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Bioengineering
Special Issues
Advanced Diffusion MRI and Its Clinical Applications
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Advanced Diffusion MRI and Its Clinical Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 6415

Special Issue Editors

Dr. Zheng Zhong

E-Mail Website
Guest Editor
Department of Radiology, Stanford University, Stanford, CA 94305, USA
Interests: magnetic resonance imaging (MRI); diffusion MRI; diffusion-weighted imaging (DWI); diffusion tensor imaging (DTI); medical imaging; cancer; tumor; grading; neurodegenerative disease
Dr. Muge Karaman

E-Mail Website
Guest Editor
Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL 60612, USA
Interests: medical imaging; MRI; diffusion imaging; machine learning

Special Issue Information

Dear Colleagues,

Diffusion magnetic resonance imaging (MRI) has become a pillar of modern clinical imaging with its sensitivity to probe underlying tissue microstructures at the microscopic level, much lower than the achievable image resolution of conventional MRI. With this capability, diffusion MRI can serve as an imaging biomarker for an array of clinical applications ranging from the assessment of acute brain ischemia and neurodegenerative disorders to tumor tissue characterization, which has been essential in the management of cancer in various organs.

The recent advances in the technological developments have substantially improved the data acquisition, processing, and analysis pipeline in diffusion MRI. The advanced image acquisition technologies with reduced field-of-view, non-Cartesian sampling, simultaneous multi-slice imaging, and many others, in conjunction with the use of state-of-the-art machine-learning methods, have substantially improved the quality of the images and increased the speed and reproducibility of data acquisition at high (i.e., 3T) or ultra-high field (i.e., 7T and above). On the other hand, the diffusion MRI research community has continuously been working on improving tissue microstructural characterization by developing advanced data-analysis techniques to extract biologically valuable information from diffusion-weighted MR signals, especially in the high b-value regime. These advancements in technology have had a significant impact on the clinical applications of diffusion MRI, including in areas such as cancer diagnosis and treatment evaluation, determining the severity of tumors, and identifying neurodegenerative diseases.

The goal of this Special Issue, entitled “Recent Advances in Diffusion MRI and Its Clinical Applications”, is to share new perspectives on the most recent research challenges in the field of diffusion MRI. We welcome original research papers, review articles, and case reports that reflect recent technical developments of diffusion MRI and its clinical applications.

Dr. Zheng Zhong
Dr. Muge Karaman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Magnetic resonance imaging (MRI);
  • Diffusion MRI;
  • Diffusion-weighted imaging (DWI);
  • Diffusion tensor imaging (DTI);
  • Medical imaging;
  • Cancer;
  • Tumor grading;
  • Neurodegenerative disease;
  • Tissue characterization;
  • Diffusion acquisition;
  • Non-Gaussian diffusion MRI;
  • Brain disease diagnosis;
  • Treatment response monitoring.

Published Papers (5 papers)

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Research

14 pages, 2577 KiB  
Article
Multimodal MRI for Estimating Her-2 Gene Expression in Endometrial Cancer
by Xiwei Li, Shifeng Tian, Changjun Ma, Lihua Chen, Jingwen Qin, Nan Wang, Liangjie Lin and Ailian Liu
Bioengineering 2023, 10(12), 1399; https://doi.org/10.3390/bioengineering10121399 - 06 Dec 2023
Cited by 2 | Viewed by 1015
Abstract
Purpose: To assess the value of multimodal MRI, including amide proton transfer-weighted imaging (APT), diffusion kurtosis imaging (DKI), and T2 mapping sequences for estimating human epidermal growth factor receptor-2 (Her-2) expression in patients with endometrial cancer (EC). Methods: A total of 54 patients [...] Read more.
Purpose: To assess the value of multimodal MRI, including amide proton transfer-weighted imaging (APT), diffusion kurtosis imaging (DKI), and T2 mapping sequences for estimating human epidermal growth factor receptor-2 (Her-2) expression in patients with endometrial cancer (EC). Methods: A total of 54 patients with EC who underwent multimodal pelvic MRI followed by biopsy were retrospectively selected and divided into the Her-2 positive (n = 24) and Her-2 negative (n = 30) groups. Her-2 expression was confirmed by immunohistochemistry (IHC). Two observers measured APT, mean kurtosis (MK), mean diffusivity (MD), and T2 values for EC lesions. Results: The Her-2 (+) group showed higher APT values and lower MD and T2 values than the Her-2 (−) group (all p < 0.05); there was no significant difference in MK values (p > 0.05). The area under the receiver operating characteristic curve (AUC) of APT, MD, T2, APT + T2, APT + MD, T2 + MD, and APT + MD + T2 models to identify the two groups of cases were 0.824, 0.695, 0.721, 0.824, 0.858, 0.782, and 0.860, respectively, and the diagnostic efficacy after combined APT + MD + T2 value was significantly higher than those of MD and T2 values individually (p = 0.018, 0.028); the diagnostic efficacy of the combination of APT + T2 values was significantly higher than that of T2 values separately (p = 0.028). Weak negative correlations were observed between APT and T2 values (r = −0.365, p = 0.007), moderate negative correlations between APT and MD values (r = −0.560, p < 0.001), and weak positive correlations between MD and T2 values (r = 0.336, p = 0.013). The APT values were independent predictors for assessing Her-2 expression in EC patients. Conclusion: The APT, DKI, and T2 mapping sequences can be used to preoperatively assess the Her-2 expression in EC, which can contribute to more precise treatment for clinical preoperative. Full article
(This article belongs to the Special Issue Advanced Diffusion MRI and Its Clinical Applications)
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16 pages, 4209 KiB  
Article
Preoperative Grading of Rectal Cancer with Multiple DWI Models, DWI-Derived Biological Markers, and Machine Learning Classifiers
by Mengyu Song, Qi Wang, Hui Feng, Lijia Wang, Yunfei Zhang and Hui Liu
Bioengineering 2023, 10(11), 1298; https://doi.org/10.3390/bioengineering10111298 - 09 Nov 2023
Cited by 1 | Viewed by 772
Abstract
Background: this study aimed to utilize various diffusion-weighted imaging (DWI) techniques, including mono-exponential DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for the preoperative grading of rectal cancer. Methods: 85 patients with rectal cancer were enrolled in this study. Mann–Whitney U [...] Read more.
Background: this study aimed to utilize various diffusion-weighted imaging (DWI) techniques, including mono-exponential DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for the preoperative grading of rectal cancer. Methods: 85 patients with rectal cancer were enrolled in this study. Mann–Whitney U tests or independent Student’s t-tests were conducted to identify DWI-derived parameters that exhibited significant differences. Spearman or Pearson correlation tests were performed to assess the relationships among different DWI-derived biological markers. Subsequently, four machine learning classifier-based models were trained using various DWI-derived parameters as input features. Finally, diagnostic performance was evaluated using ROC analysis with 5-fold cross-validation. Results: With the exception of the pseudo-diffusion coefficient (Dp), IVIM-derived and DKI-derived parameters all demonstrated significant differences between low-grade and high-grade rectal cancer. The logistic regression-based machine learning classifier yielded the most favorable diagnostic efficacy (AUC: 0.902, 95% Confidence Interval: 0.754–1.000; Specificity: 0.856; Sensitivity: 0.925; Youden Index: 0.781). Conclusions: utilizing multiple DWI-derived biological markers in conjunction with a strategy employing multiple machine learning classifiers proves valuable for the noninvasive grading of rectal cancer. Full article
(This article belongs to the Special Issue Advanced Diffusion MRI and Its Clinical Applications)
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10 pages, 4525 KiB  
Communication
Accelerating High b-Value Diffusion-Weighted MRI Using a Convolutional Recurrent Neural Network (CRNN-DWI)
by Zheng Zhong, Kanghyun Ryu, Jonathan Mao, Kaibao Sun, Guangyu Dan, Shreyas S. Vasanawala and Xiaohong Joe Zhou
Bioengineering 2023, 10(7), 864; https://doi.org/10.3390/bioengineering10070864 - 21 Jul 2023
Viewed by 1245
Abstract
Purpose: To develop a novel convolutional recurrent neural network (CRNN-DWI) and apply it to reconstruct a highly undersampled (up to six-fold) multi-b-value, multi-direction diffusion-weighted imaging (DWI) dataset. Methods: A deep neural network that combines a convolutional neural network (CNN) and recurrent neural network [...] Read more.
Purpose: To develop a novel convolutional recurrent neural network (CRNN-DWI) and apply it to reconstruct a highly undersampled (up to six-fold) multi-b-value, multi-direction diffusion-weighted imaging (DWI) dataset. Methods: A deep neural network that combines a convolutional neural network (CNN) and recurrent neural network (RNN) was first developed by using a set of diffusion images as input. The network was then used to reconstruct a DWI dataset consisting of 14 b-values, each with three diffusion directions. For comparison, the dataset was also reconstructed with zero-padding and 3D-CNN. The experiments were performed with undersampling rates (R) of 4 and 6. Standard image quality metrics (SSIM and PSNR) were employed to provide quantitative assessments of the reconstructed image quality. Additionally, an advanced non-Gaussian diffusion model was employed to fit the reconstructed images from the different approaches, thereby generating a set of diffusion parameter maps. These diffusion parameter maps from the different approaches were then compared using SSIM as a metric. Results: Both the reconstructed diffusion images and diffusion parameter maps from CRNN-DWI were better than those from zero-padding or 3D-CNN. Specifically, the average SSIM and PSNR of CRNN-DWI were 0.750 ± 0.016 and 28.32 ± 0.69 (R = 4), and 0.675 ± 0.023 and 24.16 ± 0.77 (R = 6), respectively, both of which were substantially higher than those of zero-padding or 3D-CNN reconstructions. The diffusion parameter maps from CRNN-DWI also yielded higher SSIM values for R = 4 (>0.8) and for R = 6 (>0.7) than the other two approaches (for R = 4, <0.7, and for R = 6, <0.65). Conclusions: CRNN-DWI is a viable approach for reconstructing highly undersampled DWI data, providing opportunities to reduce the data acquisition burden. Full article
(This article belongs to the Special Issue Advanced Diffusion MRI and Its Clinical Applications)
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11 pages, 3074 KiB  
Article
Feasibility Study of 3D FACT and IVIM Sequences in the Evaluation of Female Osteoporosis
by Shuo Zhang, Qianrui Guo, Yang Yang, Hongbo Feng, Yan Zhao, Peng Guo, Di Li, Xuemei Du and Qingwei Song
Bioengineering 2023, 10(6), 710; https://doi.org/10.3390/bioengineering10060710 - 11 Jun 2023
Cited by 1 | Viewed by 1077
Abstract
Background: The aim of this study is to search for the predictive value of 3D fat analysis and calculation technique (FACT) and intravoxel incoherent motion (IVIM) parameters in identifying osteoporosis in women. Methods: We enrolled 48 female subjects who underwent 3.0 T MRI, [...] Read more.
Background: The aim of this study is to search for the predictive value of 3D fat analysis and calculation technique (FACT) and intravoxel incoherent motion (IVIM) parameters in identifying osteoporosis in women. Methods: We enrolled 48 female subjects who underwent 3.0 T MRI, including 3D FACT and IVIM sequences. Bone mineral density (BMD) values and Fracture Risk Assessment (FRAX) scores were obtained. Proton density fat fraction (PDFF) in the bone marrow and the real diffusion (D) value of intervertebral discs were measured on 3D FACT and IVIM images, respectively. Accuracy and bias were assessed by linear regression analysis and Bland–Altman plots. Intraclass correlation coefficients were used to assess the measurements’ reproducibility. Spearman’s rank correlation was applied to explore the correlation. MRI-based parameters were tested for significant differences among the three groups using ANOVA analyses. A receiver operating characteristic (ROC) analysis was performed. Results: The PDFF of the vertebral body showed a negative correlation with BMD (R = −0.393, p = 0.005) and a positive correlation with the FRAX score (R = 0.706, p < 0.001). The D value of intervertebral discs showed a positive correlation with BMD (R = 0.321, p = 0.024) and a negative correlation with the FRAX score (R = −0.334, p = 0.019). The area under the curve values from the ROC analysis showed that the 3D FACT and IVIM sequences could accurately differentiate between normal and osteoporosis (AUC = 0.88 using the PDFF; AUC = 0.77 using the D value). The PDFF value demonstrated a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 78.6%, 89.5%, 84.6%, and 85.0%, respectively, in its ability to predict osteoporosis. The D value had a sensitivity, specificity, PPV, and NPV of 63.16%, 92.9%, 65.0%, and 77.8%, respectively, for predicting osteoporosis. Conclusions: The 3D FACT- and IVIM-measured PDFF and D values are promising biomarkers in the assessment of bone quality and fracture risk. Full article
(This article belongs to the Special Issue Advanced Diffusion MRI and Its Clinical Applications)
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12 pages, 3342 KiB  
Article
Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer
by Qiuhan Huang, Yanchun Wang, Xiaoyan Meng, Jiali Li, Yaqi Shen, Xuemei Hu, Cui Feng, Zhen Li and Ihab Kamel
Bioengineering 2023, 10(3), 331; https://doi.org/10.3390/bioengineering10030331 - 06 Mar 2023
Cited by 2 | Viewed by 1422
Abstract
Background: The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC). Materials and Methods: Sixty-one participants with pathologically confirmed CC [...] Read more.
Background: The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC). Materials and Methods: Sixty-one participants with pathologically confirmed CC were included in this retrospective study. The APTw MRI and ZOOMit diffusion-weighted imaging (DWI) were acquired. The mean values of APTw and DKI parameters including mean kurtosis (MK) and mean diffusivity (MD) of the primary tumors were calculated. The parameters were compared between the LNM and non-LNM groups using the Student’s t-test or Mann–Whitney U test. Binary logistic regression analysis was performed to determine the association between the LNM status and the risk factors. The diagnostic performance of these quantitative parameters and their combinations for predicting the LNM was assessed with receiver operating characteristic (ROC) curve analysis. Results: Patients were divided into the LNM group (n = 17) and the non-LNM group (n = 44). The LNM group presented significantly higher APTw (3.7 ± 1.1% vs. 2.4 ± 1.0%, p < 0.001), MK (1.065 ± 0.185 vs. 0.909 ± 0.189, p = 0.005) and lower MD (0.989 ± 0.195 × 10−3 mm2/s vs. 1.193 ± 0.337 ×10−3 mm2/s, p = 0.035) than the non-LNM group. APTw was an independent predictor (OR = 3.115, p = 0.039) for evaluating the lymph node status through multivariate analysis. The area under the curve (AUC) of APTw (0.807) was higher than those of MK (AUC, 0.715) and MD (AUC, 0.675) for discriminating LNM from non-LNM, but the differences were not significant (all p > 0.05). Moreover, the combination of APTw, MK, and MD yielded the highest AUC (0.864), with the corresponding sensitivity of 76.5% and specificity of 88.6%. Conclusion: APTw and ZOOMit DKI parameters may serve as potential noninvasive biomarkers in predicting LNM of CC. Full article
(This article belongs to the Special Issue Advanced Diffusion MRI and Its Clinical Applications)
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