDE on two microstructure models that take into account the exchange between neurites and the extra-cellular space. We applied it to both synthetic and clinical data.
Axonal fraction imaging on clinical and preclinical dMRI PGSE data
Authors: Thøgersen Thina , Dyrby Tim , Pizzolato Marco
Published: November 2024

Motivation: We wish to quantify the amount of axons in the brain using clinically-feasible in vivo human diffusion MRI. Goal(s): We want to estimate the axonal signal fraction using the conventional pulsed gradient spin echo (PGSE) sequence while reducing model degeneracy and minimizing modeling assumptions. Approach: We model spherical harmonic (SH) coefficients across two high b-value PGSE shells. We calculate ratios between SH l-band power spectra across the shells, relate them analytically to the axonal diffusivities - estimated using machine learning - and with these we calculate the axonal signal fraction. Results: We report comparable results across preclinical and clinical data and demonstrate methodological feasibility. Impact: The axonal signal fraction is proportional to the total volume of axons within a voxel and can be used to characterize pathology. This work proposes its estimation with clinically-feasible b-values and with conventional diffusion MRI data while minimizing modeling assumptions.
Diffusion spectrums of intra- and extra-cellular molecular diffusion via Oscillating Gradient Spin-Echo MRI-techniques
Authors: Avellaneda Manuel , Ferrari Ignacio Lembo , Alvarez Gonzalo
Published: November 2024

Motivation: We address the challenge of characterizing complex diffusion spectra within biological tissues, with implications for early disease detection. Goal(s): We aimed to employ OGSE sequences to probe water diffusion in phantom models, separating intra- and extracellular diffusion components. Approach: Using OGSE sequences, we varied diffusion times and gradient values, effectively disentangling diffusion spectral densities and analyzing restricted diffusion patterns. Results: We successfully separated intra- and extra-cellular diffusion spectral densities. The intra-cellular one provides the cell-sizes. In contrast, the extra-cellular spectral densities exhibited complexity, necessitating the development of models for interpretation. These insights hold promise for early disease diagnosis based on tissue-microstructure characterization. Impact: Our study successfully separates intra- and extracellular diffusion spectral densities, enabling cell-size determination, advancing medical imaging. It fosters nuanced tissue microstructure characterization, opening new avenues for early disease diagnosis and innovative clinical applications.
Enhancing subtle cortical lesion detection through multi-dimensional MRI modelling and optimization
Authors: Messaritaki Eirini , Şimşek Kadir , Aird-Rossiter Charlie , Jones Derek , Palombo Marco
Published: November 2024

Motivation: Many cortical pathologies are invisible via conventional MRI, making it difficult for clinicians to correctly diagnose and treat patients. Goal(s): Our aim was to optimize advanced MRI acquisitions, making them sensitive to subtle cortical pathologies while at the same time reducing acquisition times to clinically-feasible durations. Approach: We calculated the combined relaxation-diffusion signal to encompass surface relaxivity and T2 effects. We used Monte-Carlo simulations to model the signal from healthy and pathological cortical neurons for different PGSE schemes. Results: Our optimized sequences can distinguish pathology associated with focal cortical dysplasia from healthy tissue, and differentiate between focal cortical dysplasia subcategories. Impact: We calculate the combined relaxation-diffusion signal encompassing surface relaxivity and T2 effects, and use it in Monte-Carlo simulations to optimize MRI sequences for subtle cortical lesion detection. Our methodology can be used by researchers to investigate other cortical pathologies.
The Value of Combined Clinical-Radiomics-Deep LearningModels for Prediction Gleason Grade Group
Authors: Qiao Xiaomeng , Hu Chenhan , Bao Jie , Wang Ximing , Song Yang
Published: November 2024

Motivation: Gleason Score (GS) could only be obtained through biopsy or radical prostatectomy (RP), which might carry a multitude of complications and pose additional financial burdens and emotional strain. Goal(s): To explore the predictive value of mixed model combined clinical features, radiomics features and deep learning features for GS. Approach: The mixed model was constructed to classify grade group 0 (GG0) (benign), GG1, GG2, GG3, GG4 and GG5. DenseNet was used to establish the model. Results: The mixed model had the best predictive ability, with Kw of 0.74 and relative accuracy of 0.76. Impact: Clinicians could obtain GS without biopsy or surgery, which could avoid a lot of complications and financial burdens. Future studies could integrate automated VOI segmentation algorithm to optimize AI model.
An Information-Theoretic Paradigm in Modelling Diffusion-Weighted Imaging of Prostate Cancer: EDDIE (Entropy of Divergence of DWI Decay Curve)
Authors: Chu Rui Jian , Jambor Ivan , Taimen Pekka , Ettala Otto , Pesola Marko , Saunavaara Jani , Boström Peter , Aronen Hannu , Merisaari Harri
Published: November 2024

Motivation: Typically, diffusion-weighted imaging (DWI) modelling is assumption-based using e.g. exponential models, but nonparametric (data-based) methods have not been explored. Goal(s): We propose a information-theoretic paradigm for DWI modelling which results in a novel radiomics for DWI of prostate cancer (PCa). Approach: The proposed radiomics, EDDIE (entropy of divergence of DWI decay curve) is formulated as entropy of information lost from approximating a reference by DWI decay curves. It is subjected to classification of clinically significant and insignificant PCa using test-retest DWI datasets of 78 patients. Results: EDDIE achieved an AUC score of 0.77 and an ICC (3,1) of 0.78 which indicates good repeatability. Impact: The proposed approach is nonparametric (assumption-free), interpretable (mathematically and physically meaningful) and complete (higher-order measurement). These may contribute towards more accurate and efficient DWI modelling. Besides, the associated novel radiomics could help ushering in more information-theoretic developments in this field.
Diagnostic Value of Combined PI-RADS v2.1 and PSAD in Detecting Clinically Significant Prostate Cancer in the Gray Zone of PSA: A Dual-Center Study
Authors: Zhang Yun , Dong Zhe , Liu Baichuan , Wang Haiyi , Ye Hui-yi
Published: November 2024

Motivation: It is crucial to improve the accuracy of detection of clinically significant prostate cancer (csPCa) within the PSA gray zone. Goal(s): To combine PI-RADS v2.1 with prostate-specific antigen density(PSAD) derivatives to improve the predictive value of csPCa in the PSA gray zone. Approach: Based on a dual-center study, logistic regression was used to analyze the predictive value of the multi-parameter combination on csPCa in the training group, the receiver operating characteristic curve(ROC) curves were used to evaluate the diagnostic performance, and conducting external validation. Results: The area under curve (AUC) of combining of PI-RADS v2.1 and PSAD was the highest for predicting csPCa. Impact: The dual-center study demonstrates combining of PI-RADS v2.1 and PSAD improved the predictive performance of csPCa in the PSA gray zone, and grouping PSAD with PI-RADS risk stratification can have more direct clinical applications.
Assessment of prostate tissue response to brachy- and external beam radiotherapy using 1H MRS
Authors: Weis Jan , Johansson Adam , Jafar Maysam , Dahlman Pär , Taheri-Kadkhoda Zahra
Published: November 2024

Motivation: Monitoring time-dependent effects of prostate brachy- and radiotherapy. Goal(s): Assessment of prostate metabolic activity during and three months after the completion of brachy- and/or external beam radiotherapy. Approach: Single-voxel 1H-MRS with a surface receiver coil. Results: It is demonstrated that the proposed 1H-MRS approach is a useful tool for monitoring metabolic changes in prostate tissues treated with brachy- and/or external beam radiotherapy. We found reduction of citrate intensity close to the noise level to be the most reliable measure for identification of metabolic atrophy and response to therapy. Impact: Single-voxel 1H-MRS with a surface receiver coil is an effective method for monitoring the response of prostate tissues to brachy- and/or external beam radiotherapy. Good response to prostate radiotherapy might be characterized by low citrate intensity.
7.0T CMR-TT evaluate quantitatively the effect of nicotinamide mononucleotide on experimental autoimmune myocarditis via ferroptosis
Authors: Wang Chunhua , Wang Zhetao , Zhu Jing , Zhou Peng , Gao Fabao
Published: November 2024

Motivation: To evaluate the diagnosis and follow-up value of CMR-TT in myocarditis. To explore more treatment strategies for myocarditis and the potential mechanism. Goal(s): Using 7.0T CMR-TT to evaluate the effect of nicotinamide mononucleotide (NMN) for experimental autoimmune myocarditis and exploring the potential mechanism. Approach: Lewis rats were induced to be autoimmune myocarditis by porcine cardiac myosin and treated by NMN. The strains of rats were detected by 7.0T CMR-TT. Histopathology, biochemistry and ELISA were used for myocardial damage and ferroptosis. Results: Strains reduction in EAM was alleviated by NMN. The changes of cardiac and ferroptosis biomarkers were relieved by NMN. Impact: CMR-TT can provide the imaging evidence for the diagnosis and follow-up of myocarditis. The study will provide more options and targets for clinicians to treat myocarditis.
A 5D MRI Framework for Cardiac Motion Characterization in Radiation Therapy
Authors: Naren Tarun , Ruff Chase , Johnson Kevin , Glide-Hurst Carri , Wieben Oliver
Published: November 2024

Motivation: Current radiotherapy treatment planning does not account for cardiac substructure motion during the respiratory and cardiac cycle, thus leading to suboptimal treatment plans. Goal(s): To develop a free breathing 5D MRI framework to characterize cardiac and respiratory motion of the heart for use in radiotherapy treatment planning. Approach: A double gated (cardiac and respiratory) radial bSSFP acquisition and constrained reconstruction pipeline were implemented and motion and tested in five volunteers. Results: Left ventricle centroid analysis showed 5D images provided significant motion characterization. Advanced reconstruction allows <5 min scan time. Impact: The primary impact of this work will be on patients receiving thoracic radiotherapy. More accurate treatment planning and sparing of sensitive cardiac substructures will improve patient outcomes and reduce long term cardiotoxicities.
Effects of RAFF2 periodicity on contrast between infarct and remote myocardium
Authors: Ylä-Herttuala Elias , Michaeli Shalom , Liimatainen Timo
Published: November 2024

Motivation: Current MRI techniques are insufficiently sensitive to determine specific physiological changes in the myocardium after infarction. Goal(s): To optimize sensitivity of RAFF2 technique to specific tissue changes of the myocardium after myocardial infarct (MI) by modulating the durations of P-packets of RAFF2 pulses. Approach: Mice hearts are imaged ex vivo 7 days after the factitious myocardial infarction by using different durations of P-packets of the RAFF2 along with other rotating frame relaxation techniques. Results: Duration of the RAFF2 pulse have an impact on relaxation maps and contrast of MI area compared to the remote area. Increased rotating frame relaxation times were also detected. Impact: This study presents novel application of RAFF2 technique for quantitative ex vivo assessment of mice myocardium after infarction. This method allows to study physiological changes in the myocardium with high sensitivity.
Differentiation of cardiac inflammatory diseases by myocardial cine T2 mapping
Authors: Kato Mana , Nagao Michinobu , Yoneyama Masami , Goto Yasuhiro , Shiina Isao , Kodaira Kazuo , Sakai Shuji
Published: November 2024

Motivation: Development of a non-contrast differential method for cardiac inflammatory diseaseGoal(s): Accurate identification of cardiac sarcoidosis and myocarditisApproach: Cardiac cine imaging to project T2 values using T2prep-based T2 mapping with dynamic multiple trigger-delay frameworkResults: Diastolic cine T2 values (54.2msec.) discriminated cardiac sarcoidosis from myocarditis with an AUC of 0.879, sensitivity of 86%, and specificity of 77%. Impact: This method is a new technique that captures changes in myocardial properties during the cardiac cycle without the use of contrast media.
Accelerated ferumoxytol-enhanced free-running acquisitions for whole-heart angiography in congenital heart disease patients.
Authors: Romanin Ludovica , Roy Christopher , Yerly Jérôme , Milani Bastien , Prsa Milan , Rutz Tobias , Si-mohamed Salim , Tenisch Estelle , Piccini Davide , Stuber Matthias
Published: November 2024

Motivation: Angiography using ferumoxytol-enhanced free-running MRI can be obtained within an acquisition window of 6 minutes, which could be further reduced for faster clinical workflows. Goal(s): To determine the lower bounds of scan time for fast free-running whole-heart MRI using a data-driven reconstruction (SIMBA). Approach: Fifteen datasets were retrospectively undersampled and image quality metrics were determined as a function of scan time reduction. Results: A 3-minute acquisition provides comparable image quality to that of its original 6-minute counterpart, and when applying compressed sensing we can confidently further reduce the acquisition time of 3D MR angiography with high resolution to 2 minutes. Impact: Free-running whole-heart MRI acquisitions can be greatly sped-up by exploiting ferumoxytol contrast enhancement, in conjunction with a data-driven reconstruction, which facilitates fast whole-heart angiography in congenital heart disease patients.
Assessment of cardiac structural and functional changes in patients with Parkinson's disease by CMR-FT: a pilot study
Authors: Yue Xun , Sun Jiayu
Published: November 2024

Motivation: To evaluate the changes of cardiac structure and function in patients with Parkinson's disease (PD). Goal(s): To evaluate the structural and functional changes of left heart in PD patients by cardiac magnetic resonance feature tracking (CMR-FT), and to explore the influencing factors of left heart structure and function. Approach: Cardiac function and strain parameters of left heart were obtained, and the correlation between CMR parameters and motor dysfunction and autonomic dysfunction was evaluated. Results: Left atrium reserve and conduction function are related to the severity of motor dysfunction and autonomic dysfunction. CMR-FT can detect the early changes of cardiac function in PD patients. Impact: The prevalence of heart failure has been twice among Parkinson's Disease patients compared with overall population. Cardiac magnetic resonance feature tracking technology can be used as an effective tool for early diagnosis of cardiac dysfunction in Parkinson's Disease patients.
Clinical Study of 4D-Flow MRI in Evaluating Abdominal Aortic Compliance in Patients with Chronic Kidney Disease
Authors: Zong Qinling , Pan Liang , Wu Peng , Han Lu , Xing Wei
Published: November 2024

Motivation: Arterial stiffness is a key predictor of mortality in chronic kidney disease (CKD) patients. Goal(s): Use 4D-Flow MRI to noninvasively evaluate the abdominal aortic compliance in CKD patients. Approach: Thirteen healthy controls and seventy-three CKD patients were enrolled to evaluate the hemodynamic parameters including pulse wave velocity (PWV), peak velocity, and maximum wall shear stress (WSS) for healthy controls and different CKD groups. Results: CKD patients tend to have an increased PWV and a decreased maximum WSS in the abdominal aorta, compared to healthy controls. Correlations between PWV/WSS and estimated glomerular filtration rate (eGFR) were found. Impact: Our study shows that PWV tends to increase while WSS tends to decrease for CKD patients compared to healthy controls. Indicating that 4D Flow imaging can be used to assess changes in abdominal aortic compliance in CKD patients.
Towards Sequence Optimization for Multi-Compartment Magnetic Resonance Fingerprinting
Authors: Griesler Tom , Gram Maximilian , Stebani Jannik , Albertova Petra , Dawood Peter , Seiberlich Nicole , Jakob Peter , Blaimer Martin
Published: November 2024

Motivation: Volumetric measurements of the myelin water fraction in the human brain are hampered by long acquisition times. Goal(s): Investigate the extent to which sequence optimization can improve the encoding efficiency of multi-compartment Magnetic Resonance Fingerprinting. Approach: Implement different cost functions to optimize MRF sequence parameters. Validate the results with simulations, one-dimensional projection measurements in phantoms and in vivo measurements. Results: MRF sequence design has a significant influence on partial volume estimation with MRF. Optimization can yield improved tissue discrimination and more detailed myelin water fraction maps. Impact: MRF sequence design has a significant influence on partial volume estimation, yielding potential for optimization of, for example, tissue discrimination and myelin water fraction mapping. Further investigation of cost functions and validation of results is needed.
Tailored Synthetic and Non-Synthetic (SNS) Optimization for pediatric neuroimaging
Authors: Fernandes Tiago Timoteo , Qian Enlin , Poojar Pavan , Nunes Rita G. , Geethanath Sairam
Published: November 2024

Motivation: Quantitative MR (qMR) relaxometry allows monitoring of pediatric neuropathology. However, acquiring qualitative and qMR imaging is time-consuming and challenging, especially in pediatric patients. Goal(s): To design a pulse sequence allowing both quantitative and non-synthetic weighted imaging, addressing the need for acceleration. Approach: MR Fingerprinting (MRF) is the gold standard for simultaneous qMR mapping but does not provide non-synthetic images. We compare MRF versions to sequences designed using the 1-Look-Ahead approach to optimize gray (GM) to white matter (WM) contrast, including periods of stable signal for non-synthetic imaging. Results: A promising signal control enabling quantitative mapping and good-quality weighted in vivo images. Impact: Demonstration of the feasibility of controlled contrast and signal stability for quantitative and qualitative Magnetic Resonance Fingerprinting using one-look ahead (1LA) flip angle design for accelerated scanning in the pediatric brain, increasing quality in T2-weighted images.
Influence of Medical Implants in the Subcranial and Facial Area on the Accuracy of Quantitative Magnetic Resonance Relaxometry at 3T
Authors: Zhao Gaoyang , Zhao Shanshan , Luo Wenzheng , Wu Yanglei , Chen Feng , Zhang Yong , Cheng Jingliang
Published: November 2024

Motivation: The emergence of MR fingerprinting (MRF) technology enables accurate T1 and T2 relaxation time quantification. However, the impact of metal implants on quantitative imaging remains unclear, hindering postoperative and clinical MRI scans. Goal(s): To assess how craniofacial metal implants affect T1 and T2 relaxation time quantification using MRF at 3T. Approach: We conducted experiments on standardized MRI phantoms (ISMRM/NIST) with cranial implants using MRF and traditional T1/T2 mapping sequences. Results: Aneurysm clips had no significant impact on MRF, while cranial plates/screws exhibited mild abnormalities. Galvanized steel strips caused substantial signal anomalies. Impact: Highlighting the importance of considering the influence of metal implants on quantitative MRI results, impacting the accuracy of diagnoses. This research opens the door to further investigations into the development of metal-insensitive MRI sequences.
DAES: Self-Supervised Parameter Estimation Model for MR Fingerprinting
Authors: Tan Jinghang , Ye Huihui , Gao Mengze , Li Zihan , Tian Qiyuan , Bilgic Berkin
Published: November 2024

Motivation: Accurate estimation of relaxation parameters using MRF requires lengthy acquisitions as it benefits from having multiple spiral interleaves to boost the data quality. Goal(s): We aim to reduce acquisition time by denoising highly under-sampled data while retaining the fidelity of the estimated parameter maps. Approach: An unsupervised convolutional neural network called DAES is proposed. It combines Denoising Auto-coder (DAE) with subspace modeling, taking advantage of both denoising framework and Bloch simulation-based dictionary information. Results: DAES outperforms conventional dictionary matching in both simulated and in-vivo data for MRF, demonstrating stronger ability to estimate parameters from highly under-sampled data. Impact: Magnetic Resonance Fingerprinting with the proposed unsupervised Denoising Auto-encoder permits high-quality T1 and T2 mapping while substantially reducing the acquisition time.
Spatial Habitats Features Derived from Multiparametric MRI Predicts Prognosis in High Grade Glioma
Authors: Mazu Liwei , Ma Hui , Zeng Shanmei , Wang Mengzhu , Song Yang , Zhang Cheng-xiu , Yang Guang , Yang Zhiyun , Zhao Jing
Published: November 2024

Motivation: High-grade glioma (HGG) is a highly invasive neoplasm characterized by significant intra-tumoral spatial heterogeneity. However, the clinical relevance of the observed spatial and physical imaging characteristics remains unknown. Goal(s): To identify tumor subregions and quantify their image-based habitat characteristics associated with survival time. Approach: We retrospectively analyzed quantitative tumor habitat based on initial MRI scans in 2 groups (long-term and short-term survivals) of patients diagnosed with HGGs. Kmeans clustering, Univariate and multivariate logistic and survival analysis were used. Results: The features of the high MK and low FLAIR habitat was most effective for predicting survival groups (AUC 0.91, Sensitivity 0.844, Specificity 0.867). Impact: Tumor habitat is a novel method and It’s an earlier attempt to use habitats from diffusion and T1 based perfuison to predict the survival time of HGG. It has high prediction capabilities for prognosis.
Quantitative and qualitative parameters of DCE-MRI predict CDKN2A/B homozygous deletion in gliomas
Authors: Yang Huiquan , Zhu Zhengyang , Zhang Xin , Zhang Bing
Published: November 2024

Motivation: Homozygous deletion (HD) of cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) holds important prognostic value in gliomas. Goal(s): This study aims to explore the predictive potential of conventional MRI imaging parameters combined with dynamic contrast-enhanced (DCE) MRI parameters in predicting CDKN2A/B HD status in gliomas. Approach: Conventional MRI features and DCE-MRI qualitative parameter time-intensity curve types, quantitative parameters Ktrans, Kep, Ve, Vp, and iAUC were obtained. Logistic regression models for prediction of CDKN2A/B HD status were constructed. Results: Ktrans can serve as valuable predictive parameters for identifying CDKN2A/B HD status in all glioma patients as well as patients with IDH-mutant or IDH-wild gliomas. Impact: Our findings provide a foundation for precise preoperative non-invasive diagnosis and personalized treatment approaches for glioma patients.
Spatial comparison of MR perfusion and radio-pathomic model derived cell density in newly diagnosed glioma patients: prognostic implications
Authors: Bobholz Samuel , Winiarz Aleksandra , Lowman Allison , Flatley Michael , Duenweg Savannah , Nath Biprojit , Kyereme Fitzgerald , Connelly Jennifer , Coss Dylan , Krucoff Max , Banerjee Anjishnu , LaViolette Peter
Published: November 2024

Motivation: Deliniating non-angiogenic and early-angiogenic areas of tumor prevents detection of the full extent of glioblastoma invasion. Goal(s): This study investigated the relationship between perfusion and radio-pathomic estimates of cell density in glioblastoma. Approach: This study compared ASL- and DSC-based perfusion estimates to predicted cellularity maps in two large publicly available datasets. Results: Positive cellularity-perfusion associations were observed within contrast enhancement but not in non-enhancing regions. Per-subject positive cellularity-perfusion associations within FLAIR hyperintensity were associated with worse prognosis in glioblastoma patients following gross total resection. Impact: Areas of increased perfusion and hypercellularity can be used to direct surgical intervention to capture early-angiogenic areas of tumor missed by contrast enhancement, which may in turn improve survival outcomes. Non-angiogenic hypercellular tumor may persist outside even this margin.
Pre-RT Fiber Density-Weighted White Matter Pathlength Maps Can Predict Tumor Progression in Patients with Glioblastoma Multiforme
Authors: Liu Bo , Tran Nate , Rowley Paul , Jakary Angela , Ngan Tiffany , Braunstein Steve , Morin Olivier , Lin Hui , Lupo Janine
Published: November 2024

Motivation: Utilizing the knowledge of glioma cells' infiltration along white matter pathways to better predict GBM progression. Goal(s): To enhance GBM progression prediction by analyzing the map of adjacent white matter fibers and building models to incorporate that map with anatomical MR. Approach: Developed a novel algorithm, DW-WMPL, from Diffusion-Tensor Imaging data that adjusts white matter fiber lengths to reveal possible tumor advancement. Employed deep learning models to predict progression with anatomical MRI and DW-WMPL maps. Results: DW-WMPL-enhanced deep learning models achieved higher precision in tumor delineation and reduced normal brain inclusion versus the standard 2cm radiation margin. Impact: The introduction of density-weighted white-matter path-length maps provides valuable insights into tumor cell migration, significantly refining GBM progression prediction. This advancement indicates a pivotal step towards personalized, more effective radiation therapy planning.
Non-Stationarity of Resting-State Connectivity in Patients with Brain Tumors in the Awake and Anesthetized State
Authors: Zhang Jing , Vizioli Luca , Tatsuoka Curtis , Yacoub Essa , Chen Clark , Posse Stefan
Published: November 2024

Motivation: Map temporal fluctuations of functional connectivity (FC) in anesthetized brain tumor patients. Goal(s): Map static FC (sFC), dynamic inter-region FC (dFC), and test-retest reliability between awake and anesthetized states in patients undergoing resection of brain tumors. Approach: A sliding-window xDF method was developed to estimate variance of the correlation in spatial-temporal resolution resting-state fMRI, considering nonstationary autocorrelation and cross-correlation. Results: The largest decrease in sFC during anesthesia was observed across, rather than within, networks. The sliding-window xDF increased sensitivity compared to the static model. Test-retest reliability between cortical areas was higher during anesthesia versus awake state, in contrast to subcortical and cortical-subcortical dFC. Impact: These results demonstrate the feasibility of performing resting-state functional connectivity studies in intraoperative settings with high spatial-temporal resolution. The higher test-retest reliability within cortical areas during anesthesia versus awake state informs the design of minimum duration intra-operative resting-state fMRI protocols.
A fast, vendor-neutral protocol for multi-center, multi-parametric quantitative MRI studies in brain tumor patients
Authors: Thomas Dennis , Deichmann Ralf , Nöth Ulrike , Langkammer Christian , Ferreira Mónica , Hattingen Elke , Wenger Katharina
Published: November 2024

Motivation: Multi-centric multi-parametric quantitative MRI (mp-qMRI) studies require short and vendor-neutral protocols. Goal(s): The goal was to develop and validate an 8-minute, vendor-neutral protocol for multi-center mp-qMRI studies on brain tumor patients at 3T. Approach: 5 Volunteers were measured at two scanners. Application of the proposed method was demonstrated for one brain metastasis patient, where artefact free qMRI maps were obtained. Results: qMRI maps (T1, T2*, PD and QSM) obtained from 5 volunteers on two scanners showed a very good reproducibility. Among the parameters, PD yielded the lowest COV. Artefact free qMRI maps are demonstrated in a brain metastasis patient. Impact: We propose an 8-minute, vendor-neutral mp-qMRI protocol for 3T studies in brain tumor patients. T1, T2*, PD and QSM maps are demonstrated in 5 healthy volunteers and one brain metastsis patient. Multi-centric qMRI studies could use this fast mp-qMRI protocol.
Multiparametric analysis of early treatment changes in glioma after receiving radiation therapy
Authors: Li Yan , Autry Adam , Wang Zhongjie , Vaziri Sana , Lafontaine Marisa , Liu Bo , Villanueva-Meyer Javier , Lin Hui , Braunstein Steve , Chang Susan , Lupo Janine
Published: November 2024

Motivation: Understanding how MR imaging markers change in normal appearing brain tissue over the course of RT for different dose distributions could help shed light on which parts of the brain are more susceptible to RT. Goal(s): To examine early changes in perfusion, diffusion, and MR spectroscopic imaging metrics in normal and tumor regions during and following RT for different dose regions. Approach: Twelve patients were studied at before, in the middle of, and after the completion of RT. Results: Significant imaging differences in diffusion, perfusion, and NAA/creatine was found in the normal appearing white matter in different dosage maps after receiving RT. Impact: Our study has shown initial alterations in the normal-appearing brain after radiation therapy. We have detected these changes in the early stages.
Preoperative prediction of lymph node metastasis in endometrial cancer based on an intra- and peritumoral multiparameter MRI radiomics nomogram
Authors: Yan Bin
Published: November 2024

Motivation: Whether lymph node metastasis (LNM) affects surgical management in endometrial cancer (EC) patients. Goal(s): To develop and validate a nomogram based on intra- and peritumoral radiomics features and multiparameter MRI imaging features to preoperatively predict LNM in EC. Approach: Three hundred and seventy-four women with histologically confirmed EC were divided into training (n = 220), test (n = 94), and external-validation cohorts (n = 60). Radiomics features were extracted from intra- and peritumoral regions based on axial T2WI and ADC mapping. Results: The nomogram combining the Radscore, CA125, and TAR showed good diagnostic performance (AUCtraining = 0.878, AUCtest = 0.877, AUCexternal-validation = 0.864). Impact: The combined intra- and peritumoral region multiparameter MRI radiomics nomogram could be used to preoperatively predict LNM in EC. Moreover, different field strength data were proportionally mixed for modeling and external validation, expanding the real-world application scenarios of prediction models.
Utility of whole tumor texture analysis based on MRI and ADC values in differentiating uterine sarcomas from cellular uterine leiomyomas
Authors: Yang Zhong , Wei Cao
Published: November 2024

Motivation: The treatment methods and prognosis of cellular uterine leiomyomas (CULs) and uterine sarcomas (USs) are different. The ADC values has certain differential diagnostic value, but there is some overlap between them. Texture analysis (TA) may have some potential and complementary role in differential diagnosis. Goal(s): To explore the capability of TA based on MRI and ADC values in the differential diagnosis of USs from CULs. Approach: Combining the ADC values and texture parameters to set up diagnostic model and evaluate the diagnosis value and clinical usefulness of the model. Results: Texture analysis combined with DWI could be helpful to distinguish USs and CULs. Impact: Texture analysis combined with DWI give a better method to identify uterine sarcomas and cellular uterine leiomyomas, providing a more reliable basis for the choice of clinical treatment.
Comparing a Fully Automated Hybrid Approach on MRI and FibroScan for Triaging Clinically Significant Liver Fibrosis:A Multi-center Cohort Study
Authors: Zha Junhao , Xia Tianyi , Song Yang , Wang Yuancheng , Ju Shenghong
Published: November 2024

Motivation: MRI-derived texture analysis could assess liver fibrosis effectively, yet lacking large-scale datasets for validation or comparisons with transient elastography-based liver stiffness measurement (TE-LSM). Goal(s): develop and validate the combined radiomics-clinic model (CoRC) on MRI for triaging clinically significant liver fibrosis (≥ F2), comparing or combining with TE-LSM. Approach: This retrospective multi-center study recruited 595 patients with biopsy proven liver fibrosis. CoRC model integrated Radiomics features extracted from the ResUNet-based automated entire liver segmentation and clinical variables with multivariate logistic regression. Results: Additive value of CoRC model to TE-LSM was explored with combined AUC of 0.86, and 0.81 in the internal, and temporal sets. Impact: Complementary information provided by Radiomics features could be in combination with clinical risk factors in order to assist clinicians in assessing liver fibrosis comprehensively. CoRC models exhibited promising diagnostic performances for clinically significant liver fibrosis, complementary to TE-LSM.
Intratumoral Habitat and Peritumor Radiomics for Progression Risk Stratification of Patients with Soft Tissue Sarcoma: A Multicenter Study
Authors: Liang Hao-yu , Wang He-xiang , Hao Da-peng , Gao Chuan-ping , Zhang Meng , Li Qing , Liu Shun-li , Yang Shi-feng , Hou Feng , Duan Li-sha
Published: November 2024

Motivation: Increasing the identification accuracy of patients with high risk of progression could help guide treatment decision in soft tissue sarcoma (STS). Goal(s): To establish a radiomics nomogram that incorporated tumor habitat and peritumor features and validate its performance to predict tumor progression in patients with STS. Approach: A nomogram combining radiomics based on intratumoral habitat and peritumorwith clinical information was established. Results: This nomogram predicts tumor progression in STS patients and stratifies them according to the risk of progression. Impact: Combining radiomics features derived from the intratumoral habitat and peritumoral region resulted in superior performance for predicting progression-free-survival in patients with STS, which is helpful for clinical decision making.
AutoML Radiomics-Based Classification of Patients with Renal Cell Carcinoma Using Non-Contrast Enhanced Magnetic Resonance Imaging
Authors: Liu Ming-Cheng , Lin Yen-Ting , Hung Siu-Wan , Lyu Pin-Sian , Hsieh Yu-zhen , Chiu Tzu-Yu , Liu Yi-Jui
Published: November 2024

Motivation: Kidney cancer is often diagnosed as either clear cell renal carcinoma (ccRCC) or non-clear cell renal carcinoma (non-ccRCC) to determine treatment recommendations. Additionally, many patients with kidney cancer cannot receive contrast medium due to renal function disorders. Goal(s): for the distinction of ccRCC from other types of RCC without contrast medium administrationApproach: A model using automated machine learning (AutoML) based on radiomics features Results: Our results indicate that the best model from the AutoML process demonstrated a mean sensitivity of 0.819 and a mean specificity of 0.729 in distinguishing between ccRCC and non-ccRCC. Impact: To demonstrated that the TPOP-radiomics-based classification model can effectively discriminate between ccRCC and non-ccRCC using MRI without the need for contrast medium.