Ultrasound + Cone-Beam CT Guidance: Paper by Eugenio Marinetto in CMIG

April 22, 2017

A paper published in the journal of Computerized Medical Imaging and Graphics (CMIG) reports the integration of C-arm cone-beam CT with a low-cost ultrasound imaging probe for needle interventions such as biopsy, tumor ablation, and pain management. The research reports a rigorous characterization of imaging performance for the ultrasound probe (Interson Vascular Access probe), including spatial resolution and contrast-to-noise ratio measured as a function of frequency and depth of field. The work also integrates the ultrasound probe via the PLUS Library for ultrasound-guided interventions, using a 3D-printed geometric calibration phantom and Polaris Vicra tracking system. The accuracy of image registration between ultrasound and cone-beam CT was ~2-3 mm at the needle tip, with anticipated improvement to be gained through enhancement of ultasound image quality. The work also demonstrates the potential for multi-modality (ultrasound-CBCT) deformable image registration using normalized mutual information (NMI), cross-correlation (NCC), or modality-insensitive neighborhood descriptors (MIND) similarity metrics. The research was supported by NIH, industry partnership with Siemens Healthcare, and a collaborative PhD student exchange program with the University Hospital Gregorio Marañón and University Carlos III de Madrid, first-authored by Dr. Eugenio Marinetto as part of his doctoral dissertation on advanced image-guided interventions.

Task-Driven CT: Paper by Grace Gang in Phys Med Biol.

April 8, 2017

A paper published this month in the journal of Physics in Medicine and Biology reports a method that “takes imaging physics to task” to improve CT image quality and reduce dose. Grace Gang and coauthors Jeff Siewerdsen and Web Stayman combined methods for statistical 3D image reconstruction with mathematical models for task-based image quality to drive both the CT image acquisition and reconstruction process in a manner that is optimal to the imaging task. Among the findings is a new approach to tube current modulation that distributes x-ray fluence in a way that is completely different from conventional methods, maximizing imaging performance by reducing tube current in highly attenuating lateral views and instead spending radiation dose in less attenuating views where it has greater benefit to image quality. The result shows how model-based statistical 3D image reconstruction can completely change one’s approach to maximizing image quality. The work also shows how a joint optimization of acquisition technique and image reconstruction parameters is important in reducing radiation dose. The article can be found here and was supported by Dr. Stayman’s U01 grant on low-dose CT imaging.

Hao Dang Earns PhD in Biomedical Engineering

March 13, 2017

Hao Dang earned his Ph.D. from Johns Hopkins University after successfully defending his dissertation, entitled Model-Based Iterative Reconstruction in Cone-Beam Computed Tomography: Advanced Models of Imaging Physics and Prior Information. His thesis details the development of new model-based iterative reconstruction methods that leverage advanced models of imaging physics, task-based assessment of imaging performance, and patient-specific anatomical information from previously acquired images. The approaches uncovered in his work demonstrate substantial improvements in CBCT image quality for applications ranging from detection of acute intracranial hemorrhage to surveillance of lung nodules.

Hao Dang’s doctoral research was carried out in the I-STAR Lab) in Biomedical Engineering under supervision of Prof. Jeffrey H. Siewerdsen (primary advisor) and Prof. J. Webster Stayman (co-advisor). His Ph.D. dissertation focused on the development of new model-based iterative reconstruction methods to improve image quality – specifically, low-contrast, soft-tissue image quality – and reduce radiation dose in cone-beam computed tomography. His Ph.D. research culminated in multiple novel contributions to the field, leading to several published studies and conference presentations and subsequent translation of technology into clinical studies.

Hao’s Thesis Committee Members included Dr. Jeffrey H. Siewerdsen (Biomedical Engineering), Dr. J. Webster Stayman (Biomedical Engineering), Dr. Jerry L. Prince (Electrical and Computer Engineering) and Dr. Katsuyuki Taguchi (Radiology).

Congratulations, Hao!

Automatic Planning for Spine Surgery: Paper by Joseph Goerres

February 8, 2017

Dr. Joseph Goerres, postdoctoral fellow in the I-STAR Lab and Carnegie Center for Surgical Innovation, recently published a paper in Physics in Medicine and Biology that highlights the challenge of achieving precision guidance in spinal surgery.

His work addresses the challenging task of high-precision placement of instrumentation in spine surgery. Screw placement in particular is a challenging task due to the small bone corridors of the spinal pedicle in proximity to nerves and vessels. Spine surgeons benefit from precision guidance and navigation, as well as intraoperative quality assurance (QA) to ensure that each screw is placed safely. Implicit to both surgical guidance and QA is the definition of a surgical plan – i.e., definition of the desired screw trajectories and device selection for each vertebrae. Conventional approaches to surgical planning require time-consuming, manual annotation of preoperative CT or MRI by a skilled surgeon. Dr. Goerres’ paper demonstrates a method for automatically determining both the optimal trajectory and device (screw length and diameter). By leveraging a pre-defined atlas of vertebral shapes and trajectories in combination with deformable 3D registration to the patient’s preoperative image, the method demonstrated accurate, automatic plan definitions that agreed well with those defined by an expert spine surgeon.

Read the full paper here.

Michael Ketcha Wins Young Scientist Award at SPIE 2017

January 26, 2017

Michael Ketcha received the Young Scientist Award at the 2017 SPIE Medical Imaging Conference in Orlando FL for his paper entitled “Fundamental limits of image registration performance:effects of image noise and resolution in CT-guided interventions.” (Abstract Link)

Michael’s research tackles a largely unanswered, fundamental question in image science: How does the accuracy of image registration depend on image quality? His work yields a theoretical analysis that relates the lower bound in registration accuracy to the spatial resolution and noise in the underlying images, providing new insight on imaging techniques for image-guided interventions.

“While imaging performance is fairly well understood for detection and discrimination tasks,” says Michael, “comparatively little has been done to relate image quality factors to the task of image registration.” For CT and cone-beam CT-guided interventions, the methods derived in Michael’s work could lead to methods that involve much lower dose than conventionally used for image visualization but are still well suited to image registration. The work includes analysis of the robustness of various similarity metrics against image quality degradation and reveals a method for optimizing post-processing to minimize registration errors.

The Young Scientist Award recognizes outstanding work by early career researchers in the SPIE Medical Imaging conference on Image-Guided Procedures, Robotic Interventions, and Modeling. Michael Ketcha is a PhD student in Biomedical Engineering at Johns Hopkins University, advised by Dr. Jeff Siewerdsen in the I-STAR Lab and Carnegie Center for Surgical Innovation.