Faculty Bibliography

PURPOSE/OBJECTIVE:This prospective, non-randomized phase II single-arm pilot trial aimed to explore favorable mid-treatment nodal response (FMNR) through CT imaging to guide de-escalated chemoradiation therapy (CRT) in patients with favorable risk, node-positive HPV-associated oropharyngeal cancer (OPC). MATERIALS AND METHODS/METHODS:. At week 4, CT imaging evaluated nodal response: ≥40% reduction warranted de-escalation to 60 Gy, while <40% reduction continued standard CRT. Primary endpoint was 2-year PFS from initiation of dose de-escalated CRT. Tissue tumor modified viral (TTMV) HPV DNA samples and DW-MRI were collected at baseline and week 4. MDADI questionnaires were collected at baseline, 1, 3, 6, 12, and 24 months. RESULTS:Of 39 patients, 26 had FMNR and underwent de-escalated treatment. 13 pts had slow mid-treatment nodal shrinkage and received standard dose. At a median follow-up of 47.4 months, the 2-year PFS was 92.1% (95% CI: 0.72-0.98) for the deescalated dose group and 92.3% for the standard dose patients (95% CI: 0.57-0.99), p=0.96. With a median survival follow up of 48.9 months (range: 16.7-77.8 months), there were no deaths or distant failures. FMNR was associated with rapid TTMV HPV DNA clearance, reduced TTMV HPV DNA flare, lower baseline and week 4 MRI diffusivity, and higher baseline and week 4 MRI diffusional kurtosis. No differences in acute or late maximum grade 3-4 toxicity by patient were noted. MDADI composite scores showed minimal clinical important difference (MCID) in the de-escalated group at 1-month post-treatment while the standard group had MCID up to 1-year post-treatment. No patients required feeding tube placement. CONCLUSIONS:De-escalated CRT using CT-based mid-treatment nodal response in favorable risk, node-positive HPV-associated OPC achieved excellent 2-year PFS and OS rates and represents a potential approach in better selecting patients for treatment de-escalation.

PURPOSE/OBJECTIVE:To develop a deep learning-based method for robust and rapid estimation of the fatty acid composition (FAC) in mammary adipose tissue. METHODS:A physics-based unsupervised deep learning network for estimation of fatty acid composition-network (FAC-Net) is proposed to estimate the number of double bonds and number of methylene-interrupted double bonds from multi-echo bipolar gradient-echo data, which are subsequently converted to saturated, mono-unsaturated, and poly-unsaturated fatty acids. The loss function was based on a 10 fat peak signal model. The proposed network was tested with a phantom containing eight oils with different FAC and on post-menopausal women scanned using a whole-body 3T MRI system between February 2022 and January 2024. The post-menopausal women included a control group (n = 8) with average risk for breast cancer and a cancer group (n = 7) with biopsy-proven breast cancer. RESULTS: > 0.9 except chain length). The FAC values measured from scan and rescan data of the control group showed no significant difference between the two scans. The FAC measurements of the cancer group conducted before contrast and after contrast showed a significant difference in saturated fatty acid and mono-unsaturated fatty acid. The cancer group has higher saturated fatty acid than the control group, although not statistically significant. CONCLUSION/CONCLUSIONS:The results in this study suggest that the proposed FAC-Net can be used to measure the FAC of mammary adipose tissue from gradient-echo MRI data of the breast.

The fastMRI breast dataset is the first large-scale dataset of radial k-space and Digital Imaging and Communications in Medicine data for breast dynamic contrast-enhanced MRI with case-level labels, and its public availability aims to advance fast and quantitative machine learning research.

PURPOSE/OBJECTIVE:To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. METHODS: RESULTS: CONCLUSION/CONCLUSIONS:We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

PURPOSE/OBJECTIVE: METHODS: RESULTS: CONCLUSIONS:

BACKGROUND AND PURPOSE/OBJECTIVE:The purpose of this study is to evaluate the feasibility of using 3-dimensional (3D) ultra-short echo time (UTE) radial imaging method for measurement of the permeability of the blood-brain barrier (BBB) to gadolinium-based contrast agent. In this study, we propose to use the golden-angle radial sparse parallel (GRASP) method with 3D center-out trajectories for UTE, hence named as 3D UTE-GRASP. We first examined the feasibility of using 3D UTE-GRASP dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for differentiating subtle BBB disruptions induced by focused ultrasound (FUS). Then, we examined the BBB permeability changes in Alzheimer's disease (AD) pathology using Alzheimer's disease transgenic mice (5xFAD) at different ages. METHODS:For FUS experiments, we used four Sprague Dawley rats at similar ages where we compared BBB permeability of each rat receiving the FUS sonication with different acoustic power (0.4-1.0 MPa). For AD transgenic mice experiments, we included three 5xFAD mice (6, 12, and 16 months old) and three wild-type mice (4, 8, and 12 months old). RESULTS:The result from FUS experiments showed a progressive increase in BBB permeability with increase of acoustic power (p < .05), demonstrating the sensitivity of DCE-MRI method for detecting subtle changes in BBB disruption. Our AD transgenic mice experiments suggest an early BBB disruption in 5xFAD mice, which is further impaired with aging. CONCLUSION/CONCLUSIONS:The results in this study substantiate the feasibility of using the proposed 3D UTE-GRASP method for detecting subtle BBB permeability changes expected in neurodegenerative diseases, such as AD.

PURPOSE:In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND METHOD:-10min methods in estimating the PS values. RESULTS:-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. CONCLUSIONS:We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.

This manuscript aims to evaluate the robustness and significance of the water efflux rate constant (k_io) parameter estimated using the two flip-angle Dynamic Contrast-Enhanced (DCE) MRI approach with a murine glioblastoma model at 7 T. The repeatability of contrast kinetic parameters and k_io measurement was assessed by a test-retest experiment (n = 7). The association of k_io with cellular metabolism was investigated through DCE-MRI and FDG-PET experiments (n = 7). Tumor response to a combination therapy of bevacizumab and fluorouracil (5FU) monitored by contrast kinetic parameters and k_io (n = 10). Test-retest experiments demonstrated compartmental volume fractions (v_e and v_p) remained consistent between scans while the vascular functional measures (F_p and PS) and k_io showed noticeable changes, most likely due to physiological changes of the tumor. The standardized uptake value (SUV) of tumors has a linear correlation with k_io (R² = 0.547), a positive correlation with F_p (R² = 0.504), and weak correlations with v_e (R² = 0.150), v_p (R² = 0.077), PS (R² = 0.117), K^trans (R² = 0.088) and whole tumor volume (R² = 0.174). In the treatment study, the k_io of the treated group was significantly lower than the control group one day after bevacizumab treatment and decreased significantly after 5FU treatment compared to the baseline. This study results support the feasibility of measuring k_io using the two flip-angle DCE-MRI approach in cancer imaging.

PURPOSE:To assess the reliability of measuring diffusivity, diffusional kurtosis, and cellular-interstitial water exchange time with long diffusion times (100-800 ms) using stimulated-echo DWI. METHODS: RESULTS: CONCLUSIONS:Based on two well-established diffusion phantoms, we found that time-dependent diffusion MRI measurements can provide stable diffusion and kurtosis values over a wide range of diffusion times and across multiple MRI systems. Moreover, estimation of cellular-interstitial water exchange time can be achieved using the Kärger model for the metastatic lymph nodes in patients with head and neck cancer.

Background: Increasing evidence suggests the subtle changes of Blood-Brain Barrier (BBB) permeability in normal aging and in Alzheimer"™s disease using Dynamic Contrast-Enhanced MRI (DCE-MRI). However, measuring this subtle change poses great challenge for accurate measurement, resulting in inconsistent results among previous studies. Two major challenges are long scan times, as suggested by previous studies and selection of the arterial input function (AIF). In this study, we aim to estimate the capillary level input function (CIF) using a deep learning network to overcome these two challenges. Methods: Healthy volunteers (n= 8, ages: 21-76) were recruited for DCE-MRI scan for 28min. Golden-angle RAdial Sampling Parallel (GRASP) sequence was used to obtain the dynamic images at ∼5s/frame. Individual AIF was sampled from the superior sagittal sinus of the brain (Fig.1a). FSL was used to segment the gray and white matters (Fig.1b). Each voxel was fitted using the graphical Patlak model (Fig.2a) to assess the vascular permeability-surface area product (PS) for both 28-min data and 10-min truncated data. We used a 3x3 kernel sliding through the images (Fig.3) and feed each voxel"™s dynamic as the input to our vision-transformer. Training data were generated using individual AIFs with a mathematical model, consisting of two Gaussian and one exponential function, and used to simulate dynamic patches using the Extended Patlak model (Fig.2b). Result: When the 10-min data are used, the conventional approach with AIF results in overestimation of PS when the scan-time is reduced, while the network-predicted CIF allows more accurate estimation, with refence to the results using the 28-min data, as illustrated by an example in Figure 4. Figure 5 shows the regional permeability differences between young and old subjects, where the conventional approach with AIF does not show the difference, while the approach with CIF shows subtle increases in PS with aging. Conclusion: Our proposed CIF-based approach provides an appropriate input-function for DCE analysis, allowing assessment of subtle permeability changes in the BBB.