Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics.

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/.

Multi-rigid image segmentation and registration for the analysis of joint motion from three-dimensional magnetic resonance imaging.

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.

Evaluating foot kinematics using magnetic resonance imaging: from maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation.

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.