Qing Dai

I build real-time imaging systems for image-guided minimally invasive procedures — from algorithm development and machine learning to computational modeling and translational validation. My work bridges deep technical engineering and clinical reality, moving technologies from the benchtop through animal models and into human subjects.

Graduating Summer 2026 and exploring roles in medical devices, surgical robotics, and imaging R&D.

PhD candidate in Bioengineering at UCLA, advised by Dr. Holden Wu in the UCLA Magnetic Resonance Research Lab, affiliated with the Jonsson Comprehensive Cancer Center. Previously, M.S. in Biomedical Imaging at UCSF (advised by Dr. Peder Larson) and B.S. in Biochemistry with a Bioinformatics minor at UCLA.

Research

My PhD research spans four interconnected areas — three technical pillars and a translational thread that ties them together.

Real-time imaging and monitoring for MRI-guided thermal ablation. I develop imaging and computational methods that improve MRI-guided thermal ablation — including a software-based EMI suppression system that eliminates device interference without hardware modifications (patent pending) [1], a volumetric thermometry framework that tracks temperature across the entire liver during free-breathing with <1.5°C accuracy [2], and calibrated computational models that predict ablation outcomes for pre-procedural planning [3].

Signal ProcessingMotion TrackingImage ReconstructionComputational Modeling (EM & Bioheat Transfer)Real-Time Imaging

Theranostic imaging for nanoparticle drug delivery. I develop MRI-based methods for confirming and monitoring nanoparticle-mediated drug delivery triggered by focused ultrasound [6]. This spans imaging pipeline design, HIFU protocol optimization, and in vivo validation — demonstrating spatially targeted visualization of nanoparticle delivery with 139× signal amplification and improved therapeutic outcomes in murine cancer models. In collaboration with the Zink Group (UCLA Chemistry & Biochemistry).

Contrast EnhancementHIFUNanoparticle Drug DeliveryTheranostic ImagingImaging Pipeline DesignIn Vivo Validation

Machine learning and computer vision for medical imaging. I co-develop deep learning methods for real-time procedural guidance and clinical image analysis. This includes a keypoint detection network for needle localization during MRI-guided interventions that requires minimal annotation (<2 mm error, <35 ms inference; patent pending) [7], 2.5D deep learning frameworks for automated renal tumor grading on the UCSF RMaC dataset (800+ subjects) [8], a U-Net pharmacokinetic estimation framework for hyperpolarized ¹³C MRI [9], and automated neuron segmentation pipelines benchmarking deep learning against classical methods [10].

Machine LearningImage SegmentationDevice TrackingPharmacokinetic ModelingLarge-Scale Clinical DataReal-Time Inference

Pre-clinical translation and validation. The technical innovations above only matter if they work in practice. Working shoulder-to-shoulder with clinicians, I was a core contributor to UCLA’s pre-clinical MRI-guided intervention program across various imaging platforms (Siemens 3T/0.55T, Bruker 3T), therapeutic modalities (microwave ablation, HIFU, biopsy, surgical robotics), and teams spanning radiology, pharmacology, engineering, and industry — culminating in successful demonstrations from phantom testing through ex vivo validation to in vivo animal models including Oncopig and murine cancer models [4][5]. In collaboration with Dr. David Lu (UCLA Interventional Radiology), the Chiang Lab, and the MAC Lab (UCLA Mechanical & Aerospace Engineering).

MRICTUltrasoundMicrowave AblationHIFUSurgical RoboticsAnimal ModelsRapid PrototypingSystem IntegrationCross-Functional CollaborationPhantom-to-In Vivo Validation