A simple B 1 correction method for dynamic contrast-enhanced MRI

Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review

Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufacturers incorporate various post-acquisition image-processing techniques into the scanner's computer software tools for different post-processing tasks. These tools provide a final image of adequate quality and essential features for accurate clinical reporting and predictive interpretation for better treatment planning. Different post-acquisition image-processing tasks for MRI quality enhancement include noise removal, motion artifact reduction, magnetic bias field correction, and eddy electric current effect removal. Recently, deep learning (DL) methods have shown great success in many research fields, including image and video applications. DL-based data-driven feature-learning approaches have great potential for MR image denoising and image-quality-degrading artifact correction. Recent studies have demonstrated significant improvements in image-analysis tasks using DL-based convolutional neural network techniques. The promising capabilities and performance of DL techniques in various problem-solving domains have motivated researchers to adapt DL methods to medical image analysis and quality enhancement tasks. This paper presents a comprehensive review of DL-based state-of-the-art MRI quality enhancement and artifact removal methods for regenerating high-quality images while preserving essential anatomical and physiological feature maps without destroying important image information. Existing research gaps and future directions have also been provided by highlighting potential research areas for future developments, along with their importance and advantages in medical imaging.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging as Imaging Biomarker for Vascular Normalization Effect of Infigratinib in High-FGFR-Expressing Hepatocellular Carcinoma Xenografts

PURPOSE

Overexpression of fibroblast growth factor receptor (FGFR) contributes to tumorigenesis, metastasis, and poor prognosis of hepatocellular carcinoma (HCC). Infigratinib-a pan-FGFR inhibitor-potently suppresses the growth of high-FGFR-expressing HCCs in part via alteration of the tumor microenvironment and vessel normalization. In this study, we aim to assess the utility of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) as a non-invasive imaging technique to detect microenvironment changes associated with infigratinib and sorafenib treatment in high-FGFR-expressing HCC xenografts.

PROCEDURES

Serial DCE-MRIs were performed on 12 nude mice bearing high-FGFR-expressing patient-derived HCC xenografts to quantify tumor microenvironment pre- (day 0) and post-treatment (days 3, 6, 9, and 15) of vehicle, sorafenib, and infigratinib. DCE-MRI data were analyzed using extended generalized kinetic model and two-compartment distributed parameter model. After treatment, immunohistochemistry stains were performed on the harvested tumors to confirm DCE-MRI findings.

RESULTS

By treatment day 15, infigratinib induced tumor regression (70 % volume reduction from baseline) while sorafenib induced relative growth arrest (185 % volume increase from baseline versus 694 % volume increase from baseline of control). DCE-MRI analysis revealed different changes in microcirculatory parameters upon exposure to sorafenib versus infigratinib. While sorafenib induced microenvironment changes similar to those of rapidly growing tumors, such as a decrease in blood flow (F), fractional intravascular volume (vp), and permeability surface area product (PS), infigratinib induced the exact opposite changes as early as day 3 after treatment: increase in F, vp, and PS.

CONCLUSIONS

Our study demonstrated that DCE-MRI is a reliable non-invasive imaging technique to monitor tumor microcirculatory response to FGFR inhibition and VEGF inhibition in high-FGFR-expressing HCC xenografts. Furthermore, the microcirculatory changes from FGFR inhibition manifested early upon treatment initiation and were reliably detected by DCE-MRI, creating possibilities of combinatorial therapy for synergistic effect.

A practical method for post-acquisition reduction of bias in fast, whole-brain B1-maps

Large consistent differences have been observed between maps of the flip angle correction factor (commonly called "B₁-maps") produced with different fast methods in the human brain. We present an empirical procedure for first-order multiplicative bias correction that can be applied when more than one B₁-mapping method is available. We use a B₁-map measurement in a calibration phantom as a reference and the voxel-wise histogram mode between ratios of B₁-maps produced from different methods to calculate determine the bias as a multiplicative correcting scale factor. Institutional implementations of four common methods of B₁-mapping were assessed: Method of Slopes, FSE and EPI double angle methods (DAM), and Bloch-Siegert. In human subjects, the multiplicative bias used to correct for each of the four methods was: Method of Slopes = 1.005, FSE-DAM = 0.956, EPI-DAM = 1.080, and Bloch-Siegert = 1.128. Scaling to remove this bias between methods produces more consistent B₁-maps which enable more consistent values for any computations requiring flip angle correction. In addition, we present evidence that the corrected B₁ maps, using our calibration method, are also more accurate.