bluuebunny/crossref_metadata_2025 huggingface dataset:
Diffusion in dendritic spines: impact on permeative exchange estimation with time-dependent diffusion-weighted MRI
Authors: Şimşek Kadir , Palombo Marco
Published: November 2024

Motivation: Diffusion exchange models NEXI and SMEX assumes permeative exchange between intra- and extra-neurite compartments. Here, we hypothesize fine microstructures, like spines in dendritic segments, without permeable membranes can mimic permeative exchange. Goal(s): We aim to emphasize the significance of taking diffusion-mediated exchange into account when interpreting model-based estimates of exchange in intricate microstructures like the gray matter of the brain. Approach: Monte-Carlo water diffusion simulations in spiny dendritic branches, also featuring undulations and beadingResults: Our results question the way we interpret dMRI exchange estimates, emphasizing the need to exercise caution when inferring these estimates solely as indicators of membrane permeability. Impact: Diffusion exchange models assumes permeative exchange between intra- and extra-neurite compartments. We hypothesize fine microstructures without permeable membranes can mimic permeative exchange. We aim to emphasize considering the impact of intricate microstructures in gray matter when interpreting model-based exchange estimates
Incorporating mesoscopic orientation dependent R2 from magnetic susceptibility into the Standard Model of Diffusion in White Matter
Authors: Sandgaard Anders , Ianus Andrada , Shemesh Noam , Kiselev Valerij , Jespersen Sune
Published: November 2024

Motivation: $$$R_2$$$ in WM is orientation dependent due to microscopic magnetic anisotropy. So far, the Standard Model of diffusion (SM) has been extended to only include isotropic $$$R_2$$$ (TEdDI). Goal(s): Our goal is to characterize $$$R_2$$$ anisotropy of a multi-echo dMRI signal for long diffusion times and incorporate $$$R_2$$$ anisotropy into TEdDI (STEdDI). Approach: We simulate $$$R_2$$$ of PGSE signal in magnetized cylinders using Monte-Carlo, and fit TEdDI and STEdDI on ex vivo mouse multi-echo dMRI data acquired at 16.4T. Results: $$$R_2$$$ anisotropy outside axons are non-axially-symmetric, depends on B0 direction, gradient direction and b-value. Residuals are significantly lower with STEdDI in dMRI data. Impact: Interplay between microscopic magnetic fields and diffusion weighting affects $$$R_2$$$ in extra-axonal space. Incorporating $$$R_2$$$ anisotropy$$$\,$$$into modeling lowered the residuals and may allow rotation-free estimation of $$$R_2$$$ anisotropy, which could be useful to gain a deeper insight into brain microstructure.
Water exchange as measured by diffusion MRI with free gradient waveforms: A potential biomarker of dendritic spine morphology
Authors: Chakwizira Arthur , Şimşek Kadir , Palombo Marco , Szczepankiewicz Filip , Knutsson Linda , Nilsson Markus
Published: November 2024

Motivation: Time-dependent diffusion MRI reveals fast exchange in grey matter, but it remains unclear whether permeative or non-permeative exchange explains these findings. Goal(s): We set to investigate the effect of non-permeative exchange induced by dendritic spines on the time-dependence of the diffusion-weighted signal and the exchange estimates obtained from the signal. Approach: Monte Carlo simulations were performed in synthetic dendrites with varying spine densities and signals were generated using free gradient waveforms. The intracellular signals were analysed using a restriction-exchange framework from previous work. Results: Dendritic spines give the same signal time-dependence as permeative exchange. Estimated exchange rates increase with spine density. Impact: Dendritic spines may explain the exchange rates observed with diffusion MRI in grey matter. Furthermore, exchange measurements provide a potential biomarker of dendritic spine morphology, which is important because spine density is implicated in, for example, learning and psychiatric disorders.
Accelerated Microstructure Quantification by Q-Space Trajectory Imaging Using Machine Learning
Authors: Goedicke Oliver , Laun Frederik , Martin Jan , Rauch Julian , Neher Peter , Rokuss Maximilian , Ladd Mark , Kuder Tristan
Published: November 2024

Motivation: Tensor-encoded diffusion MRI (dMRI) methods for tissue microstructure elucidation typically require lengthy dMRI acquisitions and computationally costly, SNR-sensitive data analysis. Goal(s): Employing q-space trajectory imaging (QTI), we seek to greatly reduce both the number of required measurements and computational burden in analysis for robust estimation of parameters quantifying brain tissue microstructure. Approach: A machine learning-based estimator is trained on a 10-fold reduced subset of an extensive dMRI protocol acquired in 18 healthy volunteers. Results: The proposed method outperforms a state-of-the-art model fitting framework, yielding smoother parameter maps and showing lower deviation from the chosen ground truth, even at reduced SNR/increased resolution. Impact: Quantitative measures of brain microstructure are obtained by accelerated tensor-encoded diffusion MRI, employing a voxel-wise regression neural network. Observed resilience at reduced voxel size (1.7mm)3 appears promising regarding measurement of parameters such as microscopic fractional anisotropy in a clinical setting.
The variability of diffusion-relaxation multidimensional MRI estimates in the human brain
Authors: Manninen Eppu , Bao Shunxing , Landman Bennett , Yang Yihong , Topgaard Daniel , Benjamini Dan
Published: November 2024

Motivation: Multidimensional (MD)-MRI provides valuable subvoxel information. However, its experimental variability has never been investigated. Goal(s): Assessing the variability of MD-MRI estimates is essential for adaptation to clinical research and widespread use. Approach: Ten healthy participants were each scanned twice, using a 40-minute 2-mm3 resolution diffusion-relaxation MD-MRI protocol. Agreement, reliability, and repeatability were assessed using Bland-Altman plots, intraclass correlation coefficient, and test-retest variability, respectively. Results: We demonstrated a good to excellent reliability in the quantification of first- and second-order diffusion parameters. Our findings guide further improvement to the protocol and encourage the use of this MD-MRI framework in clinical research. Impact: Multidimensional MRI is crucial for investigating tissue microstructure, brain connectivity, and pathology. Here we present an in vivo variability study that shows strong agreement, reliability, and repeatability, especially for diffusion parameters, providing a way forward for clinical research.
Microstructural Precision: Assessing the Reproducibility and Sensitivity of Multidimensional Diffusion Metrics
Authors: Bowdler Matthew , Barker Gareth , Dell'Acqua Flavio
Published: November 2024

Motivation: The reproducibility and sensitivity of Multidimensional Diffusion (MDD) measures have not been extensively investigated. Goal(s): Our goal was to assess reproducibility and sensitivity of µFA in comparison with conventional FA metrics for multiple white and grey matter regions. Approach: Test-retest data was acquired to compute ICC scores (reproducibility) and power calculations (sensitivity) to predict the number of subjects required to detect 1%, 2%, 4% and 5% changes for each metric across multiple regions. Results: While FA shows the highest reproducibility for both WM and GM, µFA demonstrates enhanced sensitivity to detect microstructural changes for the same percentage difference. Impact: Our test-retest study demonstrates that Multidimensional Diffusion (MDD) metrics, like µFA, show good reproducibility scores and increased sensitivity in the detection of microstructural changes when compared to equivalent FA changes in both white and grey matter.
Diffusion-prepped fast spin echo sequence for prostate MR to overcome distortion and signal loss in diffusion weighted imaging
Authors: Gomella Andrew , Lee Philip , Hess Jeremiah , Hargreaves Brian , Loening Andreas
Published: November 2024

Motivation: Clinically utilized diffusion weighted imaging (DWI), an essential part of prostate MRI, relies on echo planar imaging (EPI) and is prone to geometric distortion and signal loss due to susceptibility from rectal gas and metal hip prostheses. Goal(s): To improve the robustness of prostate DWI in the setting of field inhomogeneities from hip prostheses and rectal gas. Approach: Prospective study of 10 patients undergoing prostate MRI with diffusion prepped fast spin echo (FSE) sequence, as well as single-shot and multi-shot DW-EPI, comparing prostate measurements. Results: We show feasibility of using a diffusion prepped FSE sequence in prostate MR with decreased distortion and artifact. Impact: A diffusion prepped fast-spin echo sequence avoids distortion and signal loss from susceptibility artifacts seen with echo planar imaging, and is a feasible method to obtain robust diffusion weighted imaging of the prostate.
Magnetic Resonance Elastography to Identify Prostate Phenotypes of Lower Urinary Tract Symptoms
Authors: Wells Shane , Johnson Cody , Gonzalez-Pereira Juan-Pablo , Ricke William , Grimes Matthew , Hall Timothy , Jiang Yun , Gulani Vikas , Roldan-Alzate Alejandro , Brace Christopher
Published: November 2024

Motivation: There is a critical need for significant improvements in image-based assessment of the prostate in men with lower urinary tract symptoms (LUTS). Goal(s): To develop non-invasive image-based biomarkers that will optimize clinical management of men with LUTS. Approach: Quantify transition zone complex shear modulus (henceforth ‘stiffness’) with transperineal magnetic resonance elastography (pMRE) at 90Hz and 100Hz. Results: Mean periurethral TZ stiffness increases with frequency (3.0clip_image006.png">0.4kPa). Impact: Transperineal pMRE is technically feasible, generates volumetric whole prostate quantitative parametric maps that can differentiate zonal prostate anatomy. Periurethral TZ stiffness, measured with pMRE, may be a clinically useful biomarker for identifying discrete phenotypes of LUTS.
Multiparametric Cardiovascular MR in Non-hospitalized COVID-19 Infection Subjects: An Intra-individual Comparison Study
Authors: Luo Song , Dou Wei Qiang
Published: November 2024

Motivation: To evaluate the prevalence of cardiac involvement in mild, non-hospitalized, recovered COVID-19 patientsGoal(s): investigate the changes of cardiac structure and function by comparing multiparametrical cardiac MRApproach: This prospective study enrolled 39 unhospitalized COVID-19 patients. Results: Four patients had the same late gadolinium enhancement (LGE) pattern at baseline and repeated CMR and 5 female patients had myocardial T2 ratio >2 but with normal T2 value in post-COVID-19 CMR. All other CMR parameters were in normal ranges before and after COVID-19 infection. Impact: This knowledge has an important role in can help alleviate wide social concern on COVID-19 related myocarditis.
Quantifying Repeatability of a 3D-MRF Protocol During Scanner Software Upgrades
Authors: Dupuis Andrew , Chen Yong , Griswold Mark , Boyacioglu Rasim
Published: November 2024

Motivation: Magnetic Resonance Fingerprinting relies on the stable performance of vendor hardware and software through-time. However, the effects of software upgrades on the repeatability of quantitative protocols require evaluation. Goal(s): Measure changes to MRF-derived T1 and T2 values resulting from a vendor-provided software upgrade. Approach: 8 healthy volunteers were imaged before and after a software upgrade. Regional T1 and T2 changes were evaluated using correlation plots and MNI-derived regional means. Results: The evaluated sequence was similarly repeatable in both the intra-version test-retest case (T1: -0.49+/- 3.85%, T2: -0.53+/-7.59%) and pre- to post-upgrade (T1: -0.01+/-4.23% , T2: -2.10+/-8.44%). Impact: Showing similar repeatability of 3D MRF maps before and after vendor software upgrade establishes performance reliability. The automated analysis pipeline does not require qualitative images and can be used to test MRF reproducibility in various stages of longitudinal studies.
MR Fingerprinting Enables Quantitative Measures of Basal Ganglia TissueRelaxation Times and Myelin Water Fraction in Parkinson's Disease
Authors: Li Xiaolu , Zhao Mengwan , Jiang Yueluan , Fan Guoguang
Published: November 2024

Motivation: The diagnosis and monitoring of PD present formidable challenges due to the absence of objective and specific indicators. Goal(s): Our objective is to demonstrate the potential of MRF in improving PD diagnosis and monitoring, offering a more precise and objective assessment for the benefit of patients. Approach: MRF sequences were used to quantitative brain tissue properties. By employing partial least squares and Receiver Operating Characteristic analysis, we explored MRF's potential in distinguishing PD and assessing disease severity. Results: MRF sequences yield objective and quantifiable metrics for differentiation and severity assessment in PD patients. Impact: By providing reliable objective and quantifiable metrics, the use of MRF enhances the accuracy and reliability of PD assessment, leading to more precise interventions. This has the potential to improve patient outcomes and reduce the burden on healthcare systems.
Rapid Multiparametric Quantitative Bilateral Breast MR Fingerprinting Using a Phase-Sensitive Deep Learning Network.
Authors: Martinez Jessica A. , Sutton Elizabeth J. , Otazo Ricardo , Cohen Ouri
Published: November 2024

Motivation: To rapidly obtain MRF-based multiparametric quantitative maps in the breast. Goal(s): To explore the feasibility of using PS-DRONE and an EPI-MRF sequence in breast imaging to simultaneously quantify T1, T2, PD, B1, phase, and QSM maps. Approach: MRF data were acquired at 3T. Tissue parameters were reconstructed using PS-DRONE, including QSM maps computed from the estimated phase. Results: Bilateral breast T1, T2, PD, B1 and phase (for QSM analysis) maps were obtained using PS-DRONE and an EPI-MRF sequence. Scan time was 2 minutes and 30 seconds. Parameter reconstruction time was one second. Maps presented differences between the two breasts consistent with diffusion images. Impact: Comprehensive quantitative T1, T2, PD, B1 and phase (QSM) bilateral breast imaging in under 2.5 minutes can potentially improve the detection and characterization of breast cancer and treatment response in a clinical setting without the use of a contrast agent.
Multipool CEST MRI demonstrates good performance in predicting ATRX mutation status in IDH1-mutant lower-grade gliomas
Authors: Zhu Hongquan , Zhang Xiaoxiao , Zhu Wenzhen
Published: November 2024

Motivation: ATRX gene mutation is correlated with better prognosis in IDH1-mutant lower-grade gliomas (LrGGs). Goal(s): We aimed to investigate the predictability of ATRX mutation status using multipool CEST MRI. Approach: The CEST Z-spectrum was fitted with 5 Lorentzian functions. We compared the differences in 5 metrics between ATRX mutant and wild-type gliomas. And ROC analyses were performed to evaluate predictive performances of metrics. Results: ATRX mutant IDH1-mutant LrGGs showed significantly decreased direct saturated water (DS), semi-solid magnetization transfer (MT) signals and increased amine signal compared to ATRX wild-type group. The combination of metrics yielded the highest AUC of 0.773. Impact: Multipool CEST MRI demonstrated good ability to distinguish ATRX mutant gliomas from wild-type gliomas, it may be as a useful imaging biomarker for precise prediction of ATRX mutation status and facilitate the diagnosis and prognosis of glioma patients.
Using intra-and peri-tumoral radiomics features to identify LMN and LVSI in endometrial cancer from MRI images
Authors: Li Shengyong , Zhang He , Wang Yida , Song Yang , Yang Guang
Published: November 2024

Motivation: Preoperational identification of lymph node metastasis (LNM) and lymphatic vascular space invasion (LVSI) of endometrial cancer from MRI is important to treatment planning. Goal(s): To explore power of intra/peri-tumor radiomic features from DWI, T1CE and T2W images to identify LVSI and LNM. Approach: We developed radiomics models with intra/peri-tumor features from different MRI images and compared their performance.We developed radiomics models for intra- and peri-tumoral features and compare performance. Results: For LVSI, T2W model using both intra- and peri-tumoral features achieved AUC values of 0.790/0.696 in internal/external test cohorts. For LNM, the combined model achieved AUC values of 0.801/0.976 in internal/external test cohorts. Impact: The radiomics signatures built with intra- and peri-tumoral features extracted from DWI, T1CE, T2W sequences can yield satisfactory predictions for both LVSI and LNM status in endometrial cancer.
Using Machine Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Local Advanced Rectal Cancer Based on Texture Features of MRI
Authors: Gao Fei , Tao Zhenchao
Published: November 2024

Motivation: To explore and validate the association between magnetic resonance image texture features and the efficacy of Neoadjuvant Chemoradiotherapy for rectal cancer. Goal(s): To predict the efficacy of neoadjuvant chemoradiotherapy for locally advanced rectal cancer using machine learning methods. Approach: The wavelet texture parameters of all lesions in patients' MRI images were extracted, and feature selection was performed using random forest classifier model, and then classification learning was performed using the XGBoost classifier. Results: The model based on wavelet texture feature analysis of MRI can effectively predict the effect of neoadjuvant radiochemotherapy for rectal cancer patients. Impact: Through this study, we explored and validated the relationship between MRI texture features and the efficacy of Neoadjuvant Chemoradiotherapy for rectal cancer, providing new guidance and decision support for individualized treatment strategies.
Prediction of intra-tumoural tertiary lymphoid structures in intrahepatic cholangiocarcinoma using MRI-based radiomics
Authors: Xu Ying , Yang Yi , Ye Feng , Xie Lizhi , Wang Sicong , Zhao Xinming
Published: November 2024

Motivation: Tertiary lymphoid structures (TLSs) can only be assessed by postoperative specimen and a non-invasive tool to preoperatively evaluate TLSs is still lacking. Goal(s): To explore the association between TLSs status of patients with ICC and preoperative magnetic resonance imaging (MRI) radiomics analysis. Approach: Radiomics features were subjected to LASSO regression to select the most associated features of TLSs and construct the radiomics model. Results: The AUCs of Rad-score were 0.85, 0.81, and 0.84 in the T, V1, and V2 cohorts, respectively. Low-risk group showed significantly better median RFS than that of the high-risk group, which was also confirmed in cohort V1 and V2. Impact: TLSs have been reported to have prognostic value and guiding significance to immunotherapy in ICC patients. The MRI radiomics signature could serve as a non-invasive tool to preoperatively predict intra-tumoural TLSs status of ICC patients and correlate significantly with prognosis.
T2 -Weighted MR Imaging-Based Radiomics Model for Evaluating the Activity of Thyroid-Associated Ophthalmopathy
Authors: Gou Xinyi , Peng Pai , Lian Jianxiu , Zhang Xiuyi , Cheng Jin , Hong Nan
Published: November 2024

Motivation: The assessment of thyroid-associated ophthalmopathy (TAO) activity is crucial for determining the appropriate treatment. However, it was based on clinical activity score(CAS) which relies on subjective symptoms and judgment heavily. Goal(s): The aim of this study was to establish an objective evaluating model for TAO’s activity based on MR Imaging-Derived Radiomics. Approach: MR Imaging was performed for different activity status of TAO patients, after that radiomics features were extracted and selected. Radiomics models were constructed. Results: The radiomics model of T2 weighted SPIR-based extraocular muscles can achieve satisfactory performance than those of optic nerve or interorbital tissue in evaluating the activity of TAO. Impact: This study constructed the T2 -weighted MR imaging-derived radiomics model to evaluate the activity of thyroid-associated ophthalmopathy, which could be a more objective and consistent evaluating approach than clinical activity score.
Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging
Authors: Gao Weibo , Chen Xin , Zhao Fengjun , Wei Xiaocheng
Published: November 2024

Motivation: As manual slice-by-slice analysis of breast MR images is both time-consuming and error-prone. Goal(s): To develop a deep learning-based system for the detection and classification of breast lesions in DCE-MRI. Approach: DCE-MRI images were fed into the developed cascade feature pyramid network system(CFPN), feature pyramid network, and faster region-based convolutional neural network for breast lesion detection and classification. Results: CFPN achieved the highest sensitivities in detection at the lowest FPs at both the slice level and the patient level. Impact: DL-based systems can automatically detect and classify breast lesions on DCE-MRI. These results illustrate the potential use of this technique in a clinically relevant setting.
Automatic detection of Small Hepatocellular Carcinoma (<= 2 cm) in cirrhotic liver based on Gd-EOB-DTPA-enhanced MRI using deep learning
Authors: Lei Junqiang , Xu Yongsheng , Zhu Yuanhui , Jiang Shanshan , Tian Song
Published: November 2024

Motivation: In light of the overlapping image features between small hepatocellular carcinoma (sHCC) and benign precancerous nodules, the detection of sHCC from cirrhosis liver is deemed difficult and challenging. Goal(s): To develop a fully automatic deep learning approach for the detection of sHCC in cirrhotic livers, utilizing Gd-EOB-DTPA-enhanced MRI. Approach: A 3D nnU-Net deep learning network was trained to perform automatic segmentation and detection of sHCC lesions. Results: 120 patients were included. The AUCs for discriminating between sHCC lesions and non-sHCC lesions were 0.967 and 0.864 in the training and test cohorts,, with both P<0.001. Impact: Deep learning holds promise for the noninvasive detection of sHCC, offering the potential to alleviate the workload of radiologists and mitigate the necessity for biopsies along with their associated complications.
Deep learning-based Reconstruction with Super Resolution for Abdominal Diffusion Weighted Imaging
Authors: Kwon Jihun , Sato Jiro , Yuda Kohei , Yoneyama Masami , Katsumata Yasutomo , Hamano Hiroshi , Obara Makoto , Van Cauteren Marc
Published: November 2024

Motivation: Abdominal diffusion-weighted imaging (DWI) plays a significant role in the detection and characterization of lesions. However, the spatial resolution of single-shot echo-planar imaging (ssh-EPI) readout is limited by the acquisition time. Goal(s): To enhance the image quality and sharpness of abdominal ssh-EPI-DWI image using a prototype AI-based reconstruction technique (SuperRes). Approach: We examined eight healthy volunteers using abdominal ssh-EPI-DWI, and the acquired data were reconstructed using both conventional Compressed SENSE and SuperRes. The image quality was assessed qualitatively and quantitatively. Results: SuperRes demonstrated a significant improvement in the image quality and sharpness of both DWI and ADC map. Impact: The dedicated deep learning-based super-resolution technique enhanced the image quality and sharpness in abdominal ssh-EPI-DWI. Enhanced sharpness resulted in better delineation of structures, such as the pancreas. The improvement in image quality was demonstrated in both qualitative and quantitative assessments.
Deep learning reconstructed fast non-Gaussian DWI for predicting microsatellite instability in esophagogastric junction adenocarcinoma
Authors: Zhu Yongjian , Wang Peng , Li Ying , Wang Sicong , Jiang Liming
Published: November 2024

Motivation: Microsatellite instability (MSI) in esophagogastric junction adenocarcinoma (EGA) can serve as a predictor of sensitivity to immunotherapy and affect the prognosis. Predicting MSI preoperatively can enable personalized and precise treatment for EGA patients. Goal(s): This study investigates the use of fast non-Gaussian diffusion-weighted imaging with deep learning-based reconstruction (DLRecon) to assess MSI in EGA. Approach: We compared image quality between conventional scanning (CS) and DLRecon, calculated diffusion parameters, and assessed their ability to distinguish MSI status. Results: DLRecon exhibited superior image quality and reduced scan time. Diffusion parameters effectively differentiated MSI status in EGA. Impact: DLRecon non-Gaussian DWI significantly improved image quality and reduced acquisition time. Multiple diffusion parameters may serve as imaging markers, and their combination provides high diagnostic accuracy for discriminating MSI status in EGA.
Treatment Monitoring of Irreversible Electroporation in a Potato Model with a two-shot CP/CPMG-RARE Sequence and Spiral Sampling
Authors: Belker Othmar , Gerlach Thomas , Hubmann Max Joris , Speck Oliver , Wacker Frank , Hensen Bennet , Gutberlet Marcel
Published: November 2024

Motivation: Treatment monitoring of irreversible electroporation (IRE) is only feasible on small animal MRI systems. Goal(s): Demonstrate Current Density Imaging capabilities of the two-shot CP/CPMG-RARE with spiral sampling by monitoring IRE of a potato in a clinical MR scanner. Approach: A potato is irreversibly electroporated, while the two-shot CP/CPMG-RARE sequence is acquiring. The electric field is simulated, and the current density is projected using the MRCI toolbox. T1 maps are calculated from inversion recovery sequences. Results: Simulated and projected current density fields are in good agreement. IRE monitoring in single slices is feasible in clinical MR scanners in optimal conditions. Impact: Irreversible Electroporation is a promising non-thermal ablation therapy without in-situ validation available. This work presents a step towards implementing treatment monitoring on clinical MR scanners utilizing a two-shot CP/CPMG-RARE with spiral sampling as Current Density Imaging sequence.
Water-fat Separation for the knee on a 50 mT Portable MRI Scanner
Authors: Wan Cai , He Wei , Xu Zheng
Published: November 2024

Motivation: Bright fat signals in MR images can obscure the underlying details and affect the physician's diagnosis. Especially in ultra-low-field MRI (B0 < 100 mT), lower SNR images are more important to discern fat and water signals. Goal(s): This study aims to achieve effective water-fat separation using the Dixon method at a 50 mT MR scanner. Approach: R2* effect and priori information have been added to the existing two-point Dixon method. Results: The images obtained on the 50 mT MRI scanner can clearly distinguish cartilage, muscle and fat compared to the water-fat separation images obtained on the 3T MRI scanner. Impact: Phase errors in the acquired MR images were significantly reduced after using priori phantom phase images. This work demonstrates the successful application of the Dixon method to ULF MRI. Future studies will focus on reducing imaging time.
Complex B1+ field predictions to evaluate Electrical Properties Tomography reconstructions.
Authors: Meerbothe Thierry , Jung Kyu-Jin , Cui Chuanjiang , Kim Dong-Hyun , van den Berg Cornelis , Mandija Stefano
Published: November 2024

Motivation: Electrical properties (EPs) are reconstructed from complex B1+ maps. In-vivo reconstructed EPs values presented in literature show large variations, reducing the confidence in the quality/accuracy of the reconstruction methods. Goal(s): To develop a method to compute complex B1+ fields from EPs maps, which can the verify accuracy of EPs reconstructions. Approach: Complex B1+ maps are predicted using a finite difference-based approach. The difference between the predicted and measured fields is used as surrogate error of the estimated input EPs. Results: The method shows accurate complex B1+ field reconstructions in 2 minutes and the ability to localize errors in the input EPs maps. Impact: Complex B1+ fields are predicted using tissue electrical properties maps as input. This method provides a way to assess the quality/accuracy of in-vivo electrical properties reconstructions, providing a means to gain confidence in the reconstructed electrical properties values.
An Automatically Optimised Gaussian Weighting Function Width for Magnitude-Weighted Phase-Based Electrical Properties Tomography (EPT)
Authors: Luo Jierong , Arsenov Oriana , Shmueli Karin
Published: November 2024

Motivation: Phase-based electrical properties tomography calculates conductivities by fitting the transceive phase weighted by a Gaussian function of the magnitude image with width δ. Currently, δ is selected empirically and its impact on conductivities is unknown. Goal(s): To investigate the effect of δ on conductivity maps and develop a method to automatically select δ. Approach: After evaluating relationships between δ and healthy brain conductivities at 3T, we calculated conductivities using a voxel-wise δ based on inverse phase noise and compared the results. Results: Increasing δ decreased contrast and noise in conductivity maps. Our new method to calculate a varying δ automatically optimised conductivity maps. Impact: We have developed a method to calculate a varying Gaussian weighting width for EPT. This will enable automatic optimisation of conductivity maps rather than time-consuming empirical choice of δ, facilitating the use of phase-based EPT and broadening its applicability.
Measurement of Radiation Effects in Tumor Tissues using MR-based Conductivity Imaging
Authors: Ok Ko In , Kyung Choi Bup , Ae Park Ji , Hoe Hur Young , Chul Kim Hyun , Woong Kim Jin , Joong Kim Hyung
Published: November 2024

Motivation: Ionizing radiation produces ions inside the human body that can kill cancerous tissues by damaging DNA directly or creating charged particles that damage DNA. Goal(s): Contrast mechanism of electrical conductivity is determined by the concentration and mobility of ions that make up tissues. Approach: We applied MR-based conductivity imaging to mouse brain tumors to evaluate the responses in irradiated and non-irradiated tissues during the peri-irradiation period. Results: MR-based conductivity images effectively showed acute response after irradiation in glial tumors. Impact: High-frequency conductivity images can differentiate each brain tissues including viable tumors, tumor necrosis, and normal brain. It shows potential as a tool to differentiate the therapeutic effect of radiation by measuring absolute values and calculating percentage changes.
Detection of Pathological Functional Connectome in Brains with Low-grade Gliomas Using Graph Convolutional Network
Authors: Cai Siqi , Fan Zhen , Qi Zengxin , Liu Yufei , Chen Fanfan , Cui Zhuoxu , Wang Wenxin , Li Fanshi , Shi Zhifeng , Zhang Lijuan
Published: November 2024

Motivation: Alterations in the functional connectome may serve as new biomarkers to infer the disease profile of glioma. Goal(s): To detect the pathological functional connectome (Patho-FCN) that characterizes the functional plasticity due to low grade glioma. Approach: Dynamic functional connectivity-based graph convolutional network (dFC-GCN) models were constructed to distinguish patients from healthy controls. Class activation mapping was utilized to identify the top 5% salient nodes constituting the Patho-FCN, where the information flow was assessed using the time-delay and probabilistic flow estimation. Results: The dFC-GCN model identified a contralesional Patho-FCN with altered information propagation patterns, and achieved an averaged classification accuracy of 96.1%. Impact: The pathological functional connectome detected with the proposed methodology in this study provides a novel biomarker to characterize cerebral glioma. Theranostic scheme targeting pathological connectome may innovate the management of glioma.
Identification of Prognostic Imaging Biomarkers in H3 K27-Altered Diffuse Midline Gliomas in Adults: Impact of Tumor Oxygenation Biomarkers
Authors: Sim Yongsik , Park Yae Won , Ahn Sung Soo , Lee Seung-Koo
Published: November 2024

Motivation: The prognostic markers of H3 K27-altered diffuse midline gliomas (DMGs) in adults have yet to be studied. Goal(s): To investigate prognostic markers for H3 K27-altered DMGs in adults, including clinical, qualitative, quantitative imaging phenotypes, and tumor oxygenation characteristics. Approach: Clinical, qualitative, and quantitative imaging phenotypes were analyzed in 32 adults with H3 K27-altered DMGs. Cox analyses were conducted to determine predictors of overall survival in entire patients with H3 K27-altered DMG and subgroup of contrast-enhancing (CE) tumor. Results: The presence of LM and higher rCMRO2 of CE tumor were independently associated with poor prognosis in adult patients with H3 K27-altered DMG. Impact: Tumor oxygenation imaging biomarkers may provide valuable insight into the prognosis of H3 K27-altered diffuse midline gliomas, potentially reflecting higher metabolic activity in the tumor microenvironment.
Test-retest reliability of myelin volume fraction quantified with multicomponent MPnRAGE
Authors: Weaver Jayse , Kecskemeti Steven , Guerrero-Gonzalez Jose , Dean Douglas
Published: November 2024

Motivation: There is a need for rapid and high-resolution myelin water imaging techniques, while high test-retest reliability is necessary for widespread adoption. Goal(s): Evaluate the repeatability of multicomponent relaxometry with MPnRAGE. Approach: Data from 12 adolescent subjects scanned 3 consecutive times were reconstructed with motion-correction and fit to multicomponent MPnRAGE model. Coefficient of variation (CoV) was calculated across the 3 scans for each subject for several white and gray matter regions. Results: Mean CoV was <7% across white matter regions. Myelin volume fraction trends between regions were also consistent with prior T2-based techniques. Impact: A recently developed T1-based technique for myelin water imaging with MPnRAGE demonstrates high repeatability across consecutive scans. Multicomponent relaxometry with MPnRAGE enables high-resolution and motion-corrected myelin mapping in under 10 minutes of scan time.
Simultaneous brain susceptibility, T1 and T2 quantification at 7T with phase-cycled balanced steady-state free precession
Authors: Acikgoz Berk , Martinez Cristina , Mackowiak Adele , Plähn Nils , Safarkhanlo Yasaman , Bonanno Gabriele , Peper Eva , Jorge João , Bastiaansen Jessica
Published: November 2024

Motivation: Phase-cycled(PC) balanced steady-state free precession(bSSFP) sequences offer yet-to-be-explored capabilities for quantitative susceptibility mapping(QSM), T1, and T2 mapping, particularly attractive for 7T applications. Goal(s): To determine the potential of PC-bSSFP for simultaneous QSM, T1 and T2 mapping in the brain at 7T. Approach: PC-bSSFP-based off-resonance, tissue phase, T1 and T2 maps are compared with reference maps obtained from ME-GRE and MP2RAGE, in three subjects at 7T. Results: PC-bSSFP-based off-resonance and tissue phase maps agreed with ME-GRE-based references with absolute mean errors of 13.2±3Hz and 8.9±3.7Hz, respectively. PC-bSSFP-based T1 and T2 maps matched the expected brain contrast with high precision. Impact: At 7T, PC-bSSFP enables quantitative measurements of susceptibility, T1, and T2, within one acquisition, while providing high quality weighted images with a clear distinction between different brain structures.
Influence of Body Coil Drive Mode on the MR Safety of Interventional Devices
Authors: Özen Ali , Reiss Simon , Algin Oktay , Bock Michael
Published: November 2024

Motivation: RF-induced heating of interventional devices is reduced at lower magnetic fields, but dangerous heating conditions can still occur during MR-guided interventions. Goal(s): To investigate the effect of body coil drive mode on RF-induced heating of interventional devices at 0.55T and 1.5T. Approach: Numerical simulations, E-field, transfer function and temperature measurements are used to evaluate the dependency of RF-induced heating of commonly used interventional devices on the phase and amplitude settings of body coil ports. Results: RF-induced heating can be reduced using individually adapted body coil drive modes. Impact: Dual-drive body coil technology can be used to generate implant-friendly electromagnetic fields, which can enable safe use of clinical intravascular devices during MR-guided interventions at 0.55T and 1.5T.
SAR Efficient Imaging for Patients with Deep Brain Electrodes at 3 Tesla: Coil Length Matters
Authors: Karadeniz Nejat , Hajnal Jo , Ipek Özlem
Published: November 2024

Motivation: To improve the design of pTx head coils intended to produce uniform RF fields while controlling local SAR levels for patients with DBS implants. Goal(s): To evaluate the effect of extended coil length and increased number of rows on performance using simulations. Approach: Comparison of SAR and RF homogeneity in 3-row and 4-row pTx head coil configurations using FDTD simulations on a human head model with an implanted DBS lead. Results: The 4-row coil offered the best SAR control for the whole brain, with length having a greater impact on local SAR than the number of rows. Impact: The research indicates that the length of multi-row pTx coils affects local SAR in MRI for DBS patients, enhancing efficiency and safety, and broadening imaging possibilities, potentially advancing MRI-compatible devices and personalized imaging techniques.
Radio-Frequency Energy-Induced Heating for hot spots with the same SAR averaged over 0.1 gram without perfusion
Authors: Kozlov Mikhail , Boulant Nicolas
Published: November 2024

Motivation: Evaluating induced temperature rise (ΔT) in human tissue at a hot spot. Goal(s): Comprehensive analysis of ΔT by examining cases where SAR0.1 g (SAR, specifically averaged over a mass of 0.1 gram of human tissue) remains constant but the spatial distribution of SAR inside and outside the averaging volume is different. Approach: Analytical solutions were obtained for estimating ΔT as a result of the SAR distribution exhibiting spherical symmetry with both uniform and Gaussian distributions. Results: Using SAR0.1g without consideration of the distribution of SAR within and outside the enclosed volume cannot be a reliable tool for estimation of ΔT. Impact: We have obtained analytical results on the effect of the SAR distribution on the conservatively estimated ΔT in different RF exposure scenarios.
Deep learning imaging-based reconstruction improved the image quality of synthetic high b-value DWI for prostate lesion detecting
Authors: Fan Li , Zhou Xiuxiu , Zhang Hanxiao , Dai Jiankun , Shi Jie , Jiang Song , Gu Lingling , Zhang Pei
Published: November 2024

Motivation: Synthetic-DWI (SyDWI) at high-b-value, derived from low-b-value DWI, might be beneficial for prostate cancer evaluation due to better conspicuity of lesions. Relative to conventional reconstruction (ConR), a vendor-provided deep learning reconstruction (DLR) has been reported for improving imaging quality in aspects of higher SNR and imaging sharpness. Goal(s): Investigate the impact of DLR on the image quality of SyDWI for prostate lesion detection. Approach: Low-b-value DWI was reconstructed with DLR and ConR, separately. SyDWIs were generated from DWI_DLR and DWI_ConR. The image quality and lesion conspicuity were compared. Results: SyDWI generated from DWI_DLR showed improved image quality and enhanced prostate lesion detection. Impact: The enhancement of prostate lesion detection would be beneficial for clinical examination.
SyntheticLGE.jl: An Open-Source Toolbox for Retrospective T1 Fitting and Synthetic LGE Image Generation
Authors: Sheagren Calder , Tran Brandon , Patel Jaykumar , Lau Angus , Wright Graham
Published: November 2024

Motivation: Synthetic late gadolinium enhancement (SynLGE) has been proposed as a technique to quantify cardiac fibrosis from post-contrast T1 mapping. Current SynLGE techniques use site-specific code, limiting clinical adoption and standardization. Goal(s): Develop a software toolbox for SynLGE image generation using retrospective T1* mapping. Approach: An open-source software SyntheticLGE.jl was implemented in Julia and is publicly available on GitHub with two sample MOLLI datasets for software evaluation. Results: SynLGE image generation is feasible for both SSFP and gradient-echo MOLLI imaging. Impact: We hope that SyntheticLGE.jl can enable standardized and reproducible synthetic LGE image generation in simple and challenging clinical scenarios.
Investigating Myelin Abnormalities in Major Depressive Disorder: Insights from Synthetic Magnetic Resonance Imaging
Authors: Wen Junyan , Chen Zhimin , Gong Liya , Cui Wei , Gao Liaoming , Wu Ziqi , Yang Shanshan , Hao Yanyu , Wen Ge
Published: November 2024

Motivation: Major depressive disorder is a common mental illness with biological processes, such as myelin alterations, that are not yet fully understood. Goal(s): This study aims to explore myelin abnormalities in MDD patients utilizing Synthetic MRI (SyMRI) technique. Approach: Myelin content measured by SyMRI was compared between 32 MDD patients and 51 healthy controls, and the study further investigated the relationship between myelin alterations and the depressive symptoms. Results: Several white matter regions exhibited a significant decrease in myelin content in individuals with MDD, and a correlation was found between the depressive symptoms and these myelin deficits. Impact: The present study highlights the potential of Synthetic MRI technique as a valuable tool for measuring myelin content, which could play a crucial role in understanding the underlying neurobiological mechanisms of Major Depressive Disorder.
DeepDenoiseNet: a convolutional neural network trained with synthesized images from inversion-recovery maps for SASHA denoising
Authors: Qian Xiaofeng , Wang Ancong , Fan Yingwei , Li Yafeng , Liu Bowei , Jin Yongsheng , Ding Haiyan , Guo Rui
Published: November 2024

Motivation: SASHA T1 has high accuracy but low precision due to the low SNR of T1-weighted images. Convolutional neural network has the potential to improve SASHA T1 precision by using spatio-temporal correlations. Goal(s): The aim of this study is to develope a convolutional neural network for improving SASHA T1 precision. Approach: We implemented a convolutional neural network (DeepDenoiseNet) and trained it using synthesized SASHA images from co-registered high-quality T1, T2, and M0 images. Different-level noise was added to simulate low SNR SASHA images. Results: DeepDenoiseNet could reduce the impaction from noise and improve SASHA T1 precision. Impact: The deep convolutional neural network trained with synthesized images and simulated noise could improve SASHA T1 precision.
Identification of Glioblastoma Infiltrative Areas in Peritumoral Edema Based on Expert Interaction Framework
Authors: Tu Jiaqi , Shen Chuyun , Liu Jianpeng , Xiong Ji , Wang Xiangfeng , Jin Bo , Zhu Fengping , Li Yuxin
Published: November 2024

Motivation: Infiltration and recurrence of glioblastoma is typically fatal. Conventional imaging techniques are insufficient for identifying the infiltrated regions. Goal(s): We aim to develop an interactive visualization method based on conventional MRI to identify the peri-tumor infiltration. Approach: Glioblastoma infiltrating area detection interactive framework (GIADIF) consists of two steps: delineating peritumoral edema and extracting the voxels with low fractional anisotropy value as user-interactive input; using the P-Net from the DeepIGeoS framework to output the infiltrated maps, and validating in a prospective cohort. Results: GIADIF showed reliable performance in identifying GBM-infiltrated regions (area under the receiver operating characteristic curve: 0.929 [95% CI 0.804–1.000]). Impact: GIADIF utilizes the interactive information to the conventional MRI sequences to locate areas of GBM infiltration. Its excellent performance allows for the prompt and precise selection sites for surgery and radiotherapy.
A Subregion-based RadioFusionOmics Model Discriminates between Grade 4 Astrocytoma and Glioblastoma on Multisequence MRI
Authors: Wei Ruili , Lu Songlin , Xu Yongzhou , Zhen Xin , Yang Ruimeng
Published: November 2024

Motivation: Investigated the underlying impact of subregional analysis on model performance: comparison of two volumes of interests (VOI) definition strategies. Goal(s): To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma. Approach: Subregional radiomics analysis using the K-means clustering demonstrated discriminative performance comparable to that of manual segmentation. Edematous subregion is a possible intratumoral heterogeneity phenotype that differentiates grade 4 astrocytoma from glioblastoma. Results: The RFO model that was trained using fused features achieved the AUC of 0.868 (VOI3) and 0.884 (H34) in the primary cohort (p=0.059), and 0.824 (VOI3) and 0.838 (H34) in the testing cohort (p=0.023). Impact: Fusion of features from edematous subregions of multiple MRI sequences by the RFO model identified IDH genotypes of adult type grade 4 gliomas in line with current WHO CNS 5 criteria.
Estimating right heart catheterization results from Phase-Resolved Functional Lung (PREFUL) MRI using Deep Learning
Authors: Zubke Maximilian , Wernz Marius , Alsady Tawfik Moher , Kaireit Till , Mueller Robin , Voskrebenzev Andreas , Wacker Frank , Olsson Karen , Hoeper Marius , Vogel-Claussen Jens
Published: November 2024

Motivation: Right heart catheterization (RHC) is the invasive gold standard to measure several cardiopulmonary parameters. PREFUL is a non-invasive method to quantify hemodynamics during the cardiac cycle from MRI in free breathing and without contrast agents. Goal(s): Provide an MRI-based method for non-invasive estimation of mean pulmonary arterial pressure (mPAP), pulmonary arterial wedge pressure (PAWP) and mixed venous oxygen saturation (SvO2) currently determined by RHC. Approach: Multiple deep neural networks were trained to estimate mPAP, PAWP and SvO2 from cardiac cycles of three lung slices, provided by PREFUL. Results: The estimations of mPAP, PAWP and SvO2 showed strong correlation with RHC. Impact: Estimation of mPAP, PAWP and SvO2 is possible via PREFUL MRI. The presented approach is more automatized than echocardiography and may support the diagnosis of cardiopulmonary diseases such as pulmonary hypertension where RHC is not yet available.
Staging of Primary Lymphedema Based on Radiomics Features from Subcutaneous Tissues in Lower Extremity MRI
Authors: Liu Mengke , Tian Yuchi , Li Xingpeng , Zhang Yimeng , Feng Jixue , Liang Xiaoyun , Wang Rengui
Published: November 2024

Motivation: Traditional imaging-based staging methods for PLEL rely on subjective assessments by medical professionals and often struggle to capture the micro-level changes associated with lymphedema, which limits the accuracy and granularity of staging. Goal(s): To explore the value of radiomicss features based on different components extracted from MRI for assessing the staging of PLEL. Approach: We proposed a machine learning model to integrate multi-region radiomics for automated PLEL staging and employed deep learning for automated subcutaneous tissue segmentation in the lower extremity MRI. Results: The Dice coefficient for subcutaneous tissue segmentation scored 0.92, and the AUC for lymphedema staging was 0.821. Impact: The machine learning model based on radiomics in this study shows promising potential and value in lymphedema staging, which is expected to reduce subjective variability and potentially eliminate the need for clinical assays, thus enhancing its clinical applicability.