bluuebunny/crossref_metadata_2025 huggingface dataset:
Identify molecular subtypes in breast cancer using time-dependent diffusion MRI based microstructural mapping
Authors: Wang Xiaoxia , Ba Ruicheng , Yin Ting , Wu Dan , Zhang Jiuquan
Published: November 2024

Motivation: It is imperative to noninvasively assess molecular subtypes in patients with breast cancer, as these play a vital role in guiding treatment approaches and monitoring outcomes. However, conventional apparent diffusion coefficient measurements may not reliably identify the histopathologic differences in molecular subtypes. Goal(s): We explore the feasibility of time-dependent diffusion MRI (td-dMRI) based microstructural mapping for noninvasive identification of molecular subtypes. Approach: The td-dMRI method was validated on breast cancer patients, and microstructural parameters were estimated and compared among molecular subtypes. Results: The cellularity and diameter derived from td-dMRI proved effective for identifying molecular subtypes in breast cancer. Impact: Microstructural mapping derived from td-dMRI proves to be an effective method for predicting molecular subtypes, demonstrating unique microstructural properties acroess various molecular subtypes, and thus is promising in personalizing treatment strategies.
High-Resolution MR Microscopy of Mouse Spinal Cord at 15.2 T
Authors: Dhakal Bibek , Hardy Benjamin , Anderson Adam , Does Mark , Xu Junzhong , Gore John
Published: November 2024

Motivation: The research aims to overcome the challenges of performing microscopy to assess the microstructure of mouse spinal cords at high spatial resolution. Goal(s): Our goal is to develop a micro-solenoid radiofrequency circuit, which combined with ultra-high field strength and fast imaging sequences, including diffusion MRI, can achieve microscopic-resolution images. Approach: The study involves the development of a micro-solenoid transceiver coil, and imaging at 15.2 T using fast diffusion imaging sequences to achieve images of excised specimens at microscopic resolution. Results: The micro-solenoid radiofrequency circuit significantly improved SNR, enabling high-resolution imaging and accurate data sets for implementing diffusion models at micron-scale resolution. Impact: High-resolution diffusion imaging may provide estimates of diffusion parameters at a scale more commensurate with the microstructure of the spinal cord than in vivo acquisitions. This will be useful for validating models of water diffusion in complex environments neuronal tissue.
Diffusivity and kurtosis time-dependence changes in the rat brain during somatosensory evoked response
Authors: Hertanu Andreea , Pavan Tommaso , Jelescu Ileana O.
Published: November 2024

Motivation: The microstructure-function relationship is essential for our understanding of the healthy and pathological human brain. Goal(s): In this context, the goal of our study was to investigate microstructural changes arising in the brain during neuronal activity. Approach: Differences in mean diffusivity MD and mean kurtosis MK time-dependence between rest and active states were assessed in the somatosensory cortex following rat forepaw stimulation. Results: While no changes were found in control cortical regions, the BOLD-fMRI positive cluster presented a significant decrease in MD and MK during activation. Interestingly, subcortical somatosensory relays displayed the opposite trend which could result from changes in inhibitory/excitatory balance. Impact: Neuronal activity is accompanied by a myriad of microstructural changes. The diffusion-weighted signal sensitivity to underlying brain microstructure brings new perspectives into the structure-function relationship along with the promise of a functional contrast unbound from the current limitations of BOLD-fMRI.
Sensitivity of quantitative MRI to demyelination and axonal loss: validation against myelinated and unmyelinated axons from histology
Authors: Abdollahzadeh Ali , Coronado-Leija Ricardo , Chasen Elizabeth , Novikov Dmitry , Fieremans Els
Published: November 2024

Motivation: Attaining microstructural specificity to myelinated/unmyelinated axons from macroscopic in vivo quantitative MRI. Goal(s): Quantifying changes of myelinated and unmyelinated axons using dMRI. Approach: We apply compartmental diffusion models of white matter, White Matter Tract Integrity (WMTI) and Standard Model Imaging (SMI) to the dMRI signal. We also measure Magnetic Transfer Resonance and R2. We develop automated techniques to segment 2d transmission electron microscopy (TEM) images of white matter into their constituent microstructure and apply volumetric analysis. Results: We measured axonal water fraction (AWF) using WMTI and SMI. AWF correlated strongly with our EM volumetric analysis of myelinated and unmyelinated axons. Impact: Demyelination and axonal loss occur in neurodegenerative pathologies. This validation study reveals specificity of R2 to myelin volume, while AWF from dMRI detects both unmyelinated and unmyelinated axons. Combining both modalities has the potential to differentiate demyelination from axonal loss.
In vivo investigation of recuperation dynamics of altered myocardial PDH activity induced by a ketogenic diet
Authors: Chen Jun , Erfani Zohreh , Elnwasany Abdallah , Nemri Sarah Al , Huynh Mai , Zaidi Maheen , Harrison Crystal , Wen Xiaodong , Szweda Pamela , Szweda Luke , Park Jae Mo
Published: November 2024

Motivation: Metabolic alterations associated with a ketogenic diet (KD) include suppressed carbohydrate utilization in the myocardium, but how quickly they recover by reverting to a normal diet (ND) is under-investigated. Goal(s): This study investigates recuperation dynamics of myocardial pyruvate oxidation by reverting a KD to a ND. Approach: Cardiac metabolism of healthy rats was longitudinally assessed using hyperpolarized [1-13C]pyruvate at baseline, during a KD, and a subsequent ND after the 5-week KD. Results: Cardiac [13C]bicarbonate disappeared with a KD. Reverting to ND gradually recovered the PDH flux over eight days. [1-13C]Lactate production in the KD group showed the opposite but similar dynamic patterns. Impact: Compensating the metabolic shifts by a ketogenic diet is challenged for proper assessment of cardiac metabolism and is often attempted by temporarily reverting the diet. Understanding the recuperation dynamics is crucial in proper assessment of cardiac metabolism.
Feasibility of water-fat separated free-running 3D cardiac cine imaging with a phase-sensitive approach
Authors: Wang Dinghui , Chao Tzu Cheng , Yerly Jerome , Stuber Matthias , Roy Christopher , Waddle Spencer , Browne Jacinta , Leiner Tim
Published: November 2024

Motivation: Water-fat separated (WFS) imaging can improve detection and characterization of various cardiovascular pathologies. We hypothesize that the signal phase at half the repetition time of balanced steady-state free precession (bSSFP) may be used for efficient WFS cine imaging. Goal(s): To study the feasibility of WFS free-running 3D cardiac cine imaging using the phase-sensitive approach. Approach: After reconstruction of 5D whole heart images, the global slowly varying phase was estimated and removed. Water and fat voxels were then identified according to the phase. Results: Water and fat were sufficiently separated around the heart. A field map of off-resonance can partially mitigate peripheral water/fat swaps. Impact: A phase sensitive approach for WFS only requires negligible computational cost and minor adjustment of repetition time. It might be implemented with a wide range of bSSFP cine imaging techniques for fat suppression or to provide complementary water/fat information.
Rapid Pediatric Imaging with Zero-Shot Deep Subspace Reconstruction for Multiparametric Quantitative MRI
Authors: Jun Yohan , Fujita Shohei , Cho Jaejin , Yong Xingwang , Milshteyn Eugene , Jaimes Camilo , Ferraciolli Suely , Gagoski Borjan , Gee Michael , Bilgic Berkin
Published: November 2024

Motivation: To address unmet needs for accurate, rapid, and high-fidelity quantitative MRI using a 3D-QALAS sequence. Goal(s): To enable accurate T1 and T2 mapping with reduced biases, g-factor noise amplification, and relaxation-related blurring compared to conventional QALAS. Approach: We employed a zero-shot self-supervised subspace reconstruction technique, Zero-DeepSub, which combines scan-specific deep-learning-based reconstruction with low-rank subspace modeling, and demonstrated the performance using ISMRM/NIST phantom and pediatric patients. Results: Zero-DeepSub enabled a highly accelerated, 2 min acquisition at 1 mm isotropic resolution at 3T, as well as a 5 min pediatric exam at 1.2 mm isotropic resolution at 1.5T. Impact: Zero-DeepSub enabled accurate T1 and T2 mapping with reduced biases, g-factor noise amplification, and relaxation-related blurring, showing the potential to substantially speed up pediatric brain exams, thus obviating the need for or reducing the amount of sedation and anesthesia.
Sub-Second GRASP-LLR DCE: Locally Low-Rank Subspace Constraint aided by Deep Learning
Authors: Solomon Eddy , Bae Jonghyun , Moy Linda , Heacock Laura , Feng Li , Kim Sungheon Gene
Published: November 2024

Motivation: We hope to advance the assessment of breast dynamic contrast-enhanced MRI (DCE-MRI) by enhancing image quality, temporal resolution, and temporal fidelity. Goal(s): Propose a new radial GRASP reconstruction pipeline for DCE-MRI, which enables reliable spatially localized dynamics at a sub-second temporal resolution. Approach: Presenting globally and locally low-rank reconstruction approaches for GRASP DCE-MRI aided by Residual Network (ResNet) architecture. Results: Our results suggest that GRASP-LLR offers not only enhanced tumor lesion delineation with reduced background noise but also good separation between healthy, benign, and malignant cases. Impact: We propose a new radial reconstruction pipeline for DCE-MRI which leverages a locally low-rank (LLR) subspace model in combination with deep learning approach, resulting in reliable spatially localized dynamics at a sub-second temporal resolution.
Time-Resolved Cardiac function: Myocardium Strain and First-Pass Perfusion Using MR-MOTUS
Authors: Olausson Thomas , Terpstra Maarten , Huttinga Niek , Beijst Casper , Blanken Niels , Suchá Dominika , Correia Teresa , van den Berg Cornelis , Sbrizzi Alessandro
Published: November 2024

Motivation: We aim at improving accuracy and reliability in cardiac imaging for coronary artery disease diagnosis and management. We address motion artifacts in first-pass myocardial perfusion MR imaging. Goal(s): To develop and validate motion correction techniques for motion field accuracy and strain quantification in non-ECG triggered myocardial first pass perfusion examinations. Approach: We use a modified MR-MOTUS framework for motion separation and reconstruction in patient data. Results: Our approach demonstrates higher accuracy in respiratory/cardiac motion field estimation with additional strain analysis. Impact: Our findings have the potential to improve patient care by enabling free-breathing and non-ECG triggered examinations of myocardial first-pass perfusion simultaneously with strain analysis. We also open avenues for further research in cardiac imaging and motion correction techniques.
Joint optimization of multi-echo reconstruction and quantitative map estimation in Looping Star
Authors: Xiang Haowei , Onder Ilhan , Mehta Anahita , Fessler Jeffrey , Noll Douglas
Published: November 2024

Motivation: Looping-star sequences, despite their advantages, exhibit low SNR and undersampling artifacts compared to standard GRE sequences. Goal(s): This work proposes to jointly reconstruct multi-echo data and estimate quantitative maps in looping-star to boost the SNR, reduce the undersampling artifacts, and improve image quality. Approach: Our approach frames echo image reconstruction and quantitative map estimation as a unified optimization problem. This is then split into two sub-problems, addressed alternately using CG-SENSE. Results: Compared to individual echo reconstruction, our joint optimization improves tSNR of both echo images and T2* maps and effectively mitigates image artifacts. Impact: Our method jointly reconstructs multi-echo data in looping-star, enhancing SNR and reducing artifacts, with a notable tSNR improvement. It can be adapted to the Looping-Star fMRI protocol to potentially improve functional activity estimation.
Articular Cartilage 3D T2* Mapping at 7 Tesla in Patients with Medial Meniscus Posterior Root Tears
Authors: Knutsen Karsten , Kajabi Abdul Wahed , Zbyn Stefan , Steinberger Collin , Tollefson Luke , Smith Jesse , Takahashi Takashi , Metzger Gregor , LaPrade Robert , Ellermann Jutta
Published: November 2024

Motivation: Meniscal root tears are increasingly recognised as the ‘root cause’ of osteoarthritis with articular cartilage loss as the hallmark of the disease. Goal(s): Utilising SNR gains at 7T UHF MRI to detect early changes in cartilage composition. Approach: Compare high resolution quantitative cartilage 3D T2* relaxation times at 7T in patients with arthroscopically confirmed medial meniscal posterior root tears (MMPRT’s) to matched controls. Results: Prolonged T2* relaxation times were found in the weight bearing medial and lateral cartilage of the femur in patients with meniscal tears, when compared with controls. No changes were seen in the tibial cartilage. Impact: Identifying early cartilage degeneration using high resolution T2* at 7 Tesla could inform timely treatments, such as prompt meniscal repair, potentially preventing the onset of osteoarthritis.
Clinical evaluation of spine disorders using high contrast cartilaginous endplate imaging
Authors: Athertya Jiyo , Statum Sheronda , Chen Xiaojun , Jerban Saeed , Chung Christine , Du Jiang , Chang Eric , Ma Yajun
Published: November 2024

Motivation: Lower back pain is a common cause of ailment driven by disc degeneration and endplate defects. Imaging the CEP region would increase the diagnostic accuracy of early detection. Goal(s): To develop an optimized sequence to image the CEP that is readily translatable to clinical settings. Approach: The clinically available 3D FS-SPGR sequence was optimized and applied to image the CEP with high contrast for both asymptomatic and symptomatic subjects. Results: The optimized 3D FS-SPGR sequence with T1-weighting can delineate the CEP regions with high contrast and reveal irregularities that are not visible on routinely used protocols. Impact: The fast T1-weighted 3D FS-SPGR sequence can be readily implemented on clinical scanners, used to highlight the CEP, and shows promise for the detection of intervertebral disc abnormalities.
Foundation Model based labelling of MR Shoulder images to drive Auto-Localizer workflow
Authors: M Gurunath Reddy , Shao Muhan , Anand Deepa , Manickam Kavitha , Gui Dawei , Bhushan Chitresh , Shanbhag Dattesh
Published: November 2024

Motivation: Develop automatic labelling capability on anatomical shoulder MRI images with minimal manual annotation. Goal(s): Leverage large-FOV, low resolution coil sensitivity maps to guide correct positioning of three-plane localizer for shoulder MRI planning. Approach: Use chained DINO-V2 and SAM foundation models, tuned to MRI localizers and a data driven similarity measure to label shoulder data at scale and transfer to low resolution coil sensitivity maps for CNN model training. Results: Excellent shoulder region localization with FM on anatomical (91% accuracy) and with CNN model on calibration data (error < 15 mm)Impact: A data adaptive, chained foundation model-based approach for annotating shoulder regions on MRI anatomical images at scale is shown. This allowed rapid development of model using low-resolution calibration data for correctly positioning three-plane localizer for shoulder anatomical planning and imaging.
Distribution Matching Based Personalized Federated Learning for Multi-Contrast Liver MRI Synthesis and Registration
Authors: Zheng Rencheng , Yu Hang , Li Ruokun , Wang Qidong , Chen Caizhong , Dai Fei , Zhang Boyu , Chu Ying-Hua , Chen Weibo , Wang Chengyan , Wang He
Published: November 2024

Motivation: The combined diagnosis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE)-MRI is of significant importance for liver diseases, but accurate registration between these two modalities remains a substantial challenge. Goal(s): Our goal was to design a deep learning model for accurate registration between DCE and DCE-MRI, and conduct multicenter studies based on federated learning. Approach: We proposed a multi-task synthesis-registration network (SynReg) and a personalized decentralized distribution matching federated framework (PDMa) based on SynReg. Results: The proposed SynReg and PDMa method increased the registration accuracy in most centers both in liver region and liver tumor region. Impact: Accurate and rapid registration of DWI and DCE can effectively assist clinicians in leveraging multimodal imaging for efficient diagnosis. Personalized federated learning can effectively aid single-center with limited data to leverage the abundant data from multiple centers for model development.
Accelerating DT-CMR with Deep Learning-based Tensor De-noising and Breath Hold Reduction
Authors: Tanzer Michael , Scott Andrew , Khalique Zohya , Dwornik Maria , Rajakulasingam Ramyah , De Silva Ranil , Pennell Dudley , Ferreira Pedro , Yang Guang , Rueckert Daniel , Nielles-Vallespin Sonia
Published: November 2024

Motivation: DT-CMR can revolutionise diagnosis and treatment of heart conditions by non-invasively imaging cardiomyocyte microstructure, but currently long acquisition times prevent clinical use. Goal(s): Reduce the number of breath-holds required for in-vivo DT-CMR acquisitions, resulting in significantly reduced scan times with minimal image quality loss. Approach: We developed a deep learning model based on Generative Adversarial Networks, Vision Transformers, and Ensemble Learning to de-noise diffusion tensors computed from reduced-repetition DT-CMR data. We compared model performance to conventional linear fitting methods and a baseline deep learning approach. Results: Our model reduced noise over 20% compared to previous state-of-the-art approaches while retaining known clinically-relevant myocardial properties. Impact: This breakthrough in DT-CMR acquisition efficiency could enable rapid microstructural phenotyping of the myocardium in the clinic for the first time, revolutionising personalised diagnosis and treatment by unlocking DT-CMR’s ability to non-invasively characterise heart muscle organisation at the cellular level.
3D free-breathing simultaneous myocardial T1 and T1ρ mapping with B1+ correction and subject-specific non-rigid motion correction
Authors: Qi Haikun , Lyu Zhenfeng , Diao Jiameng , Zhu Jiayu , Xu Jian , Botnar René , Prieto Claudia , Hu Peng
Published: November 2024

Motivation: Multi-parametric mapping is useful for comprehensive myocardial tissue characterization. However, 3D free-breathing cardiac multi-parametric mapping faces challenges. Goal(s): Develop a 3D free-breathing cardiac multi-parametric mapping framework that is robust to confounders of motion, fat and field inhomogeneities and validate it for joint T1 and T1ρ mapping at 3T. Approach: A subject-specific respiratory motion model was constructed to enable intra-bin 3D translational and inter-bin non-rigid motion correction. B1+ inhomogeneities were corrected with optimized dual-flip-angle strategy. A dual-echo Dixon readout was adopted for water-only mapping. Results: The proposed technique achieved good agreement with conventional techniques in measuring T1 and T1ρ in phantoms and healthy subjects. Impact: A novel framework was proposed for efficient 3D free-breathing multi-parametric mapping. The 3D simultaneous cardiac T1 and T1ρ mapping technique with scan time of ~ 5 minutes may serve as an efficient tool for diagnosing ischemic and non-ischemic cardiomyopathies.
Accelerated T1ρ,adiab Mapping using Slice Selective Spin-Lock Preparation Pulses (FAST1ρ)
Authors: Tyler Andrew , Kunze Karl , Neji Radhouene , Masci Pier Giorgio , Chiribiri Amadeo , Roujol Sébastien
Published: November 2024

Motivation: T1ρ mapping is a promising non-contrast technique for the assessment of myocardial scar. Myocardial T1ρ mapping techniques commonly acquire multiple images in one breath hold to calculate a single-slice T1ρ map. Recently, non-selective adiabatic pulses have been employed for spin-lock preparation (T1ρ,adiab). FAST1ρ provides a two-fold acceleration for multi-slice myocardial T1ρ,adiab mapping. Goal(s): To assess the T1ρ,adiab map quality of FAST1ρ compared to a single-slice 2D T1ρ,adiab mapping sequence. Approach: 10 healthy-volunteers were scanned with FAST1ρ and a single-slice sequence. Results: FAST1ρ had no significant difference in intra-subject variability or subject-wise precision, to the single-slice sequence, and successfully visualized a myocardial scar. Impact: FAST1ρ provides a two-fold acceleration for multi-slice myocardial T1ρ,adiab mapping. It has no significant difference in intra-subject variability or subject-wise precision, compared to a single-slice sequence, and shows promise for characterization a myocardial scar.
Free-breathing T1ρ dispersion imaging for myocardial fibrosis without exogenous contrast agents
Authors: Miao Qinfang , Lv Zhenfeng , Hua Sha , Hu Peng , Qi Haikun
Published: November 2024

Motivation: LGE and ECV are standard cardiac MR (CMR) techniques for detecting focal and diffuse myocardial fibrosis, requiring gadolinium contrast agents. For patients with gadolinium contraindications, non-contrast CMR techniques are needed to detect myocardial fibrosis. Goal(s): To evaluate the performance of endogenous T1ρ dispersion imaging for myocardial fibrosis. Approach: A recently proposed free-breathing T1ρ dispersion imaging technique was employed to image patients with non-ischemic cardiomyopathies. The endogenous parameters, T1ρ, myocardial fibrosis index (mFI), and native T1 were evaluated against LGE and ECV. Results: T1ρ and mFI were elevated in the LGE region and mFI showed the best correlation with ECV among the tested parameters. Impact: T1ρ dispersion imaging is a promising CMR technique for detecting myocardial fibrosis without exogenous contrast agents.
In vivo 1H MRS identifies intramyocellular lipids as early prediabetic marker in diet-induced obesity
Authors: Flocke Vera , Wegener Katja , Staub Tamara , Grandoch Maria , Flögel Ulrich
Published: November 2024

Motivation: Obesity is one of the main risk factors for type 2 diabetes and is also associated with an increased cardiovascular risk. However, the transition from the early states of impaired glucose intolerance to a more progressive disease stage still remains elusive. Goal(s): To identify biomarkers and time points for early therapeutic interventions to stop further transition. Approach: We used an established mouse model exposed for 9 weeks to a diabetogenic diet and longitudinally monitored important organs/tissues by MRI+MRS. Results: Using MRS we could detect massive accumulation of lipids in all organs which preceded even a sigificant weight gain in DD-fed mice. Impact: MRS of intramyocellular lipids was most sensitive to reveal in vivo very early alterations in the prediabetic state and, thus, may also be used in humans to identify patients at the transition point from prediabetes to diabetes.
Multiparametric MRI of a Mouse-INtraDuctal (MIND) in vivo model of human invasive lobular breast carcinoma
Authors: Lesbats Clémentine , Flaherty Renée , Sinkus Ralph , Brisken Cathrin , Robinson Simon
Published: November 2024

Motivation: Invasive lobular carcinoma (ILC) is a major subtype of breast cancer but remains very difficult to detect until late stage. There is a lack of sensitive imaging techniques for the diagnosis of ILC. Goal(s): To define clinically-translatable MRI biomarkers of the heterogeneous tumour microenvironment in ILC. Approach: A Mouse-INtraDuctal (MIND) model was used to emulate late stage ILC. Multiparametric MRI was performed on ~10 month-old tumours. Results: ILC exhibited bimodal ADC and stiffness profiles. Invasive tumour regions displayed low ADC and elevated stiffness compared to lobular in situ regions. Impact: Multiparametric MRI can inform on the evolving microenvironment within MIND model of invasive lobular carcinoma. Invasive regions displayed restricted water diffusion and elevated stiffness, highlighting the utility of DWI and MRE as informative techniques for detecting and assessing invasiveness.
Imaging of thorax and diaphragm movement in mechanically ventilated mice and rats
Authors: Wennen Myrte , Claassen Wout , van Huis Nick , Heunks Leo , Ottenheijm Coen , Garipov Ruslan , Alles Lindy , Coolen Bram , Strijkers Gustav
Published: November 2024

Motivation: The pathophysiology of diaphragm dysfunction in mechanically ventilated patients is not fully understood and adequate animal models are required to accommodate further research. Goal(s): Our goal was to develop a method to image 3D thoracic movement during mechanical ventilation of mice and rats at different pressure levels of mechanical ventilation. Approach: With our setup we visualized the movement of the thorax in mice and rats using self-gated 3D pseudo-radial k-space sampling. Results: Imaging was feasible in both animal types and increase of pressure resulted in a decrease of mean diaphragm excursion of 0.9 and 1.3 mm in mouse and rat respectively. Impact: Our proposed setup allows controlled mechanical ventilation and MR imaging of 3D thorax movement in mice and rats. This can be used to study the pathophysiology behind mechanical ventilation-induced respiratory muscle dysfunction, and ultimately guiding clinical practice in respiratory care.
Comparison of cardiomyocyte orientation assessed with diffusion tensor imaging and a high-angular resolution diffusion imaging model
Authors: Ferreira Pedro , Munoz Camila , Baxan Nicoleta , Scott Andrew , Pennell Dudley , Nielles-Vallespin Sonia
Published: November 2024

Motivation: The orientation of the cardiomyocytes can be probed non-invasively with diffusion imaging. In-vivo, this is typically assessed by the orientation of the primary eigenvector in a diffusion tensor model, although questions remain about the ability of this model to probe complex cardiomyocyte arrangements. Goal(s): To assess the agreement of cardiomyocyte orientation measures given by a diffusion tensor and a more robust q-ball model. Approach: A diffusion tensor model was compared to a q-ball diffusion model in a healthy porcine heart. Results: Median angular deviations of 7.8 and 10.8 degrees were found for the main diffusion direction between the two models. Impact: We provide an estimate of cardiomyocyte angular orientation differences between a diffusion tensor model and a more robust diffusion q-ball model with a higher angular resolution in a healthy porcine heart.
Non-invasive monitor expression of the glycolytic enzyme in vivo with deuterium MRI
Authors: Liu Xinjie , Guo Yusheng , Zhang Zhi , Pan Feng , Yang Lian , Sun Peng , Zhou Xin , Liu Chaoyang , Bao Qingjia
Published: November 2024

Motivation: Enzyme expression plays a crucial role in tumor metabolism, influencing tumor development and response to therapy. However, there is a lack of non-invasive techniques to measure the expression of glycolytic enzymes in research and clinics. Goal(s): Verify whether Deuterium metabolic imaging (DMI), a promising non-invasive technique, can effectively monitor the expression changes of glycolysis enzyme in vivo. Approach: Utilize deuterium MRS/MRI to monitor metabolic flux in two groups of mice with control tumor and HK2 knockdown tumor. Results: DMI can indirectly monitor the expression changes of glycolytic enzymes represented by HK2 in vivo by measuring metabolic flux. Impact: This study provides a non-invasive technique for measuring glycolytic enzyme expression of tumors in vivo. The proposed method might have clinical potential in cancer treatment management and response monitoring in a timely manner.
Enhancing Liver Cyst Segmentation for ADPKD Patients Through Deep Learning Assistance
Authors: Moghadam Mina Chookhachizadeh , Romano Dominick , Aspal Mohit , He Xinzi , Teichman Kurt , Hu Zhongxiu , Sabuncu Mert , Prince Martin
Published: November 2024

Motivation: Autosomal dominant polycystic kidney disease (ADPKD) often also has polycystic liver disease (PLD), impacting patients' well-being. Manually segmenting liver cysts for measuring disease burden is time-consuming and error-prone, necessitating an automated solution. Goal(s): We introduce a deep-learning (DL) framework for liver cyst segmentation in ADPKD/PLD patients. Approach: An nnUNet-based framework ensembled 2D and 3D models trained on our institute's ADPKD dataset to detect liver cysts in an external test set. Additionally, we implemented patient, cyst, and voxel-level evaluation metrics for clinical impact assessment. Results: Our model achieved an 84% cyst-level Dice score significantly reducing annotation time by 91%. Impact: This research aims to revolutionize PLD monitoring by transitioning from qualitative to quantitative, replicable, and scalable approaches. Advanced DL models can produce high-quality liver cysts annotations and introduce cyst-level evaluation metrics, aiding radiologists with precise disease assessment and clinical decisions.
Image quality assessment and longitudinal quality monitoring of clinically-applied AI-based reconstructions in MRI of rectal cancer
Authors: White Owen , Shur Joshua , Castagnoli Francesca , Charles-Edwards Geoff , Whitcher Brandon , Scurr Erica , Hopkinson Georgina , Koh Dow-Mu , Winfield Jessica
Published: November 2024

Motivation: With increasing AI adoption in MR-reconstructions, robust quality assessment becomes paramount. This study aims to ensure that AI-techniques meet clinical requirements at implementation and longitudinally. Goal(s): 1) Compare image quality of AI-imaging with standard techniques in anorectal cancer. 2) Develop longitudinal quality control (QC) assessments capable of detecting changes in AI-reconstructions without resource-intensive evaluations. Approach: A prospective study involving 40 patients utilised radiologist scoring and quantitative image-quality-metrics (IQMs). Retrospective reconstructions gauged sensitivity of IQMs to reconstruction pipeline changes. Results: AI-reconstructions demonstrated >50% time savings with improved image quality. Feasibility of quantitative-IQMs for assessing AI-reconstructions is established, providing a practical solution for ongoing QC. Impact: There is a need to develop QC assessments offering performance monitoring for AI-based reconstructions in diverse clinical settings. The study presents feasible ways to support integration of AI-imaging into clinical practice, including resource-efficient quantitative image quality assessments.
Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Breast Imaging
Authors: Yang Fan , Xiao Yitian , Sun Jiayu , Zhang Bo , Liang Huilou
Published: November 2024

Motivation: Synthetic MRI, with its unique advantages including unique signal acquisition, rapid synchronization, visualization and multiparameter maps, is gradually applied in breast cancer diagnosis. However, its extended scanning time restricts its broader use. Goal(s): To accelerate synthetic MRI while maintaining its quantitative parameters and image quality using deep learning-based reconstruction (DLR). Approach: 12 female patients were enrolled and scanned with two sets of synthetic MRI: a standard protocol and an accelerated protocol (before and after DLR). Quantitative parameters, SNR of lesion and subjective image quality were compared. Results: Comparable image quality was achieved using accelerated synthetic MRI with DLR. Impact: The combination of DLR with accelerated synthetic MRI protocol has significant benefits in promoting the practical application of synthetic MRI in breast imaging and enhancing examination efficiency.
Deep learning-based super-resolution imaging for routine clinical T1- and T2-weighted breast MRI at 1.5T
Authors: Zhang Shuo , Kwon Jihun , Lemainque Teresa , Peeters Hans , Yoneyama Masami , Bode Maike , Kuhl Christiane
Published: November 2024

Motivation: High-resolution images in breast MRI are desired for lesion detection and characterization but are restricted due to scan time constraint in routine clinical settings. Goal(s): Our goal was to use deep learning (DL)-based reconstructions to improve image resolution and quality of routine clinical breast MRI. Approach: We applied a dedicated Precise-Image-Net for both 2D T1- and T2-weighted imaging in breast cancer patients at 1.5T and compared it to conventional parallel imaging, compress sensing, and convolutional neural network (CNN) reconstructions. Results: Initial clinical data demonstrated a clear improvement of sharpness in breast T1- and T2-weighted images compared with standard reconstructions. Impact: Deep learning-based super-resolution reconstruction provides improved image resolution and sharpness in breast MRI, showing promises for better lesion detection and characterization in routine clinical settings without prolonging scan time, which is of particular importance in dynamic contrast enhanced-MRI.
Image Quality Assessment using an Orientation Recognition Network for Fetal MRI
Authors: Liu Mingxuan , Li Haoxiang , Li Zihan , Yang Hongjia , Zheng Jialan , Zhang Xiao , Tian Qiyuan
Published: November 2024

Motivation: Fetal MRI is important in clinical and scientific applications but prone to motion artifacts. Automated image quality assessment (IQA) assists data acquisition and subsequent analyses. However, training neural networks for IQA requires labor-intensive manual annotation. Goal(s): To develop a model for fetal MRI IQA that doesn't require image quality labels. Approach: A network is trained to determine the acquisition orientation of 2D T2-weighted images. The variation of orientation recognition network (ORN) inferences for central images of a brain stack is used to assess motion and the image quality. Results: High-quality and low-quality images are robustly discriminated. Image super-resolution from brain stacks is improved. Impact: ORN-IQA eradicates the necessity image quality labels for training, thereby circumventing manual annotation. ORN-IQA simplifies online image quality evaluation and permits image reacquisition during fetal MR scans. Moreover, ORN-IQA improves super-resolution reconstruction results.
Water exchange across blood-brain barrier in neuromyelitis optica spectrum disorder measured by vascular-water-exchange MRI
Authors: Jiao Bingjie , Wei Ruili , Liang Hui , Hsu Yi-cheng , Bai Ruiliang
Published: November 2024

Motivation: Limited information exists regarding the spatial characteristics of blood-brain barrier (BBB) disruption at the whole-brain level for neuromyelitis optica spectrum disorder (NMOSD). Goal(s): We explored spatial features of BBB changes in NMOSD using noninvasive, quantitative MRI technique. Approach: Vascular-water-exchange MRI was applied for quantitative analysis. Results: In the white matter, the apparent exchange rate across the BBB (AXRBBB) was significantly higher in acute-phase patients than in healthy controls, especially in some lesion-prone areas. In the gray matter, acute-phase patients had significantly higher AXRBBB values in the frontal and parietal lobes. AXRBBB was also significantly correlated with clinical-scale scores. Impact: Exploring the spatial features of BBB disruption will enable better understanding the imaging features of NMOSD and may help clinicians in differentially diagnosing NMOSD from other brain diseases using noninvasive MRI.
ASL blood-brain barrier permeability is associated with amyloid and cognitive impairment
Authors: Padrela Beatriz , Tecelão Sandra , Geier Oliver , Sneve Markus , Garcia David Vallez , Mahroo Amnah , Pålhaugen Lene , Kirsebom Bjørn-Eivind , Eickel Klaus , Thomas David , Bjørnerud Atle , Fjell Anders , Walhovd Kristine , Barkhof Frederik , Selnes Per , Günther Matthias , Petr Jan , Fladby Tormod , Mutsaerts Henk
Published: November 2024

Motivation: Blood-brain barrier (BBB) permeability changes may be implicated in Alzheimer’s Disease (AD) pathophysiology. Goal(s): To investigate if the exchange time (Tex) of water across the BBB is associated with cognitive and amyloid status. Approach: We measured Tex with a multi-echo arterial spin labeling MRI sequence in 116 adults older than 50 years and studied its association with cognition (cognitively normal vs mild cognitive impaired) and amyloid (A- vs A+) status. Results: BBB water permeability is increased in A+ participants and in patients with MCI, compared to healthy controlsImpact: Our results suggest that multi-TE ASL MRI BBB water permeability can be used as a potential early imaging biomarker of AD pathophysiology.
Repeatability of Blood-Brain Barrier Diffusion Weighted Arterial Spin Labeling MRI at Different Post-Label Delays
Authors: Zhu Yufei , Lee Quimby , Fan Audrey
Published: November 2024

Motivation: Anomalous blood-brain barrier (BBB) water transfer rate (Kw) has the potential to be a novel biomarker for neurological disorders. Goal(s): However, additional studies are needed to affirm the reliability of MRI sequences that assess Kw. Approach: This in vivo study sought to determine the intrasession repeatability of the single-delay diffusion-weighted (DW) arterial spin labeling (ASL) MRI sequence at different DW post-label delays (PLDs). [1]Results: Our findings confirmed that cerebral blood flow (CBF) and Kw were most stable at a DW PLD of 1800ms and that there exists a significant linear correlation between arterial transit time (ATT) and Kw. Impact: Our findings show that for single-delay diffusion weighted (DW) ASL MRI, properly selecting the DW PLD and consideration of ATT are crucial for robust BBB water permeability measurements. Studies like ours are necessary before Kw imaging in different disease states.
Dextran CEST MRI Agents for Quantitative Assessment of BBB Leakage in the Macromolecular Size Range.
Authors: Aafreen Safiya , Wang Wenshen , Thomas Aline , Liu Guanshu
Published: November 2024

Motivation: Dextrans (Dex) -based CEST MRI allows evaluating vascular permeability in the macromolecular size range. However, its signal intensity, similar to most CEST agents, is pH-dependent, which complicates in vivo quantification. Goal(s): To develop a dextran CEST agent that is less pH sensitive for this application. Approach: Chemically modified Dex (CM-Dex) has negatively charged carboxylate groups that can retard the exchange rate of OH protons to decrease pH effects. Results: CM-Dex has a relatively consistent CEST signal across a pH range of 6 to 7.4 and is feasible for detecting blood-brain barrier (BBB) leakage in a mouse model of multiple sclerosis (MS). Impact: The development of a second-generation dextran-based CEST agent with a more extended pH range of stable MRI signal to facilitate quantitative measurements of vascular permeability in vivo for applications wherein intra- and inter-individual pH can vary.
IL-6 knockout reduces doxorubicin-induced neurotoxicity
Authors: Yeung Jonas , Weksberg Rosanna , Guger Sharon , Schachar Russell , Ito Shinya , Nieman Brian
Published: November 2024

Motivation: Doxorubicin (DXR) is a widely used chemotherapy agent associated with inflammation and neurocognitive impairment in cancer survivors. Given that DXR has limited access to the brain, indirect mechanisms, such as the generation of systemic pro-inflammatory cytokines, are proposed to induce neurotoxicity and neuroinflammation. Goal(s): This study aims to probe this hypothesized pro-inflammatory pathway of DXR-induced neurotoxicity. Approach: We identified an elevation of the pro-inflammatory cytokine IL-6 in DXR-treated mice. Consequently, we utilized MRI to assess neuroanatomical changes after DXR treatment in wildtype and Il-6 knockout mice. Results: Our findings revealed that Il-6 knockout partially mitigated the neurotoxic effects induced by DXR. Impact: DXR leads to cognitive impairment that diminishes quality of life for cancer survivors. We demonstrated the involvement of IL-6 in the neurotoxic mechanism of DXR, suggesting a strategy for targeting IL-6 to limit neurotoxicity of cancer treatments.
Assessment of Functional connectivity in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome using 7Tesla MRI.
Authors: Inderyas Maira , Thapaliya Kiran , Marshall-Gradisnik Sonya , Barth Markus , Barnden Leighton
Published: November 2024

Motivation: To assess Functional Connectivity (FC) differences between healthy individuals and Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) patients using ultra-high-field fMRI. Goal(s): Are there significant FC differences between brainstem and cerebellum regions comparing ME/CFS and healthy controls? Is there any association between clinical measures and FC in ME/CFS?Approach: fMRI data were acquired on 7Tesla scanner during cognitive Stroop color-word task. Using a-priori regions, FC was assessed in CONN toolbox. Results: Weaker FC was observed between brainstem and cerebellum regions along with altered FC within the intrinsic network hubs which supports our hypothesis of connectivities being defective in ME/CFS within those regions. Impact: FC analyses using ultra-high-field MRI facilitates our understanding of the underlying patho-mechanisms of the cognitive deficits in ME/CFS and their progression.
TR-resolved Real-Time Low-Field CMR using Hermitian Motion Corrected Reconstructions
Authors: Cruz Gastao , Hamilton Jesse , Cummings Evan , Gulani Vikas , Seiberlich Nicole
Published: November 2024

Motivation: Real-time cardiac MR could provide new insights into function of the myocardium while avoiding artefacts arising from cardiac and/or respiratory motion. Goal(s): High temporal resolution real-time cardiac MR is particularly challenging at low field (0.55T) due reduced SNR and coil elements available on commercial systems. Approach: Here, we leverage motion compensated reconstructions and Hermitian symmetry to enable the highly undersampled reconstructions required for real-time cardiac MR at low field. Results: Experiments at 0.55T show that the proposed approach enables imaging with a temporal resolution of 6 ms (R~48x) with minimal aliasing, outperforming conventional compressed sensing (considerable aliasing) and parallel imaging (aliasing dominated). Impact: TR-resolved (6ms temporal resolution) real-time cardiac is demonstrated at 0.55T where SNR is limited. Such highly accelerated imaging may reveal finer details in myocardial function. Additionally, the high acceleration factors achieved here could also be leveraged for 3D real-time imaging.
Pilot Tone Navigated Motion Correction in DCE-MRI
Authors: Ariyurek Cemre , Chow Jeanne , Afacan Onur , Kurugol Sila
Published: November 2024

Motivation: Addressing the challenge of respiratory motion in abdominal DCE-MRI, especially in pediatric patients, to improve image quality and enhance quantitative DCE-MRI analysis. Goal(s): To develop a motion correction method using PilotTone navigators (PTnavs) to enhance DCE-MRI quality and reliability. Approach: We extract PTnav, create a linear motion model using binning based reference motion parameters. We then apply the motion model to the PTnav for each spoke to estimate its motion and correct for it. We evaluate the method on non-contrast volunteer and pediatric DCE-MRI data. Results: Successful elimination of motion artifacts and improved image quality, reduced image alignment and improved signal-time-intensity curves. Impact: The proposed PT-based motion correction effectively overcomes the challenges of previous motion correction methods, eliminating respiratory motion artifacts and enhancing image quality and misalignment in high-temporal-resolution DCE-MRI. This advancement improves diagnostic accuracy, particularly in pediatric cases with unpredictable breathing patterns.
Temporal Dynamics of ARIA-E: Quantifying Longitudinal Changes in Amyloid-Related Imaging Abnormalities
Authors: Kanani Ela , Powell Elizabeth , Alexander Daniel , Barkhof Frederik , Beament Millie , Fox Nick , Malone Ian , Mummery Catherine , Rosa-Grilo Miguel , Thomas David , Parker Geoff
Published: November 2024

Motivation: Using anti-amyloid monoclonal antibodies to treat Alzheimer’s disease (AD) can lead to ‘Amyloid-Related Imaging Abnormalities’ (ARIA), indicative of oedema (ARIA-E). ARIA-E's transient nature poses diagnostic challenges, necessitating efficient detection and monitoring, which is critical for drug development for AD. Goal(s): This study aims to develop methods for quantifying and tracking ARIA-E across multiple longitudinal scans, including subtle manifestations. Approach: Temporal variance and gradient maps were computed using serial ultra-rapid 3D FLAIR scans. Data-driven methods were employed to assess statistical significance of changes. Results: The proposed maps highlight significant regions of change in the presence of varying levels of ARIA-E. Impact: This work has facilitated the tracking of statistically significant ARIA-E over multiple serial scans, which will enable further automatic detection methods. These methods may inform dose adjustment and patient safety measures, which is important as anti-amyloid monoclonal antibodies become commonplace.
Patella Shape is Associated with ACL Injury and Changes in KOOS and T1rho Following ACLR
Authors: Peters James , Obuchowski Nancy , Subhas Naveen , Pedoia Valentina , Majumdar Sharmila , Potter Hollis , Koff Matthew , Amrami Kimberly , Jacobs Cale , Winalski Carl , Spindler Kurt , Li Xiaojuan
Published: November 2024

Motivation: PTOA progression is poorly understood and there is a relative dearth of data available on the impact of the patellofemoral joint on PTOA. Goal(s): The goal of this study was to elucidate the relationship between PTOA, knee function, and patella shape and to investigate possible indicators for PTOA progression. Approach: A shape model of the patella was used to explore longitudinal shape changes and associations with injury, sex, KOOS, and cartilage T1rho in 67 patients following ALR and 11 controls. Results: Ipsilateral patella shape was found to be associated with ACL injury, sex, and the degenerative changes accompanying PTOA. Impact: This study suggests patella shape may play a role in ACL injury and PTOA. These results should inform future biomechanical studies of the knee joint which could lead to the development of preventative orthoses and novel interventions.
Sigma-1 receptor changes in chronic knee pain using PET/MRI: Preliminary results of fifteen patients
Authors: van der Heijden Rianne , Yoon Luke , Yoon Paul , Davidson Guido , Biswal Sandip
Published: November 2024

Motivation: Diagnosis of chronic knee pain remains a challenge with conventional diagnostic methods leading to unsatisfactory treatment in a large group of patients. Goal(s): To investigate the use of sigma-1 receptor (S1R) radioligand, [18F] FTC-146 in conjunction with positron emission tomography/magnetic resonance imaging (PET/MRI) for identifying the pain generator in chronic knee pain. Approach: Comparison of [18F] FTC-146 PET-MRI imaging findings in patients with unresolved chronic knee pain to healthy volunteers. Results: All 15 patients showed statistically significant increased uptake of S1R compared to healthy control subjects in a variety of locations. At sites of abnormal PET uptake, MRI often did not demonstrate abnormalities. Impact: Future clinical implementation of S1R-PET/MR can potentially help reveal previously unidentified pain generator in patients with chronic knee pain that have exhausted standard clinical care leading to better-targeted treatment.