bluuebunny/crossref_metadata_2025 huggingface dataset:
A deuterium MRS study of the effect of the ketogenic diet on β-hydroxybutyrate metabolism in a mouse model of Dravet syndrome
Authors: Garbow Joel , Ge Xia , Song Kyu-Ho , Engelbach John , Thio Liu Lin , Neil Jeffrey , Ackerman Joseph
Published: November 2024

Motivation: Children with Dravet syndrome, a drug-resistant epilepsy, often show a positive response to the ketogenic diet (KD) where the TCA cycle is driven mainly by ketones. Goal(s): To monitor TCA cycle metabolism of ketone body β-hydroxybutyrate in wild-type and Dravet mouse models comparing normal diet (ND) vs. KD. Approach: Deuterium (2H) single-voxel brain MRS is applied to wild-type and Dravet mouse models following subcutaneous administration of [3,4,4,4-2H4]β-hydroxybutyrate. Results: Mice on KD vs. ND show enhanced brain uptake of β-hydroxybutyrate (p < 0.001) with subsequent increase in glutamine+glutamate (“Glx”) production (p < 0.002), evidence that the KD upregulates brain monocarboxylate transporters. Impact: Deuterium metabolic imaging quantification of TCA cycle flux in individual Dravet patients may (i) assess metabolic dysfunction, (ii) assist in selecting the best treatment option, and (iii) allow optimization of the diet. Hence development of a personalized treatment plan.
Predicting tumor recurrence of locally advanced rectal cancer after neoadjuvant chemoradiotherapy based on multi-task deep-learning model
Authors: Liu Zonglin , Runqi Meng , Sun Yiqun , Rong Li , Caixia Fu , Tong Tong , Dinggang Shen
Published: November 2024

Motivation: The promising application of deep learning (DL) techniques for prognostic prediction in various tumors has been reported, but mostly with single-task modelsGoal(s): Exploring the use of multi-task DL models to automate the whole process of prediction for rectal cancer patients. Approach: We designed a modality-fusion-based multi-task DL model to concurrently predict tumor volumes, patient relapse state, and patient risk scores based on a combination of multimodal MR images and clinical tabular data. Results: The multi-task DL model achieved favorable predictive performance at the stage of initial diagnosis with automatic lesion identification, and further improved with the inclusion of postoperative pathology indicators. Impact: Multi-tasking DL may be a new approach and orientation to fully automate the process of clinical prediction, and its feasibility is expected to be further explored in other oncology studies in the future.
3D Kidney Segmentation in MRI using Transformers
Authors: Sharma Kanishka , Soe Kywe Kywe , Periquito Joao , Santini Francesco , Alhummiany Bashair , Shelley David , Brown Andrew , Fulford Jonathan , Gilchrist Mark , Shore Angela , Dilharreguy Bixente , Grenier Nicolas , Gomez Maria , Gooding Kim , Sourbron Steven
Published: November 2024

Motivation: Convolutional Neural Networks (CNNs) have long been the go-to deep-learning architecture for medical image segmentation, but in recent years transformer-based architectures adapted from large language models are setting a new standard. Goal(s): The aim of this study was to test if transformers are suitable for 3D kidney segmentation on high-resolution MRI. Approach: A transformer-based deep-learning architecture (UNETR) was trained and tested against a supervised method on 82 patient datasets from the iBEAt study on diabetic kidney disease. Results: UNETR provides fast segmentation with comparable results to the supervised method, but additional refinement is needed to reduce the limits of agreement. Impact: Novel transformer-based architectures for medical image segmentation may be useful for fast 3D segmentation of individual kidneys.
Utilizing Synthetic MRI and Deep Learning Reconstruction of DWI to Distinguish Benign from Malignant Breast Lesions
Authors: Xia Wanjun , Zhang Yong , Wang Kaiyu , Liu Guiyong , Cao Zhenghao , Cheng Jingliang
Published: November 2024

Motivation: Breast cancer has emerged as the foremost global malignancy, prompting a growing inclination toward exploring novel non-invasive imaging techniques that obviate the need for contrast agent administration. Goal(s): Enhancing breast diagnostics without reliance on contrast agents. Approach: Expanding on the foundation of deep learning-based DWI reconstruction, coupled with Synthetic MRI, as a viable alternative to traditional contrast-enhanced diagnostic methodologies, the focus lies in pinpointing valuable parameters for differential diagnosis. Results: The fusion of deep learning-reconstructed DWI and Synthetic MRI yields an impressive AUC (Area Under the Curve) of 0.995 in distinguishing between benign and malignant breast pathologies. Impact: The integration of deep learning-reconstructed DWI with Synthetic MRI not only carries substantial diagnostic significance in discerning between benign and malignant breast conditions but also exhibits the promise of supplanting conventional contrast-enhanced methodologies.
Hierarchical Bayesian Microstructure Modelling Improves Voxelwise Quantification of Blood-Brain Barrier Water Exchange Rates
Authors: Powell Elizabeth , Parker Geoff , Slator Paddy
Published: November 2024

Motivation: Blood-brain barrier (BBB) water exchange (WEX) imaging techniques are increasingly used to quantify BBB dysfunction. However, WEX imaging is highly noise sensitive, which is typically addressed by averaging data spatially or across subjects. Goal(s): To obtain robust, subject-specific, voxel-wise WEX quantification from BBB filter exchange imaging (FEXI) data. Approach: We implement a hierarchical Bayesian model fitting method, which, by introducing a Gaussian prior for model parameters (estimated from the data), reduces sensitivity to voxel-wise noise. Results: Relative to conventional least-squares estimation, Bayesian model fitting improves parameter estimation qualitatively and quantitatively in synthetic and in ten test-retest volunteer datasets. Impact: Robust, subject-specific, voxel-wise WEX quantification from BBB filter exchange imaging (FEXI) data will enable localised BBB dysfunction to be identified in neurological disease, potentially enabling earlier diagnosis or discrimination between diseases.
Loss of AQP4 impairs solute clearance from the CSF space
Authors: Kato Daisuke , Kameda Hiroyuki , Kinota Naoya , Fujii Takaaki , Abe Yoichiro , Yasui Masato , Kudo Kohsuke
Published: November 2024

Motivation: Aquaporin-4 (AQP4) water channels are thought to play an important role in cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange. However, the effect of AQP4 on CSF drainage and dynamics has not been well-established. Goal(s): To investigate the effects of AQP4 on CSF drainage and dynamics using in vivo imaging. Approach: We performed a whole-brain analysis including CSF spaces and drainage pathways of AQP4 knockout rats using dynamic contrast-enhanced (DCE) MRI with intrathecal gadolinium-based contrast agent administration. Results: DCE-MRI showed that loss of AQP4 impairs solute clearance from the CSF space and reduces CSF-ISF exchange. Impact: Loss of AQP4 impairs CSF-ISF exchange as well as solute clearance from the CSF space, suggesting that AQP4 expression might affect the entire CSF dynamics.
Impaired Glymphatic Clearance Linked to Neuroinflammation and Disability in Multiple Sclerosis
Authors: Sivakolundu Dinesh , Gauthier Susan , Nguyen Thanh
Published: November 2024

Motivation: Multiple sclerosis (MS) involves a proinflammatory state leading to cellular waste accumulation and disability. This study investigates the glymphatic system's waste clearance role, hypothesizing that lymphatic dysfunction contributes to MS-related disability. Goal(s): Explore the relationship between glymphatic function, meningeal lymphatic vessels (mLV), and MS-related disability. Approach: The study examined 49 MS patients using MRI to visualize mLVs and evaluate glymphatic stasis via CSF fraction (CSFF), analyzing links between mLV volume, CSFF, and MS outcomesResults: With age, cortical CSFF increases. mLV volume also increases with CSFF. Higher cortical CSFF is associated with more lesions and disability, suggesting glymphatic dysfunction contributes to MS-related disability. Impact: Our study suggests that glymphatic dysfunction contributes to lesion burden and disability in multiple sclerosis, highlighting the importance of lymphatic clearance mechanisms in disease progression.
Neuroimaging signatures of severe depression in people with HIV
Authors: Rakshasa-Loots Arish Mudra , Dowell Nicholas , Vera Jaime , Ronen Itamar
Published: November 2024

Motivation: People with HIV and co-morbid severe depression are rarely included in research, despite a critical need for identifying biomarkers for early detection of depression in this group. Goal(s): We explored whether neuroimaging biomarkers may distinguish between people with HIV experiencing severe or mild depressive symptoms. Approach: We recruited 11 participants with HIV and severe or mild depressive symptoms, who underwent standard and diffusion-weighted MR spectroscopy and dynamic contrast-enhanced MRI. Results: We found no significant group differences, but observed correlations of depressive symptom severity with creatine (ρ = 0.66) and NAA (ρ = 0.64) diffusion, though these findings did not survive multiple comparisons correction. Impact: This is the first study to successfully quantify neurometabolite diffusion in people with HIV and depression. Intracellular diffusion of neurometabolites may be associated with depressive symptom severity in this community, and well-powered studies are needed to resolve this relationship.
Harmonizing 2D and 3D FLAIR MRIs in white matter hyperintensity quantification
Authors: Chen Yasheng , Phuah Chia-Ling , Ying Chunwei , Dai Xing , Kang Peter , Lee Jin-Moo , Ford Andria , An Hongyu
Published: November 2024

Motivation: Harmonizing neural imaging datasets respectively acquired with 2D and 3D FLAIR MRI. Goal(s): Converting 2D FLAIRs to high-resolution 3D FLAIRs. Approach: We employed a ResUNet-based deep learning approach to learn the complex transformation from 2D to 3D FLAIR. Results: The converted 3D FLAIRs bear a high resemblance to the acquired 3D FLAIR in terms of image similarity measures and white matter hyperintensity segmentation. Impact: With this proposed approach, we can harmonize the 2D FLAIRs from the ADNI study with the 3D FLAIRs in the UK Biobank study.
Learning non-rigid registration in k-space from highly-accelerated cardiac and respiratory MR data
Authors: Ghoul Aya , Hammernik Kerstin , Rueckert Daniel , Gatidis Sergios , Küstner Thomas
Published: November 2024

Motivation: Time-resolved motion estimation from accelerated MR data enables high-quality imaging, intra-modality motion correction and real-time tracking during MR-guided radiotherapy. Conventionally, image registration is solved in the image domain and, therefore, remains susceptible to aliasing artifacts for highly-accelerated acquisitions. Goal(s): We aim to propose a robust non-rigid image registration framework from highly-accelerated data without additional information. Approach: We introduce a novel Local-All-Pass Attention Network (LAPANet) that performs accurate motion estimation directly from the acquired k-space. Results: LAPANet provides reliable estimates for fully-sampled and undersampled data, up to 104-fold for cardiac motion and 148-fold for respiratory motion, and outperforms established image-based registrations in different trajectories. Impact: Our framework can reliably estimate non-rigid motion from highly-accelerated data without a-priori information. This enables faster acquisition through integration into motion-compensated reconstructions, intra-modality motion correction for other imaging methods and real-time motion characterization and tracking for guided radiotherapy and interventions.
Exploring Preclinical Imaging Biomarkers and Progressive Degeneration in Alzheimer's Disease
Authors: Liu Yu-Chen , Huang Chu-Chung , Lin Ching-Po , Lo Chun-Yi
Published: November 2024

Motivation: Preclinical stages of AD offer potential windows for intervention. Investigating individuals in this stages can yield valuable biomarkers and deepen our understanding of disease progression mechanisms. Goal(s): We aim to investigate brain degeneration mechanisms during AD's preclinical stages and explore early diagnostic markers in gray matter and superficial white matter alterations. Approach: This study involved 411 participants (including preclinal stages, aMCI and AD) and their diffusion and structural MRI and neuropsychological tests, to assess brain changes. Results: Cortical atrophy in the temporal lobe may be a trigger for disease onset, while extensive SWM degeneration appears to be associated with disease progression in AD. Impact: This study provides crucial insights into brain changes in early stages of AD. Identified imaging biomarkers are valuable for early diagnosis and interventions, and the proposed degeneration patterns enhance our understanding of AD's pathophysiology.
Alterations in superficial white matter tracts are associated with pathological deposition in early-stage Alzheimer’s disease
Authors: Wang Shuyue , Zhang Fan , Zeng Qingze , Hong Hui , Jiaken Yeerfan , Yu Xinfeng , Luo Xiao , Li Kaicheng , Xu Xiaopei , Huang Peiyu , Sun Jianzhong , Zhang Minming , O’Donnell Lauren
Published: November 2024

Motivation: Between-cortical connections largely depend on the superficial white matter (SWM) fibers, which are less studied in the AD continuum. Goal(s): To determine the relationship between superficial white matter (SWM) fiber microstructure and local pathology, and the SWM's impact on cognition. Approach: We defined cohort groups in the early AD continuum. We quantified the microstructure of SWM fiber tracts (diffusion MRI) and the regional pathological deposition (PET). We analyzed associations between SWM fiber microstructure and regional pathologies in cortical areas connected by the tract. Results: SWM tract microstructure is affected by pathology in the cortical regions connected by the tract, and this affects memory. Impact: We localize pathology-affected SWM connections, assess their roles in cognition, and provide new insights into white matter abnormalities in the AD continuum.
High-Resolution DTI Cortical Column Analysis for Early Detection of Alzheimer’s Disease in High-Risk Pre-Symptomatic Subjects
Authors: Overson Devon , Truong Trong-Kha , Ma Yixin , Madden David , Petrella Jeffrey , Song Allen
Published: November 2024

Motivation: Microstructural changes in cortical gray matter, occurring potentially well before cognitive decline in Alzheimer’s disease (AD), could serve as an early diagnosis biomarker. Goal(s): We use high-resolution diffusion tensor imaging to identify such changes among AD subjects, cognitively normal but high-risk (APOE4+) subjects, and healthy controls (APOE4-). Approach: The variation in fractional anisotropy along cortical columns was analyzed within 68 regions. Results: 20 regions exhibited a lower variation in the high-risk group compared to the control group. The AD risks of individual high-risk subjects could be further differentiated based on similarities and differences with the AD or control groups. Impact: Our cortical column-based analysis of high-resolution diffusion tensor imaging data can detect microstructural changes within specific cortical regions of pre-symptomatic subjects with high risk for Alzheimer’s disease, potentially providing a more definitive biomarker for its early diagnosis and treatment.
Thalamic nuclei atrophy in autosomal dominant Alzheimer's disease
Authors: Saranathan Manojkumar , Kaplan Elizabeth , Hornberger Michael , Baena Ana , Munera Diana , Sanchez Justin , Langella Stephanie , Lopera Francisco , Quiroz Yakeel
Published: November 2024

Motivation: The thalamus has not been investigated properly in autosomal dominant Alzheimer's disease (ADAD) despite evidence of early involvement. Goal(s): We investigated thalamic nuclei atrophy in asymptomatic presenilin carriers vs. non-carriers in an ADAD cohort using a recently proposed multi-atlas segmentation method. Approach: Thalamic nuclear atrophy and correlations of nuclei volumes with age, amyloid, and tau burden were analyzed in mutation carriers vs. non-carriers in an ADAD cohort. Results: Significant atrophy was seen in mediodorsal, pulvinar, and medial geniculate nuclei in carriers compared to non-carriers. These nuclear volumes correlated significantly with age, amyloid, and tau burden as well. Impact: The pattern of thalamic nuclear atrophy in ADAD presenilin mutation carriers can help understand mechanisms for disease progression as well as aid in possible treatment targets.
Bone Metabolic-Morphologic relation is Mediated by Gait; Cartilage Compositional-Morphologic relation isn’t: PET/MRI in Isolated PFJOA
Authors: Bhattacharjee Rupsa , Hammond Eric , Ngarmsrikam Chotigar , Jiang Fei , Han Misung , Souza Richard , Pedoia Valentina , Majumdar Sharmila
Published: November 2024

Motivation: Whether the compositional, metabolic, and biomechanical relationships have a mediated effect on one another is the next crucial step toward decoding the isolated PFJOA mechanism. Goal(s): To investigate whether the relationship of morphological-joint-degradation with SUV-values from 18NaF-PET and cartilage-T2 in isolated-PFJOA are mediated by gait-biomechanics. Approach: Linear regression was performed between (i)WORMSBME(predictor) vs. Medial-SUVmax, Lateral-SUVmax (outcomes), and (ii) WORMSCartilage(predictor) vs. Mean-T2-Deep-Medial, T2-Superficial-Medial, T2-Deep-Lateral, T2-Superficial-Lateral (outcomes) patellar and trochlear. For significant correlations, mediation analyses were done to examine the role of Normalized-Mean-Peak vGRFIP as a mediator. Results: WORMSBME vs. Medial-SUVmax, Lateral-SUVmax relationships are mediated by gait; WORMSCartilage vs. Mean-T2-Deep-Medial, T2-Superficial-Medial, T2-Deep-Lateral, T2-Superficial-Lateral aren’t mediated. Impact: This is the first step towards understanding the flow of causalities and mediated interrelationships of bone remodeling (SUV), morphological degradation (WORMS), cartilage loss (T2), and gait biomechanics in a complex joint developing isolated PFJOA
Ripple Artifact Quantification in Slice Encoding for Metal Artifact Correction (SEMAC) using MR Bloch Simulation
Authors: Wahlen Jonas , Kozerke Sebastian , Nanz Daniel , Sutter Reto , von Deuster Constantin
Published: November 2024

Motivation: Commonly used methods for the quantification of metal-induced image artifacts, such as measuring the extent of signal voids, do not capture spatial frequencies of ripple artifacts, as seen in dedicated metal artifact reduction sequences including SEMAC or MAVRIC. Goal(s): To propose a new method for the quantification of SEMAC ripple artifacts which may serve as quality metric for sequence optimizations. Approach: We applied a k-space-based metric to MR Bloch simulations of SEMAC sequences with variable slice thicknesses and RF pulse shapes (time-bandwidth product, TBW). Results: A trend towards higher absolute artifact intensity and lower spatial frequency can be observed for higher TBWs. Impact: The proposed metal artifact metric extends current quantification methods by taking the spatial frequency distribution of ripple artifacts into account. This may serve as a basis for metal artifact reduction sequence optimization, with a particular focus on RF pulse parameters.
Native T1-mapping as a Predictor of Progressive Renal Function Decline in Chronic Kidney Disease Patients
Authors: Shi Zhaoyu , Sun Chen , Zhou Fei , Yuan Jianlei , Chen Minyue , Wang Xinyu , Wang Xinquan , Pylypenko Dmytro , Yuan Li
Published: November 2024

Motivation: Chronic kidney disease (CKD) is recognized as a global public health problem. Thus, there is a pressing need for a non-invasive method to predicting the evolution of CKD. Goal(s): This study aimed to investigate the potential of Native T1-mapping in predicting the prognosis of patients with CKD.Approach: In our study of 119 CKD patients, there was a statistically significant difference in prognosis between the high and low T1 groups in terms of the occurrence of kidney endpoint events.Results: Native T1-mapping has the potential to significantly improve the identification of CKD patients with a higher risk of progressing to end-stage renal disease.Impact: CKD is increasingly recognized as a global public health problem. Traditional examination methods, such as renal biopsy, have many limitations. Therefore, we need to find a non-invasive, alternative method to evaluate the prognosis of CKD.
Diagnosis of muscle-invasive urothelial carcinoma with variant histology using biparametric MRI and VI-RADS in a radical cystectomy cohort
Authors: Arita Yuki , Kwee Thomas , Miranda Joao , Shigeta Keisuke , Ishii Ryota , Edo Hiromi , Ruby Lisa , Nincevic Josip , Waseda Yuma , Tamada Daiki , Muellner Ada , Nalavenkata Sunny , Hricak Hedvig
Published: November 2024

Motivation: To determine if contrast-free biparametric MRI (bpMRI), which offers potential cost, comfort and safety advantages, could replace multiparametric MRI (mpMRI) in diagnosing muscle-invasive bladder cancer (MIBC) in patients with urothelial carcinoma of variant histology (VUC). Goal(s): To compare the diagnostic accuracy of bpMRI and conventional mpMRI for detecting MIBC in patients with VUC who underwent radical cystectomy (the optimal reference standard). Approach: A retrospective, multicenter diagnostic study using radical cystectomy as the reference standard. Results: The diagnostic performance of bpMRI was confirmed to be weaker than that of mpMRI in the assessment of muscle invasion in bladder VUC. Impact: When applying VI-RADS, bpMRI had weaker diagnostic performance than mpMRI in assessing muscle invasion in patients with bladder VUC who had radical cystectomy (the optimal reference standard). Consequently, we recommend employing mpMRI-based methods for evaluating muscle invasion in bladder VUC.
A Subject-Specific Workflow for Radiofrequency Safety of Infants Undergoing an MRI Head Scan at 7T
Authors: Sadeghi-Tarakameh Alireza , Moser Julia , Weldon Kimberly , Thotland Jeromy , Elison Jed , Fair Damien , Adriany Gregor , Yacoub Essa , Eryaman Yigitcan
Published: November 2024

Motivation: Higher SNR promised at higher field strengths can be traded for higher spatial resolution, which is essential for imaging infant brains. Goal(s): To assess and ensure safe operation of a commercially-available RF head coil at 7T for infant subjects. Approach: We developed an EM model of the coil and experimentally validated it. Utilizing EM simulations, we calculated local and head SARs for an infant model to determine the safe operation limits. Results: We showed that the head SAR limit is reached before the local SAR limit. We acquired structural and functional MRI data from an infant's brain at 7T. Impact: Assessing and ensuring the safe operation of 7T MRI scanners for infant subjects can pave the way to exploring the early stages of human brain development, which is hardly possible at lower field strengths due to lower spatiotemporal resolution.
Characterizing blood and cerebrospinal fluid flow by D* tensor derived from intravoxel-incoherent-motion-diffusion-tensor-imaging
Authors: Voorter Paulien , Drenthen Gerald , van der Thiel Merel , Staals Julie , Gurney-Champion Oliver , Postma Alida , van Oostenbrugge Robert , Jansen Jacobus , Backes Walter
Published: November 2024

Motivation: A deeper understanding of brain physiology and pathology can be provided with an intravoxel-incoherent-motion-diffusion-tensor-imaging (IVIM-DTI) MR scan, which simultaneously measures blood and cerebrospinal fluid (CSF) flow and flow directions; parenchymal anisotropy; and microvascular perfusion. Goal(s): To demonstrate the feasibility of IVIM-DTI to provide a proxy for blood and CSF flow. Approach: A tensor of the pseudo-diffusion component (D*) was derived from IVIM-DTI and related to arterial and ventricular physiology. Results: D* ellipsoids align well with arterial blood and CSF flow. D*’s magnitude and anisotropy correspond to the expected flow in arteries and ventricles, indicating the technique's ability of characterizing flow dynamics. Impact: Assessing blood and cerebrospinal fluid flow with intravoxel-incoherent-motion-diffusion-tensor-imaging (IVIM-DTI) alongside traditional IVIM and DTI measures can provide comprehensive pathophysiological insights into neurological conditions. The finding that these physiological processes contribute to IVIM-derived f and D* is important for their interpretation.
Exploring radial asymmetry in MR diffusion tensor imaging and its impact on the interpretation of glymphatic mechanisms
Authors: Wright Adam , Wu Yu-Chien , Chen Nan-Kuei , Wen Qiuting
Published: November 2024

Motivation: Researchers have used diffusion tensor imaging along the perivascular space (DTI-ALPS) to investigate glymphatic function, but the influence of white matter properties on the ALPS-index remains unstudied. Goal(s): Establish whether a reduction in the ALPS-index could be influenced by axonal changes. Approach: A key assumption underlying the ALPS-index is that axons demonstrate symmetric radial diffusivities, such that eigenvalue-2 and eigenvalue-3 are equal (λ2=λ3). We investigated this assumption and evaluated λ2/λ3 changes in white matter tracts. Results: Contrary to the DTI-ALPS assumption, widespread radial asymmetry (λ2/λ3≈1.5) was observed within all white matter tracts, the extent of which decreased with aging and neurodegeneration. Impact: This study unveils widespread radial asymmetry of white matter tracts — a phenomenon that has been overlooked in DTI studies. The results provide evidence of axonal contributions to the ALPS-index, prompting researchers to consider axonal influences when interpreting this metric.
Investigation of Feasibility of Measurement of Neurofluid Movement by Propagator Analysis based on Q-space Imaging
Authors: Yatsushiro Satoshi , Murayama Keita , Atsumi Hideki , Horie Tomohiko , Kuroda Kagayaki
Published: November 2024

Motivation: To quantitatively measure neurofluid movement in the brain parenchyma of mouse and human by propagator analysis of q-space imaging (QSI). Goal(s): The accuracy of the propagator analysis to measure the neurofluid movement including the pulsation and the blood flow was investigated by Bloch simulation. Approach: The propagator analysis of pulsatile neurofluid or capillary blood flow in a voxel was conducted by Bloch simulation. The accuracy of the technique in the constant flow was validated by experiments. Results: The propagator analysis indicated the high accuracy of the velocity measurement of the pulsatile neurofluid movement and the blood flow. Impact: The high accuracy of the velocity measurement of QSI-based propagator analysis of pulsatile movement and blood flow in the voxel was validated by Bloch simulation. The technique may reveal neurofluid movement and clearance mechanism of neurowastes in human brain.
Optimization of Simultaneous Coherent/Incoherent Motion Imaging (SCIMI): Combined Diffusion and Velocimetry for Glymphatic Circulation
Authors: Jansen Isabelle Heukensfeldt , Abad Nastaren , Ajala Afis , DeMarco J Kevin , Morris H. Doug , Ho Vincent B , Werner Kent , Pollatou Angeliki , Kohls Gail , Yalewayker Haymanot , Hood Maureen , Skeete Sonja , Metzger Elizabeth , Shih Robert , Foo Thomas K.F. , Marinelli Luca
Published: November 2024

Motivation: To study glymphatic circulation in the brain parenchymal tissue, methods to measure sub-millimeter velocities of fluid flow in tissue must be developed. Goal(s): We evaluate the feasibility of imaging both coherent and incoherent motion (SCIMI) in brain tissue using phase-sensitive reconstruction of dMRI. Approach: Approach: By modifying a DTI sequence to achieve physiologically relevant low VENC, we demonstrate the simultaneous reconstruction of diffusion metrics highlighting incoherent motion in the brain and velocity data showing coherent motion by leveraging phase and magnitude information. Results: We measure velocity maps in the whole brain in conjunction with clinically relevant diffusion metrics. Impact: SCIMI acquisition and reconstruction of velocity in brain parenchymal tissue shows to be an important addendum that can be run parallel to existing DTI methods and provides novel insights into glymphatic circulation.
High-Quality Brain MRI Reconstruction against Unknown Degradation: A Unified Framework with Prompt Learning
Authors: Jiang Ning , Sui Yao
Published: November 2024

Motivation: Spatial resolution, signal-to-noise ratio, and motion artifacts critically matter in any MRI practices. Current methods focus on a single source of known degradation of imaging. A unified framework is desired, which allows for high-quality reconstruction in the face of multiple unknown sources of degradation. Goal(s): We reconstruct high-quality brain MRI against degradations by motion, noise, and low resolution, with an image-to-image translation-based deep neural framework. Approach: We developed a prompt-based learning approach and assessed it on a public brain MRI dataset. Results: Our method offered remarkably improved reconstructions (PSNR=30.96dB, SSIM=0.9133), as compared to two other state-of-the-art methods. Impact: We developed a new methodology that enables high-quality MRI reconstruction from scans corrupted by a mixture of multiple unknown sources of degradations, which commonly happen in clinical and research MRI studies, with a unified reconstruction framework.
T2* relaxometry of Fetal Brain Tissues using Low Field MRI
Authors: Payette Kelly , Uus Alena , Verdera Jordina Aviles , Hall Megan , Hajnal Joseph , Rutherford Mary , Story Lisa , Hutter Jana
Published: November 2024

Motivation: The complex and rapid changes during human brain development call for a matched analysis of both structure and function. T2* relaxometry delivers non-invasive insights and pairs well with low field MRI. However, regional assessment is currently lacking. Goal(s): Investigate the ability of low field MRI to quantify regional fetal brain T2*. Approach: We acquired dynamic multi-echo gradient-echo sequences at 0.55T and developed automatic high-resolution reconstruction and segmentation to obtain the mean T2* values of 7 individual brain tissues. Results: Fetal brain tissues vary both in absolute T2* value and in progression have differing T2* values and growth curves throughout gestation. Impact: Regional fetal brain T2* values, obtained with an automatic pipeline, match the complexity, speed of change and growth during early human brain development and thus carry the potential to play a significant role in future research studies and clinical monitoring.
Machine learning and the prediction of enlarged lateral ventricular postnatal development trend in fetuses with isolated ventriculomegaly
Authors: Chen Xue , Huang Zhou , Wu Peng , Zhang Jibin , Li Yonggang
Published: November 2024

Motivation: To evaluate the intracranial structures and distinct components (grew matter [GM] and white matter [WM]) adjacent to the occipital horn of the lateral ventricle T2WI radiomics features in healthy fetuses and fetuses with ventriculomegaly (FVs),Goal(s): and to predict postnatal changes in the size of the enlarged lateral ventricle in FVs. Approach: Utilizing WM-radiomics on the affected sides of FVs, the SVM algorithm effectively predicted the changes in ventricle size, Results: as evidenced by the highest area under the curve (AUC) values of 0.771 and 0.738 in both the training and validation sets based on DeLong’s test (all P < 0.05). Impact: An MRI-based occipital WM-radiomics model holds the potential to predict trends in changing ventriculomegaly.The image-based predictive model exhibits applicability in prenatal care. Leveraging image analysis and machine learning techniques may provide further insight into the pathophysiologic features of ventriculomegaly.
MRI-Based Quantitative Analysis of Placenta and Fetal Brain in SGA Pregnancies: Feasibility Insights
Authors: Xia Bingqing , Sun Taotao , Jiang Ling , Qian Zhaoxia , Qu Feifei , Wei Hongjiang , Wu Jiangjie
Published: November 2024

Motivation: Understanding placental dysfunction's role in small-for-gestational-age (SGA) fetal neurodevelopment is crucial; this study seeks to fill that gap, enhancing prenatal care. Goal(s): The primary goal is to assess whether MRI can effectively quantify the relationship between placental function and fetal brain development in SGA pregnancies. Approach: The study used IVIM, and T2* mapping to evaluate placental and fetal brain development, applying Pearson correlation and t tests for comparative analysis. Results: Significant differences in placental perfusion and cortical properties between control and SGA groups were reported, demonstrating the feasibility of using MRI for in-utero assessment. Impact: This study’s MRI approach could change prenatal care, allowing earlier detection of small-for-gestational age–related brain development issues, prompting interventions, and guiding research into neurodevelopmental support for affected neonates, with potential long-term cognitive benefits.
Common coordinate framework of neonate macaque brain based on ultra-high-resolution diffusion MRI
Authors: Kim Juri , Zhu Tianjia , Wu Fengxia , Sousa Andre , Levine Jon , Kriegstein Arnold , Huang Hao
Published: November 2024

Motivation: Macaque brain structures change dramatically from birth to adulthood. However, there is no neonate macaque brain common coordinate framework (CCF) serving as neuroanatomical reference for mapping genetic, cellular, and molecular information. Goal(s): To establish an ultra-high-resolution CCF for macaque brain at birth. Approach: We acquired ultra-high resolution diffusion MRI (dMRI) of neonate macaque brain and annotated fine neuroanatomical structures and investigated white matter tract development from neonate to adult macaque through dMRI-based tractography. Results: The established neonate macaque brain CCF is featured with 0.2mm isotropic ultra-high diffusion imaging resolution, comprehensive gray and white matter labels, and a coordinate framework. Impact: This first neonate macaque brain CCF with 0.2mm isotropic ultra-high diffusion imaging resolution serves as neuroanatomical reference, enables mapping genetic, cellular, and molecular information, and provides image templates, laying the foundation for the brain development and evolution discoveries.
Influence of hepatic steatosis on T1 values of the liver: Comparison of T1 mappings at different echo times
Authors: Tanabe Masahiro , Higashi Mayumi , Kawano Yosuke , Ito Katsuyoshi
Published: November 2024

Motivation: In evaluating T1 values in patients with hepatic steatosis, it is necessary to investigate the effect of fat because MOLLI is sensitive to fat. Goal(s): To elucidate the clinical importance of MOLLI T1 mapping of the liver using TE=1.8 (AIOP) sequence in daily practice. Approach: In 122 patients with suspected liver diseases, MOLLI T1 mapping with TE=1.8 (AIOP) was compared with that with TE=1.3 (OP) and TE=2.4 (IP). Results: The positive correlation with high correlation coefficient between T1 value of the liver in AIOP sequence and FIB-4 score was observed in patients with hepatic steatosis (PDFF ≥10%). Impact: T1 mapping using AIOP sequence will be critical for assessing liver fibrosis in patients with hepatic steatosis since T1 mapping obtained by OP and IP sequences have risk of over- and under-estimation of T1 measurements.
Identifying Subvoxel Diamagnetic and Paramagnetic Components in Liver and Spleen Using DECOMPOSE-QSM
Authors: Alpman Asli , Chen Jingjia , Wei Hongjiang , Liu Chunlei
Published: November 2024

Motivation: Fibrotic liver, a condition that may cause cirrhosis if untreated, often contains both paramagnetic iron and diamagnetic collagen. While quantitative susceptibility mapping (QSM) calculates the voxel-wise tissue susceptibility, it cannot distinguish the competing effect of iron and collagen within a voxel. Goal(s): The purpose of this work is to identify sub-voxel paramagnetic and diamagnetic susceptibility sources within the liver and spleen. Approach: For this purpose, we applied the DECOMPOSE-QSM method to abdominal 1.5T MR images of patients with various levels of iron overload. Results: The resulting paramagnetic and diamagnetic susceptibility maps demonstrated improved contrast compared to bulk susceptibility maps provided by QSM. Impact: Identifying sub-voxel paramagnetic and diamagnetic sources within the liver through DECOMPOSE-QSM can facilitate the diagnosis of conditions such as liver fibrosis and hepatic iron overload.
Automated 3D Volume Segmentation of Subcutaneous and Visceral Abdominal Fat Using Fat-Water Imaging
Authors: Merugumala Sai , Bhasin Shalender , Lin Alexander
Published: November 2024

Motivation: The study is driven by the need to accurately quantify abdominal fat, particularly visceral fat, to better understand its link with metabolic diseasesGoal(s): This research aims to develop a robust, automated 3D segmentation method for distinguishing and quantifying Subcutaneous and Visceral Fat from volumetric fat-water MRI images. Approach: Utilizing Fat-Water Imaging combined with 3D whole volume segmentation and morphology provides improved quantification. Results: The new method yielded more reliable and consistent fat compartment segmentation across subjects, outperforming the prior 2D segmentation techniques and showing promise for aiding the study of metabolic disorders. Impact: The study's automated 3D Fat-Water image segmentation technique aids the assessment of abdominal fat, enabling clinicians and researchers to efficiently study and evaluate metabolic disease risk and progression.
Deep learning models for predicting responses to neoadjuvant systemic therapy in triple-negative breast cancer using pre-treatment MRI
Authors: Xu Zhan , Son Jong Bum , Adrada Beatriz , Moseley Tanya , Candelaria Rosalind , Guirguis Mary , Patel Miral , Whitman Gary , Leung Jessica , Le-Petross Huong , Mohamed Rania , Pashapoor Sanaz , Panthi Bikash , Lane Deanna , Perez Frances , Chen Huiqin , Sun Jia , Wei Peng , Tripathy Debu , Yang Wei , Yam Clinton , Rauch Gaiane , Ma Jingfei
Published: November 2024

Motivation: Neoadjuvant systemic therapy (NAST) followed by surgery is the standard of care for triple-negative breast cancer (TNBC) patients. However, only approximately half of these patients achieve pathological complete response (pCR). Goal(s): To build a prediction model to identify non-pCR patients before the initiation of NAST. Approach: We evaluated multiple prediction models using pretreatment multi-parametric MRI from a cohort of 282 TNBC patients. Results: Our findings revealed that combined with clinical information, the best-performing model achieved an AUC of 0.74 on an independent testing set. We further observed that the performance of our models is not sensitive to the voxel selections in tumor segmentation. Impact: Deep learning models for predicting pathological complete response to neoadjuvant systemic therapy of triple-negative breast cancer were developed using baseline multi-parametric MRI data and clinical information and achieved an AUC of 0.74 on the independent testing dataset.
Prediction of lymph node metastasis after neoadjuvant chemoradiotherapy in rectal cancer with multiparametric MRI-based radiomics
Authors: Chen Weicui , Wei Qiurong , Chen Ling , Deng Kan , Hou Xiaoyan , Lin Yunying , Xie Renlong , Yu Xiayu , Zhang Hanliang , Wu Yuankui
Published: November 2024

Motivation: A precise assessment of LN restaging following nCRT is important to guide therapeutic decision and predict prognosis for LARC patients. Goal(s): To develop and validate a predictive radiomics model for assessing LNM status after nCRT in LARC. Approach: This study enrolled 150 LARC patients from two centers and constructed several radiomics models based on T2WI and DWI before or/and after nCRT to assess LNM after nCRT. Results: The multiparametric model incorporating MR radiomics features prior to and after nCRT was superior to the clinical model, modelpre_T2_DWI and the single-sequence models (external validation cohort AUC 0.831). Impact: Radiomics analysis of pre- and post-nCRT multiparameter MR images could predict LNM after nCRT in patients with LARC, and might help guide therapies and predict prognosis for LARC patients.
Deep Learning Based Multi-Scale Approach for Precision Medicine and Quantitative Imaging in Glioblastoma
Authors: Masood Anum , Naseem Usman , Rashid Junaid , Ahn Euijoon , Nawaz Mehmood , Nasim Mehwish
Published: November 2024

Motivation: Glioblastoma (GBM) is a fast-growing invasive brain tumor that presents unique treatment challenges. Early diagnosis requires manual segmentation using MRI and histopathological image analysis. Goal(s): Our proposed model can facilitate medical personnel in an efficient and accurate diagnosis of glioblastoma. Approach: We present a multiscale multilevel approach based on deep learning for precision medicine and quantitative imaging in GBM capturing image feature and providing wide-ranging contextual information. Results: Our method predicted the overall survival of GMB patients with an average accuracy of 88.63% and 91.7% DSC (Unet: 84% DSC; Swin Transformer: 87% DSC) on BraTS 2020. Impact: Our model surpasses state-of-the-art methods in Glioblastoma (GBM) segmentation and predicts patient survival with 88.63% accuracy. This research work assists in precise and efficient diagnoses of GBM, potentially contributing to early disease detection and treatment strategies.
LESEN: Label-Efficient Self-Ensembling Network for Multi-Parametric MRI-based Visual Pathway Identification
Authors: Diakite Alou , Li Cheng , Xie Lei , Feng Yuanjing , Han Hua , Zheng Hairong , Wang Shanshan
Published: November 2024

Motivation: Obtaining labeled data for visual pathway (VP) segmentation can be laborious and time-consuming. Therefore, it is crucial to develop algorithms with good performance in situations with limited labeled samples. Goal(s): The goal is to propose a label-efficient self-ensembling network (LESEN) for VP segmentation. Approach: We first introduce the LESEN model which consists of a student model and a teacher model that learn from each other using supervised and unsupervised losses. Additionally, a novel reliable unlabeled sample selection (RUSS) method is introduced to enhance the effectiveness of the LESEN model. Results: The LESEN model surpasses existing techniques on the human connectome project (HCP) dataset. Impact: The proposed LESEN model can improve visual pathway segmentation accuracy and reliability with limited labeled data, advancing multi-parametric MRI analysis in clinical and research settings.
Single Generalized Convolutional Neural Network for Automatic Liver Extraction and R2* Estimation for Iron Overload Assessment
Authors: Shrestha Utsav , Morin Cara , Abramson Zachary R. , Tipirneni-Sajja Aaryani
Published: November 2024

Motivation: Although R2*-MRI is extensively validated to assess hepatic iron content(HIC), different MRI sequences are used, hence multiple sequence-specific convolutional neural networks(CNNs) have been proposed for automated liver segmentation and HIC estimation. Goal(s): Assess feasibility of generalized CNN with limited training datasets to automate liver segmentation across various MRI sequences used to quantify HIC in clinical practice. Approach: Data of twenty-nine patients scanned using multi-echo 2D/3D breath-hold and free-breathing Cartesian and radial GRE sequences were used to train U-Net CNN using incremental learning. Results: Excellent agreement was obtained between manual and single generalized U-Net for liver segmentation and R2* estimation across multiple MRI sequences. Impact: Generalized CNN using incremental learning minimizes the need for extensive training datasets to segment liver across multiple MRI sequences. With additional fine-tuning and validation, this approach can be widely applicable for sequence-independent liver segmentation and assessment of hepatic iron content.
Characterizing spatial heterogeneity of BOLD fMRI cortical-depth profiles of activation: the average profile may not be typical
Authors: Blazejewska Anna , Gomez Daniel , Polimeni Jonathan
Published: November 2024

Motivation: Laminar-fMRI analysis routinely averages cortical-depth profiles within an ROI to estimate a typical laminar activation profile and increase SNR. Previous studies suggested heterogeneity of cortical-depth profiles measured with GE-BOLD-fMRI, therefore the assumption that profiles inside ROI are similar may not hold. Goal(s): To test whether the average cortical-depth profile is typical for the whole ROI. Approach: We applied k-means clustering to identify cortical locations within an ROI with similar BOLD-fMRI cortical-depth profiles. Results: Cortical-depth profiles vary substantially across the activated region and therefore the average response profile inside the ROI may not be resemble that of any particular activated location. Impact: In laminar-fMRI analysis, due to heterogeneity of neuronal responses and/or vascular architecture the average cortical-depth profile within an ROI may not match the profile at any one location, suggesting that averaging may lose meaningful layer-specific information within the activated region.
Enabling brain-wide mapping of directed functional connectivity at 3T via layer-dependent fMRI with draining-vein suppression
Authors: Chang Wei-Tang , Lin Weili , Giovanello Kelly
Published: November 2024

Motivation: Mapping brain-wide directed functional connectivity demands techniques with high spatiotemporal resolution yet current methods fall short. Goal(s): To improve spatial specificity of GE-BOLD EPI by reducing draining-vein contamination without compromising speed. Approach: Incorporating velocity-nulling (VN) gradients into a GE-BOLD fMRI sequence at 3T. We also integrated NORDIC denoising to enhance signal sensitivity. Results: The VN fMRI method demonstrated decent spatial specificity, evidenced by identifying double-peak activation patterns within the M1 area during a finger-tapping task. This technique showed enhanced robustness across participants compared to conventional fMRI. Our findings on directed functional connectivity reveal layer-specific relationships that closely align with the existing literature. Impact: Leveraging its comprehensive brain coverage and efficient scan time, VN fMRI has yielded promising results in directed FC studies. Given the widespread accessibility of 3T scanners, we anticipate this development will have a significant impact across multiple neuroscience research domains.
Automatic estimation of T1, T2, T1ρ and fat fraction in calf muscles for patients with diabetic peripheral neuropathy
Authors: Tibrewala Radhika , Sharafi Azadeh , Shah Jill , Rao Smita , Regatte Ravinder , Brown Ryan
Published: November 2024

Motivation: Quantitative MRI is used for muscle parameter mapping, but single-parameter techniques and manual muscle segmentations take long. Goal(s): Develop an automatic processing pipeline to generate parameter maps from 3D MRF and fat fraction images to extract quantitative biomarkers. Approach: 3D MRF and fat fraction images were acquired on patients with diabetic peripheral neuropathy, deep-learning methods and post-processing were used to generate muscle masks and parameter maps before and after exercise intervention. Results: Automatic muscle segmentation and 3D MRF are able to generate quantitative fat fraction, T1, T2, T1ρ volumetric maps within muscle ROIs for tracking changes in patients before and after exercise intervention. Impact: Both the 3D MRF sequence and automatic muscle extraction help reduce acquisition and post-processing time, allowing faster assessment of treatment response in diabetic patients.