bluuebunny/crossref_metadata_2025 huggingface dataset:
Inversion Recovery Quasi-Diffusion Tensor Imaging
Authors: Barrick Thomas , Howe Franklyn
Published: November 2024

Motivation: To provide a clinically feasible Quasi-Diffusion Tensor Imaging (QDTI) acquisition with free water suppression. Goal(s): To assess the effect of inversion recovery (IR) on QDTI measures in grey and white matter and determine measurement accuracy in clinically feasible data acquisitions. Approach: dMRI were acquired (8 b-values) with and without IR. QDTI measures were computed in brain tissue. Measurement bias was quantified for 4 and 3 b-value data subsets. Results: IR reduced free water effects by lowering grey matter diffusion coefficients in grey matter and raising tissue anisotropy. QDTI $$$\alpha$$$ was robust to effects of IR. Clinically feasible acquisitions provide accurate IR-QDTI measures. Impact: Our results suggest that IR-QDTI is a straightforward and robust method applicable to clinical studies for accurately characterising non-Gaussian diffusion in diseases of cortical grey matter, and white matter lesions/tumour where substantial numbers of voxels have high free water content.
Estimating the Spatial Distribution of Iron in Deep Gray Matter Nuclei over the Lifespan Using Quantitative Susceptibility Mapping
Authors: Li Gaiying , Zhang Miao , Jiang Wenqing , Du Yasong , Song Yang , Wang Yi , Li Jianqi
Published: November 2024

Motivation: Studies have demonstrated that iron accumulation rates in various gray matter nuclei are different throughout an individual’s lifetime, yet no study has quantitatively evaluated how the spatial distribution of brain iron might evolve with normal physiological development and aging. Goal(s): This study was to investigate the change trajectories of spatially distribution of iron in the deep gray matter nuclei as a function of ageing using QSM. Approach: 3D texture analyses were performed on QSM maps to calculate the texture features using the GLCM. Results: The quadratic regression results characterized differential age-dependent trajectories of texture features in gray matter of basal ganglia, midbrain and cerebellum. Impact: Quantitatively evaluated the spatial distribution properties of brain iron during lifespan might provide critical information for understand the brain development and aging, as well as predicting cognitive or neurodegenerative diseases.
MRS measured brain metabolite alterations in autism: a meta-analysis.
Authors: Thomson Alice , Pasanta Duanghathai , Arichi Tomoki , Puts Nicolaas
Published: November 2024

Motivation: 1H-Magnetic Resonance Spectroscopy (MRS) studies have revealed alterations in neuro-metabolite concentrations in autism, but results have been inconsistent. Goal(s): Perform a comprehensive meta-analysis of previous MRS studies in autism. Approach: Data was included from 59 relevant studies. After assessment of data quality, data was grouped by metabolite, brain region and other demographic and methodological factors. Results: We find significantly lower concentrations of GABA and NAA in autism. These alterations were most pronounced in children and in limbic brain regions involved in processes relevant to autism phenotypes. We also investigate factors that contribute to effect size variation between studies, including MRS quantification method. Impact: Our meta-analysis comprehensively summarises previous MRS studies to provide evidence of regional disruptions to brain metabolite concentrations in autism. We also examine how MRS study outcome varies due to methodological and demographic factors.
Altered Functional Connectivity in the Brain Induced by Early-life Mild Necrotizing Enterocolitis
Authors: Sung Ying-wei , Yang Chih-Hao , Hsieh Bao-Yu , Huang Chao-Ching , Lu Chia-Feng , Kao Yu-Chieh Jill
Published: November 2024

Motivation: According to the clinical findings, the survivors of necrotizing enterocolitis (NEC) may suffer from long-term neurodevelopmental impairment. However, how the local intestine inflammation impacts brain function needs further investigation. Goal(s): To probe the long-term alteration in the brain network induced by NEC in early life. Approach: After employing a neonatal rat model of NEC with lipopolysaccharide at postnatal 7 days, we measured the functional connectivity in the brain in its adolescent age (about P40). Results: Neurodevelopmental impairment induced by mild early-life NEC was reflected by rsfMRI in adolescent rats. Impact: We provide evidence showing that preterm NEC may induce the subsequent alteration in the functional brain network. Our results indicate the association between the early-life local intestine inflammation and long-term neurodevelopmental impairment, suggesting the interaction of the gut-brain axis.
Comparative study of time-dependent diffusion MRI and conventional DWI for microstructural characterization of breast lesions
Authors: Li Xue , Lu Jie , Jiang Lei , Li Chunmei , Li Haotian , Liu Kuiyuan , Wu Yanglei , Wu Dan , Chen Min
Published: November 2024

Motivation: Time-dependent diffusion MRI (dMRI), thanks to its distinct advantages in characterizing tissue microstructure, has gained increasing popularity in clinical research in recent years. Goal(s): However, its potential for distinguishing breast lesions remains uncertain. Approach: The present study was conducted to compare the diagnostic performance of time-dependent dMRI parameters and ADC metrics derived from conventional DWI for breast lesions. Results: The study results suggest that microstructural parameters based on time-dependent dMRI are superior to conventional DWI measurements in diagnosing breast lesions, and that the addition of time-dependent dMRI parameters improves the performance of conventional DWI in differentiating breast lesions. Impact: Herein, the microstructural characteristics of breast lesions were investigated using time-dependent dMRI technique and compared with conventional DWI. It was found that that adding time-dependent dMRI parameters could improve the performance of conventional DWI in the diagnosis of breast cancer.
Clinical assessment of whole-body MIPs as a survey tool in soft tissue disease with noise-corrected exponentially weighted DWI (niceDWI).
Authors: Knill Annemarie , Winfield Jessica , Barnsley Hannah , Malawo Benjamin , Hopkinson Georgina , Koh Dow-Mu , Messiou Christina , Withey Samuel , Blackledge Matthew
Published: November 2024

Motivation: Maximum intensity projections (MIPs) generated from niceDWI could be beneficial as a survey tool in patients with soft-tissue disease, helping to save time in clinical reporting. Goal(s): Perform a systematic clinical comparison of MIPs generated using niceDWI with MIPs generated using clinical DWI (clinDWI). Approach: An experienced reader of whole-body MRI scored niceDWI and clinDWI MIPs in 20 patients with metastatic melanoma for SNR, CNR, artifacts, overall quality, and presence of disease in soft-tissues. ROC curves were plotted to assess disease detection. Results: Image quality metrics were significantly worse in niceDWI, however there was no difference in ROC analysis of disease detection. Impact: Whole-body MIPs using niceDWI did not improve disease detection in patients with soft-tissue disease, despite improved interstation homogeneity. Motion of soft-tissue lesions may limit SNR of niceDWI thus it may have higher clinical impact in surveillance of bone disease.
Self-supervised learning with motion correction for fitting IVIM model parameters
Authors: Deng Feilin , Liu Baoer , Xu Yikai , Zhou Wu
Published: November 2024

Motivation: The existing fitting methods of lntravoxel incoherent motion (IVI M) parameter maps can already achieve good performance, but they all ignore the impact of internal heart beat or breathing movements on multiple b-value images for the fitting performance. Goal(s): We expect to incorporate motion correction in the fitting process to improve fitting performance. Approach: In this study, we propose an end-to-end deep network structure that combines self-supervised learning and motion correction for fitting IVIM parameters. Results: The quantitative and qualitative comparative experimental results indicate that using self-supervised motion correction can improve the fitting performance. Impact: For the first time, we consider incorporating motion correction into IVIM parameter fitting, and the self-supervised and end-to-end network design does not require training data, which can be extended to clinical applications.
Association between Glymphatic Function and Peripheral Inflammation in Different Motor Subtypes of Parkinson Disease
Authors: Lin Ruolan , Cai Guoen , Song Yang , Jiang Rifeng
Published: November 2024

Motivation: The association between glymphatic system and peripheral inflammation has not been well studied. Goal(s): Our study aimed to explore the relationship between glymphatic system and peripheral inflammation in PD patients and PD subtypes, and their link to motor symptoms. Approach: We used DTI to calculate the DTI-ALPS index, a non-invasive measure of glymphatic function, and evaluated the association between the DTI-ALPS index and clinical parameters using multiple linear regression. Results: Our study revealed a correlation between peripheral inflammation and glymphatic dysfunction in PD, particularly in TD subtype. Furthermore, the association between peripheral inflammation and clinical severity was observed in PD and PIGD patients. Impact: This study provides new insights into the pathophysiology of PD and potential therapeutic options.
Evaluation of Accelerated SEMAC at 0.55T using Hexagonal Sampling and Parallel Imaging
Authors: Barlas Bahadır Alp , Keskin Kübra , Li Bochao , Hargreaves Brian A , Nayak Krishna S
Published: November 2024

Motivation: Low-field and mid-field MRI systems have tremendous potential for imaging near metal implants with reduced artifacts, but reduced ability to do parallel imaging limits encoding options. Goal(s): To achieve 50% scan time reduction in addition to conventional parallel imaging at 0.55T. Approach: Hexagonal sampling (in ky-kf space) combined with GRAPPA-2 in SEMAC acquisitions was evaluated qualitatively and quantitatively via phantom and in vivo experiments at 0.55T. Results: GRAPPA-2 hexagonal sampling achieved comparable image quality to conventional GRAPPA-2 SEMAC with slight SNR reduction and a modest increase in artifact area, while allowing a 50% decrease in scan time. Impact: We evaluate the performance of hexagonal sampling combined with GRAPPA-2 at 0.55T where high parallel imaging factors are challenging via phantom and in vivo experiments. 50% additional scan time reduction is achieved with a modest increase in artifact area.
Volumetric self-navigated (vSNav) 3D-EPI for motion-robust functional MRI
Authors: Bayih Samuel , van der Kouwe Andre , Meintjes Ernesta
Published: November 2024

Motivation: 3D-EPI overcomes spatial resolution, spin-history and acceleration limitations of 2D-EPI but is rarely used for functional MRI (fMRI) due to its greater sensitivity to motion.Goal(s): To examine the performance of our volumetric self-navigated (vSNav) 3D-EPI sequence for fMRI acquisition. Approach: We acquired fMRI data using both our vSNav 3D-EPI and the standard 2D-EPI sequence during a block design finger tapping experiment both without and with intentional motion to compare the quality of the BOLD signal and the impact of different pre-processing steps. Results: Although data quality was similar, 3D data were more robust to spatial smoothing. Impact: A motion-robust 3D-EPI sequence will permit functional MRI with higher spatial and temporal resolutions. However, since the sequence acquires data and performs motion correction in a new way, it requires suitable preprocessing and analyses pipelines.
Investigating the feasibility of resting state functional MRI with GRE EPI on a high performance 0.5 T Scanner
Authors: Halder Arjama , Harris Chad , Wiens Curtis , Soddu Andrea , Chronik Blaine
Published: November 2024

Motivation: Resting state fMRI (rs-fMRI) provides vital neurological information in acute care. Goal(s): To demonstrate the feasibility of rs-fMRI at 0.5T. Approach: Repeated rs-fMRI acquisitions of two healthy volunteers (n = 6, n = 4) were acquired at 0.5T using a 31 min EPI based protocol. Results: All eleven well-established resting state networks were identified. Sensorimotor and language networks were very reliable across scans for both volunteers with intersession intra-class correlation coefficient values > 0.5. Impact: Resting state fMRI is feasible at 0.5T with an EPI acquisition technique. The functional connectivity detected resembles a 3T database for well-established resting-state networks.
Radial xSPEN for Non-Fourier Single-Sided MRI
Authors: Gomes Muller , Sadinski Meredith , Nacev Aleksander , Grissom William
Published: November 2024

Motivation: Developing methods for rapidly imaging in an inhomogeneous magnetic field is necessary for the general adoption of single sided MRI, which would bring MR imaging into the doctor's office. Goal(s): We aim to develop a fully spatiotemporal pulse sequence for collecting images with a single sided scanner. Approach: A hyperbolic phase is imparted to the magnetization which results in a time domain signal that is a profile of the phantom. The angle of this profile is rotated to produce an image. Results: Images with no Fourier encoding were produced and the images could be cropped without aliasing. Impact: This novel technique allows for rapid imaging in an inhomgeneous magnetic field. Furthermore, this technique allows for improved resolution because the field of view of the images collected with it can be smaller than the object without aliasing.
Absolute MR Thermometry in the breast using interleaved echo planar spectroscopic imaging
Authors: Odéen Henrik , Wong Peyton , Blatter Duane , Kim Seong-Eun , Payne Allison , Parker Dennis
Published: November 2024

Motivation: Clinically available MR thermometry methods only measures relative temperature change. Availability of absolute temperature measurements has high impact potential to improve monitoring and evaluation of thermal therapies. Goal(s): Develop an efficient and practical approach for spatial absolute MR thermometry in the breast. Approach: A multi-echo echo-planar spectroscopic imaging pulse sequence was implemented and tested in ex vivo human breast fat samples and healthy volunteers. Comparisons to single voxel spectroscopy was performed. Results: Water and fat peaks can be detected in both ex vivo samples and in vivo, and converted to absolute temperature measurements. Potential of spatial absolute thermometry is demonstrated. Impact: Absolute MR thermometry has the potential to improve treatment monitoring and evaluation of thermal therapies, and is demonstrated in the breast using a new efficient high-resolution interleaved echo planar spectroscopic imaging pulse sequence.
Scan, Aim, Go: A Compact Brain Drill Guide for Accelerating Minimally-Invasive Neurosurgeries
Authors: Lilieholm Thomas , Oakes Terrence , Lake Wendell , Ahmed Azam , Alexander Andrew , Block Walter
Published: November 2024

Motivation: Trajectory guides for MR-guided neurosurgeries have time-consuming, complex workflows. Trajectories are oriented by iterating between imaging and device manipulation. Noniterative approaches could reduce complexity and anesthesia time. Goal(s): To create and validate a trajectory guide that enables faster, accurate trajectory guidance in minimally-invasive neurosurgeries. Approach: Using new hardware and software, a single scan approach was used to perform drill guidance and device insertion on phantoms and cadaver heads. Results: The proposed methodology accurately guided needles to targets within cadaver brains using a single targeting scan. The initial design produced a radial error of 1.54±0.81mm. Impact: The proposed device and software accelerate trajectory guidance in minimally-invasive neurosurgeries by reducing the number of acquired scans and procedural steps. This approach simplifies and accelerates procedures which minimizes time under anesthesia.
Characterization of deformable tumor models for real-time adaptive radiotherapy plan evaluation
Authors: Lawrence Liam , Chugh Brige , Oglesby Ryan , Lau Angus
Published: November 2024

Motivation: Real-time adaptive radiotherapy, which involves tracking a target during internal patient motion, may soon reach clinical implementation. Quality assurance methods will be needed, especially for deformable motion. Goal(s): Develop a method of characterizing the relationship between input and target shape for deformable tumor model phantoms. Approach: We acquired dynamic MRI and static CT of a deformable phantom subject to several input waveforms. We approximated the target as an ellipsoid and created a linear model to predict the ellipsoid parameters from the input. Results: The ellipsoid parameters of the target subject to a new waveform could be predicted to within a millimeter. Impact: We demonstrated a simple method to determine the relationship between input and target geometry for deformable tumor model phantoms. Patient-specific tumor motion can be recreated by inverting the input-target geometry relationship for quality assurance of real-time adaptive radiotherapy plans.
Enhancing Undersampled MR Reconstruction Performance via Denoising Diffusion Models
Authors: Zhou Chen , Hu Yue
Published: November 2024

Motivation: Diffusion models, the latest generative modeling approach, hold significant promise for MRI reconstruction tasks. Goal(s): Due to the lengthy and slow diffusion inverse process, diffusion model are challenging to apply directly to MR reconstruction tasks. Approach: We employ SwinrnNet for initial reconstruction of undersampled images and introduce the DDDC module to supplement details, obviating the need for a protracted full reverse diffusion process. Results: Our method achieves high-quality reconstructions within 3 seconds, outperforming SOTA approaches in quantitative metrics. It exhibits superior reconstruction speed compared to other diffusion model methods, with a remarkable PSNR of 38.28 dB in the case of 5x acceleration. Impact: The DDDC module we propose effectively enhances reconstruction quality and can be applied extensively to any reconstruction model proposed by other researchers. With only a minimal time investment, it significantly improves image quality.
Simultaneous and Respiratory Motion-Synchronized T2 and T2* Mapping of the Human Kidneys
Authors: Vides Jose Raul Velasquez , Herrmann Carl , Gladytz Thomas , Shalikar Shahriar , Millward Jason M. , Waiczies Sonia , Seeliger Erdmann , Mattern Hendrik , Rose Georg , Niendorf Thoralf
Published: November 2024

Motivation: Quantitative MRI techniques, such as T2 and T2* mapping, have the potential to become important imaging biomarkers for non-invasive renal tissue assessment. However, clinical T2 and T2* mapping faces challenges posed by respiratory motion. Goal(s): This study explores the feasibility of simultaneous T2 and T2* mapping of the human kidneys with mitigated respiratory motion artifacts, using the 2in1-RARE-EPI technique. Approach: We used the distinctive acoustic noise pattern generated by the gradient coil switching during 2in1-RARE-EPI data acquisition to guide the subject to time their respiration during the scan. Results: This approach facilitates high in-plane resolution (1x1x5mm3) T2 and T2* mapping of human kidneys. Impact: Our approach for simultaneous and motion-synchronized T2 and T2* mapping of the human kidney provides a technical foundation for swift translation into the clinic and for gaining a better mechanistic understanding of renal (patho-)physiology.
Multi-Parametric MRI using Quantitative Non-Contrast Imaging for the Evaluation of Patients with Chronic Kidney Disease
Authors: Udayakumar Durga , Zhou Limin , Madhuranthakam Ananth
Published: November 2024

Motivation: Multiparametric (mp) MRI can enable functional characterization of entire kidneys for the assessment of patients with chronic kidney disease (CKD). Goal(s): To evaluate quantitative mpMRI for functional characterization in CKD patients and compare against healthy volunteers. Approach: Seven CKD patients and 6 healthy volunteers underwent MRI including ASL (perfusion), DWI (cellularity), BOLD (oxygenation), and MRE (fibrosis). Results: ASL measured perfusion was significantly lower in CKD patients compared to healthy volunteers (P<0.05), while DWI measured ADC was not different (P=0.36). BOLD measurement was statistically different in Medulla (T2*: P=0.009; R2*: P=0.05), but was not different in cortex (T2*: P=0.08; R2*: P=0.32). Impact: Multi-parametric MRI, particularly using ASL measured perfusion and BOLD measured R2* provides functional characterization of kidneys that could facilitate longitudinal monitoring and therapeutic interventions in CKD patients.
Flip Angle Optimisation for Quantitative Assessment of Pulmonary Ventilation and Perfusion at 3T
Authors: Peggs Zachary , Bolton Charlotte , Hall Ian , Gowland Penny , Francis Susan
Published: November 2024

Motivation: The optimum flip angle which provides a trade-off between ventilation and perfusion signal in free-breathing proton MRI (3 T) of the lung is unknown. Goal(s): To optimise the acquisition of free-breathing pulmonary proton MRI to maximise both ventilation and perfusion signals. Approach: Assessment of the flip angle in a 2D fast field echo (FFE) sequence in healthy volunteers utilising voxel-wise lung ventilation (VOLVE) analysis. Results: Increasing the flip angle tends to reduce the ventilation signal but increase the perfusion signal. A flip angle higher than the Ernst angle for lung parenchyma is recommended when assessing both perfusion and ventilation. Impact: Demonstration of the dependence of 2D FFE proton MRI ventilation and perfusion signal on flip angle to inform future studies. A flip angle greater than the parenchyma Ernst angle maintains a strong ventilation signal without supressing the perfusion signal.
Serum metabolic profiling of human valve diseases based on proton NMR spectroscopy
Authors: Kumar Pawan , Kumar Pradeep , Narang Rajeev , Mewar Sujeet , Arva Sudheer , Chakraborty Sandeep , Jayasundar Rama , Rajashekar Palleti
Published: November 2024

Motivation: Heart valve disease (HVD) is a complex condition with a poorly known pathogenesis. Goal(s): NMR-based serum metabolomics of HVD patients (aortic, mitral valve, and double valve replacement) and healthy controls to identify potential biomarkers for HVD. Approach: Proton NMR spectroscopy Results: The results obtained from PLS-DA and VIP score plots of metabolites in serum showed a separation between patients with HVD and HC. Impact: To understanding the potential metabolic alteration such as BCAA, and fatty acids, amino acids, and carbohydrate metabolism associated with inflammation, oxidative stress, and tissue degradation of HVD.
2D Flow-Inversion Non-Enhanced (FINE) MRA of lower extremities: comparison with 2D TOF
Authors: Zhang Xianyi , Shi Zhang , Wang Dong , Zhu Jiayu , Hu Junpu , Zhang Xijing , Yuan Jianmin , Wang He , Li Hao
Published: November 2024

Motivation: Inflow-based NCE MRA techniques have limited capability of background and venous suppression at the high field strength. Goal(s): This study described a new arterial spin labeling (ASL)-based method for lower limb MR angiography (MRA) called FINE (Flow-Inversion Non-Enhanced). Approach: The performance of FINE was evaluated on ten healthy volunteers and compared with 2D TOF MRA on 3T. Results: 2D FINE technique provides superior suppression of background and venous signal with reduced acquisition time than that of 2D TOF. Impact: The results of this study demonstrate the feasibility of FINE MRA of the lower limbs at 3T.
3D multiple overlapping-echo detachment (3D-MOLED) imaging for ultrafast simultaneous T2* and susceptibility mapping of whole-brain
Authors: Yang Qinqin , Chen Jie , Ge Nuowei , Zhu Liuhong , Wu Zhigang , Chen Zhong , Cai Shuhui , Zhou Jianjun , Zhong Jianhui , Cai Congbo
Published: November 2024

Motivation: Current high-resolution T2* and susceptibility mapping techniques remain time-consuming or suffer from geometric distortion. Goal(s): Our goal was to achieve distortion-free and time-efficient quantification of whole-brain T2* and susceptibility. Approach: The multiple overlapping-echo detachment imaging (MOLED) method was extended to 3D acquisition for collecting more echoes for robust high-resolution parametric mapping. Single scan blip-up-down operation of two echo trains combined with deep learning reconstruction was used for distortion correction. Results: 3D-MOLED enables high-quality T2* and susceptibility mapping in 32 seconds, comparable to conventional 3D-GRE in 12 minutes, with Pearson’s correlation coefficient of 0.983 and 0.986, respectively. Impact: Distortion-free whole-brain T2* and susceptibility mapping at isotropic 1 mm3 resolution can now be achieved using our newly developed 3D-MOLED technique in only 32 seconds, which significantly improves the motion robustness of quantitative imaging in clinical examinations.
Noisy-Signal2Parameter(S2P): Structure-adaptative parameter map reconstruction for filter-exchange imaging without clean data
Authors: Cheng Zhaowei , Jiang Fan , Fang Ke , Jin Xinyu , Hsu Yi-Cheng , Bai Ruiliang
Published: November 2024

Motivation: Water exchange measured by filter-exchange imaging (FEXI) is expected to serve as an important biomarker for several brain diseases. However, its estimation accuracy is easily affected by noise. Goal(s): To develop an approach for reconstruction of FEXI parameters from noisy signals. Approach: An end-to-end framework was constructed to achieve parameter reconstruction without corresponding labels. An adaptative deformable convolutional network was introduced to explore structural information. A loss function was designed to enhance network denoising performance. Results: Simulation results under SNR=30~50 showed that the S2P achieved optimal results in the reconstruction of apparent water exchange rate, with PSNR of 27.44 and SSIM of 0.9050. Impact: The S2P, an end-to-end framework, reconstructs high-quality FEXI parameter maps from only a single scan when it has been trained with noisy pairs, which can provide efficient and reliable medical images for clinical diagnosis.
Ten-fold accelerated multi-echo spiral fMRI using self-supervised physics-driven DL reconstruction
Authors: Yu Zidan , Gu Hongyi , Zhang Chi , Rettenmeier Christoph , Akcakaya Mehmet , Stenger V.Andrew
Published: November 2024

Motivation: Multi-echo fMRI holds the promise of more potential applications, however it suffers from long readout lengths. Goal(s): Explore the possibility of using deep learning(DL) reconstruction for highly under-sampled spiral multi-echo fMRI acquisition. Approach: Multi-echo data from four subjects were collected for DL training. Multi-echo fMRI data from another subject was used for testing the DL model. The DL model has been designed and modified to enable the reconstruction of ten-fold under-sampled fMRI images for BOLD analysis. Results: The DL model has not only reconstructed the multi-echo spiral fMRI with good image quality, but also preserved its BOLD sensitivity with the highly under-sampled data. Impact: With the help of DL, multi-echo fMRI may become more versatile for clinical use and future studies.
RANGR: Deep Learning Autonavigation of Free-Breathing Golden-Angle Radial Abdominal MRI
Authors: Nario Joel Jose , Murray Victor , Mekhanik Anthony , Otazo Ricardo
Published: November 2024

Motivation: Current autonavigation methodology for free-breathing MRI methods lacks reliability. Goal(s): Develop deep learning methodology to estimate a motion signal directly from the acquired data without manually tuned filtering or PCA transformation. Approach: RANGR uses an encoder network based on the popular VGG architecture to estimate a 1-D respiratory navigator signal from 1-D projections extracted directly from the data. Results: RANGR improved motion estimation and results on motion-resolved images with reduced artifacts, and was even able to detect motion even in cases where filtering+PCA completely failed. Impact: The improved robustness and automation presented by RANGR can promote the use of free-breathing motion-resolved imaging for both diagnostic and treatment guidance purposes.
Simultaneously Multi-slice Imaging by the Fusion of Reconstruction and Collecting Under-sampled Signal with Deep Learning (FoCUS)
Authors: SATO Yuki , ENDO Naoya , OUCHI Shohei , ITO Satoshi
Published: November 2024

Motivation: Simultaneous multi-slice imaging (SMS) can obtain multiple slice images simultaneously, but it requires the sensitivity distribution of the receiver coils. Goal(s): Our goal was to separate slice images using deep learning reconstruction without coil sensitivity. Approach: Different amplitude modulation is given to each slice, and the CNN separates each slice from the focal image based on the value of the amplitude modulation. Results: Simulation experiments showed that image separation was successfully achieved not only for real-valued images but also for complex-valued images. Image quality decreases when the number of excitation images was increased. Impact: SMS can be used much more easily because it does not require coil sensitivity distribution for image separation. No non-uniform residual noise will be generated. The proposed method may expand the application of SMS.
Denoising very low field magnetic resonance images using native noise modeling and deep learning
Authors: Ssentamu Tonny , Omoding Ronald , Edgar Atamba , Mukwaya Pius , William Jjuuko , Kimbowa Alvin , Geethanath Sairam
Published: November 2024

Motivation: Low-field MRI (LF-MRI) can increase accessibility in low-income countries where high-field MRI is not available due to cost, power and siting requirements. However, noise significantly affects LF-MR image quality. Goal(s): This study aims to enhance the signal-to-noise ratio (SNR) in very LF-MRI (0.05T) images using native noise modeling and deep learning. Approach: We extracted noise from 0.05T phantom MRI images, modeled it, added it to high-field brain MRI (1.5T & 3T), trained two deep-learning algorithms, and evaluated them on in vivo brain MRI images. Results: Our approach improves the SNR of in-vivo LF images by a factor of approximately two. Impact: Using native noise while developing deep-learning denoising algorithms for LF-MRI images is better than using synthetic random noise. As a result, the developed algorithms are more explainable and follow domain knowledge on noise in LF-MRI improving trust in the models.
Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography
Authors: Kim Jinho , Nickel Marcel Dominik , Knoll Florian
Published: November 2024

Motivation: We address the issue of long scan times in MR Cholangiopancreatography (MRCP), which often leads to poor image quality. Goal(s): We aim to leverage a Deep Learning-based model to accelerate MRCP acquisition. Approach: We acquired two-times parallel imaging accelerated MRCP data at 3T, trained a variational network with retrospective undersampling to a total acceleration factor of 6, and then tested the trained model with both retrospective and prospective 6-times accelerated data, acquired at both 3T and 0.55T. Results: The trained model shows potential to improve MRCP by reducing artifacts and enhancing distal ducts compared to parallel imaging and compressed sensing. Impact: The proposed method effectively removes artifacts in highly accelerated MRCP, shortening scan times from 303 seconds to 138 seconds. Moreover, the corresponding SNR enhancement enables MRCP acquisitions at 0.55T, where traditional image reconstruction methods face challenges.
Robust Partial Fourier Reconstruction with Zero-shot Deep Untrained Generative Prior
Authors: Kang* So Hyun , Kim* Jihoo , Cho JaeJin , Cooley Clarissa , Bilgic Berkin , Kim Tae Hyung
Published: November 2024

Motivation: We introduce a novel partial Fourier reconstruction method. Goal(s): The objective is to enhance partial Fourier reconstruction by integrating the traditional phase constraint with the recent zero-shot deep learning approach. Approach: The proposed method combines the virtual conjugate coils (VCC) phase constraint with zero-shot deep untrained generative prior (ZS-DUGP), assuming MRI can be nonlinearly represented by untrained networks, enabling simultaneous image reconstruction and prior learning without external training data. This approach enables robust partial Fourier reconstruction. Results: Evaluation across diverse datasets, including the fastMRI, the QALAS multi-echo data, and the low-field MR data, validates its enhanced performance compared to existing techniques. Impact: We propose a novel partial Fourier reconstruction combining virtual conjugate coils with a zero-shot untrained generative network prior. It provides robust reconstruction without external training dataset, evaluated across various scenarios (parallel imaging, multi-echo/contrast imaging, low-field MR) demonstrating its utility.
MEDL: Unsupervised Multi-Stage Ensemble Deep Learning with Diffusion Models for Denoising MRI Scans
Authors: Vora Sahil , Paul Riti , Ding Pak Lun Kevin , Patel Ameet C. , Hu Leland S. , Zhou Yuxiang , Li Baoxin
Published: November 2024

Motivation: Traditional MRI scans, necessary for high SNR and clear images, are time-consuming and discomfort for patients. Shorter scans, meant to improve the patient experience, often compromise image quality and SNR. New deep learning techniques provide a solution to denoise MRI scans, even with limited data availability. Goal(s): We aim to create an unsupervised MRI denoising method for real-world clinical settings, eliminating the need for clean or paired noisy images ensuring versatility and practicality. Approach: We use an unsupervised diffusion-based denoising approach to denoise MRI scans. Results: We achieve unsupervised denoising for MRI scans, outperforming previous methods and reducing time to 6 seconds. Impact: Our approach denoises general MRI scans without extra clean or noisy data. It's suitable for real-world clinics, reducing patient MRI time. It enhances imaging quality, ensuring accurate diagnoses and faster clinical practices for patients and doctors.
Self-supervised variational manifold learning: application to dynamic MRI of airway collapse in obstructive sleep apnea.
Authors: Alam Wahidul , Rusho Rushdi , Liu Junjie , Daele Douglas , Jacob Mathews , Lingala Sajan
Published: November 2024

Motivation: Recent manifold-based models use unsupervised generative Variational smoothness regularization on manifold framework for improved recovery. This unsupervised method lacks a well-defined automated early stopping criterion and rely on subjective qualitative assessment only. Goal(s): We aim to adapt a physics-guided early stopping criterion to V-SToRM framework leveraging non-cartesian multi-slice acquisition. Approach: We developed a self-supervised variational manifold recovery method where we modified the original variational manifold scheme to integrate an early stopping criterion. Results: With early-stopping criterion enforced, we observe a faithful reconstruction of spatiotemporal dynamics at epoch 45 and images without blocky/noise amplification artifacts at different temporal phases with suppressed temporal blurring artifacts. Impact: While preserving the integrity of the ongoing joint learning of latent variables and generator weights, the adoption of early-stopping strategy in this context streamlines the computational complexity and consequently, rendering faithful and reproducible faster reconstructions.
Quantitative Susceptibility Mapping by Controllable Diffusion Models
Authors: Xiong zhuang , Gao Yang , Sun Hongfu
Published: November 2024

Motivation: Current deep learning Quantitative Susceptibility Mapping (QSM) methods often rely on rigorous supervised training with paired data of the input and susceptibility maps and are only capable of specific one-to-one reconstructions. Goal(s): In this study, we introduce the Diffusion Model QSM (DM-QSM), a controllable generative model capable of synthesizing high-quality susceptibility maps without the need for supervised training. Approach: The DM-QSM method can produce controllable susceptibility maps with different measurements as the guidance. Results: DM-QSM is versatile and suitable for many-to-one task including QSM super resolution and dipole inversion for both simulated and in-vivo tests. Impact: This manuscript investigates the application of 3D generative models on QSM. It demonstrates robustness against acqusition artifacts for in-vivo test, and shows the potential beyond current tasks and is able to solve inverse problems like single-step QSM reconstruction.
Self-shielded RF coils for imaging in unshielded environments at low-field
Authors: Selvaganesan Kartiga , Sadinski Molly , Gomes Muller , Nacev Aleksandar , King Scott
Published: November 2024

Motivation: Access to MRI worldwide could be improved by reducing cost and increasing portability of the system. This requires imaging to occur in unshielded rooms making them highly susceptible to environmental noise; therefore, low-cost alternatives are needed for noise mitigationGoal(s): Here we have designed a self-shielded RF coil array, made of resonating coaxial cables, for low-field parallel imaging in unshielded environments. Approach: The proposed coils were tested against copper loops through phantom and clinical studies in various noise environments. Results: The results indicate the self-shield coils are robust to external noise and produce images with sufficient signal and contrast to distinguish anatomical features. Impact: With the increased interest in the community towards developing accessible MR systems, the proposed coil design presents a low-cost and resource efficient method for noise mitigation, thereby allowing MRI to be portable and available to provide point-of-care screening.
Simple and cost-effective 3D field mapping robot
Authors: Oiye Ivan Etoku , Poojar Pavan , Geethanath Sairam
Published: November 2024

Motivation: Measurement of parameters like magnetic field and temperature inside a magnet bore is of interest at all MR magnetic field strengths to characterize the system and use that information for downstream image quality improvement or to determine safety thresholds. Goal(s): Build and test a simple 3D movement robot. Approach: we design, build and test a simple 3D movement robot with a sensor holder as a standalone system using a Raspberry Pi and a 16 channel ADC hat. Results: The robot is capable of sub-millimeter measurements. We demonstrate the 3D field mapping of a 50mT scanner as an example use of the robot. Impact: We designed and built a $2000 3D field mapping robot to map a very low field scanner’s magnet. The system is standalone exploiting Raspberry Pi’s system on a chip architecture and is scalable to include 16 analog inputs (sensors).
Accelerating Prostate DWI Scans: A Combination of Local SVD and Deep Learning for Enhanced Denoising
Authors: Bolay Lukas , Pfaff Laura , Würfl Tobias , Weiland Elisabeth , Darwish Omar , Shagalov Oleg , Nickel Marcel-Dominik , Maier Andreas
Published: November 2024

Motivation: Diffusion-weighted MR images often suffer from low signal-to-noise ratio, particularly at high b-values, diminishing their diagnostic value. To counter this, multiple repetitions per diffusion direction are typically acquired and averaged, which is time-consuming and prone to motion artifacts. Goal(s): Present a method that reduces the required number of repetitions in DWI, thus shortening scan times, while preserving diagnostic value. Approach: The repetitions in DWI are jointly denoised through a combination of local Singular Value Decomposition and deep-learning-based denoising. Results: Our evaluations indicate that this approach outperforms competing methods, offering a potential solution to the problem of prolonged acquisition times in DWI. Impact: By combining local singular value decomposition with deep-learning-based denoising techniques, the necessary number of repetitions for the acquisition of diffusion-weighted MR images is substantially decreased and thus the acquisition is accelerated, while retaining comparable image quality.
Non-complete Recovery of White Matter Diffusion Metrics at One Year Post-Radiotherapy: Implications for Radiation-Induced Necrosis Risk
Authors: Pan Jie , Qiu Ziru , Fu Gui , liang Jiahui , Zhang Xinyuan , Lv Xiaofei
Published: November 2024

Motivation: Radiotherapy for nasopharynx carcinoma (NPC) can lead to various white matter (WM) abnormalities. Goal(s): Explores the connection between WM injuries and temporal lobe necrosis (TLN) and optimizing radiotherapy (RT) regimens for NPC. Approach: Multi-shell diffusion MRI (MS-dMRI) quantification using automated fiber-tract quantification (AFQ). Diffusion metrics alterations analysis using rANOVA. TLN risk factors identification using Cox regression models. Optimal RT regiments development using normal tissue complication probability (NTCP) models. Results: Most TL WM tract segments showed a "decrease/increase without complete recovery" pattern. MD_UF.L.1, NDI_CH.L.1, and NDI_ILF.R.1 were identified as TLN risk predictors. Tolerance dose for D10cc.L, D20cc.L and D10cc.R were revealed for optimal regiments. Impact: Our findings highlight the importance of "decrease/increase without complete recovery" pattern in TL WM tract segment impairments in TLN development. Identifying high-risk WM tract segments and their tolerance doses can aid in optimizing radiotherapy regimens for NPC.
Glymphatic system impairment in Type 2 diabetes mellitus associated with cognitive function
Authors: Gao Jie , Pan Peichun , Zhang Dongsheng , Yan Xuejiao , Tang Min , Li Jing , Zhang Xin , Ai Kai , Wu Peng , Lei Xiaoyan , Zhang Xiaoling
Published: November 2024

Motivation: T2DM has emerged as an important risk factor for cognitive impairment. However, the neuropathological basis of the cognitive impairment associated with T2DM is still unclear. Goal(s): To investigate whether the impaired glymphatic function is one of the mechanisms in T2DM-associated cognitive dysfunction. Approach: We compared the glymphatic function by calculating the diffusion along the perivascular space based on a diffusion tensor image, namely the DTI-ALPS index between HCs and patients with T2DM and investigated the relationship between DTI-ALPS index and cognitive functions. Results: The lower DTI-ALPS index was found in patients with T2DM, and DTI-ALPS index was correlated with worse cognitive performance. Impact: DTI-ALPS index may be applied as a useful indicator to evaluate the glymphatic system function. The impaired glymphatic system in patients with T2DM may provide a new perspective for understanding the pathophysiology of T2DM related cognitive impairment.
Feasibility of high b value in vivo cardiac diffusion in humans with ultra-high gradient strength scanner
Authors: Garrett Thomas , Kara Danielle , Chen Shi , Liu Yuchi , Bi Xiaoming , Nguyen Christopher
Published: November 2024

Motivation: New technology allows for higher gradient strengths and therefore higher b-values to be used in cardiac diffusion tensor imaging (cDTI). Goal(s): To compare cDTI parameter maps for the current standard and increased b-values both qualitatively and quantitatively. Approach: Two cDTI datasets with b=500 s/mm2 and b=1000s/mm2 were acquired from nine healthy subjects. The resulting DTI parameter maps were compared through visual analysis and a series of paired t-tests. Results: Both qualitative and quantitative data between parameter maps show improved detail and higher precision in high b-value data compared to standard b-value data. Impact: A new ultra-high-performance 3T MR system allows for higher diffusion gradients and b-values. Our study shows high b-values (b=1000 s/mm2) lead to higher levels of detail and precision in cardiac DTI of healthy subjects compared to standard b-values (b=500 s/mm2).
Repeatability and reproducibility of diffusional variance decomposition (DIVIDE) imaging
Authors: Inui Shohei , Ueyama Tsuyoshi , Suzuki Yuichi , Wakayama Tetsuya , Abe Osamu
Published: November 2024

Motivation: Novel Multidimensional diffusion encoding (MDE) technique, diffusional variance decomposition (DIVIDE), may provide more detailed insights into tissue microstructure. Goal(s): To evaluate the feasibility of DIVIDE imaging for human brain. Approach: Ten healthy-subjects underwent MDE (2D-EPI sequence with 29 linear and 26 spherical b-tensors) twice using 3T-MRI. Regional values of 20 ROIs was extracted for 10 DIVIDE metrics. Coefficient of variation (CV) and interclass correlation coefficient (ICC) were calculated. Results: Intra-subject CV was less than 5% in almost all regional metrics. Intra-subject CV was lower than that of inter-subject CV in all regional metrics. ICC showed almost perfect agreements for almost all regional metrics. Impact: Recently developed MDE technique, diffusional variance decomposition (DIVIDE), may be reliably used for measuring diffusion metrics with a potential to provide more detailed insights into tissue microstructure in complex tissues, such as crossing or kissing fiber configurations in the brain.
The value of IVIM-based tumor habitats in predicting the lymph node metastasis of breast cancer
Authors: Liu Haifa , Zhai Yingmin , Liu Hui , Wang Mengzhu , Song Yang , Zhang Chengxiu , Yang Guang , Grimm Robert
Published: November 2024

Motivation: Biopsy is usually used to diagnose lymph node metastasis in breast cancer patients. Our study evaluated the clinical significance of a non-invasive method. Goal(s): Our aim was to explore the performance of a prediction model using tumor habitats. It predicted lymph node metastasis in patients with malignant breast cancer. Approach: We developed a predictive model of tumor habitats. It used the data of intravoxel incoherent motion (IVIM) and diagnosed lymph node metastasis. Results: The IVIM indicators can segment whole tumor into four subregions, which were related to hypoxic condition. The model can effectively predict lymph node metastasis of breast cancer patients. Impact: The predictive model elucidates the features of habitats. Its performance is good for the diagnosis of lymph node metastasis in patients with breast cancer, helping clinicians make better decisions and recommend appropriate treatment options. Thus, patient outcomes are improved.
Evaluation of PD-1/PD-L1 Expression in Hepatocellular Carcinoma Using Intravoxel Incoherent Motion: A Diagnostic Perspective
Authors: Chen Qianjuan , Long Liling , Zhang Huiting , Stemmer Alto
Published: November 2024

Motivation: In clinical practice of hepatocellular carcinoma, there are lacking in preoperative and pre-medication results regarding immunosuppressive checkpoint inhibitors PD-1 and PD-L1. Goal(s): This study aimed to evaluate the expression of PD-1 and PD-L1 using intravoxel incoherent motion (IVIM) before surgery and immunotherapy. Approach: Immunohistochemistry staining was performed on liver cancer tissue to analyze the correlation between immunohistochemistry results and derived parameters (D, D*, and f) from IVIM and ADC, allowing for the assessment of their diagnostic value. Results: The parameters D and D* from IVIM had statistically significant differences between the expression of PD-1 and PD-L1. Impact: The results of this study may have an impact on clinical decision-making in the future and demonstrate a favorable outcome for scientific research in the immune microenvironment.