ABSTRACT
PURPOSE: To develop an efficient navigator-based motion and temporal B0 shift correction technique for 3D multi-echo gradient-echo (ME-GRE) MRI for quantitative susceptibility mapping (QSM) and R2* mapping. THEORY AND METHODS: A dual-echo 3D spiral navigator was designed to interleave with the Cartesian ME-GRE acquisitions, allowing the acquisition of both low- and high-echo time signals. We additionally designed a novel conjugate-phase based reconstruction method for the joint correction of motion and temporal B0 shifts. We performed both numerical simulation and in vivo human scans to assess the performance of the methods. RESULTS: Numerical simulation and human brain scans demonstrated that the proposed technique successfully corrected artifacts induced by both head motions and temporal B0 changes. Efficient B0-change correction with conjugate-phase reconstruction can be performed on less than 10 clustered k-space segments. In vivo scans showed that combining temporal B0 correction with motion correction further reduced artifacts and improved image quality in both R2* and QSM images. CONCLUSION: Our proposed approach of using 3D spiral navigators and a novel conjugate-phase reconstruction method can improve susceptibility-related measurements using MR.
ABSTRACT
Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.
ABSTRACT
The tissue engineering community has been vocal regarding the need for noninvasive instruments to assess the development of tissue-engineered constructs. Medical imaging has helped fulfill this role. However, specimens allocated to a test tube for imaging cannot be tested for a prolonged period or returned to the incubator. Therefore, samples are essentially wasted due to potential contamination and transfer in a less than optimal growth environment. In turn, we present a standalone, miniature, magnetic resonance imaging-compatible incubator, termed the e-incubator. This incubator uses a microcontroller unit to automatically sense and regulate physiological conditions for tissue culture, thus allowing for concurrent tissue culture and evaluation. The e-incubator also offers an innovative scheme to study underlying mechanisms related to the structural and functional evolution of tissues. Importantly, it offers a key step toward enabling real-time testing of engineered tissues before human transplantation. For validation purposes, we cultured tissue-engineered bone constructs for 4 weeks to test the e-incubator. Importantly, this technology allows for visualizing the evolution of temporal and spatial morphogenesis. In turn, the e-incubator can filter deficient constructs, thereby increasing the success rate of implantation of tissue-engineered constructs, especially as construct design grows in levels of complexity to match the geometry and function of patients' unique needs.
