bluuebunny/crossref_metadata_2025 huggingface dataset:
One-minute 3D ky-kz Centric FFE Thoracic Aorta Imaging with Real-Time Motion-Correction and High-Resolution Deep Learning Reconstruction
Authors: Ota Hideki , Higuchi Satoshi , Morita Yoshiaki , Nishina Takashi , Tanaka Sho , Yamashita Yuichi , Kassai Yoshimori , Nagasaka Tasuo , Miyazaki Mitsue , Takase Kei
Published: August 2024

Efficiency of 3D ky-kz centric fast field echo (FFE) acquisition was acquired using 100% efficiency of real-time motion correction in the sagittal oblique acquisition and compared with conventional 3D FFE acquisition. The thoracic aorta was acquired within 1 minute using centric ky-kz FFE and reconstructed with high-resolution deep learning reconstruction (HR-DLR), providing good image quality. Regular FFE with conventional reconstruction provides fair image quality with prominent noise and artifacts.
Machine Learned Wave Encoded Neurovascular 4D Flow
Authors: Tang Chenwei , Rivera-Rivera Leonardo , Eisenmenger Laura , Johnson Kevin
Published: August 2024

Keywords: Wave Encoding, Trajectory Optimization, 4D Flow Non-Cartesian sampling is often required for 4D Flow imaging because of more efficient sampling. Due to the heuristic nature of the optimization of such trajectories, we propose to parameterize and optimize wave encoded 3D Cartesian sampling using a gradient descent algorithm in a data-driven way. We demonstrate the feasibility of our framework in learning the sampling patterns and the wave parameters and providing high image quality for highly accelerated scans in digital phantoms, phantoms and in vivo with phase contrast.
Self-supervised Learning with Self-supervised Regularization Reconstruction for Accelerated Single- and Multiband Myocardial Perfusion MRI
Authors: Sun Changyu , Bilchick Kenneth , Salerno Michael , Altes Talissa
Published: August 2024

Physics-guided self-supervised learning (PG-SSL) of MRI reconstruction may provide high spatiotemporal fidelity and fast reconstruction of highly accelerated first-pass myocardial perfusion MRI without ground truth. We sought to develop a SSL model with self-supervised regularization (SSR) using Siamese architecture with stop gradient and re-undersampling block to generate physics-based data augmentation and regularization. PG unrolled network was used as the sub-network in Siamese structure. Self-supervised learning with self-supervised regularization (SSLR) outperformed low rank plus sparse on retrospective rate-8 undersampling single-band data and showed improved SNR and temporal fidelity on prospective multiband whole-heart coverage high resolution perfusion imaging.
19F-MRI of pulmonary ventilation: Assessment of response to treatment in asthma and COPD
Authors: Neal Mary , Holland Charlotte , Pippard Benjamin , Forrest Ian , Burns Graham , Lawson Rod , Fisher Holly , Matthews John , Simpson A John , Wild Jim , Thelwall Peter
Published: August 2024

19F-MRI is an emerging technique which holds promise for direct, quantitative imaging of pulmonary ventilation without recourse to ionising radiation or use of specialist hyperpolarised gases. We aimed to assess the sensitivity of 19F-MRI to treatment response by imaging inhaled perfluoropropane in patients with asthma and patients with COPD before and after administration of a bronchodilator, at two research sites. Significant increases in percent ventilated lung volume were measured from the 19F-MR images following bronchodilator administration in both patient groups, and a positive correlation with spirometry was measured, demonstrating good technique sensitivity and clinical validity.
Time Course Changes of Synthetic Relaxation Time during Neoadjuvant Chemotherapy in Breast Cancer.
Authors: Zhao Ruimeng , Du Siyao , Gao Si , Xie Lizhi , Shi Jing , Zhang Lina
Published: August 2024

This study investigated the time course changes of synthetic magnetic resonance imaging (MRI) parameters (T1/T2 relaxation time [T1/T2], proton density [PD]) during neoadjuvant chemotherapy (NAC) and evaluate their value as predictors for pathological complete response (pCR) in locally advanced breast cancer. The results showed that median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. And the Change of synthetic T1 after the first cycle of NAC may be an early predictor for NAC response in locally advanced breast cancer during whole treatment cycles. However, its predictive ability is significantly affected by histological grades.
Prediction of the recurrence and invasion of hepatocellular carcinoma after TACE using intravoxel incoherent motion
Authors: Zhao Xinyao , Wen Qingqing , Dou Weiqiang , Wang Junying
Published: August 2024

The accurate evaluation of tumor response after Transarterial Chemoembolization (TACE) treatment is important for tumor prognosis and subsequent treatment. In this study, we comprehensively investigated the feasibility of IVIM parameters in the TACE treatment area, peritumoral area, and hepatic parenchymal to predict tumor recurrence in hepatocellular carcinoma patients after TACE treatment. ADC and D of TACE-treated area, and D of the peritumoral area can predict the intralesional and peritumoral recurrence after TACE treatment with high accuracy, indicating that IVIM might be a useful tool in predicting the therapeutic response and the peritumoral invasion.
Quantitative DCE-MRI after P4-based error correction provides a more accurate therapy response assessment of pancreatic ductal adenocarcinoma
Authors: Holland Martin , Onuoha Ezinwanne , Khushman Moh’d , Outlaw Darryl , Akce Mehmet , El-Rayes Bassel , Williams Grant , Hashmi Salila , Reddy Sushanth , Rose J. , Morgan Desiree , Jiang Xiaoyu , Cardin Dana , Frederick-Dyer Katherine , Xu Junzhong , Kim Harrison
Published: August 2024

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures tissue perfusion by monitoring the dynamic change of MRI contrast agents. However, the inter/intra-scanner variability in quantitative DCE-MRI (qDCE) measurement remains a concern. We developed a point-of-care portable perfusion phantom (P4) that can be imaged with a human subject in a standard MRI scanner to detect and correct the inter/intra-scanner variability of qDCE measurement. We demonstrated that the PDAC response to chemotherapy could be accurately assessed using quantitative DCE-MRI after our P4-based error correction method approximately seven weeks after starting therapy in two clinics.
Comparing the effectiveness of 1D versus 2D navigated respiratory-correlated 4D-MRI acquisitions for radiotherapy guidance
Authors: Keijnemans Katrinus , Borman Pim , Raaymakers Bas , Fast Martin
Published: August 2024

Accurate characterization of internal anatomical motion is essential for correctly guiding free-breathing radiotherapy treatments on the MR-linacs. Respiratory-correlated 4D-MRI can potentially provide this crucial information during each treatment session. We acquired a 1D respiratory navigator (1D-RNAV) interleaved with simultaneous multi-slice (SMS) images, yielding a concurrent 2D navigator (2D-RNAV). Respiratory-correlated 4D-MRIs were obtained with the 1D and 2D internal navigator surrogate signals, and the amount of missing anatomical data was quantified. Using the 1D-RNAV signal yielded fewer missing data in respiratory-correlated 4D-MRIs. Improving breathing regularity using biofeedback did not substantially influence the choice of navigator.
Four Quadrant Vector Mapping of Hybrid Multidimensional MRI data for the diagnosis of prostate cancer
Authors: Chatterjee Aritrick , Fan Xiaobing , Oto Aytekin , Karczmar Gregory
Published: August 2024

This study introduces a new quantitative mapping technique referred to as “Four Quadrant Vector Mapping” of HM-MRI data, where each image voxel is represented as a vector within a 2D plot with components ‘∆T2/∆b’ and ‘∆ADC/∆TE’ with associated spatial coordinates and quadrant, distance and angle. Measured metrics provides effective cancer markers, with cancers associated with high PQ4, lower PQ2, and higher vector angle, and lower amplitude. Quadrant mapping parameters show promise for determining cancer aggressiveness as they are moderately correlated with Gleason score. Four quadrant mapping could be combined with the compartmental analysis of HM-MRI data to increase diagnostic accuracy.
Detection of new resonances in the down-field 1H MRS of human calf muscle in vivo at 7T
Authors: Nanga Ravi Prakash Reddy , Elliott Mark , Swain Anshuman , Wilson Neil , Swago Sophia , Soni Narayan Datt , Witschey Walter , Reddy Ravinder
Published: August 2024

We have detected new resonances that were not reported previously with chemical shifts of ~10.1, 10.3, and 10.9 ppm occurring in the down-field 1H MRS (DFMRS) from the human calf muscle in vivo. Based on the phantom and literature data, we speculate that the contribution to the 10.1 ppm peak might be from L-Tryptophan whereas the peaks at 10.3 and 10.9 ppm are yet to be assigned. This is for the first time that these peaks are observed and reported from the down-field 1H MRS of the human calf muscle.
Quantitative muscle MRI depicts microstructural abnormalities but no signs of inflammation or dystrophy in Post COVID-19 condition
Authors: Schlaffke Lara , Forsting Johannes , Rohm Marlena , Schwenkreis Peter , Tegenthoff Martin , Meyer-Frießem Christine , Enax-Krumova Elena
Published: August 2024

Patients with post COVID-19 condition (PCC) often suffer from musculoskeletal pain with unknown pathophysiology. qMRI of the lower limbs was used to unravel the underlying mechanisms. 20 PCC were compared to 20 age and gender matched controls with regard to muscle fatfraction (revealed by Dixon imaging) water T2 time (using T2-mapping) and structural alterations (using DTI). Quantitative MRI did not depict any signs of ongoing inflammation or dystrophic process of the skeletal muscles in PCC patients. However, differences observed in muscle DTI depicts microstructural abnormalities, which may reflect potentially reversible fiber hypotrophy due to deconditioning.
Skeletal muscle Mg2+, membrane permeability, and pH are altered in Becker muscular dystrophy: A 31P-MRS and DT-MRI study
Authors: Cameron Donnie , Hooijmans Melissa , Niks Erik , Kan Hermien
Published: August 2024

Becker muscular dystrophy (BMD) is an X-linked disorder characterised by variable, progressive muscle damage and loss of function. MR-derived fat fraction is used as a disease-progression marker, but early markers are lacking. Here we compared membrane permeability measures between BMD patients and controls to test whether these are altered in BMD. Phosphorus-(31P)-MRS showed reduced ionised magnesium [Mg2+], and increased phosphodiester/adenosine-triphosphate ratios and pH in the tibialis anterior muscle in patients, while diffusion-tensor-(DT)-MRI-derived permeability showed no inter-group differences. Future studies should determine the predictive value of 31P-MRS-measured [Mg2+], pH, and membrane breakdown for disease progression, to establish their potential as biomarkers.
Fat fraction dependent decrease of muscle 1H2O T2 in Duchenne muscular dystrophy is steeper in patients with faster disease progression
Authors: Baetscher Eric , Triplett William , Walter Glenn , Forbes Sean , Willcocks Rebecca , Mueller Robert , Barnard Alison , Vandenborne Krista , Rooney William
Published: August 2024

Fatty tissue replacement of muscle is characteristic of disease progression in Duchenne muscular dystrophy (DMD), and correlates with a decrease in muscle 1H2O T2. We report longitudinal analysis of MRS data from 86 subjects with DMD. We detect a significant effect of μ (age at 50% fat-fraction) on the slope of T2 by FF. The effect of μ on slope is a consequence of lower T2 at high FF in early progressing subjects, not an effect of T2 differences at low FF. The slope of muscle water T2 decline with FF may contain additional information related to pathology in DMD.
Magnetization transfer ratio for characterizing extra-ocular muscles changes in myasthenia gravis: a pilot feasibility study
Authors: Zhou Qin , Chen Pei , Zhao Xiaoxiao , Zhao Jing , Wang Mengzhu , Yang Zhiyun
Published: August 2024

This study looked into the feasibility of using MTI to assess the EOM morphological and pathological changes in MG. The MTR can effectively quantify fibrosis and atrophy of EOM in MG patients, which is significantly related to the poor response to medication and the long duration of the disease, implying that it can be used as a non-invasive auxiliary diagnostic tool for MG patients' prognosis evaluation.
Selective Shielding for RF Energy Harvesting Without B1 Interference
Authors: Bjoerkqvist Oskar , Pruessmann Klaas
Published: August 2024

In this abstract we present a solution for harvesting RF excitation pulses in MRI while suppressing interference caused by the harvesting system, which is otherwise known to be an issue. This approach to RF harvesting offers an alternative for powering circuitry that needs to be within the field of view free from cables and without dedicated energy transmission.
Transmission of Rotary Excitation enables Quantification of Oscillating nT-Fields using a Linear Calibration Model
Authors: Albertova Petra , Gram Maximilian , Schirmer Verena , Blaimer Martin , Herrmann Martin , Gamer Matthias , Nordbeck Peter , Jakob Peter
Published: August 2024

Rotary excitation based MRI, which enables spatially resolved, non-invasive, direct detection of biomagnetic fields, complements the measurement range accessible via MEG since detection is independent of the distance to the body surface. We present an extension of the REX method towards quantitative imaging. A calibration function can be composed from REX data of two measurements of adjustable fields projected onto the tissue by the scanner’s gradient system. In subsequent measurements, the field strength can be inferred from the calibration model. First in vivo proof-of-concept measurements show that the calibration successfully eliminates influences of tissue properties and accurately quantifies oscillatory nT-fields.
Biophysical model to correct for blood volume confounders of T2* mapping based renal MR oximetry
Authors: Gladytz Thomas , Cantow Kathleen , Millward Jason , Waiczies Sonia , Seeliger Erdmann , Niendorf Thoralf
Published: August 2024

Quantitative MRI of renal hypoxia, an early key feature in acute kidney injury, is a valuable tool for diagnostics and pathophysiological studies. En route to MR oximetry we propose a biophysical model that uses the kidney size as an additional MRI derivable information to correct for blood volume confounders of oxygenation sensitive T2* maps. The model is demonstrated in a preclinical study involving three different vascular occlusions highlighting the confounding effect of blood volume changes and its correction.
Synthesizing speech through a tube talker model informed by dynamic MRI-derived vocal tract area functions
Authors: Rusho Rushdi Zahid , Story Brad , Meyer David , Jacob Mathews , Lingala Sajan
Published: August 2024

The vocal tract encompasses the airspace from the glottis (the space between the vocal folds) to the external lips. This irregular tube filters the glottal sound source, and is modulated by many structures (e.g. the tongue, lips, and velum) to produce speech sounds (e.g. vowels and consonants). In this work, we determine the preliminary feasibility of integrating vocal tract area functions derived from a recently proposed accelerated pseudo-3D dynamic speech MRI scheme to a parametric tube talker model to synthesize speech.
Two-voxel MRS with B0 correction using a model-based reconstruction
Authors: Berrington Adam , Mougin Olivier
Published: August 2024

Performing simultaneous MRS from two-regions is often desirable in spectroscopy studies. However, achieving an optimal B0 shim in two regions is difficult, leading to poor data quality. We investigate a model-based reconstruction for two-voxel MRS, incorporating B0 information, to reconstruct data with reduced T2* decay. We test this approach using least squares and regularization approaches for Hadamard encoding in simulations and phantom. Despite a noise penalty for B0-correction (2-4)-fold, FIDs from two voxels better matched un-broadened spectra under conditions of sufficient SNR. Regularization also produced time-domain reconstructions with less noise than a least-squares approach.
Enhanced Myelin Mapping based on Histology and MRI from the Same Subjects
Authors: Liang Zifei , Lee Choong , Minteer Jennifer , Kim Yongsoo , Zhang Jiangyang
Published: August 2024

To infer cellular-level information from MR signals with high sensitivity and specificity is a challenging task. We previously demonstrated the feasibility of mapping myelin in the mouse brain based on multi-contrast MRI using deep learning, but the results were based on limited histological data and MRI data from separate cohorts. In this study, we acquired serial 2-photon and MRI data from the same mice and trained a neural network for mapping myelin. Our results demonstrated enhanced sensitivity and specificity compared to conventional MRI myelin markers, our previous network, and polynomial fitting.
Enlarged Perivascular Spaces in the Basal Ganglia Surround Arteries, not Veins
Authors: Oltmer Jan , Beck Julia , Mattern Hendrik , Yakupov Renat , Auger Corinne , Düzel Emrah , van Veluw Susanne , Schreiber Stefanie , Perosa Valentina
Published: August 2024

Fluid-filled perivascular spaces (PVS) surround brain vessels. Their enlargement is a common hallmark of cerebral small vessel disease (CSVD) and has been related to impaired clearance of toxic proteins from the brain. It is unclear whether PVS enlarge around arteries, veins, or both. Combining ultra-high resolution 7T MRI angiography, venography and histology, we show that in healthy controls and patients with CSVD, PVS enlarge around arteries more than veins within the basal ganglia. A better understanding of the anatomy and distribution of enlarged PVS can contribute to the understanding of perivascular clearance and disease mechanisms.
VICTR: Venous transit time (VTT) Imaging by Changes in T1 Relaxation
Authors: Shi Wen , Jiang Dengrong , Moghekar Abhay , Hu Zhiyi , Lu Hanzhang
Published: August 2024

Venous hemodynamics is not well-studied despite it is a crucial part of human vascular system and involves in many brain vascular diseases. Here, we developed a novel technique dubbed Venous transit time Imaging by Changes in T1 Relaxation (VICTR) to measure the time for blood to travel from capillary to veins, i.e., VTT. We verified VICTR on different locations of superior sagittal sinus and VTT showed regional dependency. The caffeine challenge study also validated VTT with the vasoconstriction effect. With excellent test-retest reproducibility, VTT may be a potential physiological biomarker to evaluate vessel tortuosity and detect venous pathologies.
An advanced reconstruction framework in stack-of-stars golden-angle radial non-contrast enhanced 4D MRA with ultra-high temporal resolution
Authors: Zhao Tianrui , Feng Li , Yan Lirong
Published: August 2024

In the present study, we developed an advanced reconstruction framework for ASL-based 4-dimensional (4D) MRA dubbed GraspMRA, which combines stack-of-stars golden-angle radial sampling with low-rank subspace-based image reconstruction to achieve ultra-high temporal resolution. The performance of GraspMRA was evaluated by comparison with three other reconstruction methods at different acceleration rates. Our results have demonstrated that GraspMRA has superior performance to the other methods, and it provides real flow dynamics at an ultra-high temporal resolution of up to 25ms per 3D volume while preserving good image quality.
A novel method of calculating cardiac pulsatility from resting state fMRI data
Authors: Beckerleg Ryan , Mitsis Georgios , Kassinopoulos Michalis , Murphy Kevin
Published: August 2024

Measures of cardiac pulsatility were generated from resting-state fMRI data using ICA information and processed HRV traces. This was achieved by performing a correlation between the ICA component and HRV time series to isolate cardiac-related components from non-cardiac-related components. Classified datasets were used to train the FIX noise correction algorithm. FIX was used to classify cardiac components in 4123 rfMRI datasets. Measures of voxelwise cardiac pulsatility were generated be determining the R2 variance explained in the voxel time series by the classified cardiac components. Comparisons with gold standard pulsatility measures derived from the HRV traces showed high correlation values.
Can HR-VWI pre-operatively predict Postoperative Restenosis of Intracranial Atherosclerotic Disease Treated by Drug-Coated Balloon?
Authors: Jiang Shu , Dou Weiqiang , Wang Xinyi , Zhang Chao
Published: August 2024

This study mainly investigated if high-resolution vessel wall MRI (HR-VWI) can predict postoperative restenosis before drug-coated balloon (DCB) treatment. We included 76 patients who underwent HR-VWI examination before DCB treatment. DSA measurement was assessed 6 months after operation to determine vessel restenosis, classifying patients into three groups of no stenosis, mild stenosis (<50%), and restenosis (>50%). Significant differences among three groups were observed in plaque length, lumen area of MLN, degree of stenosis, enhancement amplitude and plaque burden. Plaque length and EA were independent prognostic factors of postoperative restenosis. Therefore, HR-VWI has potential to predict postoperative restenosis before DCB treatment.
TPA:Two-stage Progressive Attention diagnosis framework for hepatocellular carcinoma segmentation on Dynamic Contrast Enhanced MRI
Authors: Gao Lei , Wang Yuan-Cheng
Published: August 2024

We proposed a Two-stage Progressive Attention (TPA) framework by simulating the radiologists’ decision process for hepatocellular carcinoma segmentation. The study included 400 HCC patients as an internal set and 109 patients as an external test set. To obtain sensitive and specific results, our model is divided into two stages, respectively introducing attention mechanism and residual network.
Motion Corrected DW-MRI With 3D Slice Level Motion Estimation In Pediatric Liver Tumors
Authors: VASYLECHKO SERGE , LU LINA , ARIYUREK CEMRE , AFACAN ONUR , KURUGOL SILA
Published: August 2024

Voxel misalignment due to unavoidable respiratory motion and bulk motion introduce large errors in DW-MRI quantitative parameter fitting. Apparent diffusion coefficient (ADC) is an effective tool for characterization of malignant tumors. In this study we evaluate the use of a motion correction method for DW-MRI imaging based on 3D slice level motion tracking using a rigid slice to volume registration and Kalman filtering. We show improvement in robustness of parameter estimation and reduction of blurring in b-value images for assessment of pediatric hepatoblastoma lesions.
Long-term evolution of liver imaging reporting and Data system Version 2018 category 2, 3, and 4 observations on MRI in HBV-related cirrhosis
Authors: Xing Fei , Wang Yi , Zhao Xian
Published: August 2024

This study assessed the imaging outcomes of Liver Imaging Reporting and Data System (LI-RADS) v2018 category 2, 3, and 4 observations in prospective hepatocellular carcinoma (HCC) surveillance cohort. LI-RADS observations demonstrate increasing risk of progression to HCC with increasing category. About two-fifths of LR-4 progressed to a malignant category. LR-3 observations with APHE or threshold/subthreshold growth upgraded to LR-5 were significantly higher. Most LR-2 observations that remain stable in category for at least two years.
Incorporating diffusion weighted MRI into a radiomics model improves diagnostic performance in survival prediction in PDAC
Authors: Zeng Piaoe , Cui Jingjing , Yuan Huihui
Published: August 2024

Radiomics features were extracted from multiparametric MRI including conventional MRI (T2WI, T1WI, arterial phase, portal venous phase images) and apparent diffusion coefficient (ADC). The radiomics score was built based on the least absolute shrinkage and selection operator regression model. Three models, including clinicopathological and radiographic characteristics (CPR) model, multiparametric MRI radiomics model and conventional MRI radiomics model, were built to predict recurrence-free survival (RFS) and overall survival (OS) in patients with resectable pancreatic ductal adenocarcinoma (PDAC). Multiparametric MRI radiomics model showed improved diagnostic performance in survival prediction than conventional MRI radiomics model and CPR model.
Utility of Thin-Slice Fat-Suppressed Single-Shot T2-Weighted MRI with Deep Learning Image Reconstruction for Pancreatic Cancer
Authors: Shimada Ryuji , Sofue Keitaro , Somiya Yuichiro , Horii Shintaro , Tujita Yushi , Ueno Yoshiko , Wakayama Tetsuya , Kusaka Akiko , Murakami Takamichi
Published: August 2024

We investigated the utility of breath-hold (BH) fat-suppressed single-shot T2-weighted MRI with deep learning image reconstruction (DLIR) in 42 patients with pancreatic cancer. Three fat-suppressed T2-weighted sequences of 1) single-shot fast-spin echo (SSFSE) of 6mm thickness with 1BH, 2) SSFSE of 3mm thickness with 2BH and 3) FSE of 6mm thickness with 3BH were compared. SSFSE sequences improved signal-to-noise ratio on anatomical organs, pancreas-to-lesion contrast, and image quality in terms of motion artifacts, image sharpness, and anatomical clarity in comparison with conventional FSE. The T2-weighted SSFSE with DLIR can be a useful sequence for the evaluation of pancreatic cancer.
Artifact-robust vascular segmentation for 3D phase-contrast MR angiography using a deep learning approach
Authors: Tamada Daiki , Oechtering Thekla , Takai Eisuke , Reeder Scott
Published: August 2024

We developed a segmentation algorithm for PC-MRA using a deep-learning approach, with the goal of achieving artifact-robust segmentation for PC-MRA. To simulate flow-related artifacts of MRA, Gaussian noise and phase error were added to the k-space domain of the datasets. LadderNet consists of two consecutive U-nets with skip connections, and has been adopted as a training network for vessel segmentation. Retrospective studies demonstrated superior accuracy and precision of the proposed method over a conventional level set segmentation method.
EVAC: Multi-scale V-Net with Deep Feature Conditional Random Field Layers for Brain Extraction
Authors: Park Jong Sung , Fadnavis Shreyas , Garyfallidis Eleftherios
Published: August 2024

Brain Extraction is a complicated semantic segmentation task. While Deep Learning methods are popularly used, they are heavily biased towards the training dataset. To reduce this dependency, we present EVAC (Enhanced V-net like Architecture with Conditional Random Fields), a novel Deep Learning model for Brain Extraction. Using V-net as a skeleton, we propose three improvements: multi-scale inputs, modified CRF layer and regularizing Dice Loss. Results show that these changes not only increase accuracy but also the efficiency of the model as well. Compared to the state-of-the-art methods, our model achieves high and stable accuracy across datasets.
GIF_boost : A Generalisable Hybrid Brain Tissue Segmentation with DeepLearning
Authors: WU JIAMING , Pontillo Giuseppe , Mendelsohn Zoe , Hu Yipeng , Barkhof Frederik , Prados Ferran
Published: August 2024

Image segmentation and parcellation can provide quantitative assessment of the brain and can guide diagnosis and treatment decision-making. Geodesic Information Flow (GIF) is a freely available brain tissue segmentation and parcellation MRI-based tool using a classical label fusion approach. In this work, we introduce GIF_boost, a hybrid solution that takes advantages of deep learning to accelerate the bottleneck step of the template library registration. We compared GIF_boost with the original version of GIF and FreeSurfer (a state-of-the-art method). GIF_boost performed parcellation minimum 16 times faster. Parcellations had a similar Dice coefficient and Hausdorff distance and an improved volumetric quantification.
Automatic Segmentation and Diagnosis of Breast Cancer in Multicenter Data based on Deep Learning and Tissue-specific Histogram Normalization
Authors: Bai Yansong , Zheng Rencheng , Chen Weibo , You Chao , Wang Chengyan , Wang He
Published: August 2024

This study presented an intelligent diagnosis system to segment breast tumors in dynamic contrast-enhanced (DCE) images from multicenter dataset and determine the risk of triple-negative breast cancer (TNBC) with a four-step model: a) breast segmentation with no-new Unet (nnUnet); b) multicenter data normalization using Tissue-specific Histogram Normalization (TSHN); c) tumor segmentation with nnUnet model and d) automatic diagnosis of breast cancer with radiomics analysis based on segmented masks. The proposed model exhibited a superior performance in segmentation and diagnosis of breast cancer in multicenter data.
Self-Supervised Deep Learning Reconstruction for Highly Accelerated Diffusion Imaging
Authors: Vurankaya Ismail , Jun Yohan , Cho Jaejin , Bilgic Berkin
Published: August 2024

We propose a zero-shot self-supervised learning (ZS-SSL) approach for accelerated diffusion MRI reconstruction. Our method builds on the approach in [3] for subject-specific MRI reconstruction. We perform reconstruction across all diffusion directions with a single model, rather than different models for each direction, reducing computation time. We partition the directions as training and validation directions. We train the model on training directions, while keeping track of validation loss. We test our model on entire directions, evaluating the reconstruction quality of a single network across all directions. Jointly trained ZS-SSL provides better reconstructions than standard parallel imaging, while remaining computationally efficient.
SVD Compression for Nonlinear Encoding Imaging with Model-based Deep Learning Reconstruction
Authors: Zhang Zhehong , Selvaganesan Kartiga , Ha Yonghyun , Sun Chenhao , Samardzija Anja , Sun Heng , Galiana Gigi , Constable R. Todd
Published: August 2024

Model-based deep learning reconstruction with a nonlinear encoding matrix poses unique challenges to GPU memory, due to the densely connected computational graph nodes in the physics model part. In this work, SVD compression is demonstrated as necessary for such networks, and it is applied to the highly nonlinear case of Bloch-Siegert encoding from a low-field MR scanner. The redundancy across all nonlinear encoding dimensions is exploited for compression. With the compressed encoding matrix, the model-based network is feasible to implement. It outperforms the traditional reconstruction at all levels of simulated Gaussian noise and has advantages over commonly used regularization terms.
Beyond lactate: using hyperpolarized [1-13C] pyruvate to measure human brain pH and amino acid metabolism
Authors: Khan Alixander , Kaggie Joshua , Horvat-Menih Ines , Matys Tomasz , Schulte Rolf , Locke Matthew , Grimmer Ashley , Latimer Elizabeth , Frary Amy , Graves Martin , McLean Mary , Gallagher Ferdia
Published: August 2024

Hyperpolarized 13C MRS was used to measure human brain pH for the first time following 13C imaging through measurements of 13CO2 and bicarbonate. Aspartate concentration was also found offering insight into amino acid metabolism. Global brain pH was measured in the brain to be 6.98 ± 0.06. Non-invasive measurements of cerebral pH could be particularly important in assessing cerebral pathology given the wide range of disease processes that alter acid-base balance. Aspartate was measured in 4 of 9 volunteers with no age-related trends seen. Pyruvate carboxylase flux was found to be 6% of pyruvate dehydrogenase flux.
Assessing Glutaminase Inhibition Treatment Response in Ovarian Cancer by Hyperpolarized Magnetic Resonance Spectroscopy
Authors: Dutta Prasanta , Glassman Deanna , Kim Mark , Spradlin Meredith , Bayraktar Emine , Stur Elaine , Mangala Selanere , Foster Katherine , Lee Sanghoon , Yap Timothy , Westin Shannon , Eberlin Livia , Sood Anil , Bhattacharya Pratip
Published: August 2024

The metabolic vulnerability of anti-VEGF antibody (AVA) resistant ovarian cancer with glutaminase inhibitor (GLSi) therapy was assessed by hyperpolarized 13C-pyruvate magnetic resonance spectroscopy (HP-MRS) in vivo and by desorption electrospray ionization spectroscopic (DESI-MS) imaging ex vivo. The lactate-to-pyruvate ratio was used as the treatment response imaging biomarker.
Time-resolved cardiac imaging and motion analysis using a multi-scale dynamics decomposition
Authors: Olausson Thomas , Beijst Casper , Sbrizzi Alessandro , van den Berg Cornelis , Huttinga Niek
Published: August 2024

In this work we present a time-resolved (ungated, free-breathing) dynamic cardiac MRI method which disentangles and jointly estimates motion-fields and time-varying contrast images. Different sources of intensity variations are represented with L+S decompositions and motion-fields, that can naturally provide the information required for downstream tasks such as perfusion (contrast dynamics) and myocardial strain analysis (tissue dynamics). Results indicate the feasibility of disentangling the different sources of temporal intensity variations such as respiratory motion or contrast enhancement. Moreover, no breath holds nor ECG triggering/sorting are required since the time-resolved framework can simultaneously resolve the many overlapping dynamics from a continuous data-stream.
Relationship between Lenticulostriate Arteries Numbers and White Matter Changes in Cerebral Small Vessel Disease Patients
Authors: Zhang Yukun , Chang Peipei , Liu Na , Wang Yiming , Lin Liangjie , Song Qingwei , Miao Yanwei
Published: August 2024

In this study, according to the number of lenticulostriate arteries (LSAs), cerebral small blood vessel disease (CSVD) was divided into three groups. Differences of typical CSVD markers and whole brain analysis based on diffusion tensor imaging (DTI) were explored among three groups. It was found that with the decrease of the number of LSAs, the volume of WMH and the scope and extent of fiber tract damage increased. Results show that the change of LSAs have the potential to represents extensive subcortical microvascular damage of brain tissues in CSVD.
Pre-surgical structural and functional MRI to optimize cerebellar stimulation electrode placement for chronic stroke therapy
Authors: Chen Jacqueline , Nemani Ajay , de Almeida Julio Cesar , Huang Xuemei , Bocca Leonardo Favi , Baker Kenneth , Lowe Mark , Jones Stephen , Machado Andre
Published: August 2024

To improve reproducibility of positive outcomes from cerebellar dentate stimulation for chronic stroke, we hypothesized that the volume of tissue activated (VTA) (dentateVTA) proximity to the volume functionally connected to ipsilesional motor-associated cortices (dentateFC-ipsi-motor) would be associated with improved arm impairment/function metrics. DentateVTA was estimated using computed tomography and T1-weighted MRI. DentateFC-ipsi-motor was estimated from fMRI (resting state). Significant correlations with arm metric improvement were not found for dentateVTA proximity to dentateFC-ipsi-motor, but were found for dentateVTA size, distance from center, and percentage VTA in the inferior-lateral-posterior region. These findings suggest stimulating a small target at the inferior-posterior-lateral dentate edge.
Anatomical MRI-guided deep learning-based low-count PET image recovery without the need for training data – a PET/MR study
Authors: Zhao Tianyun , Hagan Thomas , DeLorenzo Christine , Huang Chuan
Published: August 2024

The advent of simultaneous PET/MRI enables the possibility of using MRI to guide PET image reconstruction/recovery. Deep-learning approaches have been explored in low-count PET recovery, with current approaches focus on supervised learning, which requires a large amount of training data. A recently proposed unsupervised learning image-recovery approach does not require this but relies on the optimal stopping criterion. In this work, we developed an unsupervised learning-based PET image recovery approach using anatomical MRI as input and a novel stopping criterion. Our method achieved better image recovery in both global image similarity metrics and regional standard uptake value (SUV) accuracy.
Evaluation of deep learning high-resolution Dixon PET/MR attenuation correction using 16-channel head-neck and 32-channel head coils
Authors: Ying Chunwei , Chen Yasheng , Brier Matthew , Flores Shaney , Laforest Richard , Benzinger Tammie , An Hongyu
Published: August 2024

We evaluated the accuracy of a deep learning-based PET/MR attenuation correction (AC) method with vendor-provided high-resolution Dixon in- and opp-phase images as inputs (DL-HiRes). We found that the DL-HiRes AC method significantly outperformed the vendor-provided skull model AC method for both 16-channel head-neck coil and 32-channel head coil (p<0.001). Moreover, the DL-HiRes method had similar AC accuracy using different head coils.
Combining Hyperpolarized Magnetic Resonance and Positron Emission Tomography to Interrogate Prostate Cancer Metabolism
Authors: Enriquez Jose , Dutta Prasanta , Armijo Ryan , Wang Muxin , Han Jenny , Shepherd Peter , Frigo Daniel , Titus Mark , Pisaneschi Federica , Bhattacharya Pratip
Published: August 2024

Many advanced prostate cancer patients receiving anti-androgens (Enzalutamide) as the first line of treatment, develop resistance which relapses into metastatic castrate-resistant prostate cancer (mCRPC). Treatment options for mCRPC patients are limited and continue to pose a significant oncological challenge with an alarming low survival rate of only 28%. The overarching goal of this research is thus to develop personalized metabolic imaging modality to target treatment strategies of different metabolic sub-types of prostate cancer by targeting pyruvate-to-lactate metabolism by hyperpolarized [1-13C]-pyruvate MR and fatty acid oxidation by 18F-FPIA PET.
Highly Accelerated 3D EPI CEST Imaging using Unevenly Segmented RF Irradiation with Temporal Random Walk Sampling Pattern in a Brain Tumor Patient
Authors: Kim Hahnsung , Park Suhyung , Hu Ranliang , Hoang Kimberly , Sun Phillip Zhe
Published: August 2024

We proposed a new 3D EPI CEST imaging integrated with unevenly segmented RF irradiation configuration to reduce T1w relaxation-induced signal modulation, thereby allowing for a reliable CEST effect over the whole volume. In addition, a temporal random walk with variable density (VD) CAIPI undersampling is incorporated into segmented 3D EPI for optimized random encoding in CEST imaging. The proposed pulse sequence and reconstruction framework were validated on the phantom and tumor patient.
Identification of energy metabolism remodeling in a rat model of Huntington’s disease using multi-metabolic CEST imaging
Authors: Mathieu-Daudé Yohann , Pépin Jérémy , Pérot Jean-Baptiste , Maguin Cécile , Flament Julien
Published: August 2024

Huntington’s disease (HD) is an inherited neurodegenerative disease characterized by motor, cognitive and psychiatric symptoms. As glutamate has been shown to be a potential biomarker of neurodegenerative diseases, we used Chemical Exchange Saturation Transfer imaging of glutamate (gluCEST) to map cerebral glutamate distribution in a rat model of HD. Modification of glutamate levels observed at 12 months were preceded by decrease of lactate concentration and reduced glycolytic metabolism measured glucoCEST at 4 months, suggesting early remodeling of energy metabolism during asymptomatic stage.
Ultrafast Z-spectroscopic Imaging in vivo at 3T using through-slice spectral encoding (TS-UFZ)
Authors: Bie Chongxue , van Zijl Peter , Mao Deng , Yadav Nirbhay
Published: August 2024

Acquisition of high-resolution Z-spectra requires excessive scan times. Ultrafast Z-spectroscopy (UFZ) can obtain whole Z-spectra within one acquisition by encoding the Z-spectral dimension spatially via a gradient applied concurrently with the saturation RF pulse, significantly reducing the scanning time. Still, UFZ has had limited success in vivo due to tissue heterogeneity. Here, we developed a TS-UFZ imaging approach where both saturation gradient and its readout were applied in the slice direction, where there is minimal heterogeneity. Results show that TS-UFZ allows high spectral- and spatial-resolution imaging of CEST signals in phantoms and human brain at 3T.
Investigating the Direct Imaging of Neuronal Activity (DIANA) contrast mechanism using human brain organoids
Authors: Morrison Sean , Webber Ewald , Dixon Carl , Maillet Donald , Wolvetang Ernst , Cloos Martijn
Published: August 2024

Here we describe our initial results investigating DIANA’s ability to capture neuronal activation in humans brain organoids. Following drug delivery a 3min long window with 0.5% increased signal was observed. Although tantalising, further experiments are needed to confirm these results.
Blood volume based functional MRI using velocity selective pulses and SERIOS readout
Authors: Hernandez-Garcia Luis , Frey David , Nielsen Jon-Fredrik , Noll Douglas
Published: August 2024

We investigate a new fast readout sequence to collect blood volume image time series in using two different velocity selective pulses. We compare two classes of pulses that target the blood or the stationary tissue. The two approaches are demonstrated and compared in a simple viso-motor blood volume based functional MRI experiment.
Respiration-resolved 5D flow MRI: Impact of the number of respiratory states of blood flow quantification in congenital heart disease patients
Authors: Weiss Elizabeth , Baraboo Justin , Ma Liliana , Falcão Mariana , Roy Christopher , Robinson Joshua , Stuber Matthias , Rigsby Cynthia , Markl Michael
Published: August 2024

We adapted our novel respiratory gating method to evaluate the impact of respiratory state (RS) resolution on respiratory driven flow measured by 5D flow MRI. We found that the impact of respiratory state resolution was both anatomy and vessel dependent. Caval veins and measurements in single ventricle disease patients were most impacted by the reduction of respiratory states. Shunt patients and pulmonary artery measurements were more robust to reduced RS and may be able to take advantage of the decreased acceleration associated with fewer RS. 5D flow MRI is well suited for this variable need as respiratory gating is retrospective.
Phase-Unwrapping based on Graph-Cuts for Low-venc Phase-Contrast Cine MRI
Authors: Berglund Johan
Published: August 2024

The use of low velocity encoding (venc) improves the velocity-to-noise ratio in phase-contrast MRI, but increases the risk of phase wrapping. A novel phase-unwrapping technique based on graph-cuts was evaluated in five cardiac patients with high-, medium-, and low-venc acquisitions. The proposed method reduced the stroke volume error compared to a LaPlacian based reference method. In retrospectively wrapped data, the proposed method demonstrated excellent unwrapping results in most cases, even for venc as low as 25 cm/s. The graph-cut based method appears promising for low-venc acquisitions combined with phase-unwrapping.
Diffusion generative prior-based highly accelerated MR T1ρ mapping
Authors: Wang Kangping , Cao Chentao , Cui Zhuoxu , Liu Yuanyuan , Zheng Hairong , Liang Dong , Zhu Yanjie
Published: August 2024

Diffusion-based generative models have been applied to solve the inverse problem of MR reconstruction and show impressive results. However, the diffusion model requires many iterations to produce high-quality samples, prolonging the reconstruction time. It also may lead to stochastic differential equation (SDE) sequence divergence in MR reconstruction and degrades the reconstruction quality. We proposed a new SDE for diffusion-based MR reconstruction that focuses on the diffusion process in high-frequency of k-space to improve reconstruction robustness and reduce the iterations. We applied the proposed method in MR T1ρ mapping, showing that it can achieve a high acceleration of 14X.
FOD-Net 2.0: End-to-end FOD enhancement for low angular diffusion acquisitions using deep learning
Authors: Wang Xinyi , Tang Zihao , Cabezas Mariano , D’Souza Arkiev , Calamante Fernando , Liu Dongnan , Barnett Michael , Tu Sicong , Cai Weidong , Wang Chenyu
Published: August 2024

Modern structural brain connectome pipelines and tractography techniques heavily rely on the quality of the diffusion weighted image acquisition (angular resolution) and the subsequent estimation of the fiber orientation distributions (FODs) for each voxel. Generating reliable connectomes from low angular single-shell acquisitions in clinical scenarios remains a challenging task. This work presents an end-to-end deep learning framework to enhance FOD estimates according to multi-shell acquisitions from low angular single-shell acquisitions to guarantee high quality tractography and connectomes within acceptable time and resources.
Diffusion-weighted signals and intrinsic optical signal share a similar contrast mechanism
Authors: Williamson Nathan , Ravin Rea , Cai Teddy , Basser Peter
Published: August 2024

Diffusion-weighted imaging (DWI) and intrinsic optical signal (IOS) are both used to measure neural tissue structure and function. Here we demonstrate with simultaneous real-time low-field, high-gradient MR and optical microscopy that DW signals and IOS share a similar contrast mechanism, most likely the ratio between intracellular and extracellular volume. Signals monitor how cells are affected and respond to adverse conditions, providing novel insight into pathological mechanisms and links between structure and function.
Automatic segmentation of cortical cytoarchitectonic domains measured with high-resolution MAP-MRI
Authors: Pas Kristofor , Saleem Kadharbatcha , Basser Peter , Avram Alexandru
Published: August 2024

We investigate the feasibility of using advanced diffusion MRI, specifically MAP-MRI, to automatically segment microstructural domains based on subject-specific intrinsic cortical cytoarchitectonic features. Our preliminary results suggest that MAP-derived cytoarchitectonic domains delineate boundaries between cortical areas and layers in good agreement with histology. Segmentation methods that leverage the sensitivity of high-resolution MAP parameters to cortical cytoarchitecture have the potential to augment and refine current techniques for automatic cortical parcellation that rely on atlas registration.
A multi-compartment model for pathological connectomes
Authors: Bosticardo Sara , Battocchio Matteo , Schiavi Simona , Granziera Cristina , Daducci Alessandro
Published: August 2024

The white matter is the complex system of neuronal fibers in the brain. Any disruption to this circuitry may lead to a wide range of neurological diseases and, thus, it is fundamental to be able of detecting pathological conditions at early stages. State-of-the-art methods for studying brain connectivity assume constant properties along the fibers, but this assumption is not valid in pathological conditions that locally affect the tissue, e.g. multiple sclerosis. Here, we present a model for “multi-compartment connectomes” to explicitly consider the presence of focal lesions during the estimation of connectivity and show its effectiveness using realistic numerical simulations.
Selective filters of translational molecular diffusion dynamics in white matter microstructures with preclinical and clinical MRI
Authors: Saidman Ezequiel , Zwick Analia , Tambalo Stefano , Feiweier Thorsten , Jovicich Jorge , Alvarez Gonzalo
Published: August 2024

Magnetic resonance imaging is one of the most widely used methods for non-invasive medical imaging. Gradient modulation sequences can selectively extract quantitative information at micrometer and sub-micrometer scales that might be relevant for clinical applications. We approach this topic using white matter phantoms by observing the contrast generated by Non-uniform Oscillating Gradient Spin Echo (NOGSE) sequences to selectively filter the signal of molecules confined in compartments of specific sizes. The NOGSE contrast is determined by subtracting different OGSE acquisitions on a clinical scanner. Our results demonstrate a microscopic tortuosity mechanism that provides microstructural information at short time scales.