Dose Reduction While Preserving Diagnostic Quality in Head CT: Advancing the Application of Iterative Reconstruction Using a Live Animal Model.

BACKGROUND AND PURPOSE: Iterative reconstruction has promise in lowering the radiation dose without compromising image quality, but its full potential has not yet been realized. While phantom studies cannot fully approximate the subjective effects on image quality, live animal models afford this assessment. We characterize dose reduction in head CT by applying advanced modeled iterative reconstruction (ADMIRE) in a live ovine model while evaluating preservation of gray-white matter detectability and image texture compared with filtered back-projection. MATERIALS AND METHODS: A live sheep was scanned on a Force CT scanner (Siemens) at 12 dose levels (82-982 effective mAs). Images were reconstructed with filtered back-projection and ADMIRE (strengths, 1-5). A total of 72 combinations (12 doses × 6 reconstructions) were evaluated qualitatively for resemblance to the reference image (highest dose with filtered back-projection) using 2 metrics: detectability of gray-white matter differentiation and noise-versus-smoothness in image texture. Quantitative analysis for noise, SNR, and contrast-to-noise was also performed across all dose-strength combinations. RESULTS: Both qualitative and quantitative results confirm that gray-white matter differentiation suffers at a lower dose but recovers when complemented by higher iterative reconstruction strength, and image texture acquires excessive smoothness with a higher iterative reconstruction strength but recovers when complemented by dose reduction. Image quality equivalent to the reference image is achieved by a 58% dose reduction with ADMIRE-5. CONCLUSIONS: An approximately 60% dose reduction may be possible while preserving diagnostic quality with the appropriate dose-strength combination. This in vivo study can serve as a useful guide for translating the full implementation of iterative reconstruction in clinical practice.

Peeking into the Black Box of Coregistration in Clinical fMRI: Which Registration Methods Are Used and How Well Do They Perform?

BACKGROUND AND PURPOSE: Interpretation of fMRI depends on accurate functional-to-structural alignment. This study explores registration methods used by FDA-approved software for clinical fMRI and aims to answer the following question: What is the degree of misalignment when registration is not performed, and how well do current registration methods perform? MATERIALS AND METHODS: This retrospective study of presurgical fMRI for brain tumors compares nonregistered images and 5 registration cost functions: Hellinger, mutual information, normalized mutual information, correlation ratio, and local Pearson correlation. To adjudicate the accuracy of coregistration, we edge-enhanced echo-planar maps and rated them for alignment with structural anatomy. Lesion-to-activation distances were measured to evaluate the effects of different cost functions. RESULTS: Transformation parameters were congruent among Hellinger, mutual information, normalized mutual information, and the correlation ratio but divergent from the local Pearson correlation. Edge-enhanced images validated the local Pearson correlation as the most accurate. Hellinger worsened misalignment in 59% of cases, primarily exaggerating the inferior translation; no cases were worsened by the local Pearson correlation. Three hundred twenty lesion-to-activation distances from 25 patients were analyzed among nonregistered images, Hellinger, and the local Pearson correlation. ANOVA analysis revealed significant differences in the coronal (P < .001) and sagittal (P = .04) planes. If registration is not performed, 8% of cases may have a >3-mm discrepancy and up to a 5.6-mm lesion-to-activation distance difference. If a poor registration method is used, 23% of cases may have a >3-mm discrepancy and up to a 6.9-mm difference. CONCLUSIONS: The local Pearson correlation is a special-purpose cost function specifically designed for T2*-T1 coregistration and should be more widely incorporated into software tools as a better method for coregistration in clinical fMRI.