bluuebunny/crossref_metadata_2025 huggingface dataset:
Validation of a High Contrast Technique and T1 Values Obtained by Divided Subtracted Inversion Recovery (dSIR)
Authors: Bydder Mark , Ali Fadil
Published: November 2024

Motivation: To validate a 2 point high contrast T1 technique using the National Institute of Standards and Technology (NIST) T1 phantom. Goal(s): Qualitative results produced by the divided subtracted IR (dSIR) images have been shown to give unprecedented T1 contrast in case studies of neuroinflammation [1]. The present study validates the theoretical mechanism of contrast in a standardized commercial T1 phantom. Approach: Experiments were performed at 1.5T to compare the empirical signal response versus the theoretical prediction in a phantom with known T1s. Results: The signal response was found to match the predicted bipolar variation very closely. Impact: Clinicians wishing to employ 2 point T1 estimation techniques can be satisfied that the results are grounded in theory and are quantitatively validated.
Quantitative multi-parametric mapping of human subcortex at ultrahigh field
Authors: Pine Kerrin , Zubkov Mikhail , Bazin Pierre-Louis , Perlaki Gábor , Edwards Luke , Alkemade Anneke , Vandewalle Gilles , Kirilina Evgeniya , Weiskopf Nikolaus
Published: November 2024

Motivation: Many neurodegenerative diseases affect subcortical nuclei early. Quantitative MRI (qMRI) offers a unique non-invasive tool for detection of neurodegeneration at its early stage, paving the way for development of potential therapies. Goal(s): Ultra-high resolution multi-modal cartography of human subcortex capable of detecting subtle longitudinal changes in macro- and microstructure of subcortical nuclei. Approach: We combined high resolution multi-parametric mapping using 7T, pTx and advanced reconstruction with automated segmentation of subcortical structures. Performance was tested across two sites. Results: While qMRI repeatability varied strongly by structure, automated parcellation was highly repeatable, making the protocol a promising candidate for further studies. Impact: We present a method for multi-contrast quantification of subcortical microstructure. Our high-resolution MRI acquisition and analysis protocol aims to enable longitudinal multi-center studies to detect subcortical neurodegeneration at its early stage.
Ultra-Fast Submillimeter Whole Brain QSM Using Highly Accelerated 3D Echo-Planar Imaging with a Complex Convolutional Neural Network
Authors: Quah Bryan , Nair Sreekanth Madhusoodhanan , Silemek Arzu Has , Renner Brian , Gombos Elaina , Subhi Mustafa , Jin Jin , Han Fei , Binesh Nader , Maya Marcel , Li Debiao , Kaisey Marwa , Sicotte Nancy , Al-Louzi Omar , Sati Pascal
Published: November 2024

Motivation: To restore image quality of highly accelerated QSM for data interpretation. Goal(s): To evaluate the feasibility of a denoising convolutional neural network for generating high-quality submillimeter isotropic QSM acquired in 2-3 minutes at 3-Tesla. Approach: A previously developed network for denoising complex-valued MRI data was applied to accelerated 3D-EPI scans (TA: 2min and 3min). Image quality was evaluated and QSM values obtained with and without denoising were compared against reference non-accelerated non-denoised 3D-EPI (TA: 6min). Results: SNR and structural measures demonstrated improved image quality when denoising the accelerated data. Similar QSM values were observed for both highly accelerated denoised 3D-EPI and reference 3D-EPI. Impact: Our approach for denoising complex-valued 3D-EPI brain images shows the feasibility of producing high-quality, whole-brain, submillimeter isotropic QSM acquired in 2-3 minutes at 3-Tesla, facilitating its clinical adoption.
Towards rapid T2 mapping using geometry in bSSFP imaging
Authors: Dong Yiyun , Xiang Qing-San , Yang Yang , Hoff Michael
Published: November 2024

Motivation: Quantitative T2 mapping is useful for indication of neuropathological states such as multiple sclerosis, but current methods are time consuming and can be challenging at low T2 values needed to indicate pathology. Goal(s): To compute T2 maps efficiently and analytically. Approach: T2 maps are computed by exploiting the geometry of the bSSFP signal. The algorithm is enhanced with added regularization, linearization, and solution combination, and is evaluated in simulations and phantom images. Results: The improved algorithm demonstrates precision in simulations and a phantom, especially for lower T2 values. Reconstructed phantom T2 values were realistic, indicating its promise as a diagnostic tool. Impact: Standard quantitative T2 mapping requires significant scan time for adequate fitting; here an analytical T2 mapping method is demonstrated that doesn’t require additional scan time beyond the associated artifact-free bSSFP images generated, inspiring further exploration.
Investigation of RF-induced Heating of Active Implantable Medical Device in the Vicinity of Fragmented Leads under 1.5T MRI
Authors: Yang Lijian , Kurpad Krishna , Zheng Jianfeng , Guo Ran , Wang Qingyan , Chen Ji
Published: November 2024

Motivation: Electrically short fragmented leads are sometimes left behind in the human body after the extraction of an active implantable medical device (AIMD). When a new AIMD is implanted near the fragmented leads, the radiofrequency (RF)-induced heating of the newly implanted AIMD could be altered. Goal(s): Develop a method to evaluate the RF-induced heating of AIMD with fragmented leads in the vicinity. Approach: We propose to use the electric field distribution which includes the presence of the fragmented leads with the AIMD transfer function model for RF-induced heating evaluation. Results: The proposed method can effectively predict the RF-induced heating for AIMDs near fragmented leads. Impact: It was observed that the RF-induced heating for AIMD system can be altered by the nearby fragmented leads.
A new hybrid non-clustering VOP compression algorithm
Authors: Orzada Stephan , Fiedler Thomas , Ladd Mark
Published: November 2024

Motivation: Compression of SAR matrices can take very long for large data sets and large channel counts when using non-clustering algorithms that show the highest compression efficiency. Goal(s): The goal of this study was to develop an algorithm that performs the compression faster while maintaining the compression efficiency. Approach: We use a hybrid method that combines different algorithms to form a hybrid algorithm with greatly increased calculation speed. Results: The new compression algorithm outperforms the older non-clustering compression algorithms at all VOP counts while maintaining the compression efficiency. Impact: VOP compression is important for local SAR supervision and constraint pulse calculation in parallel transmission. We propose a new algorithm for non-clustered compression that greatly increases calculation speed, which is important especially at large channel counts.
Heat and torque minimization for designing brain shim coil translated from Stream Function
Authors: Su Shengyue , Henning Anke
Published: November 2024

Motivation: The safety concerns of heating and torque in ultra-high field MRI easily become a problem in implementing local devices. As our designed brain shim coil with dense wire pattern can improve the B0 homogeneity significantly, it is important to ensure safety before utilization. Goal(s): The study aims to minimize the power dissipation and torque in the designed shim coil. Approach: The study introduced regularization for power dissipation and torque, employs the wire interpolation method to enhance power efficiency, and simulates the impact of varying the number of turns in shim coils. Results: We found the optimal design parameters balancing safety and performance. Impact: We introduced a series of methods to design a safe and efficient shim coil for the brain in ultra-high field. The torque and heat results provide insight into the performance of the local shim coil.
Hardware and Procedure for Testing and Evaluation of a pTx 16-Element Transmit/ 32-Element Receive Array Coils at 9.4T.
Authors: Avdievich Nikolai , Walzog Joshi , Glang Felix , Scheffler Klaus
Published: November 2024

Motivation: Parallel transmission (pTx) is a very useful method for minimizing peak SAR and RF field inhomogeneity at UHF. To simplify the design, individual elements of pTx RF arrays are connected directly to scanner plugs and not easily accessible. In addition, in the case of a Tx-only/Rx-only (ToRo) setup, safety regulations require testing the Tx-only array without Rx-array. Goal(s): To develop testing hardware and procedure for adjusting and evaluating pTx 16-channel Tx/32-channel Rx 9.4T array coils. Approach: Electronic hardware and procedure was designed, constructed, and tested. Results: The hardware was constructed and successfully used in development of the pTx 16Tx/32Rx human head ToRo-array coil. Impact: We developed testing hardware and procedure for pTx-coils and used them in development of the human head ToRo-array. The developed hardware allows testing any pTx 9.4T RF coil with Rx-channel count up to 32 and Tx-channel count up to 16.
Transmit uniformity and SAR optimization by a deep-learning method in UHF imaging
Authors: Che Shao , Liu Jin , Cui Zhuoxu , Ding Siyuan , Wang Chengbo , Meersmann Thomas , Zhang Xiaoliang , Li Ye
Published: November 2024

Motivation: UHF imaging is limited by both transmit uniformity and local SAR. Information on RF electric field is unavailable in conventional MR scan procedure. Goal(s): This work aims to provide real-time RF electric field for joint optimization of imaging uniformity and peak local SAR. Approach: A deep-learning method is proposed to predict the real-time EM field distribution using B1+ data obtained in routine prescan of the imaging procedure. The output field data is used in combined optimization of transmit uniformity and local SAR. Results: In the torso imaging case, this method achieved both improvement of transmit field uniformity and reduction of peak local SAR. Impact: This work studied the feasibility of machine-learning methods for RF field estimation and simultaneous optimization of transmit homogeneity and peak local SAR, aiming to reduce the estimation error of local SAR and increase the available maximum B1.
On the combination of simulations from various human models to account for variabilities while limiting SAR10g overestimation for UHF pTx MRI
Authors: Destruel Aurelien , Jin Jin , Weber Ewald , Liu Feng , Guye Maxime , Callot Virginie
Published: November 2024

Motivation: Reports highlight variability in 10g-averaged specific absorption rate (SAR10g) when varying subjects and radiofrequency coil position, necessitating safety factors to prevent underestimation, leading to overconservative SAR constraints. Goal(s): To provide a method for selecting appropriate body models to balance RF safety and sequence performance, while investigating SAR10g variability in 7T spinal cord pTx MRI. Approach: The impact of tissue properties, anatomy, body-mass index, and coil positioning on SAR10g was evaluated according to multiple metrics. Results: The mean absolute percentage error can complement the safety factor to decrease SAR10g overestimation and guide the choice of human models used for SAR-monitoring. Impact: Simulations of local-SAR in generic human models are widely used to ensure RF safety at 7T. We show that optimizing the choice of models used for SAR-monitoring may lead to less conservative SAR-constraints, which may improve sequence and pTx performance.
Approach to Improving Anatomical Accuracy of Numerical Body Models Repositioned to Match Subject Posture During MRI
Authors: Brantner Douglas , Vu Jasmine , Rad Laleh , Collins Christopher
Published: November 2024

Motivation: Use of commercial tools to reposition/pose numerical body models to match subject posture in MRI can result in significant non-anatomical distortions of the model. Goal(s): Introduce approach to avoid non-anatomical distortions for a model in the seated position. Approach: “Seated” versions of a body model were produced by: 1) using commercial software alone, and 2) strategically combining portions of the seated and original models offline followed by post-processing. SAR was calculated for both models in an open-bore double-donut MRI system. Results: The Combined model avoided non-anatomical distortions seen when using the repositioning tool alone. These differences are also reflected in SAR distributions. Impact: We introduce a method for avoiding significant non-anatomical distortions occurring in numerical body models when using commercial tools to reconfigure the posture/pose of the model. Use of this approach can also avoid related inaccuracies in calculated SAR and field distributions.
A Hybrid FDTD-FEM Simulation Approach For Safety Assessment of Geometrically Complex Birdcage Coils.
Authors: Custers Koen , Sumser Kemal , Dubok Aleksei , van den Brink Johan , Paulides Maarten , Raaijmakers Alexander
Published: November 2024

Motivation: RF safety assessment requires accurate simulation results. FEM simulations offer superior performance for delicate structures while FDTD simulations are more efficient for realistic body models. Goal(s): Improve the accuracy of FDTD simulations for four-layered body coil models using a simplified, three layered, model combined with network co-simulation. Approach: In Sim4Life, a body coil is simulated using lumped element capacitors instead of layered capacitor structures. Capacitor values are optimized by network co-simulation to minimize differences in the resulting E-field distribution and that of HFSS. Results: Improved E-field and B1+ field distributions are observed in common phantoms for whole-body SAR and implant local SAR assessment. Impact: The proposed method can accurately simulate complex birdcage body coils with layered capacitor structures while also being able to include realistic body models. As such, the method combines the features of FDTD and FEM simulations.
Synthetic MRI to evaluate myometrial invasion and predict pathologic type of endometrial cancer: compared with high-resolution T2WI and DWI
Authors: Li Yue , Pei Yigang , Liu Hui , Liu Weiyin , Luo Yijing , Bai Yu , Ou Xiaorong , Li Wenzheng
Published: November 2024

Motivation: Depth of myometrial invasion (DMI), lymphovascular invasion (LI) and pathological types of endometrial cancer (EC) affect decision-making on an optimal treatment plan for uterus. Goal(s): This study aimed to investigate the feasibility of MAGIC in evaluating the DMI and predicting the pathological types of EC. Approach: The assessment performance of MAGIC in comparison to hr-T2WI on DMI and prediction of EC types in comparison to ADC were performed. Results: T2 and PD together had a superior predictive performance to ADC only, and sy-T2WI showed the similar assessment performance on DMI to hr-T2WI. Impact: One more application of MAGIC-generated contrast images and quantitative parameter maps in cervical diagnosis may provide additional information on diagnosis of DMI and classification of pathological types of EC.
Predicting the efficacy of induction chemotherapy for nasopharyngeal carcinoma: histogram analysis of quantitative synthetic MRI
Authors: Huang Junhao , Zhang Jiuquan , Ren Huanhuan , Liu Daihong , Zhang Jing , Yu Hong , Tan Yong , Nie Lisha
Published: November 2024

Motivation: In nasopharyngeal carcinoma (NPC) patients, there is a significant variation in tumor response to induction chemotherapy (ICT), which directly impacts prognosis. To address this issue, we aimed to explore a novel imaging biomarker based on pre-treatment synthetic MRI to predict which patients would benefit the most from the additional ICT. Goal(s): Evaluating the value of pre-treatment synthetic MRI quantitative parameter map histogram characteristics in predicting the efficacy of ICT for NPC. Approach: 40 NPC patients were prospectively enrolled, and each was imaged with synthetic MRI. Results: T2_Minimun and T2_RootMeanSquared show promise as imaging markers for predicting the response to ICT in NPC. Impact: The utilization of synthetic MRI may serve as an effective diagnostic tool for evaluating the response to ICT in clinical practice. Identifying patients who are unlikely to respond to ICT early on, can help offer them alternative treatment options.
Synthetic quantitative magnetic resonance imaging and quantitative susceptibility mapping to reveal brain function in Type 2 Diabetes
Authors: Zhou Hailing , Chen Wenjie , Wei Shouchao , Liang Yongsheng , Liu Weiyin
Published: November 2024

Motivation: Identification of high-risk dementia in T2DM is very important for early intervention. Goal(s): To explore a feasible imaging approach in early discovering T2DM-driven cognitive impairment. Approach: To compare ROI-based retrieved relaxation time of synthetic magnetic resonance imaging and quantitative susceptibility values between T2DM and healthy cohorts (NCs) and also correlate significant differences of all measurements with Montreal Cognitive Assessment (MoCA) scores. Results: T2DM had higher T1 and T2 relaxation time, PD and QS values in some brain regions than NCs. Moreover, T1 value of left insula was negatively associated with MoCA. Impact: Synthetic MRI and QSM can detect abnormal brain areas associated with in T2DM, and the former had more potential in clinically diagnosing early alteration in T2DM due to more direct visualization and measurements on the scanner console.
Pushing MP2RAGE boundaries: ultimate time-efficient parameterization combined with exhaustive T1 synthetic contrasts.
Authors: Bapst Blanche , Massire Aurélien , Mauconduit Franck , Gras Vincent , Boulant Nicolas , Dufour Juliette , Bodini Benedetta , Stankoff Bruno , Luciani Alain , Vignaud Alexandre
Published: November 2024

Motivation: Redefining MP2RAGE sequence for clinical efficiency. Goal(s): To provide a time-efficient MP2RAGE parameterization with on-demand synthetic T1-weighted contrasts. Approach: Sequence parameters are chosen to minimize idle time while maximizing CNRWM/GM. Synthetic contrasts are derived from T1 map. Experimental validation is carried for 7T brain imaging using plug-and-play parallel transmission. Results: Time-efficient MP2RAGE reduced acquisition time by up to 40% compared to reference, while maintaining contrast quality. Multiple sclerosis patients benefited from enhanced lesion visualization with a 10min, (0.67mm)3 time-efficient protocol. This optimization enabled shorter acquisition times or higher resolution within a given time budget, while providing an exhaustive set of brain T1-w contrasts. Impact: A time-efficient MP2RAGE sequence parameterization is proposed, resulting in faster, higher-resolution 3D T1-weighted brain imaging compared to conventional settings, combined with a complete set of synthetic T1-w contrasts generated online, with meaningful potential in clinical and research practice.
Synthesizing Missing MRI Sequences Towards Reliable Brain Tumor Segmentation Using Deep Learning
Authors: Al-Fakih Abdulkhalek , Shazly Abdullah , Mohammed Abbas , Elbushnaq Mohammed , Makary Meena , Al-masni Mohammed
Published: November 2024

Motivation: Automated and robust segmentation of brain tumors requires multiple MRI sequences and can expedite neuro-oncological clinical trials. Goal(s): Our goal was to develop a deep learning model for brain tumor segmentation, even when some MRI sequences are missing. Approach: We enhanced a GAN with attention modules to synthesize missing sequences and employed an optimized nnU-Net for segmentation using both real and synthesized sequences. Results: The proposed AI-based model significantly improved brain tumor segmentation, with overall Dice scores increasing from 0.688% when FLAIR is missing to 0.873% using synthesized FLAIR derived from T2, and achieving 0.901% with real FLAIR. Impact: The developed two-stage deep learning framework, comprising synthesis and segmentation, enhances segmentation of brain tumors in MRI, especially when real sequences are unavailable. This advancement accelerates clinical trials and reduces manual segmentation time, yielding promising results.
Synthesizing multiple realistic MR phase images using a multi-modal generative model
Authors: Deveshwar Nikhil , Rajagopal Abhejit , Lustig Michael , Larson Peder
Published: November 2024

Motivation: Deep learning MRI reconstruction methods face challenges in available datasets to train models. Clinical scans can be a source for diverse data but a challenge is obtaining MRI phase. Goal(s): We propose a method to generate multiple plausible synthetic phase images from a single magnitude-only input. Approach: We train a multi-modal generative model enforcing consistency in the latent space during training. We evaluate the effect of latent vector dimension on diversity and quality of the synthetic images with FID score and training image reconstruction models with this synthetic data. Results: Higher latent vector dimension resulted in more diverse and higher quality synthetic images. Impact: This method could be used to generate multiple plausible phase images from a single scan to model effects of varying field homogeneity, RF coils, echo time, motion, flow, and susceptibility
Machine learning based MRI radiomics model in predicting postoperative progressive cerebral edema and hemorrhage after resection of meningioma
Authors: Tan Guirong , Hu Kangjian , Liao Xueqing , Liu Weiyin , Guo Ming , Meng Zhihua , Liu Xiang
Published: November 2024

Motivation: Prediction radiomics analysis of postoperative progressive cerebral edema and hemorrhage which are the most common complications after meningioma resection, is limited. Goal(s): To develop and validate a machine learning model to predict progressive cerebral edema and hemorrhage after meningioma resection. Approach: Reviewing the preoperative MRI of 148 pathology-confirmed meningiomas, extracting radiomics features of tumor enhancement and peritumoral edema regions, and combining clinical characteristics to build machine learning multiparametric MRI radiomics predictive models. Results: The combining model including both enhancement and edema radiomics features, and clinical characteristics including systolic blood pressure, showed the best predictive performance with AUC of 0.94 for the validation set. Impact: We proposed a novel model that included clinical indicators and multi-parameter radiomics features, which can accurately and non-invasively predict progressive cerebral edema and hemorrhage after meningioma resection, enabling improving clinical management and quality of life of patients with meningioma.
Mathematical Modelling of Survival in Low Grade Gliomas at Malignant Transformation with XGBoost.
Authors: Tan Lily , Ruffle James , Jeremy Rees , Kosmin Michael , Nachev Parashkev , Hyare Harpreet
Published: November 2024

Motivation: Early detection of low-grade glioma (LGG) malignant transformation (MT) is vital for treatment decisions, prognosis, quality of life and patient-centered care. Goal(s): To develop non-linear machine learning models using XGBoost algorithm to predict overall survival using clinical, molecular, genetic and radiomic data at MT. Approach: 553 LGGs with histology and MRI underwent in-house tumour segmentation pipeline with radiomic feature extraction and masked disconnectome of map components. Results: XGB Classifier model predicted OS > 5 years from MT with an accuracy of 64%. Age, IDH1 mutation, 1p/19q co-deletion, regularity of tumour shape, and disconnectome-related perilesional components were most predictive of survival. Impact: Understanding malignant transformation of low-grade gliomas is crucial for research and the development of new treatment strategies. Defining the radiological features at malignant transformation allows for a timely shift in the treatment plan with potential to improve repsonse to therapy.
Deep learning radiomics nomograms predict IDH genotype in glioma patients: a multicenter study
Authors: Li Darui , Zhang Jing , Ai Kai , Hu Wanjun , Liu Guangyao , Ma Laiyang , Gan Tiejun
Published: November 2024

Motivation: It is unclear whether deep learning radiomics nomograms (DLRN) can noninvasively predict isocitrate dehydrogenase (IDH) genotypes in glioma patients. Goal To explore the feasibility of DLRN in predicting IDH genotype. Goal(s): To explore the feasibility of DLRN in predicting IDH genotype. Approach: T2WI-based DLRN was developed and validated in two centers (Center I, n=342 and Center II, n=60) to predict IDH genotype and evaluate its association with prognosis in glioma patients. Results: The proposed model had an area under the curve(AUC)of 0.98 in an externally validated cohort, and DLRN scores were significantly associated with the overall survival of glioma patients. Impact: The proposed DLRN can accurately predict IDH genotypes and provide a useful tool for targeted therapy of patients with IDH mutations.
Assessing the Efficacy of Radiotherapy for Brain Metastases in Advanced Non-Small Cell Lung Cancer through Raidomics Prediction
Authors: Yang Li , Kai Ai , Yongjun Cheng , Bo Gao
Published: November 2024

Motivation: Magnetic resonance imaging (MRI) raidomics has shown unique advantages and potential in non-invasive evaluation of therapeutic efficacy in cancer patients. Goal(s): To construct a model that can predict the prognosis of patients with advanced non-small cell lung cancer (NSCLC) brain metastases after radiotherapy. Approach: For patients with advanced NSCLC brain metastasis who underwent pre-treatment MRI examination, stable and reproducible raidomics features were quantitatively extracted and screened. Additionally, artificial intelligence methods were utilized to construct raidomics labels. Results: The potential of the MRI raidomics-based method in predicting the efficacy of radiotherapy for brain metastases from advanced NSCLC was preliminarily confirmed. Impact: The method based on MRI raidomics (T1, T2, DWI, DCE) can not only enhance the precision of radiation therapy efficacy assessment for patients with NSCLC brain metastasis, but also offer clinicians a more scientific basis for treatment decision-making.
Utilizing 2D UNet with Synthetic Attention for Enhanced Classification of IDH Mutations Based on Anatomical MRI in Gliomas
Authors: Bas Abdullah , Ozturk-Isik Esin
Published: November 2024

Motivation: There is a need to preoperatively assess the isocitrate dehydrogenase (IDH) mutational status in gliomas, which highly affects the treatment planning and patient prognosis. Goal(s): To develop a robust deep learning pipeline for noninvasively assessing the IDH mutational status of gliomas based on anatomical MRIApproach: Post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) MRI of 501 adult diffuse gliomas (103 IDH-mutant, 308 IDH-wildtype) of the UCSF-PGDM dataset were evaluated with a 2D UNet architecture using synthetic attention. Results: The model utilizing all three anatomical modalities achieved an accuracy of 93.31% (sensitivity=93.33%, specificity=93.24%). Impact: IDH mutational status in gliomas was identified with over 93% accuracy utilizing a 2D UNet architecture with synthetic attention for the evaluation of whole tumor slices of three standard anatomical MRI modalities.
Machine-learning-based multimodality radiomics analysis for the preoperative prediction for local relapse in osteosarcoma
Authors: Luo Zhendong , Liu Renyi , Li Jing , Liu Weiyin Vivian , Shen Xinping
Published: November 2024

Motivation: To compare the efficacy of radiomics models using four machine learning algorithms in predicting for local relapse of osteosarcoma before surgery. Goal(s): This study established a robust prediction model of local relapse to improve prognosis efficacy of osteosarcoma and aid a personalized treatment planning. Approach: Comparison of four algorithms in classifying high-risk local-relapse patients from low-risk ones based on only preoperative radiographic, MR, and both radiomic features. Results: The random-forest based prediction model using both radiograph-MRI radiomic features had the best performance on differentiating patients with local relapse from those with non-local relapse with AUC of 0.868, sensitivity of 0.909, specificity of 0.750. Impact: This study facilitated early identification of high-risk local-relapse osteosarcoma patients who may benefit from model-guided therapeutic interventions and have better long-term outcomes.
Differentiation Magnetic Resonance Images of Tuberculous and Brucellar Spondylitis Using Convolutional Neural Network Based on VGG19
Authors: Chen Jinming , Liu Xiaoming , Wei Lingzhen , Li Meilin
Published: November 2024

Motivation: Tuberculous spondylitis （TS） and brucellosis spondylitis （BS） are two common infectious diseases in spinal surgery, and the differential diagnosis of these diseases is challenging but important to ensure appropriate treatment. Goal(s): The aim of this study was to evaluate the performance of a convolutional neural network CNN) based on VGG19 in distinguishing between TS and BS on different parameter magnetic resonance imaging （MRI） and to compare it with three radiologists. Approach: MRIs of 383 patients were randomly divided into training （n = 307） and validation （n = 76） groups. Results: VGG19-based CNN outperforms radiologist assessment in distinguishing TS from BS. Impact: The proposed CNN based on VGG19 is effective in diagnosing TS and BS on MRI, which could not only help in clinical decision making, but also improve efficiency and reduce medical costs.
T2 Distribution Analysis of Inflamed Bone Marrow Compartments in MR Images with Quantitative T2-mapping
Authors: Brock Luise , Ben-Atya Hadas , Saar Galit , Folle Lukas , Maier Andreas , Vandoorne Katrien , Freiman Moti
Published: November 2024

Motivation: Conventional methods for imaging inflammation-related bone marrow (BM) changes are limited, necessitating advanced quantitative MRI approaches. However, capturing microscopic details poses challenges. Existing T2 distribution estimation methods' limitations led to the development of P2T2-Boot. Goal(s): We aimed to improve BM inflammation analysis using P2T2-Boot and assess its ability to differentiate healthy and inflamed BM. Approach: P2T2-Boot, a neural network for T2 distribution estimation, was developed with bootstrapping techniques, trained on simulated MRI signals, and tested on real mice data. Results: The bootstrapped model outperformed others at low Signal-to-Noise Ratios and demonstrated superior performance in distinguishing inflammatory and non-inflammatory mice. Impact: P2T2-Boot significantly enhances detecting BM inflammation, excelling in noisy conditions. Its superiority underscores its potential for advancing disease studies. The method's potential extensions make it a promising tool for advancing inflammation-related disease studies and clinical applications.
AI-enhanced prognostication in cardiac resynchronization therapy using displacement encoding with stimulated echoes (DENSE) MRI
Authors: Ghadimi Sona , Bivona Derek , Bilchick Kenneth , Epstein Frederick
Published: November 2024

Motivation: The need to address the challenge of a high nonresponse rate (approximately 40%) in CRT patients. Goal(s): By leveraging advanced computational methods, this research seeks to redefine risk stratification and long-term survival prediction. Approach: 3D-CAE model is designed to compress displacement trajectories into a low-dimensional latent code while preserving sufficient information for trajectory reconstruction. The survival network utilizes latent features from three specific slices for predicting 4-year survival of patients post-CRT.Results: 3D-CAE model effectively learned to extract latent features and reconstructed displacements with EPE of 0.0914. The survival network had the average AUC value for the ROC curves of 0.76 ± 0.04 Impact: This study used important features in myocardial displacement fields. It gives better AUC in comparison with human-derived descriptors of cardiac motion. Also, there are other parameters that can be added to this model to get a promising 4-year survival prediction.
Advancing prediction of bone marrow biopsy results from MRI in myeloma patients: A Neural Network Approach
Authors: Kächele Jessica , Wennmann Markus , Fischer Maximilian , Peretzke Robin , Wald Tassilo , Bernhard Juliane , Bauer Fabian , Sauer Sandra , Hillengass Jens , Mai Elias , Weinhold Niels , Goldschmidt Hartmut , Raab Marc-Steffen , Schlemmer Heinz-Peter , Delorme Stefan , Maier-Hein Klaus , Neher Peter
Published: November 2024

Motivation: While Radiomics analysis has shown predictive power for plasma cell infiltration (PCI) from MRI in Myeloma patients, convolutional neural networks (CNNs) offer an opportunity for improved performance and generalizability. Goal(s): Our objective was to develop a predictive model for PCI using CNNs while addressing the challenges posed by limited dataset size. Approach: CNNs were trained on MRI data of the pelvic bone marrow and its predictive capabilities were enriched by concatenating radiomic features in the latent space. Results: The findings revealed limitations due to the small dataset size. However, incorporating radiomic features enhanced prediction accuracy, aligning with radiomics and random forest-based methods. Impact: This study highlights the limitations of deep learning when using a small dataset. It underlines the importance of feature extraction and the need of dedicating substantial efforts to create large annotated datasets.
Decoding Sensitivity of Quantitative Susceptibility Mapping: Influence of Background Field Removal and Inversion Algorithms
Authors: Salman Fahad , Ramesh Abhisri , Prayer Mirjam , Adegbemigun Ademola , Jochmann Thomas , Bergsland Niels , Dwyer Michael , Zivadinov Robert , Schweser Ferdinand
Published: November 2024

Motivation: Quantitative Susceptibility Mapping (QSM) is widely applied in clinical research. However, its accuracy relies on the choice of background field removal (BFR) and inversion algorithms. This raises the question: What is the sensitivity of algorithms toward the detection of in-vivo group differences and over-time susceptibility changes?Goal(s): Explore the impact of BFR and inversion algorithms on the detection of over-time susceptibility changes. Approach: Utilizing six BFRs and twenty-one inversion algorithms, we studied the sensitivity to detect aging-related over-time susceptibility changes. Results: RESHARP+iSWIM within overall DGM, RESHARP+AMP-PE in putamen, PDF+IterTIK in caudate, PDF+TKD in globus pallidus and RESHARP+iSWIM in thalamus demonstrated the highest sensitivity. Impact: The importance of algorithm and reference region choice in QSM studies, impacting findings beyond demographics and clinical characteristics. Future research should employ varied QSM algorithms to assess their impact on longitudinal QSM changes, enhancing the quality of clinical investigations.
Feasibility of High-Speed R2 and R2’ Mapping Via Alternating SSFP With View-Sharing
Authors: Bae Eunseo , Oh Sungsuk , Lee Hyunyeol
Published: November 2024

Motivation: Quantifying R2 and R2' provide important implications about physiologic and functional states of tissues. Goal(s): To explore feasibility of an accelerated R2 and R2’ mapping strategy. Approach: A view-sharing method is integrated into the alternating nonbalanced SSFP-based technique enabling simultaneous R2 and R2’ mapping. Results: : In both phantom and brain scans, the parametric maps obtained via the presented scan acceleration approach are overall in good agreement with those derived from a reference method. Impact: Upon further evaluation, the method may be a useful means in a wide range of neuroimaging applications.
The effects of different expression states of δ-catenin on resting brain functional in patients with breast cancer before treatment
Authors: Xue Mingtuan , Cao Jiajun , Du Wei , Miao Yanwei
Published: November 2024

Motivation: δ-catenin is the only member of the p120ctn family that can be expressed in neurons. It has been confirmed that δ-catenin highly expressed in breast cancer patients and is significantly associated with the poor prognosis. Goal(s): Explore the changes of brain network function in BRCA patients with different expressions of δ-catenin before treatment. Approach: Categorize untreated BRCA patients into two groups based on the different expressions of δ-catenin,and subjected to neuropsychological testing, brain structural and functional MRI, along with a healthy control group. Results: The breast cancer patients with different δ-catenin expression have different effects on the indexes of brain functional network. Impact: The research results suggest that the brain-network changes caused by δ-catenin precede cognitive impairment changes. Early brain damage caused by δ-catenin protein may involve areas related to executive functions and emotional regulation.
Comparing non-invasive blood-brain barrier mapping with dynamic susceptibility contrast MRI in patients with high-grade glioma and metastasis
Authors: Hoffmann Gabriel , Preibisch Christine , Günther Matthias , Mahroo Amnah , van Osch Matthias , Václavů Lena , Metz Marie-Christin , Jung Kirsten , Zimmer Claus , Wiestler Benedikt , Kaczmarz Stephan
Published: November 2024

Motivation: Glioma-induced blood-brain barrier (BBB) disruptions can be characterized by dynamic susceptibility contrast MRI via the leakage parameter K2. However, it may lack sensitivity to subtle impairments. Recently, non-invasive ASL-based water-exchange measurements (Tex) were proposed to measure even subtle BBB-impairments. Goal(s): We hypothesized correlations of Tex with K2 in contrast-enhancing tissue (CET). Approach: K2 & Tex were compared in 22 patients with brain tumors and 19 healthy controls. Results: Tex agreed well with K2 in CET and was sensitive to pathophysiologically impaired BBB. Moreover, results indicate superior sensitivity to subtle impairments, which may improve therapy planning and progress monitoring. Impact: ASL-based Tex allows non-invasive detection of the pathophysiologically impaired blood-brain barrier in tumors. Whereas its sensitivity to subtle impairments may improve treatment planning in tumors, it could also impact diagnosis of neurodegenerative diseases such as Alzheimer's or Parkinson’s.
Differentiating Brain Metastases Recurrence from Treatment-Induced Changes Using Velocity-Selective ASL: Initial Experience
Authors: Zhu Dan , Xu Feng , Liu Dapeng , Katulis Lisa , Lin Doris , Kleinberg Lawrence , Qin Qin
Published: November 2024

Motivation: Brain metastases (BM) have increased frequency of treatment-related changes difficult to distinguish from progressive disease. To date, there are no studies evaluating arterial spin labeling (ASL) for BM patients during post-therapy follow-up. Goal(s): To evaluate the feasibility of velocity-selective ASL (VSASL) derived cerebral blood flow (CBF) mapping in differentiating metastases recurrence from treatment-induced changes. Approach: VSASL was applied to 9 BM patients, and compared with pseudo-continuous ASL (PCASL), dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI), and pathologic confirmation for some patients. Results: VSASL is clinically feasible and more comparable with DSC-PWI than PCASL in patients with treated BM at suspicion of tumor progression. Impact: This study showed that velocity-selective arterial spin labeling has the potential to offer a non-invasive alternative to dynamic susceptibility contrast perfusion-weighted imaging in differentiating tumor recurrence from treatment-induced changes among patients with brain metastases.
Comparing methods of biomarker quantification for contrast clearance analysis in patients with brain metastases
Authors: Guo Yifan , Boisanger James , Harris Emma , Benjamin Philip , Rosenfelder Nicola , Blackledge Matthew
Published: November 2024

Motivation: Differentiating tumour progression and treatment-induced changes in brain metastases is challenging, and quantitative measures of tumour burden and response are needed. Goal(s): To develop and compare quantitative contrast clearance analysis (CCA) mapping techniques with conventional qualitative approaches in 9 patients with brain metastases. Approach: We assessed the correlation of Relative Enhancement and Fractional Enhancement methods with conventional CCA in the brain and within detected tumour regions. Results: Fractional Enhancement mapping was superior to relative enhancement in terms of its statistical properties and correlation with conventional CCA. Impact: Quantitative enhancement fraction mapping of contrast clearance analysis (CCA) data is clinically feasible and demonstrates good correlation with conventional CCA. When used in combination with conventional radiological CCA, it may help quantify post-treatment changes in brain metastases following treatment.
Detecting BCL-6 overexpression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning
Authors: mingxiao wang , Lin Ma , Guoli Liu , nan Zhang , Yanhua Li
Published: November 2024

Motivation: Based on BCL-6 status，the prognosis of PCNSL can be detected，then the treatment can be adjusted. Goal(s): Detecting BCL-6 Expression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning. Approach: Using Python code to retrieve Pyromics for radiomics feature screening from T2、T2 Flair、ADC.The AUC value was used to evaluate the detection performance of the image sequence joint classifier, Obtain the best classifier. Results: The multi parameter sequence combined with SVM machine learning has the highest AUC, with BCL-6 overexpression detected in the training and validation sets of 0.945 and 0.865,sensitivity of 98% and 92.7%, specificity of 83.9% and 87.5%. Impact: Based on BCL-6 status, the patients are divided into "good risk" and "poor risk".patients who have a “poor risk”phenotype may be candidates for aggressive initial therapy with chemotherapy and radiaion.It may be desirable to defer WBRTto avoid the radiation-induced neurotoxicity.
A continuous-time random-walk diffusion model for predicting tumor consistency and extent of resection in patients with pituitary adenomas
Authors: Su Chun-Qiu , Lin Zeng-Ping , Tang Ran , Xue Ke , Shi Hai-Bin , Lu Shan-Shan
Published: November 2024

Motivation: Preoperative evaluation of the consistency of pituitary adenomas (PAs) plays a significant role in the surgical strategy. However, previous studies concerning the assessment of tumor consistency of PAs were controversial1-4. Goal(s): To evaluate the ability of the continuous-time random-walk (CTRW) diffusion model to predict the consistency and extent of resection (EOR) of PAs. Approach: The CTRW diffusion model relaxes a priori distributions of waiting times and distance increments of water molecular diffusion, providing a realistic description of the complex structures of biological tissues5. Results: CTRW diffusion model could provide information about the tumor consistency and EOR of PAs. Impact: CTRW diffusion model provides an imaging dimension for characterizing tissue microstructure of PAs and may serve as a promising tool to predict the tumor consistency and EOR in patients with PAs.
Multiparametric Simultaneous Hybrid 18F-FDG PET/MRI Incorporating Intratumoral and Peritumoral Regions for Grading of Glioma
Authors: Liu Ping , Zeng Yuping , Zhen Wanyi , Jiang Guihua
Published: November 2024

Motivation: The biological behavior and prognosis between low- and high-grade gliomas (HGG) are different, it is important to preoperatively judge the grading in clinical practice. Goal(s): Multiple parameters derived from hybrid 18F-FDG PET/MRI of the solid component and peritumoral zone (PTZ) can potentially improve the accuracy of glioma grading. Approach: We employed multiparametric simultaneous hybrid 18F-FDG PET/MRI including PET, ASL, and DWI from the solid component and PTZ of glioma to differentiate HGG from LGG. Results: The combination of multiple parameters from hybrid PET/MRI in tumor and PBZ can provide better diagnostic efficacy than a single parameter alone. Impact: Incorporating multiple tumoral regions into multiparameter from simultaneous 18F-FDG PET/MRI can optimize the workflow efficiency for glioma grading, and aid treatment decision-making to offer appropriate, patient-tailored precision medicine, and reduce the risk of unnecessary or inappropriate treatments.
The value of DCE-MRI and IVIM in predicting TERTp mutation status in glioblastoma
Authors: Zhao Jiamei , Li Zongfang , Hu Siqi , Li Xuemei , Li Linyun , Zhan Qinyong , Nie Lisha
Published: November 2024

Motivation: This study aims to predict the TERTp mutation status noninvasively before surgery, which is crucial for determining treatment strategies and prognosis in GBM patients. Goal(s): Investigate the predictive value of DCE-MRI and IVIM for TERTp mutation status in GBM patients. Approach: 68 GBM patients were enrolled according to the 2021 WHO classification, and divided into TERTp mutant group and TERTp wild group. Compare DCE-MRI and IVIM parameters between the two groups. Results: The DCE-MRI parameter Vp was significantly higher in the TERTp mutation group (AUC=0.708), indicating its potential as a predictor for TERTp mutation status. Impact: This study suggests that Vp, a DCE-MRI parameter, can predict TERTp mutation status noninvasively. This has important implications for targeted therapy and prognosis prediction in GBM patients.
Improved Detection of Target Metabolites in Brain Tumors with Intermediate TE High SNR and High Bandwidth Spin Echo Sequence at 5.0T
Authors: Sun Wenbo , Xu Dan , Song Xiaopeng , Li Huan , Xu Haibo
Published: November 2024

Motivation: challenges emerge at ultra-high fields when measuring metabolites using 1H-MRS. Goal(s): To investigate how well the high SNR and high bandwidth spin echo (HISE) technique performs at 5.0T for detecting target metabolites in brain tumors. Approach: 26 Subjects suspected of having brain tumors were enrolled. HISE and point-resolved spectroscopy (PRESS) single-voxel spectroscopy (SVS) scans were collected with a 5.0T clinical scanner with an intermediate echo time (TE=144ms). Results: HISE outperformed the clinical standard PRESS technique in detecting target metabolites of brain tumors at 5.0T, particularly Lac and Ala. Impact: In a recently developed whole-body 5.0T clinical scanner, the HISE technique has been demonstrated to be more preferable than PRESS for the clinical diagnosis of brain tumors.
Identification of glioma IDH genotypes using time-dependent diffusion magnetic resonance imaging-based microstructural mapping
Authors: Hu Wanjun , Zhang Jing , Li Darui , AI Kai
Published: November 2024

Motivation: Time-diffusion-dependent diffusion MRI (t-dMRI)provides quantitative imaging of cellular microstructure; however, its value in diagnosing molecular subtypes of IDH in gliomas remains unknown.Goal(s): Molecular subtypes of IDH were diagnosed using quantitative time-dependent diffusion imaging parameters.Approach: t-dMRI was acquired using OGSE and PGSE sequences, and quantitative parameters were then fitted using the Imaging Microstructural Parameters Using Limited spectrally edited diffusion(IMPULSED) method and evaluated for diagnostic potency for molecular subtypes of IDH in gliomas. Results: t-dMRI can identify IDH genotypes, and intracellular fraction (fin) reflects the actual state of tumor cells.Impact: t-dMRI can non-invasively identify IDH genotype.