Neuropsychological and socio-emotional factors associated with specific-arithmetic disability were investigated in an unselected sample of New Zealand children. Subjects were 17 specific-arithmetic disabled, 27 specific-reading disabled, 63 generally disabled, and 50 nondisabled 13 year olds. Evidence was sought for an association between specific-arithmetic disability and the neuropsychological and socio-emotional correlates of Nonverbal Learning Disability syndrome (NLD). NLD is characterized by a pattern of nonverbal and verbal neuropsychological strengths and weaknesses, and appears to place individuals at greater risk for internalizing psychopathology, than other learning disabilities. Only specific-arithmetic disabled subjects were found to show a neuropsychological profile reminiscent of NLD. Evidence of poor socio-emotional adjustment was found across all three learning-disabled groups, and was greatest among generally disabled subjects. We found that the specific-arithmetic-disabled subjects exhibited the greatest degree of overlap between internalizing psychopathology and a NLD neuropsychological profile. The results are interpreted as providing some support for the idea that specific-arithmetic-disabled individuals may be at greater risk for the NLD syndrome than either generally disabled or specific-reading-disabled individuals.

Intra-procedural monitoring and post-procedural follow-up is necessary for a successful ablation treatment. An imaging technique which can assess the ablation geometry accurately is beneficial to monitor and evaluate treatment. In this study, we developed an automated ablation segmentation technique for serial low-dose, noisy ablation computed tomography (CT) or contrast-enhanced CT (CECT). Methods: Low-dose, noisy temporal CT and CECT volumes were acquired during microwave ablation on normal porcine liver (four with non-contrast CT and eight with CECT). Highly constrained backprojection (HYPR) processing was used to recover ablation zone information compromised by low-dose noise. First-order statistic features and normalized fractional Brownian features (NBF) were used to segment ablation zones by fuzzy c-mean clustering. After clustering, the segmented ablation zone was refined by cyclic morphological processing. Automatic and manual segmentations were compared to gross pathology with Dice's coefficient (morphological similarity), while cross-sectional dimensions were compared by percent difference. Results: Automatic and manual segmentations of the ablation zone were very similar to gross pathology (Dice Coefficients: Auto.-Path. = 0.84 AE 0.02; Manu.-Path. = 0.76 AE 0.03, P = 0.11). The differences in ablation area, major diameter and minor diameter were 17.9 AE 3.2%, 11.1 AE 3.2% and 16.2 AE 3.4%, respectively, when comparing automatic segmentation to gross pathology, which were lower than the differences of 32.9 AE 16.8%, 13.0 AE 9.8% and 21.8 AE 5.8% when comparing manual segmentation to gross pathology. Manual segmentations tended to overestimate gross pathology when ablation area was less than 15 cm 2 , but the automated segmentation tended to underestimate gross pathology when ablation zone is larger than 20 cm 2. Conclusion: Fuzzy c-means clustering may be used to aid automatic segmentation of ablation zones without prior information or user input, making serial CT/CECT has more potential to assess treatments intra-procedurally.

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