bluuebunny/crossref_metadata_2025 huggingface dataset:
Ex-vivo MRI of Placentas from Women with Congenital Heart Disease: Reproducibility Assessment and Initial Results
Authors: Rodríguez-Soto Ana , Link-Sourani Daphna , Ben-Bashat Dafna , Rakow-Penner Rebecca , Parast Mana , Contijoch Francisco
Published: November 2024

Motivation: Women with CHD are prone to pregnancy complications associated with placental-dysfunction. Our understanding of this phenomenon remains limited, with unexplored data processing reproducibility. Goal(s): 1. Investigate maternal cardiovascular health's impact on placental vascular architecture. 2. Evaluate data processing method reproducibility. Approach: Placentas from mothers with cardiovascular disease and controls underwent ex-vivo MRI. Placental volume and vascular tree architecture were analyzed by two independent double-blinded annotators using established methods. Results: No group differences were observed. Annotator-related differences were only observed for vascular volume. We emphasize the need for expanded research into placental changes in women with cardiovascular diseases and standardization of data processing methods. Impact: This study investigates maternal cardiovascular health's impact on placental vascular architecture and evaluates data processing methods reproducibility. In order to understand placental dysfunction patterns across different populations, the need for expanded research and method standardization became evident.
Improved Visualization and Staging of Upper Tract Urothelial Carcinoma Using Coronal Reduced Field-of-View Diffusion Weighted Imaging
Authors: Wang Wei , Li Wei , Xue Ke , Qiu Jianxing
Published: November 2024

Motivation: Conventional axial full-field-of-view (fFOV) DWI poorly demonstrates upper tract urothelial carcinoma (UTUC) due to its varying morphology and anatomical position. The additional coronal reduced field-of-view (rFOV) DWI may improve evaluation. Goal(s): The objective of this study was to assess the diagnostic value and image quality of coronal rFOV DWI in the evaluation of UTUC. Approach: This study compared the image quality, tumor contrast, ADC value, and staging accuracy between coronal rFOV DWI and axial fFOV DWI in UTUC. Results: Coronal rFOV DWI provided better image quality and higher tumor contrast than fFOV DWI. It provided additional diagnostic value in the staging of UTUC. Impact: This study validates the clinical application of reduced field-of-view DWI in the assessment of upper tract urothelial carcinoma. The use of this technique improves image quality and tumor conspicuity in the coronal plane, providing additional value for diagnosis and staging.
Comparative Evaluation of Diffusion Weighted Imaging Sequences for Predicting Muscle Invasion in Bladder Cancer
Authors: Tan Yuchuan , Dan Hanli , Yang Lu , Nie Lisha , Zhang Yipeng , Zhang Jiuquan
Published: November 2024

Motivation: The limitations of current diffusion-weighted imaging (DWI) techniques in predicting muscle invasion in bladder cancer necessitate a comprehensive comparison of different DWI sequences to improve diagnostic accuracy. Goal(s): Evaluate the image quality of four diffusion sequences(ssDWI/FOCUS/MUSE/FOCUS-MUSE) and assess their effectiveness in predicting muscle invasion in bladder cancer. Approach: 44 primary bladder cancer patients underwent imaging using clinical and four diffusion weighted imaging (DWI) sequencesResults: Among the four diffusion sequences, field-of-view optimized and constrained undistorted single-shot (FOCUS) combined with multiplexed sensitivityencoding (MUSE) has the best image quality, and the highest accuracy in predicting muscle invasion of bladder cancer. Impact: FOCUS-MUSE diffusion weighted imaging can accurately predict muscle invasion of bladder cancer.
Evaluating the Prognostic Potential of High-Water Content Regions in Parkinson's Cognitive Impairment Progression
Authors: Reheman Mariyemuguli , He Naying , Buch Sagar , Pei Huang , Wu Peng , Chen Shengdi , Mark Haacke E. , Yan Fuhua
Published: November 2024

Motivation: White matter damage has been known to accumulate prior to the onset of white matter hyperintensity, but there is currently no way to calculate the extent of damage. Goal(s): Quantification of high-water content regions in Parkinson's patients based on proton spin density maps provides a new biomarker for mapping tissue damage and interpretation of WMH evolution.Approach: Distribution of high-water content regions and WMH were mapped for Parkinson patients with different cognitive levels. Heatmaps were created for groups. Results: High water content regions were mainly distributed along the ventricular wall. Water content and WMH correlated with CSF volumes with significant differences between groups. Impact: Segmentation of the high-water content regions based on proton spin density maps provides the ability to interpret WMH evolution and provide a new potential biomarker to guide the early detection. These effects were dramatically seen in Parkinson’s patients with dementia.
Abnormal brain perfusion pattern related to motor dysfunction and levodopa reactivity in Parkinson's disease
Authors: Zheng Qianshi , Wen Jiaqi , Duanmu Xiaojie , Tan Sijia , Yuan Weijin , Zhou Cheng , Wu Haoting , Guo Tao , Wu Chenqing , Qin Jianmei , Chen Jingwen , Wu Jingjing , Zhang Yong , Zhang Minming , Guan Xiaojun , Xu Xiaojun
Published: November 2024

Motivation: A kind of perfusion biomarker capable of effectively distinguishing Parkinson's disease (PD) from normal subjects and reflecting motor dysfunction and levodopa reactivity is under research. Goal(s): To construct a stable PD-related perfusion pattern based on arterial spin labelling (ASL), and to explore levodopa reactivity of motor symptoms with the pattern. Approach: Principal component analysis and the scaled sub-profile model (PCA-SSM) was used to construct and validate PD-related perfusion pattern, with correlation and predictive analysis. Results: The PD-related perfusion pattern was constructed to predict the severity of motor symptoms and assess levodopa reactivity in PD patients with axial symptom. Impact: The PD-related perfusion pattern could serve as a potential biomarker for evaluating the severity of motor symptoms and the prognosis of levodopa therapy in PD patients with axial symptom.
Neuromelanin Change in the Substantia nigra for Differentiating Multiple System Atrophy from Parkinson’s Disease
Authors: Liu Yu , Wang Ying , Wang Xinhui , He Naying , Wu Peng , Haacke Ewart , Yan Fuhua
Published: November 2024

Motivation: Multiple system atrophy (MSA) and Parkinson’s disease (PD) are sharing many common clinical presentations. The clinical diagnosis has been empirical due to a lack of a subjective imaging biomarker. Goal(s): To investigate possible NM change in the SN and to better facilitate differential diagnosis. Approach: 38 MSA , 38 PD patients and 38 HCs were scanned on a 3T MRI system. We calculated the relative contrast ratio (rCR) and NM volume of bilateral SN. Results: HCs had higher rCR and NM volume than patients with PD and MSA (p＜0.001). Of note, patients with MSA had lower NM volume than patients with PD (p＜0.001). Impact: Our finding suggests that NM degeneration and depigmentation of SN may be more severe in patients with MSA in comparison with PD. Hence, NM measures of the SN may provide promising insight into differentiating MSA and PD.
Altered cerebellar functional network topology in spinocerebellar ataxia type 3
Authors: Liu Bing , Zhang Linwei , Yang Aocai , Luan Jixin , Lv Kuan , Hu Pianpian , Ma Guolin
Published: November 2024

Motivation: Functional network changes of the cerebellum in patients with spinocerebellar ataxia type 3 (SCA3) have been scarcely assessed. Goal(s): To investigate the functional topological characteristics of cerebellar network connectivity and modular changes in SCA3. Approach: Graph theoretical method were used in this study to provide quantified topological organization and modular analyses of functional cerebellar networks. Results: The small-world organization of the cerebellum was spared in patients with SCA3. Compared with healthy controls, increased inter-modular connectivity between frontoparietal network and dorsal somatomotor network and decreased intra-modular connectivity in cerebellar default mode network were shown in SCA3 patients. Impact: This study displays the functional cerebellar topological network in SCA3. The abnormalities of cerebellar modular connectivity support SCA3 as a network disorder, which further enhance the interpretation of SCA3 from the perspective of neuroimaging.
Correlation analysis between flortaucipir tau PET and quantitative susceptibility mapping in progressive supranuclear palsy
Authors: Satoh Ryota , Ali Farwa , Botha Hugo , Lowe Val , Josephs Keith , Whitwell Jennifer
Published: November 2024

Motivation: To improve understanding of underlying mechanisms of flortaucipir tau PET uptake. Goal(s): To clarify the relationship between flortaucipir uptake and iron deposition in progressive supranuclear palsy (PSP). Approach: We performed a correlation analysis between flortaucipir PET and quantitative susceptibility mapping across subcortical regions in PSP. Results: Positive correlations between flortaucipir uptake and susceptibility were found in most subcortical regions in PSP, suggesting the possibility that some flortaucipir uptake is associated with iron in these regions. Impact: The positive correlation between flortaucipir PET uptake and magnetic susceptibility suggests that some flortaucipir uptake in subcortical structures may be associated with iron deposition in PSP. This finding improves our understanding of the underlying mechanisms of flortaucipir PET.
Hemoglobin A and S affects blood R1 differently: a comparative study in healthy controls and sickle cell disease patients
Authors: Ying Chunwei , Eldeniz Cihat , Musibay Lucas , Yoo Jenny , Fellah Slim , Lewis Josiah , Mirro Amy , Yan Yan , Chen Yasheng , Wang Yan , Binkley Michael , Lee Jin-Moo , Fields Melanie , Guilliams Kristin , Ford Andria , An Hongyu
Published: November 2024

Motivation: Blood R1 (R1Blood) is associated with hematocrit level (Hct). However, it is unclear whether Hct of normal hemoglobin (HctA) and sickle hemoglobin (HctS) have a similar impact on R1Blood. Goal(s): Evaluate the impact of HctA and HctS on R1Blood in healthy controls and SCD patients. Approach: Multiple linear regression with R1Blood as the dependent variable and Hct of combined hemoglobin A and F (HctAF), HctS, age, and sex as independent variables was performed. Results: R1Blood was associated with HctAF, HctS, age, and sex. Moreover, the association between R1Blood and HctS was significantly different from that between R1Blood and HctAF. Impact: Blood R1 is essential in various MRI applications. Our findings are crucial for developing an accurate blood R1 estimation model in sickle cell disease patients.
Perfusion dynamics in a mouse line of Parkinson’s Disease
Authors: Monteiro Sara , Baião Ruxanda , Hirschler Lydiane , Barbier Emmanuel L. , Figueiredo Patrícia , Shemesh Noam
Published: November 2024

Motivation: Parkinson disease patients show alterations in their vascular system, exhibiting lower perfusion than healthy subjects. Goal(s): Here, we harness a mouse model exhibiting extensive human α-syn deposition to investigate cerebral blood flow properties in PD. Approach: We use a novel setup enabling high resolution Pseudo-Continuous Arterial Spin Labelling, a non-invasive technique for perfusion mapping in-vivo without injection of contrast agentes. Results: We found that not only the PD mouse line but also their WT littermates have altered perfusion properties across the brain compared to control c57bl/6 mice. Impact: Our findings highlight the importance of accounting for these potential sources of variability in future work with these lines.
AI based neuromelanin MRI analysis in a multi-site longitudinal Parkinson’s Disease study
Authors: Ingalhalikar Madhura , Pham Ha , Nguyen Kim , Bracoud Luc , Hutchison Matthew , Evans Karleyton , Dam Tien , Schaerer Joel , Conklin Chris , Suhy Joyce , Scott David
Published: November 2024

Motivation: Neuromelanin (NM) MRI is a proposed biomarker of dopaminergic neurodegeneration in the substantia nigra pars compacta (SNpc). Goal(s): To automate post-hoc analysis on NM-MRI data acquired from a large multi-center clinical trial. Approach: A deep dynamic u-net model was built to segment the SNpc and the background region automatically and was used to analyze a large multi-center longitudinal PD dataset. Results: Within-subject change from baseline effects were significant at the population level for SNpc volume (left and right). These results suggest an AI-derived SNpc volume, estimating the atrophied hyperintense region on an NM-MRI scan, is a viable marker of disease progression in PD. Impact: The dynamic AI model on NM-MRI trained/tested on multiple sites/scanners accurately and robustly delineates the SNpc and may have applicability in trials where NM-MRI is used as a marker of nigrostriatal degeneration.
MEGA-PRESS Detects the Changes of Metabolite in Parkinson's Disease with Depression
Authors: Liu Xinzi , Xue jinyue , Xu Yongzhou , Han Lu , Wen Zhibo
Published: November 2024

Motivation: Altered λ-aminobutyric acid (GABA) levels have been observed in Parkinson's disease (PD), and these discrepancies may depend in differences of clinical symptoms. However, the role o GABA and glutamate concentrations of depression in Parkinson's disease (DPD) remains unexplored. Goal(s): To explore the changes of GABA+, glutamate and glutamate (Glx) in DPD. Approach: We utilized MEGA-PRESS to measure GABA+ and Glx levels in the medial frontal cortex (MFC) and thalamus among PD patients with and without depression, along with aged-matched healthy participants. Results: GABA+ levels were found to be elevated within MFC in individuals with DPD, independent of the presence of Parkinson’s disease. Impact: These findings suggest that GABAergic alterations in specific brain regions might be linked to the clinical symptoms of PD. Modulating GABAergic function could be a potential approach for treating DPD.
Dorsal nigral hyperintensity abnormality in 7T MRI is a biomarker for diagnosis of Parkinson’s disease and atypical parkinsonisms
Authors: Su Dongning , Zhang Zhijin , Zhang Zhe , Yan Rui , Zhu Wanlin , Wei Ning , Suo Yue , Liu Xinyao , Zhao Huiqing , Wang Zhan , Ma Huizi , Zhou Junhong , Lam Joyce , Li Yuan , Wu Tao , Jing Jing , Feng Tao
Published: November 2024

Motivation: The dorsal nigral hyperintensity (DNH) abnormality is a characteristic feature of PD and 7T MRI has proved useful for its visualization. Goal(s): To investigate the diagnostic efficiency of DNH abnormality at different stages of PD and in atypical parkinsonisms using 7T MRI. Approach: PD, RBD, MSA-P, MSA-C, and PSP patients and controls underwent 7T T2* with DNH abnormality assessed for diagnostic performance. R2* mapping and principal component analysis were performed in substantia nigra. Results: MSA-C and RBD demonstrated higher preservation rate of DNH than PD, MSA-P, and PSP. DNH scoring criteria proved an optimal diagnostic method of PD, RBD, MSA-P, MSA-C, and PSP. Impact: MSA-C and RBD patients had higher dorsal nigral hyperintensity (DNH) preservation rate compared with PD, MSA-P, and PSP. The DNH scoring criteria proved an optimal diagnostic method of PD, RBD, MSA-P, MSA-C, and PSP.
Reduced functional connectivity induced by longitudinal changes of structure and perfusion in hemodialysis patients with cognitive impairment
Authors: Yang Wenbo , Song Lijun , Li Mingan , Xu Boyan , Yang Zhenghan , Wang Hao , Wang Zhen-chang
Published: November 2024

Motivation: Hemodialysis (HD) leads to cognitive impairment; however, the pathophysiology of maintenance HD remains unclear. Goal(s): This study aimed to investigate the longitudinal alterations in gray matter volume (GMV), cerebral blood flow (CBF) and functional connectivity (FC) in patients on follow-up HD compared with baseline HD. Approach: All participants underwent high-resolution T1-weighted imaging, arterial spin labeling, and functional MR imaging to measure GMV, CBF, and FC. Results: The right and left medial superior frontal gyrus (SFGmed.L) exhibited significantly lower GMV and CBF in patients on follow-up HD compared with baseline HD and lower FC between the SFGmed.L and left middle temporal gyrus. Impact: Our findings provide unique insights into the pathological mechanisms of patients on maintenance HD with cognitive impairment.
Radiomic analysis of quantitative T2-mapping and conventional MRI in predicting histologic grade of bladder cancer
Authors: Lei Ye , Zhang Xiaoxiao , Chen Yuntian , Xiang Wanxin , Yao Jin , Song Bin , Lei Ye
Published: November 2024

Motivation: MpMRI has been extensively used for the local staging of bladder cancer (BCa), it is worth using mpMRI for the preoperative evaluation of the pathological grade.Goal(s): We explored the added value of a radiomics based on quantitativeT2-mapping and conventional MRI to evaluate the histologic grade of BCa pre-operatively. Approach: Pelvic MRI including T2-mapping and diffusion-weighted imaging before any treatment were analyzed. We constructed different prediction models using mean signal values and radiomic features from both T2-mapping and apparent diffusion coefficient (ADC) maps. Results: Radiomics could provide more information than direct evaluation of T2 and ADC values in differentiating histological grades of BCa. Impact: Our observation of significantly improved performance using the radiomics model suggests that it incorporates tumor appearance, margin, and texture features, making it more representative of tumor characteristics. The incorporation of this model provides valuable additional information for BCa management.
Functional Brain Alterations in Children with Autism Spectrum Disorder after Fecal Microbiota Transplantation: a preliminary study.
Authors: Zhou Di , Hua Ting , Zhao Xiance , Tang Guangyu
Published: November 2024

Motivation: The combination of fecal microbiota transplantation (FMT) and resting-state fMRI (rs-fMRI) can provide a new imaging perspective for understanding the neural mechanism of gut microbiota in ASD. Goal(s): This study is to evaluate the functional alterations and investigate the potential mechanisms of FMT treatment in ASD. Approach: Amplitude of low-frequency fluctuation (ALFF) and Regional Homogeneity (ReHo) based on rs-fMRI are useful methods to characterize brain function and activity. Results: After FMT treatments, the higher ALFF and ReHo values in the left angular gyrus and bilateral lingual gyrus, which could become the underlying mechanism of improvements. Impact: FMT can improve the symptoms of ASD and influence the brain functional activity. Combining FMT and MRI can provide a new imaging perspective for understanding the neural mechanism and assist clinical follow-up of ASD.
Selective Vulnerability of the Brain Regions in Neurodegenerative Disorders
Authors: Fotoohinasab Atiyeh , Ugonna Chidi Patrick , Chen Nan-kuei
Published: November 2024

Motivation: Understanding neurodegenerative diseases requires considering selective brain region vulnerability and progressive atrophy. Goal(s): We seek to develop a computational method to identify dynamic, selectively vulnerable brain regions affected by specific diseases. Approach: This study introduces a novel graph theory-based technique focusing on functional interactions in atrophy progression. Results: In Parkinson's disease, our approach demonstrated widespread chronological atrophy across motor and non-motor brain regions, showcasing its effectiveness. Impact: This pioneering study reveals selective vulnerability in functional brain networks, by employing graph theory and computational methods, advancing neurodegenerative disease understanding. These findings hold promise for tracking disease-specific atrophy patterns, enabling longitudinal assessment and the development of effective therapeutic strategies.
Functional Network-Based Statistics Reveal Abnormal Resting-State Functional Connectivity in Parkinson’s Disease with Apathy
Authors: Xu Haikun , Sa Sha , Jiang Yueluan , Zhang Mengchao , Liu Lin
Published: November 2024

Motivation: Apathy is a common and disabling symptom of Parkinson’s disease (PD), yet brain networks involved in Patients with PD with apathy (PD-A) remain underexplored. Goal(s): The aim of our study was to identify brain networks of PD-A using network-based statistics (NBS). Approach: Resting-state fMRI data was obtained from twenty-eight patients with PD-A, 19 PD patients without apathy (PD-NA), and 32 healthy controls (HCs). A network-based statistic analysis was used to isolate networks of interconnected nodes that differ among the three groups. Results: PD-A showed decreased connectivities in control network, default network, attention network, somatomotor network, temporoparietal network, and visual network. Impact: We performed NBS analysis to identify brain networks related to PD-A at the whole-brain functional connectome level for the first time. NBS is a validated nonparametrical statistical approach for understanding the neural mechanisms of PD-A.
Evolution of resting-state network efficiency after stroke: an individual confrontation with the norm.
Authors: van Dokkum Liesjet , Deverdun Jeremy , Clain Guillaume , de Champfleur Nicolas Menjot , Laffont Isabelle , le Bars Emmanuelle
Published: November 2024

Motivation: To provide personalized rehabilitation after stroke, we need to identify brain biomarkers that inform us about what differs from a normal organization to target rehabilitation accordingly. Goal(s): Evaluate the potential of a norm-based functional brain network organization analysis in the individual follow-up post-stroke. Approach: Compare fMRI resting-state network functioning of 21 people post-stroke before and after rehabilitation with the norm, based on 569 controls, while taking into account the motor deficit. Results: Using a norm, we showed that targeted motor rehabilitation improves the motor network efficiency for recovering patients, whereas executive network efficiency remained suboptimal, potentially negatively interfering with motor recovery. Impact: Comparing people with a norm, not only post-stroke but also with other central neurological deficits, facilitates personalized medicine, for instance by providing targets for non-invasive brain stimulation, or by identifying processes that require specific training, like attention direction or proprioception.
MR Optimized Reconstruction of Simultaneous Multi-Slice Imaging Using Diffusion Model
Authors: Zhao Ting , Cui Zhuoxu , Jia Sen , Zhu Qingyong , Liu Congcong , Zhou Yihang , Zhu Yanjie , Liang Dong , Wang Haifeng
Published: November 2024

Motivation: Diffusion model has been applied to MRI reconstruction, including single and multi-coil acquisition of MRI data. Simultaneous multi-slice imaging (SMS), as a method for accelerating MR acquisition, significantly reduces scanning time, but further optimization of reconstruction results is still possible. Goal(s): In order to optimize the reconstruction of SMS, we proposed a method to use diffusion model based on slice-GRAPPA and SPIRiT method. Approach: Specifically, our method characterizes the prior distribution of SMS data by score matching and characterizes the k-space redundant prior between coils and slices based on self-consistency. Results: With the utilization of diffusion model, we achieved better reconstruction results. Impact: The application of diffusion model can further reduce the scanning time of MRI without compromising image quality, making it more advantageous for clinical application.
Glucose Chemical Exchange Saturation Transfer MRI for Predicting the Histological Grade of Rectal Cancer
Authors: Meng Nan , Dai Bo , Liu Xue , Wu Yaping , Yu Xuan , Li Zheng , Shen Zhiwei , Wang Meiyun
Published: November 2024

Motivation: The histological grade of rectal cancer has a considerable involvement in treatment strategies and prognosis in relevant patients. Goal(s): To evaluate the utility of glucose chemical exchange saturation transfer (glucoCEST) MRI with non-contrast injection in predicting the histological grade of rectal cancer. Approach: This prospective analysis included 60 patients with rectal cancer who underwent pelvic glucoCEST, APTWI, and DWI. Results: Elevated MTRasym (1.2 ppm), MTRasym (3.5 ppm) values and lower ADC values were observed in the high-grade group compared with low-grade cases (all P < 0.01). The AUCs of MTRasym (1.2 ppm), MTRasym (3.5 ppm), and ADC were 0.792, 0.839, and 0.855, respectively. Impact: These preliminary data demonstrate that glucoCEST MRI without contrast injection has the potential to provide a non-invasive assessment of histological grade in rectal cancer and add value to current tools used for the differentiation between high- and low-grade rectal cancer.
Multi-center comparison of whole-brain high-resolution CEST mapping at ultra-high field using PUSHUP saturation
Authors: Völzke Yannik , Löwen Daniel , Stirnberg Rüdiger , Pracht Eberhard , Lamalle Laurent , Goa Pål , Stöcker Tony
Published: November 2024

Motivation: CEST suffers from saturation inhomogeneities, especially at ultra-high fields. Universal pulses enable calibration-free mitigation of these inhomogeneities. However, it is not obvious how well PUSHUP translates to different scanners. Goal(s): The goal of this work is to evaluate the feasibility of PUSHUP-CEST to obtain homogeneous whole-brain, high-resolution CEST maps in multi-center studies. Approach: PUSHUP was calculated using a joined database of three sites. Multiple CEST measurements were performed at each site. The mean CEST amplitudes were compared in four brain segments. Results: The resulting CEST maps were homogeneous and without major image artifacts. No significant bias could be found between sites. Impact: PUSHUP allows for high-resolution, whole-brain CEST mapping at 7T. The feasibility of PUSHUP saturation for multi-center CEST studies and the use of joined databases for pulse calculation are demonstrated. No site-bias is found. This fosters CEST mapping in large-scale studies.
APTw/CEST-MRI for body oncology with SPIR fat suppression using improved Z-spectral modeling for olefinic fat components
Authors: Keupp Jochen , Eggers Holger , Dimitrov Ivan , Vinogradov Elena
Published: November 2024

Motivation: Fat-artifacts are challenging in CEST-MRI for body applications as they overlap spectrally with clinically relevant signals like amide or hydroxyl. Goal(s): Improve the quality of in vivo fat correction based on previously explored Z-spectral fitting of data obtained with SPIR fat suppression. Approach: Olefinic fat is modeled separately while scaling and inverting aliphatic components. Fitting was tested on Z-spectral data from breast ROIs with variable fat content. Results: The new model is accurate for a large range of fat fractions. The opposite phase of aliphatic and olefinic fat components provides a better explanation for the observed Z-spectra in vivo. Impact: Understanding and accurate modeling of fat signals in fat-suppressed APT/CEST-MRI will allow unbiased assessment of CEST effects in body-oncology (breast, kidney cancer).
Improved Differentiation of Glioma Grades through Fluid Exponential Suppression in Chemical Exchange Saturation Transfer (CEST) Imaging
Authors: Zhou Longjie , Zhang Hongxi , Yong Xingwang , Zhou Haichun , Guo Jing , Chen Weibo , Shen Zhipeng , Zhang Yi
Published: November 2024

Motivation: Fluid-induced artifacts often hamper differentiation between high- and low-grade gliomas in CEST imaging. Goal(s): Our goal was to develop a novel fluid exponential suppression factor to eliminate fluid-related artifacts. Approach: We extended the original linear correction factor into a nonlinear exponential factor. The metrics with and without fluid suppression factors were compared using a dataset of 140 glioma patients. Results: CEST metrics combined with the fluid exponential suppression factor substantially reduced the fluid-related artifacts and yielded higher AUCs for grading gliomas than linear correction and no correlation. Impact: The novel fluid exponential suppression strategy can substantially improve the quality of CEST maps and enhance the performance of diagnosing gliomas. The proposed method is easy to adopt and can be applied to existing data retrospectively.
Renal Metabolic CEST-MRI Based on Golden-Angle Radial Sampling Under Free Breathing
Authors: Tao Quan , Chen Zelong , Wu Zhigang , Deng Kan , Zhang Yizhe , Zhang Qianqian , Zhang Wenyan , Lin Ting , Feng Yanqiu
Published: November 2024

Motivation: Respiration motion may induce artifact in CEST image and the in-accurate quantization of CEST signal. Goal(s): We aimed to develop clinical motion-insensitive CEST imaging of kidney. Approach: Here, we used turbo filed echo (TFE) based on golden-angle radial sampling to readout renal CEST image under free breathing of three normal volunteers and also evaluated its effectiveness for motion artifact suppression. Results: The renal Z-spectrum is float and there is no motion artifact in CEST images. This motion in-sensitive sequence showed high repeatability for renal CEST imaging. Impact: It may provide a motion in-sensitive CEST imaging sequence for renal imaging in clinical nephropathy patients under free breathing, and improve the accuracy of injury detection.
A generalized QUCESOP method with evaluating CEST peak overlap
Authors: Wang Yi , Fan Yang , Song Xiaolei , Gao Jia-Hong
Published: November 2024

Motivation: When quantifying the CE-parameters (fb, kb and R2b) of the specific CEST solute with existence of other unknown solutes, the peak overlap condition needs to be assessed to evaluate the reliability of the quantification result. Goal(s): To develop a method for CE-parameters quantification and peak overlap assessment. Approach: CEST data was acquired with various saturation offsets and powers. When fitting the targeted CE-parameters, the peak overlap was evaluated using RMSE between the trajectories of the acquired and synthesized data. Simulation and experiments were taken to test the performance. Results: The feasibility of the approach in CE-parameters quantification and peak overlap assessment was verified. Impact: The proposed method would be helpful for evaluating the reliability of the empirically set model for CE-parameters quantification, and then that of the quantification result.
Towards Temperature-Corrected pH Mapping using CEST Imaging: Quantification of the Underlying Calibration as a Function of Temperature
Authors: Boyd Philip , Lechner Jana , Wirtz Jannis , Ladd Mark , Bachert Peter , Korzowski Andreas
Published: November 2024

Motivation: CEST-based pH mapping relies on a strongly temperature-dependent calibration, yielding the parameter kc characterizing the exchange rate. Previously, this calibration has only been performed at T=37°C, thus impeding quantitative pH mapping at different temperatures. Goal(s): Enabling more reliable, i.e. temperature-corrected, quantitative CEST-based pH mapping. Approach: We quantified the temperature dependency of kc(T) for amide, amine and guanidino protons at B0=9.4T using creatine and protamine model solutions at various pH and temperatures. Results: For all proton species, the measured kc(T) showed exponential temperature dependencies with doubling of kc(T) for distinct temperature increases (ΔT=Tdouble) of around 4−11°C. Interestingly, each proton species exhibited a characteristic Tdouble. Impact: These findings enable CEST-based pH mapping at any desired temperature, which, previously, has only been possible at T=37°C. Prospectively, quantitative pH mapping will now also be possible in anatomical regions, e.g. human lower leg, where temperatures may deviate from T=37°C.
Obtaining exchange rate maps using Multitasking SS-CEST
Authors: Cheema Karandeep , De Righi Dante , Shen Chushu , Christodoulou Anthony , Xie Yibin , Li Debiao
Published: November 2024

Motivation: To make qCEST clinically feasible by reducing the imaging timeGoal(s): Multitasking steady-state (SS) approach was used to get 3D volume acquisition that reduced the imaging time per slice by a factor of 22 as compared to conventional qCEST. Approach: 6 Yucatan minipigs were scanned at four time points post interverbal disk injury. The injury was done at the lower three lumbar discs. Conventional 2D qCEST and 3D Multitasking steady state CEST were performed. Results: Scan time for conventional qCEST is 24 minutes/slice while the imaging time for Multitasking SS qCEST is 36 minutes for 32 slices, representing a factor of 22 improvement. Impact: This study represents the initial step in validating an accelerated qCEST using Multitasking. The development of MRI-based pain assessment biomarkers in this model is crucial for advancing our understanding and treatment of lower back pain.
Removing lipid artifacts in CEST imaging with a two-point turbo-spin-echo Dixon method
Authors: Huang Shengxiang , Dai Zhechuan , Wen Junjie , Yong Xingwang , Hsu Yi-Cheng , Zhang Yi
Published: November 2024

Motivation: Chemical Exchange Saturation Transfer (CEST) imaging has advanced by capturing molecular-level information of tissue metabolites. However, strong fat artifacts can affect the contrast of CEST signals. Goal(s): We aim to seek a fat suppression technique that maintains high image signal-to-noise efficiency. Approach: By combining TSE-CEST and flexible two-point Dixon methods, utilizing accurate multi-peak fat models, the obtained water-only images are used as CEST images. Results: Z-spectra and MTRasym of ROIs in the water-fat-Creatine phantoms and high-fat fraction regions near the human knee demonstrate that accurate fat suppression achieved in the CEST images. Impact: We proposed a two-point turbo-spin-echo Dixon technique, which utilizes TSE-CEST instead of the conventional gradient echo Dixon acquisition. Robust fat suppression was achieved in the phantoms and human knee by utilizing Dixon on the two images acquired for each offset.
Evaluating the effectiveness of distortion self-correction for CEST-EPI
Authors: Huang Jianpan , Park Se Weon , Chan Kannie W. Y.
Published: November 2024

Motivation: EPI-based CEST (CEST-EPI) is fast but suffers from image distortion caused by field susceptibility. Goal(s): We aimed to evaluate the effectiveness of using the field map generated by the Z-spectra to achieve the distortion self-correction (DISC) for single-shot CEST-EPI without additional acquisition of a field map. Approach: The effectiveness of DISC strategy was demonstrated in CEST-EPI experiments of a creatine phantom and in vivo mice. CEST-RARE was used as a reference. Results: Without acquiring an additional field map, DISC retrospectively and effectively corrected geometric distortion in CEST-EPI, leading to improved SSIM and spatial CEST contrasts. Impact: We evaluated the effectiveness of using the field map generated by the Z-spectra to achieve the distortion self-correction for single-shot CEST-EPI. Results showed that DISC retrospectively and effectively corrected geometric distortion in CEST-EPI without acquiring an additional field map.
An efficient CEST workflow using joint optimization of sampling, reconstruction and quantification
Authors: Liu Chuyu , Li Zhongsen , Song Xiaolei
Published: November 2024

Motivation: As an exciting ‘label-free' molecular imaging technique, CEST workflow is always time-consuming, because of the seconds-long TR and multiple frequency repetitions in acquisition, the iteration in reconstruction, and the pixel-by-pixel in B0 correction and quantification. Goal(s): To achieve rapid and high-quality sampling, reconstruction and quantification of CEST-MRI. Approach: We constructed a data-driven CEST framework, by joint optimization of k-space sampling, reconstruction and quantification. Results: Retrospective experiments on human brain demonstrated the feasibility of combination with acceleration techniques including parallel imaging, compress sensing or deep learning, allowing 6X under-sampling rate and reconstruction of high-quality contrast maps in one second. Impact: A data-driven CEST framework enabled joint optimization of k-space sampling,reconstruction and quantification. Retrospective experiments demonstrated that the the framework allows 6X under-sampling rate and reconstruction of high-quality contrast maps in one second. This one-stop workflow may facilitate more clinical needs.
A scanner-inline software for performing permuted random forest for CEST frequency importance analysis
Authors: Guo Rui , Liu Chuyu , Song Xiaolei
Published: November 2024

Motivation: CEST quantitation typically relies on model-based fitting and always performs off-scanner. Besides, model-based fitting requires collection a number of saturation frequencies, hindering the clinical applications. Goal(s): To facilitate CEST applications by implementing a scanner-inline software through model-free analysis. Approach: We implemented CEST frequency importance analysis on Philips pride platform, which could rank the acquired frequencies according their contribution to lesion classification, using a permuted random forest algorithm. Results: Without specific requirement for sampled frequencies, this software allows researchers to extract frequency importance feature, either between lesion voxels and control ones, or between two different time points or different subjects. Impact: Compared with the conventional analysis based on fitting line-shape of the spectra, this PRF method does not have specific requirement for sampled frequencies on spectra, but fully explore all acquired ones, which is user-friendly and facilitate CEST applications.
Improving Image Quality of Dynamic Contrast Enhanced Abdominal MRI Using a Novel Deep Learning Reconstruction
Authors: Milshteyn Eugene , Ghosh Soumyadeep , Nakrour Nabih , Cochran Rory , Mercaldo Nathaniel , Wang Xinzeng , Tsai Leo , Guidon Arnaud , Harisinghani Mukesh
Published: November 2024

Motivation: Free breathing DCE imaging utilizes stack-of-stars sampling, which can lead to streak artifacts and noise reduction when too few spokes are used. Goal(s): Our goal was to validate application of deep learning to 3D DISCO-Star DCE imaging in the abdomen via image quality assessment and noise characterization. Approach: DL and conventionally reconstructed images were assessed by two radiologists across different IQ attributes. Noise characteristics were evaluated by calculation of total variation. AUC was also calculated. Results: The radiologists preferred DL across many of the IQ attributes, with noticeably lowered noise and decreased streaks in DL images. AUC was similar between the two reconstructions. Impact: The application of DL to DISCO-Star DCE imaging provides enhanced diagnostic quality, with reduced streaking, higher SNR, and better in-plane resolution. This has the potential to improve care for abdominal patients who have trouble holding their breath.
DL based reconstruction method for an undersampled PROPELLER MRI data
Authors: C Florintina , Chatterjee Sudhanya , Patil Rohan , Rajamani Sajith , Joel Suresh
Published: November 2024

Motivation: Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) is a popular MRI acquisition scheme used for clinical and research MRI data acquisition due to its robustness to motion. However, it is known to have long scan times. Goal(s): Reduce scan time for PROPELLER scans to make it feasible for usage in regular clinical settings. Approach: An unrolled algorithm based deep learning reconstruction method for PROPELLER scans has been proposed, which performs reconstruction at the blade level. Results: Proposed method has been demonstrated to perform good reconstruction on single coil data for multiple anatomies and contrasts. Impact: This method has the potential to reduce PROPELLER scan times and make it a popular choice for acquisition in clinical settings.
Feature-Image Variational Network for Accelerated MRI Reconstructions
Authors: Giannakopoulos Ilias , Johnson Patricia , Kim Jesi , Breen Matthew , Lui Yvonne , Lattanzi Riccardo
Published: November 2024

Motivation: To improve learning-based MRI reconstructions to achieve higher clinical accuracy and detail retention. Goal(s): To introduce modifications in the end-to-end (E2E) variational network (VarNet) to enhance its performance for undersampled MRI reconstructions. Approach: We performed training in feature-space instead of image-space and included an attention layer that leverages the spatial locations of the Cartesian undersampling artifacts. We combined the new network with the E2E VarNet into Feature-Image VarNet to facilitate cross-domain learning. Results: Reconstructions were evaluated using standard metrics and clinical scoring. Feature-Image VarNet outperformed all open-source models in the fastMRI leaderboard and preserved small anatomical details that were blurred with E2E VarNet. Impact: We propose the Feature-Image (FI) variational network (VarNet), which performs cross-domain learning between feature and image spaces. FI VarNet significantly enhances the reconstruction quality of undersampled MRI and could enable clinically acceptable reconstructions at higher acceleration factors than currently possible.
Automated Quality Control for Arterial Spin Labelling (ASL) MRI Using a VAE-based Neural Network
Authors: Hu Jian , Knight Silvin , Deng Bowen , Kenny Rose , Chen Xin , Chappell Michael
Published: November 2024

Motivation: Quality Control (QC) of ASL data is primarily a manual and subjective process that is time-intensive and can yield inconsistent results due to rater variability, while existing tools provide limited diagnostic metrics mostly focused on specific error source. Goal(s): To develop a QC detector to automatically detect outliers for ASL data. Approach: VAE-GAN model was applied to extract the latent representation of ASL data by which the decision boundary can be determined. Results: The AUROC of our QC detector on test dataset is 0.82 with accuracy=0.81. Impact: Our QC detector could help radiologists and researchers working on ASL MRI to automatically identify outliers. Consequently, appropriate operations can be used to correct or exclude outliers to avoid biases in the outcomes and ensure accurate interpretations.
Asymmetry-Aware Pretraining: A Path to Universal Brain MRI Analysis Model
Authors: Ma Yang , Wang Dongang , Liu Peilin , Barnett Michael , Cai Weidong , Wang Chenyu
Published: November 2024

Motivation: Many brain disorders present with asymmetrical features, which can be utilized to advance the classification of disease through deep learning. Goal(s): We aim to investigate how asymmetry awareness can improve model performance to identify brain disorders, subsequently facilitating better early diagnosis and intervention. Approach: Our approach involved initial pretraining with a focus on asymmetry awareness using conditional contrastive learning. Subsequently, finetuning was performed for various downstream tasks with limited training data. Results: Our approach is proven superior in identifying brain disorders compared with baseline methods that either directly trained for specific diseases or with pretrained model without considering symmetrical features of input images. Impact: Our research pioneers the integration of asymmetry awareness in pretraining models for the detection of brain disorders. The superior performance fully illustrated the feasibility and potential of leveraging the asymmetry nature of brain disease for broad deep learning tasks.
AUTOMATED CEREBRAL CAVERNOUS MALFORMATION LESION SEGMENTATION FROM SUSCEPTIBILITY WEIGHTED MR IMAGING
Authors: Lee Jinhee , Mabray Marc , Genc Ozan , Nelson Jefferey , Kim Helen , Lupo Janine
Published: November 2024

Motivation: Familial cerebral cavernous malformation (CCM) cases typically present with multi-focal lesions of varying size, count, and location making automatic segmentation for studying lesion burden challenging. Goal(s): We aimed to develop a deep learning framework for automated detection and volumetric quantification for ongoing large-scale, multi-site evaluation of patients with CCM. Approach: We implemented a framework that ensembles two-staged deep learning networks for large and small CCM lesions. Results: Our model achieved an overall Dice-score of 70%. We demonstrated the feasibility of a deep learning approach for detecting and segmenting various-sized lesions in patients with CCM using SWI acquired from multiple sites with various parameters. Impact: Our study found that total lesion volume quantified with our deep learning approach was statistically associated with increased odds of intracranial hemorrhage history. This demonstrates the potential benefit of our approach in providing a more accurate assessment of disease burden
Using ResNet to utilize 4-class T2-FLAIR slice classification based on the cholinergic pathways hyperintensities scale for pathological aging
Authors: Tsai Wei-Chun , Liu Yi-Chien , Yu Ming-Chun , Chou Chia-Ju , Yan Sui-Hing , Fan Yang-Teng , Huang Yan-Hsiang , Chiu Yen-Ling , Chuang Yi-Fang , Wang Ran-Zan , Shih Yao-Chia
Published: November 2024

Motivation: Cholinergic Pathways Hyperintensities Scale (CHIPS) is a visual rating scale to evaluate the burden of cholinergic white matter hyperintensities in T2-FLAIR image, indicating the severity of dementia. However, it is still time-consuming to screen slices throughout the whole brain to choose 4 specific slices for rating. Goal(s): To develop a deep-learning-based model to automatically select 4 slices specific to CHIPS. Approach: We used ADNI T2-FLAIR dataset (N=150) to train a 4-class slice classification model (BSCA) utilized by ResNet, and a local dataset (N=30) to test its performance. Results: Our model achieved the accuracy of 99.82% and F1-score of 99.83%. Impact: BSCA can be an automatic screening tool to efficiently provide 4 specific T2-FLAIR slices covering the white matter landmarks along the cholinergic pathways for clinicians to help evaluate whether patients have the high risk to develop clinical dementia.
Post-Surgical Cavity Segmentation on MRI for Glioma Patients with Deep Learning and Transfer Learning Approach
Authors: Yadav Virendra , Kandpal Ankit , Jafari Raufiya , Singh Rakesh , Gupta Rakesh , Agarwal Sumeet , Singh Anup
Published: November 2024

Motivation: Currently, manual/semiautomatic methods are being used for post-surgical cavity segmentation in glioma patients, which is crucial for precise treatment planning and monitoring. Goal(s): To evaluate the potential of deep learning models for automatic post-surgical cavity segmentation. Approach: Deep learning models were trained on pre-surgery and post-surgery (within 72hours) MRI data and tested on post-surgery data. The concept of data augmentation and transfer learning were also used. Results: Initially trained on the pre-surgery BraTS’2021 challenge dataset, the models underwent further refinement with local hospital's post-surgical data, resulting in 36% improvement in post-surgical cavity segmentation (U-Net3, DSC = 0.64). Impact: This study demonstrates the potential of deep-learning in automatic segmentation of post-surgical cavity on MRI images. Proposed automatic segmentation models can assist in fast and objective treatment planning. Further optimizations for improved accuracy and validation on large dataset is required.