Submit to Diagnostics Review for Diagnostics Propose a Special Issue

Journal Browser

Diffusion-Weighted Imaging: Technique and Medical Applications

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 7554

Share This Special Issue

Special Issue Editor

Dr. Hans-Jonas Meyer

E-Mail Website
Guest Editor

Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstraße, 2004103 Leipzig, Saxony, Germany
Interests: MRI; CT; DWI; oncologic imaging; radiomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the role of imaging has rapidly evolved from the sole detection and measurement of malignant lesions to prognostication, treatment prediction and the prediction of histologic features of tumors.

One important imaging modality is diffusion-weighted imaging, which is a functional sequence of MRI. Diffusion-weighted imaging can show the cellularity and microstructure of tissues and can reflect tumor biology throughout oncology. This will be important in clinical routine and could change oncologic treatment. Moreover, the role of diffusion-weighted imaging in neurological and musculoskeletal imaging is of great significance in daily practice.

In this Special Issue, we aim to highlight the recent advances of diffusion-weighted imaging in preclinical, translational and clinical studies. Papers which investigate these novel aspects of functional imaging are welcome.

Dr. Hans-Jonas Meyer
Guest Editor

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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

MRI
DWI
ADC
texture analysis
radiomics
oncologic imaging

Published Papers (3 papers)

Download All Papers

Research

Jump to: Review

13 pages, 2144 KiB

Open AccessArticle

Undersampled Diffusion-Weighted ¹²⁹Xe MRI Morphometry of Airspace Enlargement: Feasibility in Chronic Obstructive Pulmonary Disease

by Samuel Perron, David G. McCormack, Grace Parraga and Alexei Ouriadov

Diagnostics 2023, 13(8), 1477; https://doi.org/10.3390/diagnostics13081477 - 19 Apr 2023

Viewed by 997

Abstract

Multi-b diffusion-weighted hyperpolarized gas MRI measures pulmonary airspace enlargement using apparent diffusion coefficients (ADC) and mean linear intercepts (L_m). Rapid single-breath acquisitions may facilitate clinical translation, and, hence, we aimed to develop single-breath three-dimensional multi-b diffusion-weighted ¹²⁹Xe MRI using k-space undersampling. We evaluated multi-b (0, 12, 20, 30 s/cm²) diffusion-weighted ¹²⁹Xe ADC/morphometry estimates using a fully sampled and retrospectively undersampled k-space with two acceleration-factors (AF = 2 and 3) in never-smokers and ex-smokers with chronic obstructive pulmonary disease (COPD) or alpha-one anti-trypsin deficiency (AATD). For the three sampling cases, mean ADC/L_m values were not significantly different (all p > 0.5); ADC/L_m values were significantly different for the COPD subgroup (0.08 cm²s⁻¹/580 µm, AF = 3; all p < 0.001) as compared to never-smokers (0.05 cm²s⁻¹/300 µm, AF = 3). For never-smokers, mean differences of 7%/7% and 10%/7% were observed between fully sampled and retrospectively undersampled (AF = 2/AF = 3) ADC and L_m values, respectively. For the COPD subgroup, mean differences of 3%/4% and 11%/10% were observed between fully sampled and retrospectively undersampled (AF = 2/AF = 3) ADC and L_m, respectively. There was no relationship between acceleration factor with ADC or L_m (p = 0.9); voxel-wise ADC/L_m measured using AF = 2 and AF = 3 were significantly and strongly related to fully-sampled values (all p < 0.0001). Multi-b diffusion-weighted ¹²⁹Xe MRI is feasible using two different acceleration methods to measure pulmonary airspace enlargement using L_m and ADC in COPD participants and never-smokers. Full article

(This article belongs to the Special Issue Diffusion-Weighted Imaging: Technique and Medical Applications)

► Show Figures

Figure 1

12 pages, 3153 KiB

Open AccessArticle

Preoperative Prediction of Microsatellite Instability in Rectal Cancer Using Five Machine Learning Algorithms Based on Multiparametric MRI Radiomics

by Yang Zhang, Jing Liu, Cuiyun Wu, Jiaxuan Peng, Yuguo Wei and Sijia Cui

Diagnostics 2023, 13(2), 269; https://doi.org/10.3390/diagnostics13020269 - 11 Jan 2023

Cited by 2 | Viewed by 1537

Abstract

Objectives: To establish and verify radiomics models based on multiparametric MRI for preoperatively identifying the microsatellite instability (MSI) status of rectal cancer (RC) by comparing different machine learning algorithms. Methods: This retrospective study enrolled 383 (training set, 268; test set, 115) RC patients between January 2017 and June 2022. A total of 4148 radiomics features were extracted from multiparametric MRI, including T₂-weighted imaging, T₁-weighted imaging, apparent diffusion coefficient, and contrast-enhanced T₁-weighted imaging. The analysis of variance, correlation test, univariate logistic analysis, and a gradient-boosting decision tree were used for the dimension reduction. Logistic regression, Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and tree machine learning algorithms were used to build different radiomics models. The relative standard deviation (RSD) and bootstrap method were used to quantify the stability of these five algorithms. Then, predictive performances of different models were assessed using area under curves (AUCs). The performance of the best radiomics model was evaluated using calibration and discrimination. Results: Among these 383 patients, the prevalence of MSI was 14.62% (56/383). The RSD value of logistic regression algorithm was the lowest (4.64%), followed by Bayes (5.44%) and KNN (5.45%), which was significantly better than that of SVM (19.11%) and tree (11.94%) algorithms. The radiomics model based on logistic regression algorithm performed best, with AUCs of 0.827 and 0.739 in the training and test sets, respectively. Conclusions: We developed a radiomics model based on the logistic regression algorithm, which could potentially be used to facilitate the individualized prediction of MSI status in RC patients. Full article

(This article belongs to the Special Issue Diffusion-Weighted Imaging: Technique and Medical Applications)

► Show Figures

Figure 1

Review

Jump to: Research

21 pages, 6709 KiB

Open AccessReview

Pitfalls of Diffusion-Weighted Imaging: Clinical Utility of T2 Shine-through and T2 Black-out for Musculoskeletal Diseases

by Yuri Kim, Seul Ki Lee, Jee-Young Kim and Jun-Ho Kim

Diagnostics 2023, 13(9), 1647; https://doi.org/10.3390/diagnostics13091647 - 7 May 2023

Cited by 5 | Viewed by 4628

Abstract

Diffusion-weighted imaging (DWI) with an apparent diffusion coefficient (ADC) value is a relatively new magnetic resonance imaging (MRI) sequence that provides functional information on the lesion by measuring the microscopic movement of water molecules. While numerous studies have evaluated the promising role of DWI in musculoskeletal radiology, most have focused on tumorous diseases related to cellularity. This review article aims to summarize DWI-acquisition techniques, considering pitfalls such as T2 shine-through and T2 black-out, and their usefulness in interpreting musculoskeletal diseases with imaging. DWI is based on the Brownian motion of water molecules within the tissue, achieved by applying diffusion-sensitizing gradients. Regardless of the cellularity of the lesion, several pitfalls must be considered when interpreting DWI with ADC values in musculoskeletal radiology. This review discusses the application of DWI in musculoskeletal diseases, including tumor and tumor mimickers, as well as non-tumorous diseases, with a focus on lesions demonstrating T2 shine-through and T2 black-out effects. Understanding these pitfalls of DWI can provide clinically useful information, increase diagnostic accuracy, and improve patient management when added to conventional MRI in musculoskeletal diseases. Full article

(This article belongs to the Special Issue Diffusion-Weighted Imaging: Technique and Medical Applications)

► Show Figures