The development of new imaging systems is at the heart of I-STAR research, with applications ranging from diagnostic radiology to image-guided interventions. Such work includes the modeling of image quality, design of system geometry and components, construction of prototypes, measurement of imaging performance, and translation to first clinical studies. Drawing from mathematical models of image quality and physical experimentation on laboratory imaging bench, example systems include: (1) a mobile cone-beam CT system for imaging patients with traumatic brain injury at the point-of-care; (2) a compact CBCT scanner for imaging of the weight-bearing foot, ankle, and knee as well as upper extremities for MSK radiology and orthopaedics; (3) mobile C-arms for high-quality intraoperative cone-beam in surgery; (4) novel configurations of ultrasound imaging, endoscopy, and surgical navigation systems; (5) advanced CT systems based on photon-counting detectors and low-noise, high-resolution CMOS detectors; and (6) cone-beam CT on a linear accelerator for guidance of radiation therapy.
Mathematical models of image quality can fuel the development of new imaging systems, optimize system design, identify methods to improve image quality and reducing dose, and accelerate the translation of new technologies to first clinical application. Among the most exciting areas of digital x-ray imaging physics over the last decade has been the development of cone-beam CT (CBCT) systems in a broad scope of applications ranging from diagnostic imaging to image-guided interventions. Enabled by the emergence of high-performance flat-panel x-ray detectors and fully 3D reconstruction techniques, the proliferation of CBCT raises new challenges in understanding fully 3D imaging performance (e.g., the fully 3D noise-power spectrum, noise-equivalent quanta, and task-based detectability) and new opportunities in a broad variety of innovative imaging platforms tailored to specific new applications and clinical tasks.
A general penalized likelihood (PL) framework has been developed for model-based 3D image reconstruction in cone-beam CT, providing a valuable basis for novel reconstruction techniques such as KCR, PIR-PLE, and MCR and providing dramatic improvement in image quality compared to conventional FBP under conditions of low dose and/or sparsely sampled data. Increasingly sophisticated forward models within the PL framework leverage a knowledge of the imaging chain gained from cascaded systems analysis of detector efficiency, blur, electronic noise, and other non-idealities conventionally ignored in model-based reconstruction.
Deformable image registration is essential to accurately aligning information in the reference frame of the most up-to-date point in an interventional procedure. Research in the I-STAR Lab has developed a framework for fast deformable registration that adapts methods such as the Demons algorithm to applications in image-guided radiation therapy and a spectrum of image-guided surgeries. Extensions include robust implementation within a morphological pyramid, an intensity-invariant form allowing CT-to-CBCT registration, and a novel super-dimensional form allowing registration in the presence of missing tissue and/or devices introduced between image pairs.
The development of high-performance intraoperative 3D imaging promises to overcome the limitations of conventional image-guided surgery with up-to-date images during surgery that accurately present changes associated with patient setup, tissue deformation, and target excision. A prototype mobile C-arm for high-quality cone-beam CT has been developed in collaboration with industry partners to answer such clinical challenges. The prototype provides volumetric images with sub-mm spatial resolution and soft-tissue visibility at low radiation dose and is the centerpiece of an integrated surgical navigation system undergoing translation to key surgical applications.
Prototype cone-beam CT imaging systems are under development for musculoskeletal (MSK) radiology as well as imaging of traumatic brain injuary (TBI) and stroke. Such systems open new possibilities for point of care imaging. In each case, the I-STAR Lab is developing hardware systems, image reconstruction methods, and artifact correction methods that overcome conventional limitations of cone-beam CT image quality. Methods include flat-panel detector cone-beam CT, dual-energy imaging, photon counting / spectral imaging, and tomosynthesis. Recent work combines task-based image quality models not only with processes for system design, optimization, and image quality assessment but also as the objective function in the design of "task-driven imaging" methods.