Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children.

For more than a century, neurologists have hypothesized that the arcuate fasciculus carries signals that are essential for language function; however, the relevance of the pathway for particular behaviors is highly controversial. The primary objective of this study was to use diffusion tensor imaging to examine the relationship between individual variation in the microstructural properties of arcuate fibers and behavioral measures of language and reading skills. A second objective was to use novel fiber-tracking methods to reassess estimates of arcuate lateralization. In a sample of 55 children, we found that measurements of diffusivity in the left arcuate correlate with phonological awareness skills and arcuate volume lateralization correlates with phonological memory and reading skills. Contrary to previous investigations that report the absence of the right arcuate in some subjects, we demonstrate that new techniques can identify the pathway in every individual. Our results provide empirical support for the role of the arcuate fasciculus in the development of reading skills.

MicroTrack: an algorithm for concurrent projectome and microstructure estimation.

This paper presents MicroTrack, an algorithm that combines global tractography and direct microstructure estimation using diffusion-weighted imaging data. Previous work recovers connectivity via tractography independently from estimating microstructure features, such as axon diameter distribution and density. However, the two estimates have great potential to inform one another given the common assumption that microstructural features remain consistent along fibers. Here we provide a preliminary examination of this hypothesis. We adapt a global tractography algorithm to associate axon diameter with each putative pathway and optimize both the set of pathways and their microstructural parameters to find the best fit of this holistic white-matter model to the MRI data. We demonstrate in simulation that, with a multi-shell HARDI acquisition, this approach not only improves estimates of microstructural parameters over voxel-by-voxel estimation, but provides a solution to long standing problems in tractography. In particular, a simple experiment demonstrates the resolution of the well known ambiguity between crossing and kissing fibers. The results strongly motivate further development of this kind of algorithm for brain connectivity mapping.

Think global, act local; projectome estimation with BlueMatter.

Estimating the complete set of white matter fascicles (the projectome) from diffusion data requires evaluating an enormous number of potential pathways; consequently, most algorithms use computationally efficient greedy methods to search for pathways. The limitation of this approach is that critical global parameters--such as data prediction error and white matter volume conservation--are not taken into account. We describe BlueMatter, a parallel algorithm for global projectome evaluation, which uniquely accounts for global prediction error and volume conservation. Leveraging the BlueGene/L supercomputing architecture, BlueMatter explores a massive database of 180 billion candidate fascicles. The candidates are derived from several sources, including atlases and multiple tractography algorithms. Using BlueMatter we created the highest resolution, volume-conserved projectome of the human brain.

ConTrack: finding the most likely pathways between brain regions using diffusion tractography.

Magnetic resonance diffusion-weighted imaging coupled with fiber tractography (DFT) is the only non-invasive method for measuring white matter pathways in the living human brain. DFT is often used to discover new pathways. But there are also many applications, particularly in visual neuroscience, in which we are confident that two brain regions are connected, and we wish to find the most likely pathway forming the connection. In several cases, current DFT algorithms fail to find these candidate pathways. To overcome this limitation, we have developed a probabilistic DFT algorithm (ConTrack) that identifies the most likely pathways between two regions. We introduce the algorithm in three parts: a sampler to generate a large set of potential pathways, a scoring algorithm that measures the likelihood of a pathway, and an inferential step to identify the most likely pathways connecting two regions. In a series of experiments using human data, we show that ConTrack estimates known pathways at positions that are consistent with those found using a high quality deterministic algorithm. Further we show that separating sampling and scoring enables ConTrack to identify valid pathways, known to exist, that are missed by other deterministic and probabilistic DFT algorithms.

Identifying the human optic radiation using diffusion imaging and fiber tractography.

Measuring the properties of the white matter pathways from retina to cortex in the living human brain will have many uses for understanding visual performance and guiding clinical treatment. For example, identifying the Meyer's loop portion of the optic radiation (OR) has clinical significance because of the large number of temporal lobe resections. We use diffusion tensor imaging and fiber tractography (DTI-FT) to identify the most likely pathway between the lateral geniculate nucleus (LGN) and the calcarine sulcus in sixteen hemispheres of eight healthy volunteers. Quantitative population comparisons between DTI-FT estimates and published postmortem dissections match with a spatial precision of about 1 mm. The OR can be divided into three bundles that are segmented based on the direction of the fibers as they leave the LGN: Meyer's loop, central, and direct. The longitudinal and radial diffusivities of the three bundles do not differ within the measurement noise; there is a small difference in the radial diffusivity between the right and left hemispheres. We find that the anterior tip of Meyer's loop is 28 +/- 3 mm posterior to the temporal pole, and the population range is 1 cm. Hence, it is important to identify the location of this bundle in individual subjects or patients.

CT colonography: influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection.

PURPOSE: To retrospectively determine if three-dimensional (3D) viewing improves radiologists' accuracy in classifying true-positive (TP) and false-positive (FP) polyp candidates identified with computer-aided detection (CAD) and to determine candidate polyp features that are associated with classification accuracy, with known polyps serving as the reference standard. MATERIALS AND METHODS: Institutional review board approval and informed consent were obtained; this study was HIPAA compliant. Forty-seven computed tomographic (CT) colonography data sets were obtained in 26 men and 10 women (age range, 42-76 years). Four radiologists classified 705 polyp candidates (53 TP candidates, 652 FP candidates) identified with CAD; initially, only two-dimensional images were used, but these were later supplemented with 3D rendering. Another radiologist unblinded to colonoscopy findings characterized the features of each candidate, assessed colon distention and preparation, and defined the true nature of FP candidates. Receiver operating characteristic curves were used to compare readers' performance, and repeated-measures analysis of variance was used to test features that affect interpretation. RESULTS: Use of 3D viewing improved classification accuracy for three readers and increased the area under the receiver operating characteristic curve to 0.96-0.97 (P<.001). For TP candidates, maximum polyp width (P=.038), polyp height (P=.019), and preparation (P=.004) significantly affected accuracy. For FP candidates, colonic segment (P=.007), attenuation (P<.001), surface smoothness (P<.001), distention (P=.034), preparation (P<.001), and true nature of candidate lesions (P<.001) significantly affected accuracy. CONCLUSION: Use of 3D viewing increases reader accuracy in the classification of polyp candidates identified with CAD. Polyp size and examination quality are significantly associated with accuracy.

Alternative input devices for efficient navigation of large CT angiography data sets.

PURPOSE: To compare devices for the task of navigating through large computed tomographic (CT) data sets at a picture archiving and communication system workstation. MATERIALS AND METHODS: The institutional review board approved this study, and all subjects provided informed consent. Five radiologists were asked to find 25 different vascular targets in three CT angiography data sets (average number of sections, 1025) by using several devices (trackball, tablet, jog-shuttle wheel, and mouse). For each trial, the total time to acquire the targets (T1) was recorded. A secondary study in which 13 nonradiologists performed seven trials with an artificial target inserted at a random location in the same image data was also performed. For each trial, the following items were recorded: time until first target sighting (t2), time to manipulate the device after seeing the target, sections traversed during t2 (d1), time from first sight to target acquisition (t4), sections traversed during t4 (d2), and total trial time. Statistical analysis involved repeated-measures analysis of variance (ANOVA) and pairwise comparisons. RESULTS: Repeated-measures ANOVA revealed that the device used had a significant (P < .05) effect on T1. Pairwise comparisons revealed that the trackball was significantly slower than the tablet (P < .05) and marginally slower than the jog-shuttle wheel (P < .10). Further repeated-measures ANOVA for each secondary outcome measure revealed significant differences between devices for all outcome measures (P < .005). Pairwise comparisons revealed the trackball to be significantly slower than the other devices in all measures (P < .05). The trackball was significantly (P < .05) more accurate than the other devices for d1 and d2. CONCLUSION: The trackball may not be the optimal device for navigation of large CT angiography data sets; the use of other existing devices may improve the efficiency of interpretation of these sets.

Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection.

PURPOSE: To compare the performance of radiologists and of a computer-aided detection (CAD) algorithm for pulmonary nodule detection on thin-section thoracic computed tomographic (CT) scans. MATERIALS AND METHODS: The study was approved by the institutional review board. The requirement of informed consent was waived. Twenty outpatients (age range, 15-91 years; mean, 64 years) were examined with chest CT (multi-detector row scanner, four detector rows, 1.25-mm section thickness, and 0.6-mm interval) for pulmonary nodules. Three radiologists independently analyzed CT scans, recorded the locus of each nodule candidate, and assigned each a confidence score. A CAD algorithm with parameters chosen by using cross validation was applied to the 20 scans. The reference standard was established by two experienced thoracic radiologists in consensus, with blind review of all nodule candidates and free search for additional nodules at a dedicated workstation for three-dimensional image analysis. True-positive (TP) and false-positive (FP) results and confidence levels were used to generate free-response receiver operating characteristic (ROC) plots. Double-reading performance was determined on the basis of TP detections by either reader. RESULTS: The 20 scans showed 195 noncalcified nodules with a diameter of 3 mm or more (reference reading). Area under the alternative free-response ROC curve was 0.54, 0.48, 0.55, and 0.36 for CAD and readers 1-3, respectively. Differences between reader 3 and CAD and between readers 2 and 3 were significant (P < .05); those between CAD and readers 1 and 2 were not significant. Mean sensitivity for individual readings was 50% (range, 41%-60%); double reading resulted in increase to 63% (range, 56%-67%). With CAD used at a threshold allowing only three FP detections per CT scan, mean sensitivity was increased to 76% (range, 73%-78%). CAD complemented individual readers by detecting additional nodules more effectively than did a second reader; CAD-reader weighted kappa values were significantly lower than reader-reader weighted kappa values (Wilcoxon rank sum test, P < .05). CONCLUSION: With CAD used at a level allowing only three FP detections per CT scan, sensitivity was substantially higher than with conventional double reading.