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.

Ali Uneri Earns Ph.D. in Computer Science

January 13, 2017

Ali Uneri successfully defended his Ph.D. dissertation, entitled “Imaging and Registration for Surgical Guidance: Systems and Algorithms for Intraoperative C-Arm 2D and 3D Imaging” in December 2016.   His work realized methods for mobile C-arm 2D and 3D imaging integrated with surgical navigation and advanced image registration methods. Among the breakthroughs in Ali’s work is the Known-Component Registration (KC-Reg) framework for extracting 3D information from 2D fluoroscopic views to give 3D guidance capability beyond that of conventional surgical tracking.

Dr. Üneri conducted his research advised by Dr. Jeff Siewerdsen in the I-STAR Lab, with work encompassing: (1) an extensible software platform for integrating navigational tools with cone-beam CT, including fast registration algorithms using parallel computation on general purpose GPU; (2) a 3D–2D registration approach that leverages knowledge of interventional devices for surgical guidance and quality assurance; and (3) a hybrid 3D deformable registration approach using image intensity and feature characteristics to resolve gross deformation in cone-beam CT guidance of thoracic surgery.

His PhD thesis examiners included Prof. Jeff Siewerdsen (Biomedical Engineering), Prof. Russ Taylor (Computer Science), Dr. Jerry Prince (Electrical and Computer Engineering), Dr. Jean-Paul Wolinsky (Neurosurgery), and Dr. Peter Kazanzides (Computer Science).

Congratulations, Ali!

Hao Dang Tackles Truncation Using Multi-Resolution CT Reconstruction

January 4, 2017

A recent paper published in Physics in Medicine and Biology by Hao Dang and coauthors in the I-STAR Lab reports a multi-resolution CT image reconstruction method that efficiently overcomes truncation effects, which are a particularly important problem in cone-beam CT (which often has limited field of view) and can confound iterative model-based image reconstruction (MBIR) methods.

Data truncation in CBCT results in artifacts that reduce image uniformity and challenge reliable diagnosis. For a recently developed prototype CBCT head scanner, truncation of the head and/or head holder can hinder the detection of intracranial hemorrhage (ICH).

The multi-resolution method is based on a similar approach shown by Qian Cao and coauthors for orthopaedic imaging, which allows simultaneous high-resolution reconstruction of bone regions and lower-resolution (lower-noise) reconstruction of surrounding soft tissue. In Hao Dang’s paper, a similar concept is used to overcome truncation artifacts by performing a high-resolution reconstruction of the interior with a lower-resolution reconstruction outside the RFOV.

The algorithm was tested in experiments involving CBCT of the head with truncation due to a carbon-fiber head support. Conventional (single-resolution) MBIR,  showed severe artifacts and poor convergence properties, and the proposed method with a multi-resolution extension of the RFOV minimized truncation artifacts. Compared to brute-force reconstruction of the larger RFOV, the multi-resolution approach reduced computation time by as much as 95% (for an image volume up to 10003 voxels).

The findings provide a promising method for minimizing truncation artifacts in CBCT and may be useful for MBIR methods in general, which can be confounded by truncation effects.

Read the full paper in Phys Med Biol here.

Ja Reaungamornrat Earns PhD in Computer Science

January 3, 2017

Sureerat (Ja) Reaungamornrat successfully defended her PhD dissertation, entitled “Deformable Image Registration for Surgical Guidance Using Intraoperative Cone-Beam CT” in December 2016. Her work addresses new methods for deformable image registration in image-guided interventions, including: (1) a hybrid model for resolving large deformations of the tongue in multi-modality image-guided transoral robotic surgery; (2) a free-form registration method with rigid-body constraints on bones moving within an otherwise deformable soft-tissue context; and (3) a modality-insensitive neighborhood descriptor (MIND) method for registering preoperative MRI to intraoperative CT or cone-beam CT. Ja was supervised in both her Master’s and Doctoral work by Dr. Jeff Siewerdsen (Biomedical Engineering), and her PhD thesis examiners included Dr. Jerry Prince (Electrical and Computer Engineering), Dr. Russ Taylor (Computer Science), and Dr. A. Jay Khanna (Orthopaedic Surgery). Congratulations, Ja!