Faculty Bibliography

Protein-based nanocarriers bear highly desirable properties such as biodegradability and ability to facilitate passage through biological barriers such as the blood-brain-barrier. Using modular protein engineering, we develop a strategy for iteratively improving the delivery efficacy of hydrophobic small molecules for the treatment of glioblastoma multiforme (GBM). By increasing the multiplicity m of the coiled-coil and RGD peptide targeting regions from 1 to 2, we can increase both the hydrodynamic micellar size and drug loading capacity of targeted multidomain protein assembly (TMPA) relative to its predecessor thermoresponsive assembled protein (TRAP). An upper limit of m is likely determined by steric interactions. TMPA shows a 1.7-fold increased encapsulation of doxorubicin (Dox) compared to TRAP and demonstrates improved uptake by U87 human GBM cells. Near-infrared (NIR) dye-labelled TMPA (NIR-TMPA) is injected into mice orthotopically implanted with GBM cells and fit to a 2-compartment pharmacokinetic model. NIR-TMPA demonstrates prolonged short-phase half-life in tumor-bearing mice compared to control with similar slow-phase half-life, leading to an increased area-under-the-curve clearance and pointing to its sequestration in the tumor site. Endpoint fluorescence analysis of mouse organs ex vivo also supports this conclusion. TMPA and its future derivatives therefore bear potential for targeted treatment of GBM.

Ultra low-field (ULF) MRI is an accessible neuroimaging modality that can bridge healthcare disparities and advance population-level brain health research. However, the inherently low signal-to-noise ratio of ULF-MRI often necessitates reductions in spatial resolution and, combined with the field-dependency of MRI contrast, challenges the accurate extraction of clinically relevant brain morphology. We evaluate the current state of ULF-MRI brain volumetry utilizing techniques for enhancing spatial resolution and leveraging recent advancements in brain segmentation. This is based on the agreement between ULF and corresponding high-field (HF) MRI brain volumes, and test-retest repeatability for multiple ULF scans. In this study, we find that accurate brain volumes can be measured from ULF-MRIs when combining orthogonal imaging directions for T₂-weighted images to form a higher resolution image volume. We also demonstrate that not all orthogonal imaging directions contribute equally to volumetric accuracy and provide a recommended scan protocol given the constraints of the current technology.

OBJECTIVES/OBJECTIVE:Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number of false positives, especially for intermediate-risk cases. In this work, we determine whether a deep learning model trained with PI-RADS-guided representation learning can disambiguate the PI-RADS 3 classification, detect csPCa from bi-parametric prostate MR images, and avoid unnecessary benign biopsies. MATERIALS AND METHODS/METHODS:This study included 28,263 MR examinations and radiology reports from 21,938 men imaged for known or suspected prostate cancer between 2015 and 2023 at our institution (21 imaging locations with 34 readers), with 6352 subsequent biopsies. We trained a deep learning model, a representation learner (RL), to learn how radiologists interpret conventionally acquired T2-weighted and diffusion-weighted MR images, using exams in which the radiologists are confident in their risk assessments (PI-RADS 1 and 2 for the absence of csPCa vs. PI-RADS 4 and 5 for the presence of csPCa, n=21,465). We then trained biopsy-decision models to detect csPCa (Gleason score ≥7) using these learned image representations, and compared them to the performance of radiologists, and of models trained on other clinical variables (age, prostate volume, PSA, and PSA density) for treatment-naïve test cohorts consisting of only PI-RADS 3 (n=253, csPCa=103) and all PI-RADS (n=531, csPCa=300) cases. RESULTS:On the 2 test cohorts (PI-RADS-3-only, all-PI-RADS), RL-based biopsy-decision models consistently yielded higher AUCs in detecting csPCa (AUC=0.73 [0.66, 0.79], 0.88 [0.85, 0.91]) compared with radiologists (equivocal, AUC=0.79 [0.75, 0.83]) and the clinical model (AUCs=0.69 [0.62, 0.75], 0.78 [0.74, 0.82]). In the PIRADS-3-only cohort, all of whom would be biopsied using our institution's standard of care, the RL decision model avoided 41% (62/150) of benign biopsies compared with the clinical model (26%, P<0.001), and improved biopsy yield by 10% compared with the PI-RADS ≥3 decision strategy (0.50 vs. 0.40). Furthermore, on the all-PI-RADS cohort, RL decision model avoided 27% of additional benign biopsies (138/231) compared to radiologists (33%, P<0.001) with comparable sensitivity (93% vs. 92%), higher NPV (0.87 vs. 0.77), and biopsy yield (0.75 vs. 0.64). The combination of clinical and RL decision models further avoided benign biopsies (46% in PI-RADS-3-only and 62% in all-PI-RADS) while improving NPV (0.82, 0.88) and biopsy yields (0.52, 0.76) across the 2 test cohorts. CONCLUSIONS:Our PI-RADS-guided deep learning RL model learns summary representations from bi-parametric prostate MR images that can provide additional information to disambiguate intermediate-risk PI-RADS 3 assessments. The resulting RL-based biopsy decision models also outperformed radiologists in avoiding benign biopsies while maintaining comparable sensitivity to csPCa for the all-PI-RADS cohort. Such AI models can easily be integrated into clinical practice to supplement radiologists' reads in general and improve biopsy yield for any equivocal decisions.

OBJECTIVES/OBJECTIVE:Quantitative proton density fat fraction (PDFF) and R2* estimation at lower field strengths, such as 0.55 T, is challenging due to lower signal-to-noise ratio, reduced fat water chemical shift, and increased T2* relaxation times. In this study, we propose a 3D hybrid technique for abdominal imaging at 0.55 T that enables the simultaneous acquisition of T2-weighted and T1-weighted images and quantification of fat fraction and R2* parameters. MATERIALS AND METHODS/METHODS:Numerical simulations were performed to optimize a prototype radial hybrid turbo spin echo gradient echo (TSE-GRE) acquisition scheme for improved PDFF and R2* estimation accuracy. Phantom imaging experiments with and without motion were performed to evaluate the sensitivity of the estimation to external motion. Eleven volunteers were imaged on a prototype 0.55 T system. Data were acquired using the proposed technique under free-breathing conditions, and motion-compensated reconstruction was performed using the respiratory signal from a pilot-tone device. Image contrast and estimation performance were compared with conventional acquisition schemes in vitro and in vivo. RESULTS:Numerical simulations indicated R2* estimation accuracy was more sensitive to the choice of echo time compared with PDFF. Performing motion compensation reduced the mean error in R2* from 24 to 5 s-1 while the mean error in PDFF only reduced from 2.7% to 1.6%. The proposed technique generated T2-weighted images with comparable relative liver-spleen contrast as conventional imaging and there were no significant differences (P>0.05) in the PDFF and R2* values estimated from the hybrid technique compared with conventional multi-echo GRE. Further, the free-breathing acquisition allowed improved slice coverage while overcoming breath-hold limitations of conventional acquisition schemes. CONCLUSIONS:The use of a hybrid TSE-GRE acquisition technique can allow simultaneous morphological and quantitative PDFF and R2* estimation at 0.55 T under free-breathing conditions.

OBJECTIVE:This study aims to determine the diagnostic performance of sub-5-min combined sixfold parallel imaging (PIx3)-simultaneous multislice (SMSx2)-accelerated deep learning (DL) super-resolution 3T knee MRI in children and adolescents. MATERIALS AND METHODS/METHODS:Children with painful knee conditions who underwent PIx3-SMSx2-accelerated DL super-resolution 3T knee MRI and arthroscopy between October 2022 and December 2023 were retrospectively included. Nine fellowship-trained musculoskeletal radiologists independently scored the MRI studies for image quality and the presence of artifacts (Likert scales, range: 1 = very bad/severe, 5 = very good/absent), as well as structural abnormalities. Interreader agreements and diagnostic performance testing was performed. RESULTS:Forty-four children (mean age: 15 ± 2 years; range: 9-17 years; 24 boys) who underwent knee MRI and arthroscopic surgery within 22 days (range, 2-133) were evaluated. Overall image quality was very good (median rating: 5 [IQR: 4-5]). Motion artifacts (5 [5-5]) and image noise (5 [4-5]) were absent. Arthroscopy-verified abnormalities were detected with good or better interreader agreement (κ ≥ 0.74). Sensitivity, specificity, accuracy, and AUC values were 100%, 84%, 93%, and 0.92, respectively, for anterior cruciate ligament tears; 71%, 97%, 93%, and 0.84 for medial meniscus tears; 65%, 100%, 86%, and 0.82 for lateral meniscus tears; 100%, 100%, 100%, and 1.00 for discoid lateral menisci; 100%, 95%, 96%, and 0.98 for medial patellofemoral ligament tears; and 55%, 100%, 98%, and 0.77 for articular cartilage defects. CONCLUSION/CONCLUSIONS:Clinical sub-5-min PIx3-SMSx2-accelerated DL super-resolution 3T knee MRI provides excellent image quality and high diagnostic performance for diagnosing internal derangement in children and adolescents.

Magnetic resonance fingerprinting (MRF), as an emerging versatile and noninvasive imaging technique, provides simultaneous quantification of multiple quantitative MRI parameters, which have been used to detect changes in cartilage composition and structure in osteoarthritis. Deep learning (DL)-based methods for quantification mapping in MRF overcome the memory constraints and offer faster processing compared to the conventional dictionary matching (DM) method. However, limited attention has been given to the fine-tuning of neural networks (NNs) in DL and fair comparison with DM. In this study, we investigate the impact of training parameter choices on NN performance and compare the fine-tuned NN with DM for multiparametric mapping in MRF. Our approach includes optimizing NN hyperparameters, analyzing the singular value decomposition (SVD) components of MRF data, and optimization of the DM method. We conducted experiments on synthetic data, the NIST/ISMRM MRI system phantom with ground truth, and in vivo knee data from 14 healthy volunteers. The results demonstrate the critical importance of selecting appropriate training parameters, as these significantly affect NN performance. The findings also show that NNs improve the accuracy and robustness of T₁, T₂, and T_1ρ mappings compared to DM in synthetic datasets. For in vivo knee data, the NN achieved comparable results for T₁, with slightly lower T₂ and slightly higher T_1ρ measurements compared to DM. In conclusion, the fine-tuned NN can be used to increase accuracy and robustness for multiparametric quantitative mapping from MRF of the knee joint.

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

BACKGROUND:The methods for predicting time-to-total knee replacement (TKR) do not provide enough information to make robust and accurate predictions. PURPOSE/OBJECTIVE:Develop and evaluate an artificial intelligence-based model for predicting time-to-TKR by analyzing longitudinal knee data and identifying key features associated with accelerated knee osteoarthritis progression. METHODS:A total of 547 subjects underwent TKR in the Osteoarthritis Initiative over nine years, and their longitudinal data was used for model training and testing. 518 and 164 subjects from Multi-Center Osteoarthritis Study and internal hospital data were used for external testing, respectively. The clinical variables, magnetic resonance (MR) images, radiographs, and quantitative and semi-quantitative assessments from images were analyzed. Deep learning (DL) models were used to extract features from radiographs and MR images. DL features were combined with clinical and image assessment features for survival analysis. A Lasso Cox feature selection method combined with a random survival forest model was used to estimate time-to-TKR. RESULTS:Utilizing only clinical variables for time-to-TKR predictions provided the estimation accuracy of 60.4% and C-index of 62.9%. Combining DL features extracted from radiographs, MR images with clinical, quantitative, and semi-quantitative image assessment features achieved the highest accuracy of 73.2%, (p=.001) and C-index of 77.3% for predicting time-to-TKR. CONCLUSIONS:The proposed predictive model demonstrated the potential of DL models and multimodal data fusion in accurately predicting time-to-TKR surgery that may help assist physicians to personalize treatment strategies and improve patient outcomes.

Tractography is a unique modality for the in vivo measurement of structural connectivity, crucial for understanding brain networks and neurological conditions. With increasing b-value, the diffusion-weighting signal becomes primarily sensitive to the intra-axonal signal. However, it remains unclear how tractography is affected by this observation. Here, using open-source datasets, we showed that at high b-values, DWI reduces the uncertainty in estimating fiber orientations. Specifically, we found the ratio of biologically-meaningful longer-range connections increases, accompanied with downstream impact of redistribution of connectome and network metrics. However, when going beyond b = 6000 s/mm², the loss of SNR imposed a penalty. Lastly, we showed that the data reaches satisfactory reproducibility with b-values above 1200 s/mm². Overall, the results suggest that using b-values above 2500 s/mm² is essential for more accurate connectome reconstruction by reducing uncertainty in fiber orientation estimation, supporting the use of higher b-value protocols in standard diffusion MRI scans and pipelines.