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Muzhen He¹ and Yang Song^2
1Fujian Provincial Hospital，China, Fuzhou，Fujian, China, ²MR Research Collaboration, Siemens Healthineers, City, China, China, China

Keywords: IVIM, Breast

Motivation: A nomogram incorporated radiomics and morphology was developed to predict pCR.

Goal(s): This study developed a predictive model using baseline imaging of morphology and radiomics for the purpose of predicting the pCR to NACT.

Approach:  Radiomics and conventional magnetic resonance imaging signs analysis were performed. To build a nomogram model that integrates the radiomics signature and conventional imaging, a logistic regression method was employed.

Results: The nomogram showed good performance and was a valuable tool in clinic.

Impact: Through the validation process, the nomogram developed in this study showed good performance, effectively predicting pCR in breast cancer. Hence, it serves as a valuable tool for informing individualized treatment decision-making for breast cancer patients. 

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Yanbo Li¹, Yuchen Xue², Jinxia Guo³, Lizhi Xie³, and Hong Lu^1
1Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, ²Tianjin Medical University General Hospital, Tianjin, China, ³GE Healthcare, Beijing, China, Beijing, China

Keywords: IVIM, Breast

Motivation: The motivation behind this study lies in the need for accurate prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer.

Goal(s): Our goal was to assess the potential of IVIM-derived histogram parameters as predictive markers before NAC in breast cancer patients. 

Approach: We conducted a comprehensive analysis of 287 breast cancer cases, utilizing IVIM model-based parameters and multivariate logistic regression. The study included voxel-wise analysis.

Results: The prediction model, combining the selected IVIM parameters and PR status, exhibited strong discriminative power. Additionally, we identified the minimum of D, Skewness of D* and D as significant volumetric predictors associated with pCR.

Impact: The impact of this study is significant, as it enhances breast cancer treatment by providing a reliable predictive model for neoadjuvant chemotherapy response, potentially improving patient outcomes and treatment strategies.

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Sawako Hayami¹, Mami Iima^2,3, Tomomi Nobashi², Yuko Someya^2,4, Hirohiko Imai⁵, Denis Le Bihan^6,7, and Yuji Nakamoto^2
1Faculty of Medicine, Kyoto University, Kyoto City, Kyoto, Japan, ²Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto City, Kyoto, Japan, ³iACT, Kyoto University Hospital, Kyoto City, Kyoto, Japan, ⁴Department of Radiology, Kobe City Medical Center General Hospital, Kobe City, Hyogo, Japan, ⁵Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto City, Kyoto, Japan, ⁶Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto City, Kyoto, Japan, ⁷NeuroSpin, CEA Paris-Saclay Center, Gif-sur-Yvette, Essonne, France

Keywords: IVIM, Cancer

Motivation: Although intravoxel incoherent motion (IVIM) parameters are garnering attention as a DWI analytical method, their effectiveness in evaluating the immunotherapy efficacy is still largely unvalued.

Goal(s): Our goal was to assess if IVIM/DWI parameters reflect tissue changes subsequent to anti-PD-1 antibody treatment in vivo.

Approach: Colon (CT26) and breast (4T1) cancer xenograft murine models received the treatment and afterward MR study.

Results: f_IVIM (flowing blood volume fraction) was significantly lower in the treated tissue in the CT26 model, while no change was observed in the 4T1 model. Also, there was no difference in the lesion size in both models.

Impact: The difference of f_IVIM (flowing blood volume fraction) parameter and lesion size between two anti-PD-1 antibody-treated xenograft models might reflect a different level of treatment efficacy, which may indicate the IVIM potential to observe tissue heterogeneity.

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Lu Yang¹, Xiaohui Duan¹, Mengzhu Wang², Haodong Qin³, Zhao Chen³, Huijun Hu¹, Zhuoheng Yan¹, and Xu Yan^2
1Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Roa, GuangZhou, China, ²MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China, ³MR Research Collaboration, Siemens Healthineers, Guangzhou, China

Keywords: DWI/DTI/DKI, Diffusion/other diffusion imaging techniques

Motivation: Non-Gaussian diffusion models can effectively characterize the microstructure of tissues.

Goal(s): To investigate the potential predictive value of multiple non-Gaussian diffusion models for assessing cervical cancer (CC).

Approach: Diffusion parameters derived from continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC) and intravoxel incoherent motion (IVIM) were calculated. The most significant histogram features selected by univariate analysis and multivariate logistic regression were used to establish predictive models. The predictive performance was evaluated by receiver operating characteristic (ROC) analyses.

Results:  The combination of multiple non-Gaussian diffusion models and whole-tumor histogram analysis could distinguish pathological types and differentiation degree in CC.

Impact: Predicting pathological types and differentiation degree of cervical cancer is crucial for appropriate treatment and prognosis. The use of multiple non-Gaussian diffusion models combined with whole-tumor histogram analysis offers a precise and non-invasive solution to this clinical issue.

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Xu yang Wang¹, Shuang shuang Xie¹, and Wen Shen^1
1Department of Radiology, Tianjin First Central Hospital, Tian Jin, China

Keywords: fMRI Analysis, Diffusion/other diffusion imaging techniques, intravoxel incoherent motion

Motivation: Scholars are proposing the use of mesenchymal stem cells (MSCs) to promote liver regeneration and prevent postoperative liver function failure; however, no method currently exists for evaluating their effectiveness in vivo.

Goal(s): We assessed whether intravoxel incoherent motion (IVIM) could be used to assess changes in liver regeneration in rats after partial hepatectomy.

Approach: Rats undergoing stem cell therapy underwent imaging examinations at baseline and postoperative days 1, 2, 3, 5, 7, and 14, and were compared with untreated rats.

Results: Liver D, D*, and PF differed significantly between control rats and those receiving MSCs at all time points.

Impact: Mesenchymal stem cells can promote liver regeneration by inducing cell hypertrophy and extending the proliferation period of liver cells. IVIM parameters enable monitoring changes in liver blood perfusion after mesenchymal stem cell therapy and show correlations with pathological indicators.

Haifa Liu¹, Yingmin Zhai¹, Hui Liu¹, Mengzhu Wang², Yang Song², Chengxiu Zhang³, Guang Yang³, and Robert Grimm^4
1Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China, ²MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China, ³East China Normal University, Shanghai, China, ⁴MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany

Keywords: IVIM, Diffusion/other diffusion imaging techniques, IVIM，MRI, breast tumor, Lymph node metastasis, habitats

Motivation: Biopsy is usually used to diagnose lymph node metastasis in breast cancer patients. Our study evaluated the clinical significance of a non-invasive method.

Goal(s): Our aim was to explore the performance of a prediction model using tumor habitats. It predicted lymph node metastasis in patients with malignant breast cancer. 

Approach: We developed a predictive model of tumor habitats. It used the data of intravoxel incoherent motion (IVIM) and diagnosed lymph node metastasis.

Results: The IVIM indicators can segment whole tumor into four subregions, which were related to hypoxic condition. The model can effectively predict lymph node metastasis of breast cancer patients.

Impact: The predictive model elucidates the features of habitats. Its performance is good for the diagnosis of lymph node metastasis in patients with breast cancer, helping clinicians make better decisions and recommend appropriate treatment options. Thus, patient outcomes are improved.

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Qianjuan Chen¹, Liling Long¹, Huiting Zhang², and Alto Stemmer^3
1Department of Radiology, The First Affiliated Hospital of Guangxi Medical University,, Naning, China, ²MR Research Collaboration, Siemens Healthineers Ltd., Wuhan, China, ³MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany

Keywords: IVIM, Diffusion/other diffusion imaging techniques, PD-1/PD-L1

Motivation: In clinical practice of hepatocellular carcinoma, there are lacking in preoperative and pre-medication results regarding immunosuppressive checkpoint inhibitors PD-1 and PD-L1.

Goal(s):  This study aimed to evaluate the expression of PD-1 and PD-L1 using intravoxel incoherent motion (IVIM) before surgery and immunotherapy.

Approach: Immunohistochemistry staining was performed on liver cancer tissue to analyze the correlation between immunohistochemistry results and derived parameters (D, D*, and f) from IVIM and ADC, allowing for the assessment of their diagnostic value.

Results: The parameters D and D* from IVIM had statistically significant differences between the expression of PD-1 and PD-L1.

Impact: The results of this study may have an impact on clinical decision-making in the future and demonstrate a favorable outcome for scientific research in the immune microenvironment.  

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Shuo Zhang¹, Yonghao Du¹, Ting Liang¹, Xuyin Zhang¹, Yinxia Guo¹, Jian Yang¹, Xianjun Li¹, and Gang Niu^1
1Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China

Keywords: IVIM, Cancer, Stiffness

Motivation: Exploring non-invasive techniques with the potential of replacing invasive pathological analysis of pulmonary neoplasms remains urgent.

Goal(s): To investigate the feasibility of IVIM-based vMRE to provide quantitative estimates of tissue stiffness in pulmonary neoplasms and to verify the diagnostic performance in distinguishing neoplasm property.

Approach: sADC and virtual stiffness values of neoplasm were extracted, and the diagnostic performance of vMRE in distinguishing benign and malignant and detailed pathological type were explored. 

Results: Virtual stiffness values of malignant neoplasms were significantly higher than benign ones. Subsequent sub-type analyses showed adenocarcinoma and squamous cell carcinoma were mostly stiffer than non-specific benign neoplasms. 

Impact: IVIM-based vMRE has good feasibility in reflecting stiffness of pulmonary neoplasms. Virtual stiffness and sADC values showed significant difference between malignant and benign lesions.The application of non-invasive vMRE is promising as a new method for clinical diagnosis.

Mengting Duan¹, Yingmin Zhai¹, Mengzhu Wang², Yang Song², Chengxiu Zhang³, Guang Yang³, Haifa Liu¹, and Hui Liu^1
1Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China, ²MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China, ³East China Normal University, Shanghai, China

Keywords: IVIM, Radiomics, IVIM, MRI, habitats, breast tumor, estrogen receptor

Motivation: Immunohistochemistry remains the gold standard for diagnosing the estrogen receptor (ER) factor in breast cancer. However, the non-invasive IVIM radiomics method offers a meaningful alternative for predicting ER expression. 

Goal(s): The objective of this study is to assess the feasibility and performance of a prediction model based on tumors habitats in predicting ER expression in malignant breast cancer patients.

Approach: We developed and evaluated a predictive model that utilizes tumors habitats to diagnose ER expression. 

Results: The IVIM indicators can be employed for tumor subregions’ segmentation to hypoxia. The model demonstrates effective prediction capabilities for determining the probability of ER-positive in breast cancer.

Impact: Our research highlights the excellent performance of the predictive model based on tumors habitats in predicting ER expression in breast cancer. The model can aid clinicians in making more informed treatment decisions and subsequently improve patient outcomes.

Mi Zhou¹, Meining Chen², Qin Zhang³, and Hongyun Huang^1
1Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China, ²MR Research Collaboration, Siemens Healthineers, Chengdu, China, ³MRI clinical application, Customer Service Department, Siemens Digital Medical Technology Co., LTD, Shanghai, China

Keywords: IVIM, Diffusion/other diffusion imaging techniques

Motivation: The prognosis of rectal cancer (RC), depends on pathological prognostic factors. Despite the contributions of MRI, accurate preoperative prediction of these factors remains challenging.

Goal(s): Evaluate the values of DWI, IVIM, and DKI in predicting RC’s pathological prognostic factors. 

Approach: In 162 rectal cancer patients, we compared DWI, IVIM, and DKI MRI techniques to predict EMVI, LNM, and histological differentiation, analyzing correlations with histological data.

Results: MK was identified as a powerful predictor, especially for histological differentiation and LNM. The combine of MK+MD exhibited robust potential to predict EMVI, highlighting the diagnostic values of these models for rectal cancer prognosis.

Impact: This research enhances MRI's predictive capabilities for critical rectal cancer prognostic factors, potentially refining treatment planning and improving the prognosis of patients.

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Li Fan¹, Jie Li^1,2, Yi Xia¹, Jiankun Dai³, Jie Shi³, Guangyuan Sun⁴, Meiling Xu¹, Xiaoqing Lin^1,2, and Shiyuan Liu^1
1Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China, ²College of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China, ³MR Research,GE Healthcare, Beijing, China, ⁴Department of Thoracic Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, China

Keywords: IVIM, Lung, Deep learning reconstruction; Lung cancer; Magnetic resonance imaging; Diffusion weighted imaging; Intravoxel incoherent motion.

Motivation: DWI-based IVIM model provides information about tumor microenvironment and is reported useful in discriminating pulmonary lesions. However, lung DWI suffers low SNR and substantial susceptibility artifacts. Recently, a vendor-provided deep-learning reconstruction (DLR), relative to conventional reconstruction (ConR), is provided to improve image quality.

Goal(s): Investigate the impact of DLR on IVIM parameters for distinguishing malignant from benign pulmonary lesion.

Approach: DWI was acquired using FOCUS-MUSE to alleviate susceptibility artifacts and was reconstructed with DLR and ConR, separately. SNR and diagnostic performance of IVIM were compared.

Results: DLR significantly increased DWI image SNR and improved diagnostic performance of IVIM parameters for pulmonary lesion discrimination.

Impact: The application DLR would be beneficial for lung DWI in term of image quality and the performance of IVIM quantitative parameters for distinguishing benign from malignant pulmonary lesion.

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Erick O Buko^1,2, Suhail P Parvaze^1,2, Ferenc Tóth¹, and Casey P Johnson^1,2
1Veterinary Clinical Sciences, University of Minnesota, Saint Paul, MN, United States, ²Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Keywords: IVIM, Ischemia, perfusion, bone

Motivation: Intravoxel incoherent motion (IVIM) is a promising noninvasive technique to measure tissue diffusion and perfusion using a single multi-b-value diffusion-weighted imaging (DWI) acquisition.

Goal(s): To determine whether IVIM is sensitive to acute femoral head ischemia in the piglet model using RESOLVE and single-shot EPI.

Approach: 24 piglets underwent bilateral hip imaging using a  3T MRI before and after surgical induction of unilateral femoral head ischemia. IVIM and apparent diffusion coefficient (ADC) values were compared between the pre- and post-operative scans.

Results:  IVIM is sensitive in detecting ischemia and subsequent injury to the femoral head. RESOLVE outperforms ssEPI in detecting changes in perfusion.

Impact: IVIM may be a clinically useful non-contrast-enhanced alternative to CE-MRI to measure femoral head ischemia and perfusion changes for detecting, staging, and monitoring LCPD and other forms of osteonecrosis of the femoral head.

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Wenhui Shao¹, Yuwei Cao¹, Feifei Qu², Feiyun Wu¹, and Ting Chen^1
1The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China, ²MR Research Collaboration, Siemens Healthineers, Shanghai, China

Keywords: IVIM, Reproductive, polycystic ovary syndrome, vessels, hormonal levels

Motivation: This study evaluated endometrial receptivity in women with polycystic ovary syndrome (PCOS) impacting embryo implantation and pregnancy outcomes.

Goal(s): The primary objectives were to evaluate endometrial receptivity by measuring uterine junctional zone (UJZ) and myometrium parameters and to explore the correlation between these imaging parameters and hormonal levels. 

Approach: Using intravoxel incoherent motion (IVIM), the study measured the quantitative parameters of the UJZ and myometrium in women with PCOS. 

Results: Patients with PCOS had lower UJZ and myometrium f values than controls, indicating reduced perfusion affecting embryo implantation. A positive correlation with free thyroxine levels was observed.

Impact: Intravoxel incoherent motion imaging is vital in assessing endometrial receptivity. Patients with polycystic ovary syndrome had lower f values of the uterine junctional zone and myometrium than controls, suggesting less blood perfusion, which might be associated with poor pregnancy outcomes.

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Mingyue Yang¹, Siyao Du¹, Ruimeng Zhao¹, Yueluan Jiang², Yang Song³, and Lina Zhang^1
1The First Hospital of China Medical University, Shenyang, China, ²research Collaboration Team, Siemens Healthineers, Beijing, China, ³research Collaboration Team, Siemens Healthineers, Shanghai, China

Keywords: IVIM, Cancer

Motivation: Intravoxel incoherent motion (IVIM) analysis gives information on diffusion and perfusion and may  have a potential for tumor tissue characterization and distinguish the HER2 expression.

Goal(s): This work aims to cluster tumor subregions based on IVIM parameter maps and assess the optimal number of clusters and parameter of tumor subregions related to HER2 expression.

Approach: we used kmeans method to split the ROI into different sub-regions based on IVIM maps, and imaging features to diagnose HER-2 expression.

Results: We found the optimal number of clusters was 3, and F_Median in subregion 1, D_Median and F_Volume in subregion 2 strongly associated with HER2 expression.

Impact: These subregions features are expected as a non-invasive way to obtain diffusion and perfusion information, leading to the discovery of reliable imaging biomarkers for guiding precision cancer therapy.

Shichao Li¹, Ting Yin², and Zhen Li^1
1Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, ²MR Research Collaboration Team, Siemens Healthineers Ltd., Chengdu, China

Keywords: Diffusion Modeling, Diffusion/other diffusion imaging techniques

Motivation:  Accurate preoperative grading of clear cell renal cell carcinoma (ccRCC) is essential for treatment decisions, particularly in patients with comorbidities.

Goal(s): This study aims to investigate the utility of various diffusion-weighted imaging (DWI) models, including mono-exponential, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and continuous time random walk (CTRW), in preoperative ccRCC grading to facilitate personalized treatment strategies.

Approach: We conducted MRI scans on 105 ccRCC patients with multi-b value DWI and employed machine learning for model construction for ccRCC grading. 

Results:  This study demonstrates the protential of multi-parametric DWI models for accurate ccRCC grading, yield an AUC of 0.861.

Impact:  This study has the potential to reshape the landscape of preoperative ccRCC grading, promote objectivity in treatment planning.

deshuo dong¹, anliang chen¹, lina zhang¹, ailian liu¹, qingwei song¹, jie yang², haonan guan³, and lizhi xie^3
1The First Affiliated Hospital of Dalian Medical University, Dalian, China, ²Dalian Medical University, Dalian, China, ³GE Healthcare, MR Research China, Beijing, Dalian, China

Keywords: fMRI Analysis, Breast

Motivation: Breast adenosis (AD) detection is crucial for early breast cancer diagnosis. Traditional mammography has limitations.

Goal(s): To enhance AD diagnosis using MRI and evaluate MRI's diagnostic value for AD detected in mammography. Assess the combined potential of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion (IVIM) sequences.

Approach: Collected data from AD-diagnosed patients who underwent breast MRI. Analyzed DCE-MRI and IVIM parameters, compared benign and malignant AD, and assessed their combined diagnostic efficiency.

Results: MRI exhibited high sensitivity (100%) and NPV (100%) for malignancy exclusion. DCE-MRI and IVIM combined yielded superior results with an AUC of 0.984.

Impact: This study demonstrates that combining DCE-MRI and IVIM significantly enhances the diagnostic accuracy of AD changes. It offers a non-invasive approach to differentiate benign from malignant lesions, reducing unnecessary invasive procedures and improving breast cancer management.