Efficacy of distortion correction on diffusion imaging: comparison of FSL eddy and eddy_correct using 30 and 60 directions diffusion encoding.

Diffusion imaging is a unique noninvasive tool to detect brain white matter trajectory and integrity in vivo. However, this technique suffers from spatial distortion and signal pileup or dropout originating from local susceptibility gradients and eddy currents. Although there are several methods to mitigate these problems, most techniques can be applicable either to susceptibility or eddy-current induced distortion alone with a few exceptions. The present study compared the correction efficiency of FSL tools, "eddy_correct" and the combination of "eddy" and "topup" in terms of diffusion-derived fractional anisotropy (FA). The brain diffusion images were acquired from 10 healthy subjects using 30 and 60 directions encoding schemes based on the electrostatic repulsive forces. For the 30 directions encoding, 2 sets of diffusion images were acquired with the same parameters, except for the phase-encode blips which had opposing polarities along the anteroposterior direction. For the 60 directions encoding, non-diffusion-weighted and diffusion-weighted images were obtained with forward phase-encoding blips and non-diffusion-weighted images with the same parameter, except for the phase-encode blips, which had opposing polarities. FA images without and with distortion correction were compared in a voxel-wise manner with tract-based spatial statistics. We showed that images corrected with eddy and topup possessed higher FA values than images uncorrected and corrected with eddy_correct with trilinear (FSL default setting) or spline interpolation in most white matter skeletons, using both encoding schemes. Furthermore, the 60 directions encoding scheme was superior as measured by increased FA values to the 30 directions encoding scheme, despite comparable acquisition time. This study supports the combination of eddy and topup as a superior correction tool in diffusion imaging rather than the eddy_correct tool, especially with trilinear interpolation, using 60 directions encoding scheme

Optimizing GPT-4 Turbo Diagnostic Accuracy in Neuroradiology through Prompt Engineering and Confidence Thresholds

Background and Objectives: Integrating large language models (LLMs) such as GPT-4 Turbo into diagnostic imaging faces a significant challenge, with current misdiagnosis rates ranging from 30–50%. This study evaluates how prompt engineering and confidence thresholds can improve diagnostic accuracy in neuroradiology. Methods: We analyze 751 neuroradiology cases from the American Journal of Neuroradiology using GPT-4 Turbo with customized prompts to improve diagnostic precision. Results: Initially, GPT-4 Turbo achieved a baseline diagnostic accuracy of 55.1%. By reformatting responses to list five diagnostic candidates and applying a 90% confidence threshold, the highest precision of the diagnosis increased to 72.9%, with the candidate list providing the correct diagnosis at 85.9%, reducing the misdiagnosis rate to 14.1%. However, this threshold reduced the number of cases that responded. Conclusions: Strategic prompt engineering and high confidence thresholds significantly reduce misdiagnoses and improve the precision of the LLM diagnostic in neuroradiology. More research is needed to optimize these approaches for broader clinical implementation, balancing accuracy and utility

Representative diffusion-weighted images with the 60 directions encoding scheme.

<p>The images were from the same subject and slice location. The NC60 with forward phase-encoding blips had an artifactual signal pileup around the temporal base of the skull, which was not corrected in EC60, but corrected in ET60. Again, the EC30 with trilinear were blurred, compared with EC30 with spline interpolation.</p

Comparison of ET30 and ET60 images.

<p>The FA values for ET60 were significantly higher than those for ET30 in most of the white matter, with slight left hemisphere predominance. These data were overlaid onto the MNI152_T1 template, and the mean FA skeleton is shown in green. The significance level was set at a <i>P</i> value of <0.05 with FWE correction.</p

Representative diffusion-weighted images with the 30 directions encoding scheme.

<p>The images were from the same subject and slice location, with forward phase-encoding blips (left panel), and with reversed phase-encoding blips (right panel). NC30 had an artifactual signal pileup around the frontal base of the skull, which was corrected not in EC30 with trilinear or spline but in ET30. The EC30 with trilinear interpolation were blurred, compared with EC30 with spline interpolation.</p

Efficiency of correction schemes acquired with 60 directions diffusion encoding.

<p>Tripled paired group comparisons with TBSS were conducted between NC60 and EC60 with spline interpolation in the upper, NC60 and ET60 in the middle, and EC60 with spline and ET60 in the lower row. The FA values for ET60 were significantly higher than those for NC60 or EC60 in most of the white matter skeleton (blue/lightblue). In contrast, white matter skeleton with significantly higher (blue/lightblue) and lower (red/yellow) FA values for EC60 with spline interpolation than those for NC60 were observed in various areas. These data were overlaid onto the MNI152_T1 template, and the mean FA skeleton is shown in green. The significance level was set at a <i>P</i> value of <0.05 with FWE correction.</p