کتاب دانلود مقالاتمقالات لاتین
اصلاح تضعیفی با استفاده از یادگیری عمیق و توالی دیکسون یکپارچه UTE / multi-echo: ارزیابی در تصویربرداری آمیلوئید و tau PET

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging
سال انتشار: a2020
زبان فایل: انگلیسی
فرمت فایل: pdf
قیمت: 100,000ريال

افزودن به سبد دانلود

DOI: 10.1007/s00259-020-05061-w

Abstract

Purpose

PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer’s disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging.

Methods

Thirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon-based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy.

Results

The Dice coefficients of the deep learning methods based on MPRAGE, Dixon, and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal, and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices.

Conclusion

Deep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.