bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid multi-contrast multi-parametric whole brain imaging based on AI-accelerated multi-shot echo-planar imaging (msEPI)
Authors: Wu Yanglei , Li Yuan , Fan Xiaoyun , Feng Feng , Feiweier Thorsten , Clifford Bryan , Qu Jianxun
Published: November 2024

Motivation: To enhance MRI efficiency for brain imaging, improve image quality, and enable multi-parametric mapping for diagnosing neurological diseases. Goal(s): Develop a rapid imaging protocol using AI-accelerated multi-shot echo-planar imaging (msEPI) to simultaneously acquire multi-contrast images and quantitatively map R2, R2', R2*,R1, M0, and MTR across the entire brain. Approach: Utilize msEPI with AI-enhanced reconstruction, scan five healthy volunteers, adjust parameters for different contrasts, and conduct whole-brain quantification using MATLAB. Results: The acquisition technique collects FLAIR and FGATIR-like multi-contrast images with high SNR and enable multi-parameter quantification in just 5 minutes. This approach holds the potential to streamline diagnostics and enhance the patient’s experience. Impact: Our multi-contrast fast quantification MRI protocol, founded on an AI-accelerated multi-shot echo-planar imaging sequence, substantially shortens scanning time while delivering high-quality multi-parametric brain images, offering a promising advancement in efficient and effective diagnostic processes for neurological diseases.
High-Resolution Quantitative T2 and T1 Mapping of the Hippocampus
Authors: Wiskoski Haley , Johnson Kevin , Arias Juan , Pugazhendhi Arun , Mushtaq Raza , Ahanonu Eze , Bilgin Ali , Weinkauf Craig , Trouard Theodore , Altbach Maria
Published: November 2024

Motivation: The hippocampus is important in memory and is an early target in neurodegenerative diseases. Structural MRI may lack sensitivity to microstructural changes in this region, necessitating more quantitative techniques. Goal(s): We evaluate reproducibility of new radial MRI methods for T2/T1 mapping in the brain and examine age-related changes. Approach: Quantitative maps were generated using novel pulse sequences and reconstruction algorithms. We assessed reproducibility and age-related trends. Results: We observed strong correlations in T2 and T1 measurements between imaging sessions. We observed positive relationships between T2 and T1 measurements and age in white matter and negative relationships in gray matter regions. Impact: This study introduces novel radial MRI techniques for mapping of the brain and hippocampus, with potential to offer insights into early neurodegenerative changes. Findings on reproducibility and age-related trends contribute to improved understanding of brain health and potential diagnostic applications.
Whole-tumor histogram models based on quantitative maps from SyMRI for predicting axillary lymph node status in invasive ductal breast cancer
Authors: Zeng Fang , Yang Zheting , Tang Xiaoxue , Lin Lin , Wu Pu-Yeh , Xue Yunjing
Published: November 2024

Motivation: Breast cancer is closely associated with ALN status, influencing prognosis. Sentinel lymph node (SLN) biopsy, a common ALN staging method, has limitations. Goal(s): This study aimed to explore a non-invasive predictive approach for ALN status in IDC patients using SyMRI images and histogram analysis. Approach: We included 212 patients, and compared the performance of SyMRI histogram models in differentiating N0 and N+ groups (further divided into N1 and N2-3). Results: Combining quantitative map features with clinical data achieved the highest diagnostic accuracy. Additionally, specific histogram features were found to differ significantly between N1 and N2-3 groups. Conventional parameters were less discriminative. Impact: We demonstrated efficacy of histogram analysis of SyMRI as a non-invasive method for predicting ALN status. Model combining SyMRI quantitative maps and clinical features yielded satisfactory performance, highlighting the potential of our proposed model in ALN management.
Development and external validation of a combined clinical-mammographic-MRI model for differentiating benign and malignant NME breast lesions
Authors: Wu Linhua , Yang Wei , Li Jian
Published: November 2024

Motivation: Differential diagnosis of nonmass enhancement (NME) breast lesions is difficult. Goal(s): A combined clinical-mammographic-MRI based on DWI model can distinguish benign and malignant NME lesions. Approach: We retrospectively enrolled consecutive female patients with NME breast lesions who underwent pretreatment MG and breast MRI as the development cohort and prospectively collected eligible candidates as an internal validation group and an external validation group at our centre. A combined discriminatory model was developed through multivariable logistic regression and was validated internally and externally. Results: The combined model incorporating mammography, MRI, and clinical variables showed good discriminability in the development, internal validation, and external validation cohorts. Impact: Based on SHAP analysis, suspicious calcification on mammography and internal NME patterns were significant contributors to the performance of the model, and radiologists and clinicians should improve their awareness of complementary mammography to improve diagnostic performance in classifying NME lesions.
Preoperative Prediction of Recurrence Risk in Breast Cancer Patients Based on MRI Features
Authors: Zhou Jiejie , Zhang Yang , Wang Jinhao , Lin Yezhi , Wang Hailing , Liu Yan-lin , Chen Jeon-Hor , Wang Meihao , Su Min-ying
Published: November 2024

Motivation: Early prediction of recurrence risk is essential to treatment decision-making for breast cancer patients. Goal(s): To explore potential predictors of recurrence risk based on MRI features and to construct a preoperatively predictive model of risk. Approach: MRI features of 588 patients were investigated, 397 in training and 191 in testing data. Four machine learning methods were used to construct the predictive model. Results: Multiple lesions, irregular shape, spiculated margin, and peritumor edema were identified as predictive factors and used to construct the model. SVM showed the best predictive performance with AUC 0.87 (95%CI 0.83-0.91) and 0.73 (95%CI 0.75-0.81) in training and testing data. Impact: A preoperative predictive model based on MRI features could be a valuable tool for predicting recurrence risk and assisting in the personalized treatment of breast cancer patients.
Evaluation Molecular Receptors Status in Breast Cancer Using an mpMRI-based Feature Fusion Radiomics Model: Mimicking Radiologists’ Diagnosis
Authors: Liang Fangrong , Zhang Wanli , Li Jiamin , Zhen Xin , Jiang Xinqing , Yang Ruimeng , Xu Yongzhou
Published: November 2024

Motivation: Investigating the performance of a feature fusion radiomics (RFF) strategy that mimicked the routine diagnostic practices of radiologists in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively. Goal(s): Develop an RFF model that incorporates optimal mpMRIs for BC’s molecular receptor status identification. Approach: Constructed and analyzed 150 models to determine the top four optimum sequences for identifying distinct BC’s molecular receptor statuses. Then the optimal single sequence models (Rss) and combined sequences models (RFF) were developed and compared. Results: The RFF model integrating mpMRI radiomics features exhibited promising ability to imitate radiologists’ diagnosis for preoperative identification of BC’s molecular receptors. Impact: A multiparametric MR-based RFF model, mimicking the radiologists’ daily diagnostic approach, which fused radiomics features with dominant MR sequences, was able to distinguish different molecular receptor statuses of breast cancer.
Quantitative Background Parenchymal Enhancement: Association with Lifetime Risk Factors on Breast Cancer Screening MRI
Authors: Yan Ran , Murakami Wakana , Mortazavi Shabnam , Yu Tiffany , Lee-Felker Stephanie , Sung Kyunghyun
Published: November 2024

Motivation: Breast MRI background parenchymal enhancement (BPE) has been associated with breast cancer risk. The association of BPE with various breast cancer lifetime risk factors remains controversial. Goal(s): Our goal was to determine which risk factors are associated with BPE, specifically quantitative BPE due to variability in qualitative BPE. Approach: We used linear regression to evaluate the association between quantitative BPE and BRCA gene mutation status, age, body mass index (BMI), menopausal status, hormonal treatment, and fibroglandular tissue (FGT) level. Results: Both univariate and multivariate analyses of quantitative BPE showed significant correlations with age, BMI, menopausal status, and FGT level. Impact: Quantitative background parenchymal enhancement (BPE) is correlated with lifetime risk factors, such as age, BMI, menopausal status, and FGT level, on breast cancer screening MRI. This can provide potential insight into the cancer pathophysiological mechanisms underlying lifetime risk models.
Prediction of axillary lymph nodes metastases in patients with breast cancer : Can synthetic MRI provide additional value to DWI?
Authors: Yang Xiao , Sun Zongqiong , Dou Weiqiang
Published: November 2024

Motivation: The aim of this study was to investigate whether synthetic MRI-derived quantitative maps can predict metastasis of axillary lymph nodes (ALNs) in breast cancer. Goal(s): Searching for a noninvasive method to predict lymph node metastasis in breast cancer. Approach: 61 breast cancer patients were recruited for the study and the status of ALNs was confirmed by pathology. T1, T2, and PD maps were obtained for each patient. Results: Statistically significant differences in PD, minimal ADC , anatomic features of tumor shape and size were observed between patients with and without metastatic ALNs metastasis. It was shown that PD+Min ADC+shape+size with highest AUC of 0.86. Impact: it can be concluded that syMRI derived relaxation maps may be useful for predicting ALNs status in breast cancer.
DCE-MRI Tumor Volumetric Changes Predict Response to Neoadjuvant Immunochemotherapy in Triple Negative Breast Cancer Patients
Authors: Rauch Gaiane , Moseley Tanya , Guirguis Mary , Whitman Gary , Candelaria Rosalind , Leung Jessica , Patel Miral , Sun Jia , Le-Petross Huong , Lane Deanna , Scoggins Marion , Perez Frances , Mohamed Rania , Xu Zhan , Pashapoor Sanaz , Son Jong Bum , Hwang Ken-Pin , Chen Huiqin , Wei Peng , Tripathy Debu , Yang Wei , Yam Clinton , Ma Jingfei , Adrada Beatriz
Published: November 2024

Motivation: There is unmet need for noninvasive biomarkers for neoadjuvant immunochemotherapy (NICT) response prediction in triple negative breast cancer (TNBC) to guide least toxic and most effective treatment regimens. Goal(s): To evaluate if DCE-MRI tumor volume changes measured early during NICT can predict treatment response. Approach: DCE-MRI tumor volume reduction (TVR) was calculated in 64 TNBC at baseline, after 2 and 4 cycles of NICT and correlated with surgical pathology using ROC analysis. Results: DCE-MRI TVR after 2 cycles of NICT was able to predict pCR with AUC of 0.71 (95%CI:0.57-0.84) and after 4 cycles with AUC of 0.81 (95%CI:0.69-0.92). Impact: DCE-MRI tumor volume changes early during neoadjuvant immunochemotherapy can identify triple negative breast cancer patients with high/low likelihood of pathologic complete response, triaging them to appropriate management for de-escalation trials versus targeted therapies, avoiding unnecessary toxicity of ineffective treatment.
Enhancing Breast Lesion Diagnosis Through DISCO and Deep Learning Reconstruction-Based DWI
Authors: Xia Wanjun , Zhang Yong , Wang Kaiyu , Xu Tianyong , Fan Ruilin , Cheng Jingliang
Published: November 2024

Motivation: With breast cancer now ranking as the predominant global cancer, there is a pressing need to enhance diagnostic accuracy and reduce unnecessary biopsies through the utilization of advanced imaging techniques. Goal(s): Our aim is to augment the precision of breast disease diagnosis by improving the contrast-enhanced MRI and DWI in routine scans. Approach: We developed a model that combines DISCO with deep learning-reconstructed DWI at a b-value of 800 s/mm² for differential diagnosis. Results: The integration of deep learning-reconstructed DWI and DISCO serves to significantly enhance the capability to differentiate between benign and malignant breast conditions. Impact: This advancement directly heightens the diagnostic efficiency of breast cancer within routine scanning sequences, contributing to more effective clinical solutions, and ultimately elevating both the quality of life and survival rates for patients.
Tissue-Independent SNR Measurement for Supine Breast Coil Assessment
Authors: Hess Jeremiah , Moran Catherine , Vincent Jana , Robb Fraser , Lan Patricia , Guidon Arnaud , McKay-Nault Jessica , Daniel Bruce , Hargreaves Brian
Published: November 2024

Motivation: Breast MRI typically is performed with a patient in the prone position. Imaging supine would dramatically improve patient comfort and possibly reduce setup and positioning times. Goal(s): Compare image quality in breast MRI between the supine and prone positions with specialized coils using signal-to-noise ratio. Approach: To account for tissue deformation between supine and prone positions, we compare “tissue-independent” relative SNR, which uses the body coil SNR as a reference, in the breast tissue between prone and supine. Results: In multiple patients, the supine position consistently showed an improvement in SNR over the prone position. Impact: Improvements in motion correction approaches and development of flexible coils may enable supine breast imaging, which is much more comfortable for the patient than prone imaging. Using a careful comparison, we demonstrate substantially higher SNR in supine imaging.
Predicting response to neoadjuvant chemotherapy in breast cancer by biomechanics quantified with Magnetic Resonance Elastography.
Authors: Sinha Aaditya , Jurrius Patriek , Darwish Omar , Shida Belul , Peterson Zhane , Jeffery Hannah , Welsh Karen , Metafa Anna , Spence John , Kothari Ashutosh , Hamed Hisham , Bitsakou Georgina , Karydakis Vasileios , Thorat Mangesh , Shaari Elina , Sever Ali , Satchithananda Keshthra , Ng Tony , Rigg Anne , Pinder Sarah , Sinkus Ralph , Purushotham Arnie
Published: November 2024

Motivation: To achieve de-escalation of surgery in breast cancer patients having complete response after primary chemotherapy, we require better accuracy than the current gold standard DCE-MRI, which has a sensitivity of 80-90% and a specificity of 37-97%. Goal(s): Investigate the diagnostic value of biomechanical imaging biomarkers quantified via MR-Elastography (MRE) for gauging complete pathological response non-invasively. Approach: Incorporate MRE into standard Breast MRI protocol for NACT patients within standard-of-care MRI protocol. Results: Biomechanical changes in conjunction with DCE MRI improved sensitivity to 94.4% and specificity to 95.7%, making this approach a promising new concept to predict complete response after NACT. Impact: Quantifying biomechanics throughout chemotherapy via MR-Elastography (MRE) in conjunction with DCE-MRI in breast cancer patients undergoing primary chemotherapy yields more accurate results in predicting complete pathological response than current international imaging gold standard methods.
Validating the Use of Breast MRE as a Non-Invasive Diagnostic Technique for Breast Cancer
Authors: Deavela Akhil , Hawley Jeffrey , Griffith Brandy , Thompson Kristin , Kolipaka Arunark
Published: November 2024

Motivation: While breast tumors can be identified and characterized through biopsy, MR Elastography (MRE) could provide a method of diagnosis with similar sensitivity and specificity while being non-invasive. Goal(s): Validate the use of MRE against histological grading in biopsy and contrast enhanced MRI breast tumors. Approach: MRE was performed on patients with known breast tumors to measure the stiffness non-invasively and validate it against biopsy. Results: Statistically significant correlation was determined between MRE-derived tumor stiffness and histological grade of biopsy. Impact: The statistically significant association between breast MRE results, a non-invasive method of breast cancer diagnosis, and histological grading, an invasive method, invites the possibility that physicians could diagnose breast cancer non-invasively through further study.
Radiomics nomogram based on ultrafast DCE-MRI and DWI: preoperative evaluation of axillary lymph node status in invasive breast cancer
Authors: Zeng Fang , Yang Zheting , Tang Xiaoxue , Lin Lin , Wu Pu-Yeh , Xue Yunjing
Published: November 2024

Motivation: Accurately evaluating the ALN status is pivotal for both prognosis and treatment planning, especially in the context of decreasing axillary surgery. Goal(s): This retrospective study aimed to develop a radiomics nomogram for preoperative prediction of ALN status in clinical T1-2 stage invasive breast cancer patients. Approach: Radiomics features were extracted from ultrafast DCE-MRI and DWI, and clinical characteristics were incorporated to construct a radiomics nomogram. Results: A radiomics signature achieved AUCs of 0.831 and 0.821 in training and validation cohorts, respectively. The radiomics nomogram, incorporating this signature, multifocality, and MRI-reported ALN status, showed superior performance with AUCs of 0.890 and 0.905. Impact: The proposed nomogram could be valuable for noninvasively assessing ALN status, aiding prognosis and treatment planning while reducing the need for axillary surgery in breast cancer patients.
The Optimal Timepoint for Lesion depiction in Ultrafast DCE MRI: Correlation with lesion size and Invasive Breast Cancer Subtype
Authors: Kataoka Masako , Sakaguchi Rena , Honda Maya , Iima Mami , Takada Masahiro , Ando Kumiko , Nakamoto Yuji
Published: November 2024

Motivation: To know the factors affecting optimal timing of lesion evaluation in ultrafast (UF)- DCE MRI other than BPE. Goal(s): To investigated the optimal timing for lesion depiction on UF-DCE MRI in relation to lesion size and invasive breast cancer subtype. Approach: Optimal timing of lesion evaluation over 400 lesions were assessed by experienced readers. Its association with lesion size and subtype were examined. Results: Optimal timing of breast lesion evaluation on UF-DCE MRI can be earlier for those with larger lesion, and not affected by subtype in case of invasive carcinoma. Impact: UF-DCE MRI normally involves hundreds of images with multiple timing. Predicting optimal timing before scanning may help in efficient reading, efficient post-processing/reconstruction, and protocol improvement. The current results indicate the impact of lesion size, not subtype on optimal timing.
Diagnosis of Breast Lesions on MRI using BI-RADS and Kaiser Score in Mass and Non-Mass Enhancement: Benefit of KS and KS+ for Different Readers
Authors: Zhou Jiejie , Liu Huiru , He Yun , Ye Shuxin , Chen Zhongwei , Miao Haiwei , Zhang Yang , Liu Yan-lin , Pan Zhifang , Chen Jeon-Hor , Su Min-ying , Wang Meihao
Published: November 2024

Motivation: Kaiser score (KS) could help diagnose lesions of breast MRI by an intuitive flowchart. Goal(s): To compare the diagnostic performance of BI-RADS and KS and to evaluate the benefit of the modified KS+ by incorporating diffusion. Approach: 630 patients were analyzed independently by three radiologists with different experiences, including 458 mass and 172 non-mass enhancement (NME) lesions. Results: KS outperforms the unstructured BI-RADS, especially for less experienced readers. The diagnosis for NME was more difficult than for masses, true for three readers regardless of the method used. The accuracy of KS+ was improved for masses, but not for NME. Impact: Kaiser Score provides an intuitive method for lesion interpretation, primarily helpful for mass lesions read by less experienced readers. KS+ is mainly applicable to mass lesions. For NME, the KS criteria need to be improved.
Cellular microstructural mapping (cell size imaging) by time-dependent diffusion MRI for prediction of prognostic factors in breast cancer
Authors: Wang Xiaoyan , Zhang Yan , Cheng Jingliang , Lin Liangjie , Wu Zhigang , Sun Peng , Hu Ying , Wang Anfei , Wang Ruhua , Zhang Yong , Li Ying , Zhang Kun , Zhang Wenhua
Published: November 2024

Motivation: Recent advances in time-dependent diffusion MRI (td-dMRI) for microstructural modeling provide the opportunity to characterize cancer pathology in vivo. Goal(s): This study aims to evaluate the accuracy of td-dMRI–based microstructural mapping for noninvasively characterizing of breast tumors, and further to evaluated whether the tumor microstructural properties could be used to distinguish prognostic factors in breast cancer. Approach: All patients underwent T1-weighted imaging, T2-weighted imaging, diffusion weighted imaging, td-dMRI, and dynamic contrast enhancement scans on a 3T scanner. Results: Results showed that td-MRI parameters showed significant differences between benign and malignant breast tumors, and can also be used for prediction of prognostic factors in breast cancer. Impact: The cellular microstructural mapping by time-dependent diffusion MRI show great potential for noninvasive evaluation of pathologic characteristics in breast cancer in a clinical setting.
The value of quantitative synthetic MRI in differentiating breast cancer patients with HER2 zero-, low- and over-expressing
Authors: Zhan Ting , Dai Jiankun , Lu Chunhua
Published: November 2024

Motivation: Half of breast cancers exhibits HER2 low-expressing and benefits from new antibody-drug conjugates. However, it remains lack of non-invasive technique to identify HER2 expression level. T1, T2, and proton density (PD) are quantitative MRI parameters widely used to characterize tumors. Goal(s): Investigate if T1, T2, and PD extracted from synthetic MRI (syMRI) can be used to classify HER2 expression level. Approach: One-hundred patients were enrolled. T1, T2, and PD of tumor were extracted from syMRI and were compared among different HER2 expression level. ROC was used to assess the diagnostic performance. Results: T1 and PD can be used to diagnose HER2 expression levels. Impact: Our results suggested quantitative SyMRI can be used as non-invasive biomarker to classify HER2 zero-, low- and over-expressing breast cancer patients. It would be helpful in guiding treatment selection and dynamically monitoring HER2 expressing status during treatment process over time.
Short Supine Breast Magnetic Resonance Imaging to Assess Preoperative Breast Radiotherapy Boost Targets
Authors: Le-Petross Huong , Ma Jingfei , Christopher Walker , Sun Jia , Muruganandham Manickam , Yang Wei , Shaitelman Simona
Published: November 2024

Motivation: Breast MRI is performed prone and has not been optimized for supine treatment position and with the personalized radiation mold, limiting application for radiation treatment monitoringGoal(s): To evaluate the image quality and tumor size accuracy from short supine breast MRI (ssbMRI). Approach: ssbMRI studies were acquired before and after neoadjuvant radiotherapy along with mammography and ultrasound in 20 patients. Image quality of ssbMRI was evaluated by expert readers and image derived tumor sizes were correlated with other imaging modalities and final pathology. Results: ssbMR had acceptable image quality in 70% of the cases and best correlation with pathology. Impact: Breast MRI in supine positioning is of diagnostic quality despite the technical challenges for patients with breast cancer. This pilot study allows the development of a short supine diagnostic breast MRI for radiation planning and possibly fusion with CT.
Volumetric TSE-Based DWI Acquisition with a Pseudospiral K-Space Trajectory and Phase Correction Algorithm: Application in the Breast
Authors: Fonseca Sebastian , Udayakumar Durga , Madhuranthakam Ananth
Published: November 2024

Motivation: Current image quality limitations of conventional diffusion weighted sequences in the breast offer an exciting opportunity for novel acquisitions that can generate distortion-free images within clinically feasible scan times. Goal(s): To test a novel TSE-based 3D DWI sequence (VD-CASPR) in the breast of healthy volunteers and assess image quality with an incorporated phase correction algorithm. Approach: Four healthy volunteers were scanned with current reference standard DW-EPI and DW-TSE for breast serving as benchmark for our 3D DWI sequence. Results: Our VD-CASPR sequence with phase correction was able to generate high quality images at various b-values within shorter scan times compared to DW-TSE. Impact: Our novel 3D DWI sequence and reconstruction pipeline generated high SNR images free of geometric distortions in the breast of healthy volunteers. Volumetric coverage without geometric distortion can facilitate co-registration with anatomical images for improved tumor characterization.
DCE-MRI Derived Water Exchange Rate Constant in Multi-Center and Multi-Platform Assessment of Breast Cancer Therapy Response
Authors: Li Xin , Moloney Brendan , Hirano Michael , Eddin Assim , Biswas Debosmita , Kazerouni Anum , Tudorica Alina , Li Isabella , Bryant Mary , Wille Courtney , Pyle Chelsea , Rahbar Habib , Hsieh Su Kim , Rice-Stitt Travis , Dintzis Suzanne , Bashir Amani , Hobbs Evthokia , Zimmer Alexandra , Specht Jennifer , Phadke Sneha , Fleege Nicole , Holmes James , Partridge Savannah , Huang Wei
Published: November 2024

Motivation: Investigate water exchange rate constant (kio) in monitoring breast cancer (BC) response to neoadjuvant chemotherapy (NAC). Goal(s): Evaluate changes in kio and voxel fraction of filtered kio during NAC. Approach: BC patients treated with NAC underwent longitudinal high spatiotemporal resolution DCE-MRI at three sites using different 3T vendor systems. Voxel kio values were obtained with the shutter-speed modeling and filtered with a biologically relevant and DCE achievable range. Results: Fractions of filtered kio decreased throughout the NAC course. Tumor kio and its heterogeneity were reduced in the pathologic complete response (pCR) group compared to the non-pCR group at midpoint and end of NAC. Impact: Quantitative high spatiotemporal resolution Shutter-Speed Model (SSM) DCE-MRI can be implemented in multi-center and multi-platform settings with the SSM-exclusive kio parameter providing potentially complementary information to the conventional Ktrans parameter in assessment of BC response to NAC.
Optimization of Parallel Imaging in Flip Angle Modulated 2D Sequential CSE-MRI for SNR-Efficient and Motion-Robust Liver Fat Quantification
Authors: Tang Jiayi , Tamada Daiki , Souza Raphael do Vale , Faacks Aaron , Fullerton Garrett , Buelo Collin , Starekova Jitka , Kammerman Jeff , Brittain Jean , Reeder Scott , Hernando Diego
Published: November 2024

Motivation: Flip-angle modulation (FAM) in a 2D sequential acquisition is one potential means to improve the motion-robustness and SNR of CSE-MRI for liver fat quantification, and the motion-robustness and SNR-efficiency of FAM can be further improved through parallel imaging and capturing respiratory motion in-plane. Goal(s): To determine the feasibility and performance (with respect to SNR, repeatability, and bias) of FAM-based free-breathing liver fat quantification acquired across imaging orientations and accelerations. Approach: Trials of various parameters of FAM in volunteers and in a phantom with controlled PDFF and T1 values. Results: Accelerated FAM demonstrates few artifacts, low bias, and increased SNR-efficiency compared to unaccelerated FAM. Impact: Increasing the motion-robustness and SNR-efficiency of FAM may enable and enhance its use for improved detection, staging, and monitoring of steatotic liver disease. Non-axial acquisitions may mitigate through-plane respiratory motion compared to axial acquisitions.
Quantifying liver fibrosis via diamagnetic susceptibility sources using MRI: An ex-vivo feasibility study.
Authors: Li Chao , Zhang Jinwei , Li Jiahao , González Anne , Prince Martin , Brittenham Gary , Spincemaille Pascal , Nguyen Thanh , Wang Yi
Published: November 2024

Motivation: Liver biopsy, traditionally regarded as the benchmark for liver fibrosis staging, is an invasive procedure that may cause infection and bleeding. Goal(s): To develop a non-invasive method to evaluate liver fibrosis using MRI. Approach: Diamagnetic sources are separated from paramagnetic sources using a biophysical model and multi echo gradient echo data. The value of the diamagnetic sources can be used to quantify the amount of accumulation of collagen due to liver fibrosis. Results: Significant differences in liver negative susceptibility were observed between lower stage, medium stage and higher stage of fibrosis. Impact: Multi-echo gradient echo data may provide a way to non-invasively stage liver fibrosis.
Free-breathing 3D Radial DIXON Look-Locker sequence for whole-liver simultaneous quantification of water/fat separated T1 and PDFF
Authors: Ueda Yu , Fukukura Yoshihiko , Moriya Kazunori , Shibata Shigeru , Amo Kota , Obara Makoto , Yoneyama Masami , Kwon Jihun , Tamada Tsutomu , Van Cauteren Marc
Published: November 2024

Motivation: T1 and PDFF mapping, which could serve as imaging biomarkers for liver disease, require breath-holds; T1 mapping commonly used in clinical practice, such as MOLLI, has limited spatial coverage. In addition, the presence of liver fat reduces T1 accuracy. Goal(s): Our goal is to enable simultaneous whole liver water/fat separated T1 and PDFF quantification with clinically acceptable accuracy without breath-holds. Approach: We introduced the 3D Radial DIXON LL sequence and validated in phantom and in vivo. Results: Free-breathing 3D Radial Dixon LL sequence results in a proportional bias for T1 measurement and the fixed bias for PDFF measurement compared to current standard techniques. Impact: The 3D Radial Dixon LL sequence did not result in sufficiently good agreement with reference methods for water/fat separated T1 and PDFF, suggesting that further technical validation and the optimization of imaging parameters will be needed.
Preliminary feasibility study of localized 31P spectroscopy in normal human pancreas at 3 Tesla
Authors: Zhang Zhongping , Guo Yinghua , Wang Yiming , Yang Yanzhao , Chai Weimin
Published: November 2024

Motivation: Phosphorus (31P) MR spectroscopy is quite challenging in deeply lying pancreas due to limited coil sensitivity. To our knowledge, its feasibility at 3T has not been reported yet. Goal(s): This study aims to investigate the feasibility of localized 31P magnetic resonance spectroscopy of normal human pancreas at 3T. Approach: 2D CSI 31P spectroscopy sequence were applied on pancreas of 4 healthy volunteers and phosphate metabolite ratios were calculated. Results: 31P spectrum acquired from one normal pancreas achieved comparable quality and similar PME/PDE ratio to a previous pancreas 31P spectroscopy report at 7T. Others failed due to severe contamination from neighboring muscle or gallbladder. Impact: In one among the four attempts, we obtained good 31P spectrum and PME/PDE ratio in normal pancreas. Despite low success rate, this preliminary exploration showed that localized 31P spectroscopy of human pancreas at 3T is promising although challenging.
Evaluation of MRS- and MRI- PDFF and Liver Biopsy measure of hepatic fat fraction in a clinically relevant cohort
Authors: Simonsson Christian , Cai Shan , Tellman Jens , Karlsson Markus , Cedersund Gunnar , Ignatova Simone , Nasr Patrik , Ekstedt Mattias , Kechagias Stergios , Dahlström Nils , Lundberg Peter
Published: November 2024

Motivation: Metabolic Associated Fatty Liver Disease (MAFLD) will increase the burden on future healthcare. Reliable screening alternatives such as MR-techniques e.g., nP-Dixon imaging and proton MR-spectroscopy are widely used. However, the number of studies comparing both MR-techniques with biopsies in clinically relevant cohorts is limited. Goal(s): Compare MR Dixon imaging and proton MR-spectroscopy fat fraction with histological evaluation of steatosisApproach: We have compared three different PDFF measurements; MRS, 6- and 2-point Dixon PDFF, in a prospective study, which included 84 clinically relevant patients with diffuse liver disease. Results: All three techniques could accurately characterize histology based steatosis scores in this clinically relevant cohort. Impact: In this prospective study, with a clinically relevant cohort, a difference between MRS and Dixon determined PDFF was measured. Both methods could distinguish between different histological levels of steatosis, and both methods could therefore characterize the patient group accurately.
Diffusion and Perfusion Measurements of Uterine Corpus Fluctuate During Menstrual Cycle: an IVIM based study
Authors: Deng Lei , Ren Wanxu , Shang Ali , Wei Xiaocheng , Li Xiaohui , Yang Quanxin
Published: November 2024

Motivation: Correct identification of the typical appearance of the distinct uterine corpus layers during the menstrual cycle is crucial for evaluating uterine lesions. Goal(s): To investigate the diffusion and perfusion of uterine corpus fluctuate during menstrual cycle. Approach: Thirty-one volunteers underwent standard pelvic IVIM-DWI in menstrual period, mid-proliferative period and mid-secretory period. The differences of D, D*, f and f ×D* among three-layer structure or different menstrual period were compared. Results: It was found that an exacerbation of diffusion restriction, occurring from the menstrual period to the mid-secretory period, and perfusion driven by IVIM of uterine corpus was generally decreased, especially in endometrium. Impact: Considering the menstrual status is necessary on assessment of uterine lesions. It is recommended to study uterine lesions within the same menstrual cycle.
VAR-NAV: Motion-resolved 4D MRI using vision-assisted reference auto-navigation
Authors: Murray Victor , Otazo Ricardo
Published: November 2024

Motivation: Motion navigation with improved data efficiency and robustness is still needed for free-breathing MRI. Goal(s): To develop VAR-NAV, a new 2D auto-navigation technique with 100% data efficiency for stack-of-stars acquisitions. Approach: VAR-NAV directly estimates the position of the liver dome using 2D projection images and AM-FM demodulation. Results can be corrected retrospectively, like in soccer video assistant referee (VAR). VAR-NAV is used to sort continuously acquired data and reconstruct motion-resolved 4D images. Results: VAR-NAV outperforms conventional 1D PCA-based navigation and does not require additional acquisition as in previous 2D navigation techniques, offering an efficient and accurate method for clinical 4D MRI. Impact: VAR-NAV estimates a motion signal that represents displacements on 2D images, outperforming conventional PCA-based 1D navigation. No extra navigation data are required, and results can be retrospectively analyzed like in soccer video-assisted-referee (VAR). VAR-NAV promises to improve clinical 4D MRI.
A Dictionary Matching-Based Motion Correction Method for Cardiac Multi-Parametric Mapping
Authors: Chen Haiyang , Emu Yixin , Chen Zhuo , Gao Juan , Hu Chenxi
Published: November 2024

Motivation: Motion correction (MoCo) for cardiac parametric mapping can be challenging due to the dynamic signal variations. Traditional model-based methods need an analytical model, which is often unavailable for multi-parametric mapping applications. Goal(s): To propose a model-free dictionary matching-based MoCo method for cardiac multi-parametric mapping. Approach: The method alternates between dictionary matching and image registration. In vivo validation was performed in 10 healthy subjects for cardiac joint T1 and T2 mapping with controlled breathing. Results: Compared with non-MoCo, the proposed method significantly reduced inter-image misalignment and improved the quality of the T1 and T2 maps. Impact: The proposed MoCo method can be applied to any quantitative MRI application with a signal dictionary, which includes both single-parametric and multi-parametric mapping.
Effect of respiratory motion on gated torso MRI in an upright 0.5 T scanner with seated and standing subjects
Authors: Bortolotti Laura , Chapman Barnaby , Mougin Olivier , Glover Paul , Bowtell Richard , Gowland Penny
Published: November 2024

Motivation: The study focuses on the development of a MoCo technique for torso MRI in upright scanner. Goal(s): Respiratory gated MRI acquisition was achieved by tracking respiratory-related torso motion under the rigid-body assumption. Approach: Optical tracking of torso motion was used to trigger the MRI acquisition at the beginning of each exhalation period. Results: Respiratory gating reduces motion induced artefacts in MRI of seated subjects, but was not able to eliminate motion effects in MRI of standing subjects. In its future development it would be paired with retrospective motion correction techniqueImpact: The aim is to improve MR image quality on an 0.5T upright scanner where body motion is mostly unconstrained. It would particularly improve image quality in patients who are not supine, so increasing patient comfort and reducing the scanning time.
Evaluating Pilot Tone Navigator and BioMatrix Sensor for Motion Assessment using Sliding-Window MR Fingerprinting
Authors: Yin Zhiqing , Li Xinzhou , Kretzler Madison , Griswold Mark , Chen Yong , Boyacioglu Rasim
Published: November 2024

Motivation: Respiratory monitoring is critical for robust free-breathing MRI and various methods have been developed. Goal(s): To leverage sliding-window MRF to evaluate the accuracy of respiratory motion assessment obtained from the Pilot Tone (PT) navigator and BioMatrix Sensor and apply to free-breathing abdominal MRF. Approach: Sliding-window MRF was used to measure respiratory motion with a temporal resolution of 0.5 sec to compare with the respiratory motions acquired using PT navigator and BioMatrix Sensor during in vivo scans. Results: PT navigator presents slight improvement in monitoring respiratory motion, but MRF maps reconstructed based on the motion waveforms from all these approaches are consistent in quality. Impact: The knowledge obtained in this study will help design free-breathing abdominal imaging in general and provide critical information in performing quantitative abdominal MRI using MRF.
Patient adaptive intelligent MRI scanning with consistent Image Quality
Authors: Agarwal Harsh , Abraham Tisha , Shanbhag Dattesh , Nikbeh Fara , Rajamani Sajith , Quarterman Patrick , Fung Maggie , Banerjee Suchandrima , Venkatesan Ramesh , Washburn Sheila
Published: November 2024

Motivation: Adapt the MR imaging for patient specific anatomical coverage. Goal(s): Adapt MRI protocol while maintaining contrast, SNR and scan time in patient specific MR imaging. Approach: The Intelligent slice placement estimated the brain segmentation and key landmarks for determining imaging volume center, orientation and coverage. The MRI protocol is then adjusted to demonstrate that contrast, SNR and scan time can be maintained within limits. Results: The MRI images with patient adaptive MR imaging have similar view and coverage of anatomy while contrast, SNR and scan time is maintained. Impact: MRI protocol adaption (while maintaining contrast, SNR and scan time within limits) demonstrated in this abstract along with anatomical coverage for patient adaptive scanning is essential for consistent high quality MRI imaging within and across clinical sites.
A Deep Learning Model to Generate Customizable RF Pulse Shapes for Power Independent of Number of Slices Simultaneous Multi-Slice Imaging
Authors: Nelson Seger , Reich Jason , MacMillan Erin , Feldman Rebecca
Published: November 2024

Motivation: Designing radiofrequency pulses can be a challenging, time consuming, iterative process. Goal(s): Simplify the radiofrequency pulse design process using deep learning. Approach: First, a complex model was trained on a dataset of ~34k RF pulses. Second, a simpler model was trained on a dataset of 1.2M RF pulses. Both models output the characteristics needed to generate a fully sampled radiofrequency waveform. Results: Model 2 performed better than Model 1, however, the root mean squared error in expected vs. generated slice profiles on a subset of the test data was still high at 36.5%. Future work will implement an optimization loop. Impact: A fully functioning deep learning model could serve as a tool for researchers designing power independent of number of slices pulses to improve slice profiles for SMS imaging as well as novel applications such as in ex-nuclei imaging.
Deep learning-based image reconstruction for higher resolution cardiac T1 mapping
Authors: Amsel Daniel , Vornehm Marc , Wetzl Jens , Schmidt Michaela , Tillmanns Christoph , Gebker Rolf , Giese Daniel , Knoll Florian , Küstner Thomas
Published: November 2024

Motivation: The MOLLI acquisition scheme is frequently used for T1 mapping of the heart. MOLLI restricts the spatial resolution of the resulting T1 maps due to acquiring the inversion recovery images in single-shot fashion. Goal(s): To allow the acquisition of higher spatial resolution T1 maps. Approach: Single-shot acquisitions are accelerated and image sets are reconstructed using a neural network. The deep learning-based reconstruction is integrated into an existing T1 mapping sequence. Results: The proposed method produces higher spatial resolution T1 maps. The corresponding T1 values do not differ significantly from T1 values computed by the vendor sequence. Impact: The acquisition of higher spatial resolution T1 maps is achieved. The proposed method may improve the detection of small focal lesions without increasing the required scan time or breath hold duration.
Deep Learning Reconstruction-based Accelerated Rectal MRI: Image Quality, Diagnostic Performance, and Reading Efficiency Assessment
Authors: Peng Wenjing , Wan Lijuan , Tong Xiaowan , Yang Fan , Wang Sicong , Li Lin , Zhang Hongmei
Published: November 2024

Motivation: Accelerated MRI is an imminent need clinically to satisfy the growing disease burden of rectal cancer. Goal(s): This study aims to conduct clinical assessment of DLR-based accelerated rectal MRI, encompassing image acquisition, image quality, diagnostic performance, and reding efficiency. Approach: Two sets of T2WI using standard fast spin-echo (FSEstandard) and DLR-based accelerated FSE (FSEDL) were prospectively compared. Results: FSEDL showed superior image quality and reading efficiency than FSEstandard, with a 65% reduction in acquisition time. DLR could assist to enhance the accuracy of T-staging for junior radiologists, preserving equivalent diagnostic performance in N staging, EMVI, and MRF. Impact: This study offered a comprehensive and viable perspective on the application of DLR in rectal MRI, which facilitated improved image quality and reading efficiency, while reducing acquisition time. Moreover, it enhanced the accuracy of T-staging for junior radiologists.
Deep Learning-based Human MRI Reconstruction and Preprocessing with Artificial Fourier Transform Network (AFT-Net)
Authors: Yang Yanting , Tian Jeffery , Guo Jia
Published: November 2024

Motivation: Complex-valued deep learning framework has not been fully investigated in human normal-field and low-field MRI reconstruction and preprocessing. Goal(s): We aim to replace conventional numerical methods with deep learning network, which reconstruct and preprocess the k-space data in parallel. Approach: An artificial Fourier transform network (AFT-Net) is proposed to directly processes the complex-valued raw data in the sensor domain. Results: An evaluation of accelerated reconstruction and denoised reconstruction shows that AFT-Net demonstrated the ability to reconstruct the data with significantly accelerate acquisition and random Gaussian noise. The proposed AFT-Net is an efficient and accurate approach for MRI reconstruction and preprocessing from raw data. Impact: MRI reconstruction and preprocessing with AFT-Net should be able to determine the domain-manifold mapping and process k-space data directly, which shows superior performance and can be served as an efficient and accurate approach for human high-field and low-field MRI acquisition.
MRI-NUFFT: An open source Python package to make non-Cartesian MR Imaging easier
Authors: Comby Pierre-Antoine , Daval-Frérot Guillaume , GR Chaithya , Vignaud Alexandre , Ciuciu Philippe
Published: November 2024

Motivation: Non-Cartesian imaging remains complicated to use for MRI due to the high computational cost of the Non-Uniform Fourier Transform for image reconstruction. Goal(s): To provide a uniform interface for reconstructing magnetic resonance images from non-Cartesian k-space data and a collection of non-Cartesian sampling trajectories Approach: We propose an open-source Python package (https://github.com/mind-inria/mri-nufft/) providing a standard interface to existing NUFFT libraries, with extended models for multi-coil imaging and static-field (B0) inhomogeneities correction. Results: MRI-NUFFT can generate sampling trajectories, compliant with hardware constraints, as well as simple forward/adjoint operations and density compensation for use in advanced image reconstruction scenarios. Impact: With MRI-NUFFT, non-Cartesian MRI trajectories and reconstruction algorithms become accessible, efficient, and affordable to everyone for research and education purposes.
Tensor MP-PCA Denoising for Prostate MRI
Authors: Gundogdu Batuhan , Chatterjee Aritrick , Akca Benan , Lee Grace , Oren Nisa , Karczmar Gregory , Oto Aytekin
Published: November 2024

Motivation: Prostate MRI primarily relies on diffusion-weighted imaging (DWI) but is notoriously challenged by low SNR, impacting the diagnostic process. Goal(s): To implement the state-of-the-art tensor denoising method for prostate DWIApproach: We applied the tMPPCA algorithm that makes use of the redundancy in multi-dimensional data to separate the most significant components (the diffusion-signal) and the remaining the thermal/scanner noise. We quantified the denoising efficacy with comprehensive qualitative and quantitative analysis. Results: The tMP-PCA method, previously proved to be efficient on ex-vivo scans are extremely effective to enhance in-vivo prostate MRI images when a similar multi-dimensional protocol is followed. Impact: The tMPPCA can effectively reduce noise without the trade-off of blurring—an achievement that has critical implications in cancer detection. This study is the first in-vivo implementation of tMPPCA for enhancing prostate DWI, employed under 10 minutes of scan time.
An accurate interpreter of Pulseq files for GE scanners
Authors: Nielsen Jon-Fredrik , Wang Kang , Schulte Rolf , Zhu Ante , Ajala Afis , Huang Sherry , Gupta Dinank , Peltier Scott , Egan Maximillian , Fung Rex , Zaitsev Maxim , Noll Douglas
Published: November 2024

Motivation: To enable vendor-neutral MR pulse sequence programming for rapid prototyping and reproducible research. Goal(s): Implement an accurate interpreter of Pulseq files for GE scanners. Approach: We convert the Pulseq file, which is a streaming-based description, to a ‘segment’-based representation where each segment consists of sub-sequences (groups) of Pulseq blocks that are always executed together and in a particular order. This segment is then executed repeatedly with different waveform amplitudes. Results: The proposed Pulseq interpreter for GE scanners allows the execution of arbitrary Pulseq sequences. The same interpreter source code (written in EPIC) can be compiled for all recent GE MRI scanner software versions. Impact: The proposed interpreter allows researchers to design sequences in Pulseq and execute them on GE scanners. This allows rapid prototyping of novel sequences, and identical implementations across sites. Pulseq and the proposed ‘segment’-based description are equivalent vendor-independent sequence descriptions.