Yangqiu Hu

While advances in genome sequencing technology make population-scale genomics a possibility, current approaches for analysis of these data rely upon parallelization strategies that have limited scalability, complex implementation and lack reproducibility. Churchill, a balanced regional parallelization strategy, overcomes these challenges, fully automating the multiple steps required to go from raw sequencing reads to variant discovery. Through implementation of novel deterministic parallelization techniques, Churchill allows computationally efficient analysis of a high-depth whole genome sample in less than two hours. The method is highly scalable, enabling full analysis of the 1000 Genomes raw sequence dataset in a week using cloud resources. http://churchill.nchri.org/.

The foot consists of many small bones with complicated joints that guide and limit motion. A variety of invasive and noninvasive means [mechanical, X-ray stereophotogrammetry, electromagnetic sensors, retro-reflective motion analysis, computer tomography (CT), and magnetic resonance imaging (MRI)] have been used to quantify foot bone motion. In the current study we used a foot plate with an electromagnetic sensor to determine an individual subject's foot end range of motion (ROM) from maximum plantar flexion, internal rotation, and inversion to maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation. We then used a custom built MRI-compatible device to hold each subject's foot during scanning in eight unique positions determined from the end ROM data. The scan data were processed using software that allowed the bones to be segmented with the foot in the neutral position and the bones in the other seven positions to be registered to their base positions with minimal user intervention. Bone to bone motion was quantified using finite helical axes (FHA). FHA for the talocrural, talocalcaneal, and talonavicular joints compared well to published studies, which used a variety of technologies and input motions. This study describes a method for quantifying foot bone motion from maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation with relatively little user processing time.

We report an image segmentation and registration method for studying joint morphology and kinematics from in vivo magnetic resonance imaging (MRI) scans and its application to the analysis of foot and ankle joint motion. Using an MRI-compatible positioning device, a foot was scanned in a single neutral and seven other positions ranging from maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation. A segmentation method combining graph cuts and level set was developed. In the subsequent registration step, a separate rigid body transformation for each bone was obtained by registering the neutral position dataset to each of the other ones, which produced an accurate description of the motion between them. The segmentation algorithm allowed a user to interactively delineate 14 foot bones in the neutral position volume in less than 30 min total (user and computer processing unit [CPU]) time. Registration to the seven other positions took approximately 10 additional minutes of user time and 5.25 h of CPU time. For validation, our results were compared with those obtained from 3DViewnix, a semiautomatic segmentation program. We achieved excellent agreement, with volume overlap ratios greater than 88% for all bones excluding the intermediate cuneiform and the lesser metatarsals. For the registration of the neutral scan to the seven other positions, the average overlap ratio is 94.25%, while the minimum overlap ratio is 89.49% for the tibia between the neutral position and position 1, which might be due to different fields of view (FOV). To process a single foot in eight positions, our tool requires only minimal user interaction time (less than 30 min total), a level of improvement that has the potential to make joint motion analysis from MRI practical in research and clinical applications.

We present our work on fusion of MR and CT images of the cervical spine. To achieve the required registration accuracy of approximately 1mm, the spine is treated as a collection of rigid vertebrae, and a separate rigid body transformation applied to each (Hawkes). This in turn requires segmentation of the CT datasets into separate vertebral images, which is difficult because the narrow planes separating adjacent vertebrae are parallel to the axial plane of the CT scans. We solve this problem by evolving all the vertebral contours simultaneously using a level set method, and use contour competition to estimate the position of the vertebral edges when a clean separation between adjacent vertebrae is not seen. Contour competition is based in turn on the vertical scan principle: no part of a given vertebra is vertically below any part of an inferior vertebra. Once segmentation is complete, the individual rigid body transforms are then estimated using mutual information maximization, and the CT images of the vertebrae superimposed on the MR scans. The resultant fused images contain the bony detail of CT and the soft tissue discrimination of MR and appear to be diagnostically equivalent, or superior, to CT myelograms. A formal test of these conclusions is planned for the next phase of our work.

CT-Myelography (CTM) is routinely used for planning surgery for degenerative disease of the spine, but its invasive nature, significant potential morbidity, and high costs make a noninvasive substitute desirable. We report our work on evaluating CT and MR image fusion as an alternative to CTM. Because the spine is only piecewise rigid, a multi-rigid approach to the registration of spinal CT and MR images was developed (SPIE 2004), in which the spine on CT images is first segmented into separate vertebrae, each of which is then rigidly registered with the corresponding vertebra on MR images. The results are then blended to obtain fusion images. Since they contain information from both modalities, we hypothesized that fusion images would be equivalent to CTM. To test this we selected 34 patients who had undergone MRI and CTM for degenerative disease of the cervical spine, and used the multi-rigid approach to produce fused images. A clinical vignette for each patient was created and presented along with either CT/MR fusion images or CTM images. A group of spine surgeons are asked to formulate detailed surgical plans based on each set of images, and the surgical plans are compared. A similar study assessing diagnostic agreement is being performed with neuroradiologists, who also assess the accuracy of registration. Our work to date has demonstrated the feasibility of segmentation and multi-rigid fusion in clinical cases and the acceptability of the questionnaire to physicians. Preliminary analysis of one surgeon's and one neuroradiologist’s evaluation has been performed.