bluuebunny/crossref_metadata_2025 huggingface dataset:
Propeller Diffusion-Weighted Imaging of Endometrial Cancer with Deep Learning Reconstruction
Authors: Bhosale Priya , Wang Xinzeng , Iyer Revathy , Guidon Arnaud , Hwang Ken-Pin , Ma Jingfei
Published: November 2024

Motivation: Diffusion-weighted imaging (DWI) is used in endometrial cancer imaging for improved specificity and accuracy in determining the depth of myometrial invasion compared to T2-weighted imaging alone. However, conventional echo planar imaging based DWI, including reduced FOV EPI, is prone to artifacts from field inhomogeneity in the area of endometria and from peristalsis. Goal(s): To improve the diffusion-weighted imaging of endometrial cancerApproach: Propeller DWI is robust to field inhomogeneity and motion. Deep learning (DL) reconstruction is used to mitigate its SNR deficiency and overcome the need for long scan time. Results: DL DW-PROPELLER improved the SNR and in-plane resolution of the conventional DW-PROPELLERImpact: DL DW-PROPELLER improved the SNR and in-plane resolution of the conventional DW-PROPELLER, enabling body DW-PROPELLER in clinically feasible scan time. Compared to the rFOV DW-EPI, DL DW-PROPELLER significantly improves the geometric accuracy and the readability of high b-value images.
Does Amide proton transfer-weighted mri have diagnostic and differential value in ovarian cystic and predominantly cystic lesion ？
Authors: Wang Fang , Ding Dawei , Chang Lingyu , Pylypenko Dmytro , Dou Weiqiang , Yu Dexin , Wang Qing
Published: November 2024

Motivation: Ovarian cystic and predominantly cystic lesions are common tumor-like formations within the ovaries, necessitating precise diagnosis and differential analysis for effective clinical management. Goal(s): This study examines the effectiveness of amide proton transfer-weighted (APTw) MRI in differentiating cystic or predominately cystic ovarian lesions. Approach: 49 patients with ovarian lesions were prospectively scanned by APTw before surgery, and 20 volunteers were also scanned by APTw as a control group. Results: The results demonstrate APTw MRI's substantial value in identifying and distinguishing common ovarian cystic and predominantly cystic lesions, offering enhanced diagnostic precision in ovarian pathology. Impact: MR diagnostic techniques for ovarian lesions, including DWI and DCE-MRI, have their limitations. A timely diagnosis is crucial for improving patient prognosis. APTw imaging has shown research progress across various systems, yet its application in ovarian studies remains limited.
Quantitative Apparent Diffusion Coefficient Metrics for MRI-only Suspicious Breast Lesions: Any Added Clinical Value?
Authors: Li Xue , Chen Min
Published: November 2024

Motivation: A simple, objective, and noninvasive approach is required to improve the diagnostic accuracy of MRI-only suspicious lesions. Goal(s): We aimed to assess whether using standard DWI sequences could increase the diagnostic performance of MRI for MRI-only breast lesions and reduce unnecessary MRI-guided biopsies. Approach: In our current work, we intend to use the quantitative assessment of the heterogeneity of the ADC value to explore the diagnostic value of conventional DWI sequences for MRI-only lesions. Results: Our results showed that quantitative ADC metrics based on large 2D-ROI measurements, particularly mean ADC values, exhibited superior performance over BI-RADS features in distinguishing MRI-only suspicious lesions. Impact: DWI quantitative parameters may significantly improve diagnostic ability of BI-RADS-based features of breast MRI and provide additional value for the discrimination of MRI-only suspicious lesions.
Pilot implementation of vendor-provided on-scanner gradient nonlinearity correction for breast tumor ADC measures in a prospective trial
Authors: Biswas Debosmita , Malyarenko Dariya , Surento Wesley , Peeters Johannes , Ahn Hye Shin , Turley Dallas , Rahbar Habib , Huang Wei , Chenevert Thomas , Partridge Savannah
Published: November 2024

Motivation: Improve accuracy of ADC measurement by correcting spatial nonuniformity of diffusion weighting caused by gradient nonlinearity (GNL) using novel vendor implemented on-scanner tools Goal(s): Evaluate GNL correction of breast tumor ADC in a treatment response study Approach: Implement on-scanner GNL correction, evaluate uncorrected and corrected tumor ADCs, evaluate GNL bias and ADC changes pre-treatment and post one cycle of neoadjuvant chemotherapy. Results: Preliminary results from this study indicate adequate performance of the vendor implemented GNL correction of ADC in breast DWI assessment of response to neoadjuvant chemotherapy. Impact: This pilot study demonstrates vendor-implemented GNL-correction (GNC) of spatially dependent b-value bias can dramatically simplify the process of obtaining more accurate ADC measures, which can improve robustness of ADC as a biomarker for treatment response.
Optimizing b-value Sampling Strategies with Integrative DWI: A Comparison Study on DISMANTLE Parameters in Breast
Authors: Escutia Jacqueline , Dan Guangyu , Yen Albert , Neuschler Erin , Zhou Xiaohong , Karaman Muge
Published: November 2024

Motivation: This study is driven by the pressing need for the standardization of breast diffusion-weighted imaging (DWI) techniques. Goal(s): The objective is to explore the influence of b-value selection on the parameters estimated by using an integrated DWI approach (DISMANTLE) that aims to simultaneously assess tissue cellularity, vascularity, and heterogeneity. Approach: We conducted a systematic analysis by evaluating DISMANTLE parameters in healthy breast tissue using diverse b-value sampling strategies. Results: Our findings identified an ideal b-value distribution for accurate implementation of DISMANTLE in breast. This dataset included 12 b-values, reducing scan time by 34% compared to the comprehensive DWI protocol featuring broad b-value range. Impact: This study emphasizes the role of b-value sampling in advanced breast DWI. Our systematic evaluation contributes to the potential success of advanced DWI techniques in breast imaging and the current discussion of the need for standardization in advanced DWI.
Comparison of different simulation approaches to predict signal evolutions and quantitative values in diffusion-sensitized MR Fingerprinting
Authors: Grund Christina , Feiweier Thorsten , Buonincontri Guido , Gebhardt Matthias
Published: November 2024

Motivation: For quantitative methods like MRF an accurate yet fast simulation of signal evolutions is essential. For fast simulations, ignoring time-evolutions of RF-pulses or actual diffusion directions are common practice even though these can affect the resulting fingerprints. Goal(s): We investigate some of these simplifications and their influence on the resulting signal evolutions and quantitative values. Approach: For this we simulate and analyze some possible impacts separately and compare the resulting signal evolutions and quantitative values. Results: In conclusion, while ignoring diffusion directions has negligible impact on the results, assuming instantaneous RF-pulses or ignoring B1-field variances leads to significant differences in the results. Impact: Simplifications like ignoring B1-inhomogeneities, slice profile or time-evolution of RF-pulses lead to significant differences in simulated fingerprints. On the other hand, ignoring the diffusion directions in case of Gaussian diffusion has negligible influence on the signal evolution and quantitative values.
A semi-data-driven cellular microstructural model considering cell size distribution in diffusion MRI
Authors: Shi Diwei , Liu Fan , Li Sisi , Chen Li , Jiang Xiaoyu , Zheng Quanshui , Xu Junzhong , Guo Hua
Published: November 2024

Motivation: Incorporating the impact of cell size distribution is challenging in current dMRI-based microstructural imaging. All relevant models fail to provide an analytical signal expression, but instead replace the intracellular signal with the sum of signal terms corresponding to different cell diameters. Although this is intuitive, subsequent equations are usually ill-conditioned and hard to resolve. Goal(s): To derive the analytical expression for dMRI signals and rebuild a cellular microstructural model. Approach: We performed theoretical modelling based on simulated signals and validations on numerical simulations and in-vitro cell experiments. Results: A semi-data-driven cellular microstructural model is proposed and it outperforms the published method. Impact: This work provides the first analytical expression for dMRI signals while incorporating cell size distribution. The proposed microstructural model can extract not only accurate mean cell size, but also distribution information, which provides an additional biomarker for tumor monitoring.
Beyond the dipole: unveiling the hidden impact of nondipolar shifts on MRI phase contrast anisotropy in the brain
Authors: Jochmann Thomas , Kügler Niklas , Omira Ahmad , Zivadinov Robert , Haueisen Jens , Schweser Ferdinand
Published: November 2024

Motivation: To elucidate the contribution of nondipolar frequency shifts to the anisotropy of phase contrast in brain MRI and decode the underpinning biophysical mechanisms of phase contrast. Goal(s): To differentiate the roles of magnetic susceptibility and nondipolar frequency shifts across various head orientations in MRI scans. Approach: Utilizing DEEPOLE QUASAR, the study compares susceptibility estimates and nondipolar frequency shifts in anisotropic brain regions across multiple head orientations. Results: Nondipolar frequency shifts played a substantial role in the anisotropy of frequency shifts, with DEEPOLE QUASAR providing more stable susceptibility estimates than conventional QSM, regardless of head orientation. Impact: The study establishes the substantial influence of nondipolar frequency shifts on MRI phase contrast anisotropy, questioning established assumptions from susceptibility tensor imaging and quantitative susceptibility mapping, thereby paving the way for more accurate brain tissue characterization.
Increase in Cerebral Oxygen Extraction Fraction in Early Parkinson’s Patients
Authors: Candan Huseyin , Lee Dongkyu , Cho Junghun , Lee Hansol , Cho HyungJoon
Published: November 2024

Motivation: Parkinson's disease is the fastest growing neurological disorder. However, comorbidities of the disease complicates its diagnosis and creates need for the discovery more biomarkers to help with diagnosis. Goal(s): Our aim is to noninvasively measure the cerebral oxygen extraction fraction which is an important metric for cerebral oxygen metabolism and see if it can be utilized as a biomarker for early Parkinson's Disease. Approach: MRI scans from early Parkinson's patients and healthy controls were obtained. Oxygen extraction fraction (OEF) maps for subjects were generated and two groups' oxygen metabolism was compared. Results: An overall increase in cerebral OEF was observed for Parkinson's patients. Impact: This study will potentially help with the diagnosis of early Parkinson's by providing another quantifiable biomarker. Additionally, difference in oxygen metabolism can help to understand Parkinson's disease better.
Simultaneous Dual-Band Saturation for Reduced FOV Neuromelanin Imaging at 7T
Authors: Ma Jun , Middlebrooks Erik , Patel Vishal , Westerhold Erin , Tao Shengzhen , Lin Chen , Zhou Xiangzhi
Published: November 2024

Motivation: In neuromelanin (NM) imaging at 7T, placing bilateral sagittal saturation bands near the midbrain can mitigate ghosting artifacts and provide equivalent MT contrast compared to traditional MT pulses. Goal(s): To present the feasibility of a proposed simultaneous dual-band saturation scheme to provide MT contrast in NM imaging at 7T. Approach: Simultaneous dual-band saturation pulse was designed for the proposed simultaneous saturation scheme. Its performance in providing MT contrast was compared to the conventional sequential scheme for 3D GRE- and PETRA-based in-vivo NM imaging. Results: The proposed simultaneous dual-band saturation schemes yielded favorable contrast between the NM-rich SNpc and the surrounding tissues. Impact: The proposed simultaneous dual-band saturation scheme can provide symmetric saturation and MT effects in high-resolution rFOV NM imaging at 7T. The proposed scheme may also work for efficient out-of-volume suppression for MRS and blood suppression in certain MR applications.
Longitudinal Quantitative MRI Study on Abnormal Brain Iron Progression in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease
Authors: Gaurav Rahul , Lejeune François-Xavier , Pyatigorskaya Nadya , Santin Mathieu , Valabrègue Romain , Perot Jean-Baptiste , Mangone Graziella , Leu-Semenescu Smaranda , Arnulf Isabelle , Vidailhet Marie , Corvol Jean-Christophe , Lehéricy Stéphane
Published: November 2024

Motivation: Although Parkinson’s disease (PD) and isolated REM sleep behavior disorder (iRBD) demonstrate elevated substantia nigra (SN) iron concentration, topography of change is still debated. Goal(s): To quantify abnormal brain iron progression over time using quantitative susceptibility mapping (QSM). Approach: Subjects were assessed three times 2 years apart. Mean QSM template was developed to quantify SN and its subdivisions, and subthalamic nucleus. Then caudate nucleus, putamen, globus pallidus and thalamus were also quantified. Results: At baseline, PD without RBD demonstrated +17.8% posteroventral SN iron overload and during the follow-up, 26.2% overload versus controls. None of the other tested regions showed significant differences between groups. Impact: Abnormal iron concentration was only found in the posteroventral SN and not in any other basal ganglia region over time. Further, PD without RBD showed highest iron concentration. Iron-sensitive quantitative MRI could be a potential longitudinal biomarker of the future.
Locus Coeruleus Visualisation at 7T: Optimisation through Multi-Contrast Acquisition and Wavelet-Based Fusion
Authors: Malekian Vahid , Weston Philip , Thomas David , Josephs Oliver , Callaghan Martina
Published: November 2024

Motivation: Magnetisation transfer (MT) weighting has been used to visualise the Locus Coeruleus (LC) at 7T, but robust visualisation remains challenging due to its small size and complex contrast mechanisms. Goal(s): Investigate how proton-density- (PD) and T1-weighting interact with MT-weighting and optimise an advanced post-processing pipeline for LC visualisation. Approach: Multi-echo 3D gradient echo images were acquired with MT-weighting and either PD- or T1-weighting from two individuals, including scan-rescan acquisitions. Results: MT-PD-weighting enhanced visualisation of the LC with respect to the surrounding tissue, whereas MT-T1-weighting enhanced contrast with respect to the nearby CSF. Combining these via wavelet-based fusion maximises these respective benefits. Impact: Enhancing visualisation of the Locus Coeruleus with our proposed acquisition and processing technique will pave the way for neurologists and cognitive neuroscientists to understand the key role this structure plays in human cognition and neurodegeneration.
MRI Correlates of 18F-AV133 in Parkinson's Disease
Authors: Langley Jason , Hu Xiaoping
Published: November 2024

Motivation: The limited availability and high cost of 18F-AV133 PET impedes its widespread adoption for use in diagnosis of Parkinson’s disease. Goal(s): Our goal was to assess the relationship between 18F-AV133 PET and MRI imaging markers in Parkinson's disease. Approach: Magnetization transfer and relaxometry imaging metrics derived in the basal ganglia of Parkinson's disease patients were compared to standardized uptake ratios from 18F-AV133 PET. Results: Significant correlations were seen between 18F-AV133 PET and MRI imaging metrics. These results suggest that MRI is able to capture neurodegenerative processes in Parkinson's disease. Impact: Our demonstration of the relationship between 18F-AV133 PET and MRI imaging markers suggests MRI imaging markers may serve as a surrogate measure for the integrity of the nigrostriatal system in Parkinson's disease.
Asymmetric impairment of hippocampus in patients with type 2 diabetes mellitus related with cognitive function
Authors: Pan Peichun , Gao Jie , Zhang Dongsheng , Tang Min , Li Jing , Ai Kai , Wu Peng , Lei Xiaoyan , Zhang Xiaoling
Published: November 2024

Motivation: To explore the relationship between hippocampal perfusion asymmetry and hippocampal enlarged perivascular spaces (H-EPVS) and cognitive function in patients with T2DM. Goal(s): To explore the relationship between hippocampal perfusion asymmetry and H-EPVS and cognitive function in patients with T2DM. Approach: CBF in hippocampus,H-EPVS and cognitive function were compared between HCs and patients with T2DM .The relationship between CBF in hippocampus, H-EPVS counts and cognitive measurement was analyzed. Results: In patients with T2DM, hippocampal perfusion was reduced and H-EPVS counts were higher on the left side. Decreased right hippocampal perfusion and increased left H-EPVS count were associated with cognitive impairment in patients with T2DM . Impact: Asymmetric impairment of hippocampal in patients with T2DM , and the asymmetry may be caused by a variety of mechanisms and may contribute to cognitive impairment.
Putaminal spontaneous activity characterizes and predicts wearing-off in Parkinson’s disease
Authors: Wu Chenqing , Wu Haoting , Guan Xiaojun , Xu Xiaojun , Zhang Minming
Published: November 2024

Motivation: Delayed detection of wearing off (WO) in Parkinson’s disease (PD) has a negative impact on quality of life. However, there is currently no method for WO prediction prior to treatment. Goal(s): To investigate whether resting-state functional MRI (rs-fMRI) could aid in WO prediction. Approach: Fractional amplitude of low-frequency fluctuation (fALFF) was quantified from rs-fMRI to determine whether spontaneous activity dysfunction could be a predisposing vulnerability related to WO. Results: Putaminal fALFF reduction was the independent predictor of WO development. Impact: Detection of putaminal fALFF reduction prior to treatment provides a quantitative metric to facilitate WO prediction and improve prognosis.
Accuracy of AI-driven susceptibility map-weighted MRI analyses to differentiate neurodegenerative from non-neurodegenerative parkinsonism
Authors: Wallert Elon , van de Giessen Elsmarieke , Beudel Martijn , Shin Dong Hoon , van Mierlo Tom , Blankevoort Jeroen , Berendse Henk , de Bie Rob M.A. , Booij Jan
Published: November 2024

Motivation: Susceptibility map-weighted imaging (SMWI) of the substantia nigra is a novel MRI sequence that has the potential to aid the diagnosis of patients with clinically uncertain parkinsonian syndromes (CUPS). Goal(s): To investigate the accuracy of AI-driven automated SMWI software in a clinically relevant population. Approach: We acquired SMWI in patients who received a dopamine transporter (DAT)-SPECT because of CUPS. The diagnostic software (Heuron IPD) results were compared with the DAT-SPECT results as a reference. Results: Preliminary analysis of 120 patients demonstrated an accuracy of 88% for the diagnostic software to differentiate neurodegenerative from non-neurodegenerative parkinsonism in patients who presented with CUPS. Impact: Susceptibility map-weighted imaging (SMWI) demonstrates a diagnostic accuracy of 88% in patients with clinically uncertain parkinsonian syndromes (CUPS). These findings are promising for the use of SWMI as diagnostic marker and warrant prospective studies in CUPS patients.
Abnormal Resting-state Functional Connectivity and cerebral blood flow in Overactive Bladder Syndrome
Authors: Chen Shichun , Zhang Zongpai , Zhang Yakun , Wengler Kenneth , Tam Justina , Weissbart Steven , Dai Weiying , He Xiang
Published: November 2024

Motivation: Overactive Bladder (OAB) significantly impacts patients' quality of life. However, the brain-bladder control mechanism during the bladder filling was not known. Goal(s): To investigate abnormality of cerebral blood flow (CBF) and functional connectivity (FC) during the bladder filling in OAB patients. Approach: Arterial spin labeling (ASL) images were acquired with bladder filling volumes of 0, 50, 100, 200, 350 and 500mL. Results: We observed medio-prefrontal cortex (mPFC) CBF and mPFC-PCC (posterior cingulate cortex) FC are compromised in OAB patients during bladder filling. Impact: We offered a new perspective for the role of ASL perfusion and functional connectivity in understanding brain mechanism that controls urinary continence. ASL may be used to monitor treatment effects of OAB patients.
Altered topological organization and functional brain connectome in breast cancer patients with fear of cancer recurrence
Authors: Lin Tianye , Zhang Yihe , Zhang Yening , Yang Yang , Tang Lili , Sun Yingshi
Published: November 2024

Motivation: Fear of cancer recurrence (FCR) is a common psychological condition in breast cancer patients. Goal(s): To reveal the FCR neural mechanism on the whole-brain connectome network level. Approach: We collected resting-state functional MRI from 43 breast cancer patients and 40 healthy controls (HCs). Results: Graph theory analyses revealed a decreased clustering coefficient in breast cancer patients compared to healthy control subjects. Breast cancer patients showed lower FCS in hub regions of DMN and higher FCS in frontoparietal, DMN, dorsal attention and primary somatomotor networks. Among the hyperconnected regions, the left inferior frontal operculum (IFO) showed a significant positive correlation with FCRI-severity. Impact: Our study indicated that breast cancer patients have a more random-like functional network. FCS in some specific brain regions of breast cancer patients such as left IFO was associated with FCR severity.
Arterial spin labeling and blood oxygenation level-dependent MRI to detect early kidney injury in chronic kidney disease
Authors: gu chengli , li xue , zhou minghui , yang aoran , zhang chen , Bernd Kühn , shi yu
Published: November 2024

Motivation: Multiparametric magnetic resonance imaging (mpMRI) provides the potential for a more comprehensive noninvasive assessment of organ structure and function .however few studies have combined arterial spin labelling (ASL) and blood oxygen level-dependent (BOLD) to assess the kidney. Goal(s): To detect early kidney injury in chronic kidney disease(CKD) by ASL and BOLD MRI. Approach: This study assessed CKD patients and healthy volunteers by combining ASL and BOLD imaging. Results: mpMRI distinguishes well between healthy people and patients with CKD patients, especially ASL. Impact: Our study found that ASL and BOLD are potentially useful in identifying CKD patients who require renal biopsy. Additionally, these noninvasive techniques show promise in detecting underlying pathological injuries in early-stage CKD patients.
Frequency-dependent alterations in functional connectivity associated with the progression from aMCI to AD
Authors: Hu Hanjun , Wang Luoyu , Wen Qingqing , Feng Qi , Tang Xue , Liao Zhengluan , Din Zhongxiang
Published: November 2024

Motivation: In Alzheimer’s Disease (AD) spectrum disorders, rs-fMRI signals in the cerebral cortex may possess distinct characteristics across different frequency ranges. Goal(s): To investigate potential alterations in functional connectivity among patients with amnestic mild cognitive impairment (aMCI) and AD and to determine whether these changes vary across different frequency bands. Approach: The changes in functional connectivity were analyzed using the VMHC and DC metrics in patients with aMCI and AD across three frequency bands:classic, slow-5, and slow-4. Results: Notable changes in functional connectivity were observed across multiple brain regions in patients with aMCI and AD, with the most pronounced in slow-5 frequency band. Impact: This study further advances our comprehension of the pathological and physiological mechanisms associated with AD. Furthermore, it highlights the significance of researchers taking into account various frequency bands in their investigations of brain function.
Functional alterations in white-gray matters bipartite network in preclinical Alzheimer’s disease
Authors: Xu Lyuan , Zhao Yu , Li Muwei , Schilling Kurt , Lawless Richard , Choi Soyoung , Rogers Baxter , Ding Zhaohua , Anderson Adam , Landman Bennett , Gore John , Gao Yurui
Published: November 2024

Motivation: The significance of changes in functional connectivity (FC) measures involving white matter (WM) at preclinical stages of Alzheimer’s disease (AD) remains unclear. Goal(s): Our goal was to reveal alterations in correlations in BOLD signals between WM and gray matter (GM) in the AD continuum, focusing on preclinical AD. Approach: We used a novel bipartite graph model to evaluate network properties at multi-scales and compared preclinical AD, AD subjects with controls. Results: We found declines in local specific WM-GM FC and WM FC density, without a manifest decline in global efficiency of WM-involved functional networks in the preclinical AD group. Impact: Our observation of a decline in local WM-GM FC and WM FC density but an intact global efficiency of functional networks in preclinical AD may help explain why cognition remains normal despite the presence of pathology during the preclinical stage.
Alterations in the Pain-Related Functional Matrix among RCVS Patients during Resting-State fMRI.
Authors: Hsu Tun-Wei , Wu Hsiu-Mei , Lirng Jiing-Feng
Published: November 2024

Motivation: Reversible cerebral vasoconstriction syndrome (RCVS) is a severe headache condition marked by recurrent explosive headaches. We investigate changes in the functional connectivity of brain regions associated with pain matrix in both RCVS patients and healthy controls. Goal(s): Our hypothesis the functional connectivity mechanisms underlying pain processing differ between two groups. Approach: We utilizing rs-fMRI imaging with graph theory analysis. Results: The findings contribute to a more profound comprehension of the activation processes occurring within brain regions responsible for pain perception RCVS patients.We ascertain whether the mechanisms of pain processing in RCVS patients deviate from those observed in healthy controls. Impact: We utilizing rs-fMRI imaging with graph theory to investigate functional connectivity mechanisms of pain matrix. The findings contribute comprehension of the activation processes occurring within brain regions responsible for pain perception RCVS patients deviate from those observed in healthy controls.
Dual-polarity SENSE with calibration refinement enables robust Nyquist ghost correction on a high-performance gradient system
Authors: Jiang Yuancheng , Llorden Gabriel , Fujita Shohei , Cho Jaejin , Yong Xingwang , Guo Hua , Bilgic Berkin
Published: November 2024

Motivation: Echo-planar imaging (EPI) is prone to Nyquist ghosts, which are exacerbated on scanners with high-performance gradients. While dual-polarity GRAPPA (DPG) helps alleviate these artifacts, it does not permit regularized multi-shot reconstruction. Goal(s): To introduce a SENSE-based method to address Nyquist ghosts on high-performance gradient systems and to lend itself to regularized multi-shot reconstruction. Approach: We propose dual-polarity SENSE (DPS) for ghost correction where phase differences between even and odd lines are captured in ESPIRiT coil sensitivity estimates based on a tailored calibration scan. Results: DPS effectively reduces Nyquist ghost artifacts in phantom and in vivo data on high-performance gradient systems. Impact: We provide a robust SENSE-based Nyquist correction method that can be integrated with advanced multi-shot EPI reconstruction techniques and can address challenging Nyquist ghosts on high-performance gradient systems.
Promotion of Reconstruction Performance for Equally Spaced Under-sampled Signals Using Amplitude Modulation Pulses
Authors: ITO Satoshi , ADACHI Kotaro
Published: November 2024

Motivation: In compressed sensing, it is impossible to separate folded-over images using equally spaced under-sampled signal when the subject is a real-valued image. Goal(s): Our goal was to enable image reconstruction using equally spaced under-sampled signal, and to generalize it to phase-varied images. Approach: Amplitude modulation in the phase-encoding direction makes the folded image complex-valued and thus facilitates separation of overlapping images in the image space. Results: The image reconstruction was successful up to a speedup factor of 4 with U-Net. Phase images were successfully reconstructed by partial continuous signal acquisition and phase correction. Impact: Reconstruction performance is improved by applying specific amplitude-modulated pulses prior to signal acquisition in equally spaced under-sampling CS, which is advantageous for sharp image reconstruction.
Deep learning based chemical shift artifact reduction in Zero Echo Time (ZTE) MRI
Authors: Mandava Sagar , Carl Michael , Wiesinger Florian , Fung Maggie , Lebel R.
Published: November 2024

Motivation: Chemical Shift artifacts in non-Cartesian MRI scans can lead to blurring and other artifacts at tissue interfaces. ZTE scans are particularly sensitive to this issue. Goal(s): Addressing this artifact can enable the generation of more accurate ZTE images. Additionally, subsequent post-processing tasks like bone volume rendering, pseudo CT etc., can benefit from mitigating this artifact. Approach: We introduce a deep learning based method to address this artifact and demonstrate its performance on phantom and in-vivo cases. Results: The results demonstrate that gross chemical shift artifact can be corrected using the proposed method. Impact: ZTE suffers from poor intrinsic SNR and chemical shift related blurring. Scanning at high field helps SNR but makes blurring more serious. Our proposed method helps mitigate chemical shift artifacts and opens up new possibilities for ZTE imaging.
Clinical Pulsed CEST MRF Optimization using the Cramer-Rao Bound and Sequential Quadratic Programming
Authors: Vladimirov Nikita , Weinmüller Simon , Zaiss Moritz , Perlman Or
Published: November 2024

Motivation: The lack of a protocol optimization technique suitable for practical pulsed acquisition impedes the clinical translation of CEST MRF. Goal(s): To develop a pulsed CEST MRF protocol optimization method, enabling improved parameter discrimination ability and accelerated acquisition. Approach: The Cramer-Rao bound for variance assessment was employed on Bloch-McConnell-based simulated signals, followed by a numerical sequential quadratic programming optimization of MRF saturation pulse powers. Validation was performed using L-arginine phantoms for preclinical (7T) and clinical (3T) scanners. Results: The proposed optimization approach resulted in significantly lower CEST-MRF reconstruction error (p<0.001) compared to baseline and a drastically short acquisition time (<20 s). Impact: A pulsed CEST MRF optimization technique was developed, bridging the gap imposed by the lack of accurate analytical solutions and CEST optimization methods for practical clinical settings. The technique increases the translation potential of quantitative and rapid CEST imaging.
Whole-cerebrum guanidino and amide CEST mapping at 3T by 3D EPI GRE and 3D stack-of-spiral GRE acquisitions
Authors: Wang Kexin , Ju Licheng , Song Yulu , Xie Kevin , Liu Claire , Li Anna , Zhu Dan , Xu Feng , Liu Guanshu , Heo Hye-Young , Yadav Nirbhay , Oeltzschner Georg , Edden Richard , Qin Qin , Blair Lindsay , Kamson David , Xu Jiadi
Published: November 2024

Motivation: We are driven by the critical need to efficiently map guanidino and amide CEST in the human brain at 3T, enabling the investigation of brain creatine and protein while adhering to clinical scan time constraints. Goal(s): Our aim is to validate the efficacy of 3D EPI GRE and 3D stack-of-spiral (3DSOS) GRE techniques for rapid guanidino and amide CEST mapping at 3T. Approach: We optimized saturation parameters, conducted a comparative analysis of SNR and reproducibility, and demonstrated 3DSOS in a low-grade glioma patient. Results: Both techniques yielded similar CEST signal intensities, with 3DSOS showing superior reproducibility. Impact: While 3DEPI and 3DSOS yielded similar signal intensity in whole-cerebrum guanidino and amide CEST mappings at 3T, the latter is recommended for clinical application with its enhanced reproducibility and showed an increase of guanidino CEST in the tumor.
Periodically Rotated Spiral Sampling (PRSS) and Multi-offset Transformer Reconstruction (MoTR) for CEST MRI
Authors: LIU Huabing , LIU Yang , ILYAS Abdul-Mojeed , HUANG Jianpan , SHEN Dinggang , CHAN Kannie
Published: November 2024

Motivation: CEST MRI is often limited in its application due to its time-consuming nature. Also, multiple saturation frequency offsets are required to accurately measure CEST effects. Goal(s): Accelerate CEST imaging by undersampling k-space of each frequency offset below Nyquist rate. Approach: Periodically rotated spiral sampling (PRSS) is proposed to make adjacent offsets capture different k-space subregions. Besides, a multi-offset transformer reconstruction (MoTR) network is further presented to fuse complementary information from multiple offsets. Results: Compared to fully-sampled images, reconstructed images using our method can achieve an average SSIM/MAE of 0.9899/0.0032, resulting a coefficient R value of 0.93/0.95 in terms of CEST signals at -3.5ppm/+3.5ppm. Impact: Our method can realize 4× acceleration of CEST imaging without sacrificing down-stream CEST analysis performance. Besides, our method has the potential to work on MRI scanner for CEST acceleration, which can further expand the application of CEST MRI.
Superior HyperCEST Performance of Membrane-Anchored Xenon Hosts in Nanocarriers with Variable Membrane Fluidity
Authors: Schröder Leif , Schnabel Felix , Jayapaul Jabadurai
Published: November 2024

Motivation: The efficiency of CEST agents for hyperpolarized 129Xe depends on the exchange rate of Xe in/out of tailored host structures. For liposomal designs, this may be influenced by the phospholipid membrane fluidity. Goal(s): This study investigates how cholesterol, which is often added for liposome stability, impacts the HyperCEST performance. Approach: We compared the changes in CEST buildup from liposomes with variable cholesterol content and either membrane-anchored (i.e., lipopeptide-based) or freely diffusing Xe host. Results: The HyperCEST efficiency for membrane-anchored Xe hosts is much less sensitive to membrane stiffening than for unbound hosts. Lipopeptide-based HyperCEST agents are thus a powerful approach for biosensor design. Impact: The HyperCEST performance of membrane-anchored xenon hosts in liposomal nanocarriers shows reduced susceptibility to membrane stiffening compared to non-functionalized hosts. These less susceptible lipopeptide-based hosts are thus the preferred approach for future in vivo applications.
Computationally Efficient IMplicit Training Strategy for UNrolled NEtworks (IMUNNE) for Reconstruction of Accelerated Cardiac Dynamic MRI
Authors: Iakovlev Nikolay , Schiffers Florian , Fan Lexiaozi , Tapia Santiago , Hong KyungPyo , Bishara Dima , Wilcox Jane , Lee Daniel , Katsaggelos Aggelos , Kim Daniel
Published: November 2024

Motivation: Unrolled networks (UN) achieve state-of-the-art performance in undersampled dynamic MRI reconstruction but suffer from long training times and extensive GPU memory cost. Goal(s): To apply an implicit training strategy for UNs (IMUNNE) in combination with transfer learning to develop an efficient and versatile reconstruction technique for accelerated dynamic cardiac MRI. Approach: We compare IMUNNE with a complex denoiser U-Net and an end-to-end UN on three different highly undersampled dynamic cardiac MRI datasets. Results: For all datasets, we observed that: (1) both unrolled architectures outperform CU-Net with respect to image quality; (2) compared to end-to-end UN, IMUNNE significantly reduced both training and inference times. Impact: This work has the potential to facilitate a more widespread adoption of highly-accelerated, cardiac MRI by reducing training time, inference time and memory cost of state-of-the-art unrolled reconstruction methods, thereby lowering the clinical hardware requirements and the requisite energy consumption.
Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI
Authors: Xu Di , Miao Xin , Yang Yang , Liu Hengjie , Scholey Jessica , Yang Wensha , Feng Mary , Ohliger Michael , Lao Yi , Sheng Ke
Published: November 2024

Motivation: Densely sampled k-space leads to high-quality MR but can be impractical due to lengthy scanning time. Accelerating MR acquisition by reducing sampling density can decrease image quality and/or increase reconstruction complexity and time. Goal(s): This work aims to design an algorithm for efficient and high-quality reconstruction of highly accelerated radial-sampling liver 4D MR. Approach: We proposed a novel Paired Conditional generative adversarial network term Re-Con-GAN, evaluated on a 4D liver MR dataset at 3x, 6x, and 10x acceleration ratios. Results: Re-Con-GAN achieved better PSNR, SSIM, and RMSE with sub-second inference speed (0.15s) than compressed sensing (120s) and non-GAN deep learning methods (0.15s-0.73s). Impact: A robust and efficient framework, Re-Con-GAN, is proposed in the current work with sub-second inference speed (0.15s) and promising reconstruction results demonstrated on an in-house curated 4D liver MRI dataset.
Curation of Training Data for Supervised Deep Learning Reconstruction of Speech Real-Time MRI
Authors: Lee Kevin , Kumar Prakash , Iskarous Khalil , Nayak Krishna
Published: November 2024

Motivation: Supervised deep learning (DL) reconstruction requires large training sets and computationally demanding training. Real-time MRI offers large temporal redundancy which yields high reconstruction performance from training on a subset of frames. Goal(s): To develop a method for curating small DL training datasets that capture the variance of the entire training set and provide performance non-inferior to the entire training set, with reduced training time. Approach: We use clustering for each training speech task followed by selecting a fraction of each cluster to train U-Nets for reconstruction. Results: We achieve improved image quality metrics with comparable image quality metrics with 10x improved training time. Impact: By using curated training data based on identification and clustering of vocal tract postures, we demonstrate supervised DL-reconstruction of speech RT-MRI with 10-fold training time reduction and comparable NRMSE, PSNR, and SSIM. This may be generalized to other dynamic reconstructions.
Image-feature-understanding data consistency for under-sampled MRI reconstruction
Authors: Wang Sha , Zhang Lijun , Wang Chunyao , Zhang Zhenxi
Published: November 2024

Motivation: The optimal selection of data consistency (DC) weight is task-dependent and a challenge in current deep learning unrolled reconstruction network which may result in compromised image quality, and thus deserves further investigations. Goal(s): To propose a method to obtain adaptive data consistency weight which is superior to existing methods. Approach: An image-feature-understanding data consistency (IFUDC) modulator is integrated into network to obtain adaptive DC weight based on input images. Results: Image quality metrics (SSIM, PSNR) of proposed method are higher than those of existing method. Impact: IFUDC is effective to modulate DC weight adaptively and helps to mitigate the difficulty in optimal DC weight selection.
Denoising Arterial Spin Labeling Perfusion MRI using Deep Learning Methods
Authors: Han Zixuan , Ning Zihan , Zhao Xihai
Published: November 2024

Motivation: The low SNR of arterial spin labeling (ASL) perfusion weighted images (PWI) directly affects the accuracy of cerebral blood flow (CBF) quantification. Goal(s): Our goal was to improve SNR of ASL PWI and access accurate CBF quantification. Approach: We developed XtwASL, a deep learning model for denoising ASL PWI. Evaluation included SNR, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for PWI quality as well as ICC and Bland-Altman analysis for CBF assessment. Results: Compared with other methods, the proposed XtwASL significantly improved SNR of ASL in the case of short acquisition time and the CBF after XtwASL denoising was more accurate. Impact: The ASL denoising method XtwASL not only improves SNR but also has good clinical usability in CBF quantification, and this may reduce patient discomfort and artifacts caused by long scan time especially in multi-PLD ASL and high-resolution ASL field.
Deep Learning Based Key Point Detection Algorithm to Assist Accurate Vascular Centerline Extraction and Automatic Quantitative Plaque Analysis
Authors: Zhang Xiqian , Sun Wanqing , Yang Xiong , Mao Yufei , Li Ye , Liang Dong , Liu Xin , Zheng Hairong , Zhang Na
Published: November 2024

Motivation: Magnetic Resonance Vessel Wall Imaging is a crucial method for assessing arterial plaques. Goal(s): Achieving a rapid and accurate algorithm for vascular centerline extraction and automatic detection of the pituitary stalk, thereby enabling fast, automatic, and quantitative analysis of plaques. Approach: Proposed a point detection algorithm based on V-NET, designed to achieve rapid and accurate extraction of vascular centerlines and automatic localization of the pituitary stalk. Results: Accurate key point detection had been achieved, enhancing the precision of vascular centerline extraction and enabling fast and accurate automatic localization of the pituitary stalk. Impact: The accuracy of vascular centerline detection has been improved, and rapid and automatic quantitative analysis of plaque enhancement has been realized. This can assist in enhancing diagnostic efficiency in clinical settings.
Semi-Nonlinear Self-Adaptation Normalization Generator (SNSAN) to Improve Generalization of White Matter Hyperintensities Segmentation
Authors: Cheng Yu , Wang Chengyan , Fei Beini , Lo Chun-Yi Zac , Wang He
Published: November 2024

Motivation: Self-adaptation normalization (SAN) method generalizes lesion segmentation to new sites by using a linear generator to convert inputs to a site-independent style. But it fails when the domain difference is mostly nonlinear. Goal(s): Develop a semi-nonlinear self-adaptation network(SNSAN) to generalize the White Matter Hyperintensity(WMH) segmentation model to an external site. Approach: To replace the linear generator, the method blends two SAN results with a pseudo correlation map, later use the gradient reversal method to guide the result to a site-unrelated style. Results: SNSAN normalizes the input data close to a Gaussian distribution and improves the generalization performance on the data from external site. Impact: We provide a simple and efficient semi-nonlinear normalization method to enhance the domain generalization, and its performance is better than SAN when the domain gap is affected by more nonlinear factors.
Whole body imaging at 3T with multi-coil array and compressed sensing for metastasis screening with cryo-fluorescence tomography validation.
Authors: Aristizabal Orlando , Eskow Nicole , Pandya Neelam , Monge Eva , Wadghiri Youssef
Published: November 2024

Motivation: Cancer metastasis involves multiple organs and traditional bioluminescent imaging lacks spatial resolution to locate metastatic foci. New MRI imaging protocols need to be developed to image metastasis distribution over the whole mouse body. Goal(s): Acquire high resolution 3D volumes in an acceptable time frame. Validate findings using cryo-fluorescence tomography (CFT). Approach: An 8-channel array coil was used to acquire whole body isotropic volumes with compressed sensing. Post imaging CFT datasets were acquired for validation. Results: High contrast volumes of mouse body were acquired at 200x200x200 μm in 16 minutes. Liver metastatic foci observed. Correspondence of mouse anatomy with MRI and CFT achieved. Impact: Whole body imaging with 8-channel array coil permits longitudinal visualization of major organs in normal and diseased states. Compressed sensing optimizes workflow for high throughput screening, and Cryo-Fluorescence tomography can then be used as gold standard for validation of MRI.
Repeatability of a quantitative multiparametric MRI protocol for imaging hypoxia, cellularity, and perfusion in a murine model of glioblastoma
Authors: Das Ayesha , Hormuth David , Virostko Jack , Yankeelov Thomas
Published: November 2024

Motivation: Heterogeneous distributions of hypoxic tissues lead to divergent radiotherapy response. Thus, developing a practical, accurate, and reproducible method of quantifying hypoxia is of great interest. Goal(s): Establish a repeatable, multiparametric MRI protocol for quantifying hypoxia. Approach: We induced C6 brain tumors in rats and conducted test-retest TOLD-MRI, DCE-MRI, and DW-MRI exams on each rat. We then analyzed the MRI data to identify spatially distinct physiological clusters within the tumor. Results: An initial assessment of repeatability of hypoxia, normoxia, and necrotic tissue localization within a tumor. Impact: Developing a quantitative and repeatable protocol for quantifying tumor hypoxia will have immediate applications in studies seeking to design, assess, and optimize radiotherapy regimens.
Assessing host-tumour vasculature and glycolytic metabolism in response to hypoxia in a chick CAM model of GBM using MRI and MRS
Authors: Gash Elisabeth Non , Barnett Sarah , Maguire Mahon , Pickup Stephen , Rocha Sonia , Coulson Judy , Poptani Harish
Published: November 2024

Motivation: Understanding vasculature, hypoxia and glycolysis in GBM is paramount towards understanding its resistance to therapies, but cannot be done in vitro. The CAM model is attractive as it provides the interaction between host vasculature and the tumour. Goal(s): Optimise MRI and MRS to assess hypoxia, tumour-host vasculature and glycolytic metabolism in the GBM-CAM. Approach: GBM-CAM xenografts were created under normoxic- and hypoxic-conditions. Imaging was performed using microscopy and MRI, and lactate detection via MRS. Results: Distinct morphological differences in vasculature were identified between conditions. MRI revealed vessel penetration, MRS detected lactate levels, which were significantly higher in hypoxic tumours than in the CAM. Impact: Developing tools to characterise vascular morphology and quantify lactate levels in the CAM GBM model can shed light on fundamental biological mechanisms, and support the development of therapeutic strategies for the clinic.
MRI Assessment of Sensitivity to Carbon Ion Irradiation in Rat C6 Glioma
Authors: Li Yu , Liu Jian
Published: November 2024

Motivation: What is the specific efficacy of carbon ion radiation in the treatment of brain gliomas?Goal(s): To assess the specific effect of carbon ion irradiation in inhibiting the growth and peritumor infiltration of gliomas. Approach: Establishment of a rat C6 brain glioma model with carbon ion irradiation and assessment of the efficacy response of brain glioma after carbon ion irradiation using MRI multiparametric imagingResults: MRI multiparametric imaging can reflect the inhibitory effect of carbon ion irradiation on the growth and invasion of gliomas. Impact: CIRT for glioma is still in the stage of clinical trials, the number of subjects is limited, and its specific efficacy still needs to be explored. Our study provides an experimental basis for the clinical application of CIRT for gliomas.