bluuebunny/crossref_metadata_2025 huggingface dataset:
T1 mapping of the entire abdomen using a time efficient free breathing neural network radial Look Locker approach
Authors: Ahanonu Eze , Johnson Kevin , Goerke Ute , Toner Brian , Deshpande Vibhas , Bilgin Ali , Altbach Maria
Published: August 2024

The interest in developing quantitative metrics in abdominal imaging has grown in recent years. In particular, abdominal T1 mapping plays a role in the characterization of abdominal pathologies. However, current T1 mapping of the abdomen is limited by poor anatomical coverage, long acquisitions related to sufficient sampling of the T1 recovery curve and recovery times, and reduced T1 accuracy secondary to respiratory motion. Here we present a novel approach for free-breathing T1 mapping of the abdomen, which leverages the undersampling robustness of radial MRI and combines fast data acquisition with deep learning for accurate and efficient abdominal T1 mapping.
High-resolution three-dimensional MR-STAT for musculoskeletal applications
Authors: Liu Hongyan , van der Heide Oscar , Versteeg Edwin , Fuderer Miha , Xu Fei , Froeling Martijn , van den Berg Cornelis A.T. , Sbrizzi Alessandro
Published: August 2024

MR-STAT is a framework for simultaneously acquiring multi-parametric quantitative maps from one single short scan. In this work, we design a new 3D MR-STAT sequence and the corresponding two-step reconstruction strategy based on previous work. The framework is improved by designing a faster acquisition framework, and more accurate signal modeling for reconstruction. The proposed sequence takes 7 minutes after retrospectively undersampling, and is validated in a phantom experiment. Furthermore, we apply this 3D MR-STAT sequence the first time for musculoskeletal applications. Knee and lower-leg experiments of healthy volunteers are shown here.
Quantifying Free-breathing Murine Tracheal Dynamics Using Retrospectively-Gated Ultra-short Echo-time (UTE) in MRI
Authors: Wang Qing , Xiao Qiwei , Fugate Elizabeth , Willmering Matthew , Lindquist Dianna , Higano Nana , Bates Alister , Cleveland Zackary
Published: August 2024

The trachea expands and retracts almost uniformly while breathing, but these dynamics are altered by congenital malformation and injury to tracheal cartilage. Tracheal collapse—tracheomalacia—is a common comorbidity in of disorders, including bronchopulmonary dysplasia (BPD), idiopathic pulmonary fibrosis (IPF) and cystic fibrosis (CF) and can cause life-threatening airway obstruction. While tracheomalacia is clinically diagnosed via bronchoscopy, no tool exists to noninvasively assess tracheal dynamics in small animal models. Here we use retrospectively gated, 3D UTE to resolve changes in tracheal caliber during tidal breathing and show these dynamics change as a function of tracheal position and breathing rate.
Evaluating Echo Planar Spectroscopic Imaging with a Columnar Excitation for "Virtual Biopsies"
Authors: Yao Michael , Van Andrew , Gee James , Grossman Murray , Irwin David , Tisdall M Dylan
Published: August 2024

The acquisition of high-resolution quantitative measurements is of particular interest in studying the laminar structure and layer-specific pathology in the cerebral cortex. In this work, we propose a method to address this need by acquiring 1-D echo planar spectroscopic imaging (EPSI) as a "virtual biopsy" with 200 µm resolution along the readout direction. Our sequence yields expected spectra for common compounds and produces high-quality quantitative T2* and off-resonance measurements in ex vivo brain tissue. Our goal is to use this quantitative virtual biopsy to discriminate laminar variations in cortical iron deposition in diseases such as frontotemporal lobar degeneration (FTLD).
COnstrained Reference frame diffusion TEnsor Correlation Spectroscopic (CORTECS) MRI
Authors: Avram Alexandru , Saleem Kadharbatcha , Basser Peter
Published: August 2024

We propose a practical new framework for mapping non-parametric diffusion tensor distributions (DTDs). For diffusion MRI data with sufficiently high spatial resolution, we can constrain all microscopic diffusion tensors of the DTD to be diagonalized using a single orthonormal reference frame estimated from the entire mesoscopic voxel. The constrained DTD is determined by the correlation spectrum of the corresponding microscopic principal diffusivities and can be measured very efficiently using Inverse Laplace Transform methods and single diffusion encoded measurements. cDTD spectral components measured in cortical tissue show good sensitivity to cytoarchitectonic domains and reveal lamination patterns observed in corresponding histological images.
Probing lactate exchange in Gray Matter via time-dependent DW-MRS
Authors: Mougel Eloïse , Malaquin Sophie , Palombo Marco , Valette Julien
Published: August 2024

Time-dependent DW-MRS can probe the underlying tissue microstructure. However, it has been previously shown that time-dependent apparent diffusivity and apparent kurtosis exhibit different behaviors for water and intracellular metabolite. These differences may be largely explained by exchange between intra- and extracellular spaces occurring for water. The aim of this work is to measure time-dependent diffusion of lactate which, like water, is present in and exchanges between both compartments, but for which the exchange rate is unknown. Comparison with water and intracellular metabolites indicates that lactate exchange is slow (relative to the probed diffusion times up to 500 ms).
Exchange-driven Microscopic Kurtosis in Correlation Tensor MRI
Authors: Jespersen Sune , Alves Rita , Olesen Jonas , Henriques Rafael , Shemesh Noam
Published: August 2024

Correlation Tensor MRI (CTI) provided strong evidence for non-vanishing microscopic kurtosis (µK) in neural tissues, both in animals and in humans. However, µK sources remain to be elucidated. Standard Model with Exchange (SMEX) contrasts have been recently proposed by Olesen et al. for mapping exchange properties, especially in gray matter. Here, we derive an expression for µK originating from the SMEX biophysical model due to exchange, and in a model of stroke compare µK derived from SMEX to µK measured with CTI. Our findings suggest that µK(CTI)>µK(SMEX), suggesting a degree of microstructural origin for the CTI contrast.
Diffusional exchange versus microscopic kurtosis from CTI: Two conflicting interpretations of the same data
Authors: Chakwizira Arthur , Szczepankiewicz Filip , Nilsson Markus
Published: August 2024

Correlation tensor magnetic resonance imaging (CTI) is a recently proposed technique that uses diffusion MRI with double diffusion encoding to disentangle microscopic from isotropic and anisotropic kurtosis. One assumption of the method is that intercompartmental exchange is negligible. Another approach that assumes multi-Gaussian exchange (MGE) can also be applied to data acquired with a CTI protocol, but it interprets the contrast as exchange rather than microscopic kurtosis. Using Monte Carlo simulations in substrates of permeable spheres, cylinders and ellipsoids, we observe that both the CTI parameter μK and the estimated exchange rate from MGE increase with the underlying exchange rate.
3D diffusion MRI with twin-navigator-based GRASE for cortical gray matter time-dependency measurements in the human brain
Authors: Li Haotian , Zhu Qinfeng , Hsu Yi-Cheng , Sun Yi , Wu Dan
Published: August 2024

3D oscillating gradient sequences enable diffusion measurement at short diffusion-time (td), but it suffered from low resolution and low SNR in clinical systems, and thus, the td-dependency in complex structures, such as cortical gray matter, was not characterized. Here we proposed a twin-navigator-based 3D oscillating gradient diffusion-weighted gradient spin-echo sequence for high-resolution whole-brain td–dMRI acquisition. We demonstrated that different cortical regions exhibited distinct td-dependency patterns with different diffusion dispersion exponent (θ) based on the power-law. We found θ was greater than 0.5 with the highest dispersion in the pre- and post-central cortex and lowest value in the frontal region.
High-SNR whole-brain microstructure diffusion MRI using Romer-EPTI
Authors: Wang Fuyixue , Dong Zijing , Lee Hong-Hsi , Huang Susie , Polimeni Jonathan , Wald Lawrence
Published: August 2024

Diffusion MRI is a widely-used non-invasive imaging method for studying tissue microstructure but often suffers from low signal-to-noise ratios when using high b values. Here, we present a diffusion MRI acquisition technique that can achieve significantly higher SNR efficiency while providing distortion-free high-quality images. It achieves high robustness to phase variations and motion and provides an efficient acquisition technique for microstructural diffusion imaging. Ultra-high b-value and time-dependent experiments were performed to evaluate the improved SNR efficiency of Romer-EPTI.
Detecting temperature-driven microstructural modulations in tissue using diffusion MRI
Authors: Veraart Jelle , Nunes Daniel , Shemesh Noam
Published: August 2024

In this study, we uncover that the signal fraction of a fully restricted diffusion compartment in fixed white matter tissue, the so-called dot fraction, is dependent on the tissue temperature. In particular, we report a sudden increase in its nominal value of this fraction when the tissue temperature drops below approximately 32°C. The underlying mechanism remains unexplained, but its impact on modeling of ex vivo diffusion MRI cannot be underestimated. The observation corroborates our hypotheses of a temperature-dependent disruption of the multilamellar structure of the myelin sheath, leading to water trapped in vacuoles that are formed in the myelin sheath.
Establishing a Correlation between Diffusion Heterogeneity and Tissue Structural Heterogeneity Using Stereotactic Biopsies on Brain Tumors
Authors: Karaman Muge , Maraka Stefania , Valyi-Nagy Tibor , Khalid Syed , Slavin Konstantin , Atwal Gursant , Daher Ahmad , Dan Guangyu , Scotti Alessandro , Schonfeld Dan , Zhou X.
Published: August 2024

Non-Gaussian diffusion MRI with a continuous-time random-walk (CTRW) model offers a unique avenue to probing tissue microstructural heterogeneity. The CTRW parameters, α and β, corresponding to temporal and spatial intravoxel diffusion heterogeneities, have empirically been linked to tumor tissue heterogeneity in clinical studies. This study aims at directly establishing the correlation between the CTRW parameters from patients suspected of glioma and tissue microstructural heterogeneity revealed by histology on stereotactic brain biopsies. We developed a practical protocol that integrates quantitative imaging techniques and surgical procedures to perform MR-histology correlation, and demonstrated that lower CTRW parameters correspond to increased tissue microstructural heterogeneity.
Large vessel occlusion detection on TOF images using deep learning
Authors: BENTATOU Zakarya , BOUTELIER Timothé , BERNARD Anais , RASOANANDRIANINA Henitsoa
Published: August 2024

Large vessel occlusion (LVO) in stroke patients is mostly detected using deep-learning-based automated methods on CT angiography but there has been no report on such methods using time-of-flight magnetic resonance angiography (TOF-MRA). Our study includes 460 stroke patients with 230 LVO-positive cases. The first step was vessel segmentation, and the output mask was used for the LVO detection. Both steps were deep-learning based using TOF-MRA. Our model successfully detected 95% LVO-positive and 92% LVO-negative patients. The high detection rate and short processing time (< 60 seconds) suggested that our model is highly adequate in a clinical emergency context.
Identification of high-risk basilar artery plaque with HR-VWI-based radiomics and machine learning
Authors: Bai Juan , Wu Jiang , Li Ning , Li Xuan , Wang Kaiyu
Published: August 2024

The stability of plaques is the key cause of cerebral ischemic in patients with basilar atherosclerosis. High-resolution vessel wall imaging (HR-VWI) is a useful technique for study of plaques. Radiomics are widely used in recently years to discover the radiographic information of diseases and to improve diagnostic performance. In this study, we constructed six machine learning models based on radiomics features from HR-VWI to predict the stability of plaques of the basilar artery, finding support vector machine has the best performance.
Super-resolution neural network-driven mouse cerebrovascular mapping with MION-based MRI
Authors: Zhou Xiaoqing , Hike David , Zhang Bei , Xie Zeping , Liu Xiaochen , Rosen Matthew , Iglesias Juan , Yu* Xin
Published: August 2024

High-resolution cerebrovascular mapping is crucial for the understanding of brain function. While the existing invasive imaging modalities map brain vasculature at the microscopic scale, they are mostly restricted to the cortical surface in small animals. The non-invasive penetrating cerebrovascular mapping for larger animals remains the privilege of MRI. To achieve high spatial resolution with MRI, we trained 3D U-net to super-resolve images from 75µm to 37.5µm isotropic resolution. Our results show a significantly improved segmented micro-vessel network and offer a powerful tool to identify the anatomical distribution of the microvessels in normal and degenerative brains longitudinally.
The Fuzzy MAD Stroke Conjecture, using Fuzzy C Means to classify Multimodal Apparent Diffusion for stroke stratification
Authors: Damen Frederick , Su Changliang , Anderson Thomas , Valyi-Nagy Tibor , Tai Leon , Jiang Rifeng , Cai Kejia
Published: August 2024

The ischemic stroke cascade is a complex menagerie of processes causing disruption of neuronal function and willful and/or reactive breakdown of the neural vascular unit (NVU), and, prevails to recovery, repair, or patient demise. To better understand the underpinnings of the stroke cascade we applied our Multimodal Apparent Diffusion (MAD) method to multi b‑value diffusion weighted magnetic resonance imaging, up to b‑value of 10K s/mm2, on 41 consecutive stroke patients. Using Fuzzy C Means we were able to discern 13 normal appearing tissue types and 16 lesion types. There are several findings that should be contemplated in current clinical imaging.
Prediction of Ischemic Stroke Based on Carotid Plaque VW-HRMRI Radiomics
Authors: Han Na , Hu Wanjun , Ma Yurong , Ma Laiyang , Zheng Yu , Zhang Jing , Ai Kai
Published: August 2024

The study of the relationship between carotid atherosclerotic plaque and ischemic stroke must go beyond the assessment of the basic imaging characteristics of plaque or the degree of vascular stenosis, and should adopt a new model which carotid plaque imaging combined with artificial intelligence. This study established a radiomics model of carotid plaque based on vessel wall high-resolution MRI (VW-HRMRI) to accurately predict the risk of ischemic stroke. Our study showed that the radiomics_T1CE model and combined_T1CE model outperformed other models, whereas the combined model showed no significant difference with the radiomics model.
Self-supervised contrastive learning for MR image reconstruction of cardiac CINE on accelerated cohorts
Authors: Xu Siying , Früh Marcel , Hammernik Kerstin , Gatidis Sergios , Küstner Thomas
Published: August 2024

Existing methods for deep-learning reconstruction require abundant fully-sampled images as labels, which is challenging or impractical in practice. To address this issue, we propose a self-supervised learning (SSL) approach that can be trained on subsampled data, avoiding the need for fully-sampled datasets. Specifically, we pre-train a feature extractor by contrastive learning as the first step. In the second step, the pre-trained feature extractor assists the self-supervised network during reconstruction by feature embedding. Results indicate that the proposed SSL method can effectively reconstruct cardiac CINE images without fully-sampled data. It outperforms existing SSL networks and shows comparable results to supervised learning.
Deep learning-based stroke segmentation and patient outcome prediction
Authors: Moon Hae Sol , Heffron Lindsay , Mahzarnia Ali , Obeng-Gyasi Barnabas , Holbrook Matthew , Badea Cristian , Feng Wuwei , Badea Alexandra
Published: August 2024

We compared the ability of 2D and 3D U-Net Convolutional Neural Network (CNN) architectures to segment ischemic stroke lesions and predict patient outcome using single-contrast (DWI) and dual-contrast images (T2w FLAIR and DWI). The predicted lesion segmentation metrics and location relative to corticospinal tract correlated with post-stroke patient outcome measured by National Institutes of Health Stroke Scale (NIHSS). The 2D multi-modal CNN achieved the best results with mean Dice of 0.74. The highest correlation was for weighted-lesion load with both baseline and 90-days NIHSS (80%, p<0.001). Our results support that multi-contrast MR helps automate lesion segmentation and predict post-stroke outcomes.
The GRAPH-CRAFITY Score: a novel prognostic tool for patients with hepatocellular carcinoma treated with targeted therapy and immunotherapy
Authors: Xu Ying , Li Lu , Yang Yi , Xie Lizhi , Wang Sicong , Ye Feng , Zhao Xinming
Published: August 2024

One-hundred and sixty-six patients (55.6±10.4 years) treated with lenvatinib plus anti-PD-1 antibody were included in training cohort and 77 patients (55.4±10.7 years) treated with lenvatinib or bevacizumab plus anti-PD-1 antibody were included in validation cohort. Based on independent risk factors (Gender, intRatumoral fAt, enhancing tumor caPsule, gross growtH type and CRAFITY Score) identified by the multivariate analysis, a novel prognostic tool named GRAPH-CRAFITY Score was developed to predict OS. GRAPH-CRAFITY Score by integrating Gender, MR features and laboratory tests is an effective and user-friendly tool to predict OS of HCC patients treated with targeted therapy and immunotherapy.
Value of diffusion kurtosis MR imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer
Authors: Zhang Zehua , Fu Caixia , Grimm Robert , Zhou Zhengrong
Published: August 2024

This study firstly compared diffusion kurtosis imaging (DKI) and traditional diffusion weighted imaging (DWI) in evaluating first-line chemotherapy in unresectable pancreatic cancer. The result demonstrated that higher efficacy was shown in DKI derived parameters than DWI by the ROC curve analysis. DKI has potential to become a new non-invasive tool for clinical efficacy evaluation and help clinicians select individualized treatment.
Reduction of cryoneedle artifacts due to cryoneedle-RF coil interaction during MRI-guided cryoablation
Authors: Lu Aiming , Krishnamoorthy Guruprasad , Browne Jacinta , Thompson Scott , Adamo Daniel , Woodrum David
Published: August 2024

MRI-guided cryoablation is a widely used minimal invasive treatment that uses extreme cold to destroy localized diseased tissues. The cryoneedles used are expected to introduce little metal artifacts by themselves in the MRI images. However, severe artifacts along the cryoneedle shaft are sometimes observed during MRI guided cryoablations in practice, which can not only complicate the cryoablation procedures, but also potentially cause complications and negatively affect treatment outcome. This work demonstrated that the artifacts are likely due to coupling of the cryoneedles and the RF coils, and can be minimized by proper positioning of the cryoneedles, RF coils and patients.
eGantryMate: A Piezo-Motor driven lean and flexible Assistance System for MR-Guided Interventions at 1.5T
Authors: Reichert Andreas , Özen Ali , Reiss Simon , Hickey Samantha , Lottner Thomas , Verloh Niklas , Milosavljevic Srdjan , Vogele Michael , Bock Michael
Published: August 2024

We present a small and flexible piezo-driven assistance system, which can be steered from outside the magnet bore via a control unit. The assistance system is combined with a tracking sequence, which is able to follow in-bore manipulations of a dedicated end effector in real-time. For an initial evaluation, the assistance systems is equipped with a biopsy needle and targeting experiments are performed in a phantom setting.
No Wait Inversion – a novel model for T1 mapping from inversion recovery measurements without the waiting times
Authors: Slawig Anne , Bibiano Juliana , Köstler Herbert
Published: August 2024

This study proposes a combined fit of an inversion-prepared and non-prepared measurement for robust fast T1-mapping. Therefore, 24 slices in the brain were acquired with and without a global inversion pulse before each slice and no waiting time was heeded in between. Results of exponential model reconstructions for inversion measurement only, non-prepared measurement only and combined fitting of both measurements were compared towards their performance for T1-mapping. Robust and accurate T1-maps were achieved by the proposed combined model even in cases of imperfect inversion. It can thus eliminate long waiting times in between inversions or the necessity of perfect inversion pulses.
Utilizing High Performance Gradients to Image Human Brain Arterial Vasculature with High Resolution 7T Time-of-flight MR Angiography
Authors: Ma Samantha , Beckett Alexander , Laub Gerhard , Feinberg David
Published: August 2024

Time of flight (TOF) 3D MR Angiography (MRA) has been shown to effectively image small (50-300um diameter) pial vessels. However, such high resolution requires strong flow compensation (flow-comp) gradient pulses and short echo times which are limited by peripheral nerve stimulation and hardware capability in most scanners. We demonstrate high resolution TOF MRA acquisition using an advanced head gradient system (200mT/m, 900 T/m/s) at ultrahigh field 7T.
Effects of Aquaporin-4 Inhibition on Relative Cerebrovascular Reactivity using Resting-state Functional MRI
Authors: Faiq Muneeb , Chan Russell , Sajitha Thajunnisa , Chan Kevin
Published: August 2024

Aquaporin-4 (AQP4) is most abundant water channel responsible for cerebrospinal fluid influx in the brain, and has important bearings on cerebrovascular diseases. However, it remains unclear how aquaporin-4 functions in the cerebrovasculature. We used relative cerebrovascular reactivity (rCVR) mapping derived from task-free, resting-state blood-oxygenation-level-dependent functional MRI to determine the effects of AQP4 suppression on the healthy mouse brains using the AQP4 inhibitor TGN020. We observed different patterns of rCVR responses across cortical and subcortical brain regions, indicating the heterogeneity of AQP4 functions in the cerebrovasculature, which may explain the varying vulnerability of different brain regions to cerebrovascular diseases.
Comparing 3D, 2.5D, and 2D Approaches to Brain MRI Segmentation
Authors: Avesta Arman , Hossain Sajid , Lin MingDe , Aboian Mariam , Krumholz Harlan , Aneja Sanjay
Published: August 2024

We compared 3D, 2.5D, and 2D approaches to brain MRI auto-segmentation and concluded that the 3D approach is more accurate, achieves better performance when training data is limited, and is faster to train and deploy. Our results hold across various deep-learning architectures, including capsule networks, UNets, and nnUNets. The only downside of 3D approach is that it requires 20 times more computational memory compared to 2.5D or 2D approaches. Because 3D capsule networks only need twice the computational memory that 2.5D or 2D UNets and nnUNets need, we suggest using 3D capsule networks in settings where computational memory is limited.
DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging
Authors: Fang Zhenghan , Lai Kuo-Wei , van Zijl Peter , Li Xu , Sulam Jeremias
Published: August 2024

The application of STI in human in vivo has been practically infeasible because of its time-consuming acquisition scheme. We propose a novel image reconstruction algorithm for STI that leverages data-driven priors to tackle this issue. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that resembles the proximal operator of a regularizer function. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results demonstrate superior performance of DeepSTI over state-of-the-art methods. DeepSTI is the first reconstruction method to achieve high quality results for human STI with fewer than six orientations.
PDH flux is a sensitive biomarker of mitochondrial dysfunction in acute TBI
Authors: Chen Jun , Hackett Edward , Ingle Laura , Nemri Sarah , Plautz Erik , Bartnik-Olson Brenda , Park Jae Mo
Published: August 2024

Due to the rapid and complex progression of metabolic alteration after traumatic brain injury (TBI), prompt assessment of cerebral metabolism is critical in preventing the subsequent injury processes and developing proper therapeutic strategies. In this study, we assessed longitudinal changes in bicarbonate production from hyperpolarized [1-13C]pyruvate in a rat TBI model. In addition to elevated lactate production, we observed significantly reduced bicarbonate production in the injured site. The contrast of bicarbonate signals between the injured region and the contralateral brain peaked at one day post-injury. A subset of TBI rats demonstrated markedly increased bicarbonate in contralateral brain regions post-injury.
Improving Quantification of Hyperpolarized 13C-Pyruvate Metabolism Using Pyruvate Metabolite Specific bSSFP and Variable Flip Angles
Authors: Bennett Anna , Liu Xioaxi , Sinha Avantika , Sahin Sule , Larson Peder , Wang Charlie
Published: August 2024

A pyruvate-specific bSSFP protocol was defined, based on prior work, demonstrating increased signal efficiency over GRE sequences. Monte Carlo simulations evaluated signal efficiency and kinetic performance of potential flip angle schemes. A sigmoid-based flip scheme, varying from 5 to 80 degrees, showed greatest performance of kPL estimation compared to simulated GRE and constant flip angle bSSFP. Small animal studies validated the expected signal increase and kPL estimation, with resulting kinetic parameter maps showing consistency among methods and qualitative noise reduction.
A Generative Subspace Model for High-dimensional MR Imaging
Authors: Peng Xi , Lam Fan
Published: August 2024

Subspace or low-rank models have been demonstrated useful in producing efficient representations and reconstructions for high dimensional imaging problems. In this study, we propose an unsupervised learning-based approach by generalizing the subspace model using a deep generative network. The generative subspace model can then be incorporated into the physics-based reconstruction formalism. The network parameters can be self-trained by minimizing the cost function with the flexibility to integrate with conventional constraints. We demonstrated the effectiveness of the proposed method over standard linear subspace and deep image prior models using in vivo T2 mapping dataset.
Investigations on non-Cartesian parallel imaging with time-varying receive sensitivities
Authors: Glang Felix , Valsala Praveen , Nikulin Anton , Avdievich Nikolai , Steffen Theodor , Scheffler Klaus
Published: August 2024

Non-Cartesian trajectories have several advantageous properties, including favorable parallel imaging performance. Recently, a novel concept of improving parallel imaging by electronically modulated time-varying receive sensitivities has been introduced. In the present work, we investigate if these two concepts can be combined, i.e., how dynamic sensitivity modulation impacts non-Cartesian parallel imaging reconstruction. To that end, numerical experiments are performed based on data from the novel reconfigurable coil array. We find improvement in convergence, reconstruction error and noise amplification due to rapid sensitivity modulation for radial, spiral, and Cartesian trajectories, implying the potential of this method for advanced encoding and reconstruction schemes.
Pose-dependent field reconstruction enabled by prospective motion navigation and randomized sampling
Authors: Riedel Malte , Ulrich Thomas , Pruessmann Klaas
Published: August 2024

Head motion is accompanied by a multitude of second order motion effects like changing coil sensitivity maps, background field inhomogeneities, and susceptibility-induced fields. While scan geometries and shims can be corrected in real-time, the scanner has no means to counteract changes of the coil sensitivity maps or the susceptibility-induced fields requiring data-driven retrospective motion correction algorithms for these issues. Randomized sampling can further be exploited to improve the problem conditioning of the parameter estimations on temporal sub-segments of the scan. In this work, we evaluate the combination prospective motion navigation with randomized sampling in a pose-dependent field correction algorithm.
Motion Compensated Structured Low-rank Reconstruction for Robust 3D Multi-shot EPI fMRI
Authors: Chen Xi , Wu Wenchuan , chiew Mark
Published: August 2024

Structured low-rank (SLR) reconstruction has been successfully used in 3D multi-shot EPI for fMRI to improve its robustness to inter-shot phase variations. This work proposed a motion compensated structured low-rank (mcSLR) reconstruction, which further improves the robustness of 3D multi-shot EPI by joint modelling of both inter-shot motion and phase variations.
Alternating Low-Rank Tensor Reconstruction for Improved Multi-Dimensional MRI with MR Multitasking
Authors: Cao Tianle , Xie Yibin , Li Debiao , Christodoulou Anthony
Published: August 2024

Low-rank tensor modelling is promising for multi-dimensional MR imaging. In this work, we developed a new low-rank tensor reconstruction approach using alternating minimization of spatial and temporal bases from the whole k-t space data instead of from split subsets of data. The approach was evaluated for 2D motion-resolved myocardial T1/T2/T2*/fat-fraction mapping and could potentially be used for imporving reconstruction quality and/or further reducing scan time.
Accelerated Simultaneous Multislice Imaging via Linear Phase Modulated Extended Field of View (SMILE)
Authors: Zhao Shen , Wang Junyu , Salerno Michael
Published: August 2024

Slice leakage is a significant issue in simultaneous multislice (SMS) imaging. In this work, we extend POMP and develop a new efficient accelerated SMS acquisition technique: Simultaneous Multislice Imaging via Linear phase modulated Extended field of view (SMILE). SMILE transforms the SMS problem into a 2D imaging task and enables direct implementation of 2D non-SMS-only reconstruction algorithms. SMILE increases the sampling degree of freedom by a factor of the number of slices and could theoretically avoid the significant "slice-leakage" issue.
A Time-Saving View-Sharing Interleaved Block-Segmented Diffusion-Tensor Imaging (VS iblocks-DTI)
Authors: Liang Liyuan , Chu Mei-Lan , Chen Nan-Kuei , Chang Hing-Chiu
Published: August 2024

Recently, a novel self-navigated multi-shot EPI data acquisition scheme called iblocks-EPI was proposed to achieve high-resolution DWI images with further reduction of distortion compared to the conventional interleaved EPI. However, the total acquisition time of iblocks-EPI is several times longer than the conventional interleaved EPI because it requires multiple patch patterns for covering a complete k space. Consequently, it is time-consuming and impractical for routine acquisition of DTI data. In this work we propose a view-sharing iblocks-DTI scheme which can substantially reduce the scan time for iblocks-DTI acquisition while providing accurate DTI tensor calculation.
Oxygen-Enhanced MRI Assessment of Radiotherapy-Induced Treatment Effects in Patients with Head and Neck Cancer
Authors: Dubec Michael , Gaffney John , Buckley David , Berks Michael , Datta Anubhav , McHugh Damien , Little Ross , Cheung Susan , Hague Christina , Matthews Julian , Choudhury Ananya , van Herk Marcel , McPartlin Andrew , Parker Geoff , O'Connor James
Published: August 2024

Hypoxia is common to most solid tumours and mediates treatment resistance. Oxygen-enhanced (OE)-MRI can identify, map and quantify hypoxia in multiple tumour types including H&N cancer. We build upon previous work to show that OE-MRI biomarkers: (i) can be deployed on multiple MRI systems; (ii) are repeatable; (iii) can detect cohort level response to radiotherapy; (iv) can identify which individual lesions have significant changes in their oxygenation, hypoxic volume and hypoxic fraction.
Semiautomated Pelvic Lymph Node Treatment Response Evaluation for Patients with Advanced Prostate Cancer: Based on MET-RADS-P Guidelines
Authors: liu xiang , wang xiaoying
Published: August 2024

This retrospective study aims to develop and evaluate a deep learning-based algorithm for semiautomated treatment response assessment of pelvic lymph nodes. A total of 162 patients who had undergone at least two scans for follow-up assessment after advanced prostate cancer metastasis treatment were enrolled. A previously reported deep learning model was used to perform automated segmentation of pelvic lymph nodes. Our results showed that the accuracies of automated segmentation-based response assessment were high for all the target lesions, nontarget lesions and nonpathological lesions according to MET-RADS-P criteria and achieved good consistency with the attending radiologist and fellow radiologist.
An Analytic Solution for the Modified WASABI Method: Application to Simultaneous B0, B1 and T1 Mapping and Correction of CEST MRI
Authors: Schuenke Patrick , Zimmermann Felix , Kaspar Kerstin , Zaiss Moritz , Kolbitsch Christoph
Published: August 2024

CEST MRI provides a contrast sensitive to exchange processes between solute and water protons that was proven to add value in clinical MRI. However, the contrast is susceptible to B0- and B1 field inhomogeneities as well as the T1 relaxation time. Here we present an analytical solution for a modified WASABI method that enables the quantitative mapping of all three parameters simultaneously from a single CEST-like MRI scan. We show that the generated parameter maps and reference maps match well and demonstrate their applicability for the B0, B1 and T1 correction of CEST MRI data.
B0-Robustness and Exchange Weighting of Optimal Control Pulses Compared to State of the Art Pulses for CEST Imaging
Authors: Stilianu Clemens , Huemer Markus , Graf Christina , Diwoky Clemens , Rund Armin , Zaiss Moritz , Stollberger Rudolf
Published: August 2024

The exchange weighting and robustness to B0-inhomogeneities are essential properties for accurate CEST-MRI saturation measurements. Therefore, we investigated these properties for a new numerical optimized saturation pulse train and for state-of-the-art saturation pulse strategies in simulation and phantom measurements. We have found that the optimized pulse train generates superior CEST contrast and has very high stability against B0-inhomogenities while maintaining the natural shape at the water peak which improves CEST-spectrum fitting and B0-correction.
Quantum-Sensing MRI for Non-Invasive Detection of Neuronal Firing in Human Brain: Initial Demonstration via Finger-Tapping Task
Authors: Qian Yongxian , Chen Xingye , Lin Ying-Chia , Henin Simon , Kumbella Nahbila-Malikha , Aguilera Liz , Rockowitz Zena , Clayton Ashley , Babb James , Ge Yulin , Masurkar Arjun , Liu Anli , Lui Yvonne , Boada Fernando
Published: August 2024

Neuronal firing is the electrophysiological basis of brain function. BOLD-based functional MRI indirectly detects neuronal activity through an uncertain neuro-hemodynamic mechanism and is also limited to the detection of slow neuronal activity such as postsynaptic potentials due to its relatively low temporal resolution. Recent efforts have improved temporal resolution to milliseconds or even sub-milliseconds, under the assumption that neuronal activity is exactly repeatable in time, though unlikely to be practical. Here we demonstrate the potential of quantum-sensing MRI to directly detect neuronal firing using a finger-tapping task.
Impact of regularization factor on quantification of flow and turbulence in highly undersampled 4D flow MRI
Authors: Dirix Pietro , Buoso Stefano , Vishnevskiy Valery , Kozerke Sebastian
Published: August 2024

We investigate the impact of regularization on the quantification of flow and turbulence in highly undersampled data. To overcome the uncertainties due to the lack of ground truth, patient-specific aortic geometry and inflow conditions were extracted from in-vivo 2D cine and 2D phase-contrast MRI and used to generate synthetic personalized ground truth flow fields. Simulation results were embedded into the corresponding patient-specific 4D flow MRI effectively resulting in personalized synthetic datasets with known aortic ground truth and realistic background. The reconstruction of multiple undersampled datasets showed two distinct optimal regularization ranges for the quantification of flow velocities and turbulence.
Joint Reconstruction of Image Repetitions in DWI using Cross-Instance Attention
Authors: Gadjimuradov Fasil , Pfaff Laura , Benkert Thomas , Nickel Marcel , Maier Andreas
Published: August 2024

Despite its proven clinical value, Diffusion-weighted Imaging (DWI) suffers from several technical limitations associated with prolonged echo trains in single-shot sequences. Parallel Imaging with sufficiently high under-sampling enabled by Deep Learning-based reconstruction may mitigate these problems. Newly emerged architectures relying on transformers demonstrated high performance in this context. This work aims at developing a transformer-based reconstruction method tailored to DWI by utilizing the availability of multiple image instances for a given slice. Redundancies are exploited by jointly reconstructing images using attention mechanisms which are performed across the set of instances. Benefits over reconstructing images separately from each other are demonstrated.
A Machine-Learning Approach for Saturation-Prepared Turbo FLASH B1+ Maps Calibration
Authors: Dubiner Michael , Sedlacik Jan , Wilkinson Tom , Bridgen Pip , Mauconduit Franck , Amadon Alexis , Giles Sharon , Neji Radhouene , Hajnal Joseph , Malik Shaihan , Tomi-Tricot Raphael
Published: August 2024

A fast B1+ mapping sequence such as saturation-prepared turbo FLASH (satTFL) is desirable for online pulse design at ultra-high field, but it often results in inaccuracies. Previous work performed linear fitting of the B1+ magnitude obtained with the satTFL over that of the longer but more accurate Actual Flip angle Imaging (AFI) sequence to obtain calibration parameters that would correct the satTFL B1+ on the fly for brain imaging. In this work we introduce a new machine-learning-based method that uses additional features to create a more precise and less location-dependent accuracy.
Diattenuation and retardance metrics from complete polarimetry differentiate microscale and macroscale anisotropy
Authors: Carlson Rhea , Comrie Courtney , Bonaventura Justina , Morara Kellys , Daigle Noelle , Hutchinson Elizabeth , Sawyer Travis
Published: August 2024

This study uses complete polarimetry to capture diattenuation and retardance in post-mortem ferret optic chiasm (OC) chosen for its well understood fiber structure. These methods are novel due to the limited analysis of diattenuation metrics on tissue. Our results suggest that retardance measurements are sensitive to microscale features rather than to macroscale geometry when compared to fractional anisotropy (FA) and propagator anisotropy (PA). In addition, we have shown fiber orientation distributions (FODs) created through direct optical measurement of the tissue on a 2D plane are comparable to diffusion FODs.
Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI
Authors: Hoffmann Emily , Gerwing Mirjam , Niland Stephan , Niehoff Rolf , Masthoff Max , Wilken Enrica , Berger Philipp , Vogl Thomas , Eble Johannes , Maus Bastian , Helfen Anne , Wildgruber Moritz , Faber Cornelius
Published: August 2024

Immune cells are major players of the tumor microenvironment (TME), having profound effects on tumor development and metastatic progression. We present oscillating-gradient diffusion-weighted MRI (OGSE-DWI) as non-invasive imaging approach to monitor the intratumoral immune cell infiltrate, relying on size differences between cancer cells, T-cells and macrophages. By applying the Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED) model to sine-shaped OGSE-DWI, changes within the TME and its specific immune cell composition were monitored and compared in syngeneic murine breast cancer models with different degrees of malignancy during tumor progression, clodronate liposome-mediated depletion of macrophages and immune checkpoint inhibitor treatment.
Cluster analysis of VERDICT MRI for identification of tumor subregions with distinct histological features
Authors: Lundholm Lukas , Montelius Mikael , Jalnefjord Oscar , Forssell-Aronsson Eva , Ljungberg Maria
Published: August 2024

Characterization of tumor tissue may aid in grading and assessment of cancer treatment. Time-dependent diffusion MRI can provide non-invasive estimates of parameters relating to tissue microstructure in vivo. In this study, a cluster analysis was performed to group parameters estimated by the time-dependent diffusion MRI model VERDICT. Parameter cluster maps were compared with maps derived from classification of histological images. The results showed good agreement between VERDICT cluster maps and histology maps, indicating that the method shows potential in identifying tumor subregions of distinct morphological features.
Rotation-invariant microstructure mapping revisited
Authors: Christiaens Daan , Tournier J-Donald , Sunaert Stefan , Maes Frederik
Published: August 2024

Microstructure imaging with diffusion MRI relies on non-linear fitting of a biophysical tissue model to the data, often at low signal-to-noise ratio. In this work, we derive a new rotation-invariant feature set for microstructure mapping based on a rank-1 decomposition of the multi-shell diffusion MRI signal in spherical harmonics. Simulations show that using this feature set avoids non-central-χ bias that is present at low SNR in parameter estimation based on conventional rotation-invariants. Results in human brain imaging data acquired with free waveform diffusion encoding show robust parameter estimates across white matter.
Diffusion simulations in human myelinated axons from 3d EM evaluate the impact of realistic axon geometry on diameter mapping
Authors: Lee Hong-Hsi , Tian Qiyuan , Coronado-Leija Ricardo , Fieremans Els , Novikov Dmitry , Huang Susie
Published: August 2024

To disentangle the effect of complex features on axon diameter mapping, we segmented myelinated axons in a human brain electron microscopy data and use these to create artificial cylinders with undulations and caliber variations that can be tuned to varying degrees. Diffusion simulations in segmented real axons and artificial axons showed that undulations and beadings lead to overestimation and underestimation of the axon diameter, respectively.
Motion-compensated diffusion imaging with phase-contrast (MC-DIP) for the quantification of regional cerebral blood flow
Authors: Ohno Naoki , Miyati Tosiaki , Nambu Genki , Tanaka Daichi , Makino Yuki , Alperin Noam , Ueda Yu , Van Cauteren Marc , Gabata Toshifumi , Kobayashi Satoshi
Published: August 2024

Diffusion imaging with phase-contrast (DIP) can quantitatively evaluate regional cerebral blood flow (rCBF), in which total CBF (tCBF) from phase-contrast magnetic resonance imaging (PC-MRI) converts the brain’s perfusion-related diffusion parameters into rCBF values. However, it is sensitive to bulk motion (i.e., brain pulsation), potentially causing an overestimation of DIP-derived rCBF values. To overcome this issue, we propose a motion-compensated DIP (MC-DIP) that incorporates motion-compensated diffusion gradients. DIP with second-order motion-compensated diffusion gradients (2nd-MC-DIP) improved the fitting accuracy of the biexponential analysis and showed the ability to quantitatively evaluate rCBF in gray and white matter.
Explainable artificial intelligence for automatic detection of early nasopharyngeal carcinoma on MRI
Authors: Wong Lun , Ai Qi-Yong , So Tiffany Y , Lam Jacky WK , King Ann
Published: August 2024

In this study, we propose a simple method to improve the explainability of artificial intelligence, specifically convolutional neural networks (CNNs), for the automatic detection of early nasopharyngeal carcinoma (NPC) on magnetic resonance imaging (MRI). We show a long-short-term-memory (LSTM) unit can be introduced into a CNN to read 3-dimensional medical image series. A risk curve can be extracted from the LSTM to visualize the “thought process” of the network when it reads through the input MRI slice-by-slice. This modification improves the explainability of the network without reducing performance for the early NPC detections of the original CNN.
Deep Learning-Based Automatic Pipeline for 3D Needle Localization on Intra-Procedural 3D MRI
Authors: Zhou Wenqi , Li Xinzhou , Zabihollahy Fatemeh , Lu David , Wu Holden
Published: August 2024

Needle localization in 3D images during MRI-guided interventions is challenging due to the complex structure of biological tissue and the variability in the appearance of needle features in in-vivo MR images. Deep learning networks such as the Mask Regional Convolutional Neural Network (R-CNN) could address this challenge by providing accurate needle feature segmentation in intra-procedural MR images. This work developed an automatic coarse-to-fine pipeline that combines 2.5D and 2D Mask R-CNN to leverage inter-slice information and localize the needle tip and axis in in-vivo intra-procedural 3D MR images.
Active dictionary learning: fast and adaptive parameter mapping for dynamic MRI
Authors: An Hongjun , Kim Jiye , Lee Jongho
Published: August 2024

A new parameter mapping method, active dictionary learning, for dynamic MRI is proposed. This method trains a neural network adaptively by AI-guided MR signal simulation. For an MRF sequence with M0, T1, T2, B1, and ΔB0, our method successfully estimates the parameters much faster than conventional methods (ours: 30 min for whole process; dictionary methods: 6 hours for generation, 36 hours or 3 hours for matching). AI-guided active dictionary learning enables adaptive quantification of out-of-range parameters and efficient computation, suggesting the usefulness of the method not only in dynamic imaging but also in applications where adaptation to parameters is necessary.
Automated Surfaced-based Detection of Focal Cortical Dysplasia using MR Fingerprinting
Authors: Su Ting-Yu , Hu Siyuan , Wang Xiaofeng , Adler Sophie , Wagstyl Konrad , Ding Zheng , Choi Joon Yul , Sakaie Ken , Blümcke Ingmar , Murakami Hiroatsu , Jones Stephen , Najm Imad , Ma Dan , Wang Zhong Irene
Published: August 2024

Focal cortical dysplasia (FCD) is a common pathology in medically intractable focal epilepsy and often difficult to detect by visual inspection of conventional MRI. We developed a framework for automatic FCD detection using surface-based processing of conventional MRI and MR fingerprinting data. Thirty-six patients with FCD and 48 healthy controls were included. Improved vertex-wise and cluster-wise performance was seen when MRF and FLAIR features were added to T1w data. A second-stage cluster-wise classifier showed efficacy to reduce false-positive clusters. Interim results of patient-level sensitivity of 76% and low false-positive clusters in controls supported potential clinical applicability of the proposed framework.