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Optimal Tikhonov regularization for DEER spectroscopy.

ABSTRACT: Tikhonov regularization is the most commonly used method for extracting distance distributions from experimental double electron-electron resonance (DEER) spectroscopy data. This method requires the selection of a regularization parameter, ?, and a regularization operator, L. We analyze the performance of a large set of ? selection methods and several regularization operators, using a test set of over half a million synthetic noisy DEER traces. These are generated from distance distributions obtained from in silico double labeling of a protein crystal structure of T4 lysozyme with the spin label MTSSL. We compare the methods and operators based on their ability to recover the model distance distributions from the noisy time traces. The results indicate that several ? selection methods perform quite well, among them the Akaike information criterion and the generalized cross validation method with either the first- or second-derivative operator. They perform significantly better than currently utilized L-curve methods.

SUBMITTER: Edwards TH

PROVIDER: S-EPMC5840305 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Optimal Tikhonov regularization for DEER spectroscopy.

Edwards Thomas H TH Stoll Stefan S

Journal of magnetic resonance (San Diego, Calif. : 1997) 20180201

Tikhonov regularization is the most commonly used method for extracting distance distributions from experimental double electron-electron resonance (DEER) spectroscopy data. This method requires the selection of a regularization parameter, α, and a regularization operator, L. We analyze the performance of a large set of α selection methods and several regularization operators, using a test set of over half a million synthetic noisy DEER traces. These are generated from distance distributions obt ...[more]

PMID: 29414064

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Project description:In contrast to ordered subset expectation maximization (OSEM), block sequential regularized expectation maximization (BSREM) positron emission tomography (PET) reconstruction algorithms can run until full convergence while controlling image quality and noise. Recent studies with BSREM and 18F-FDG PET reported higher signal-to-noise ratios and higher standardized uptake values (SUV). In this study, we investigate the optimal regularization parameter (?) for clinical 68Ga-PSMA PET/MR reconstructions in the pelvic region applying time-of-flight (TOF) BSREM in comparison to TOF OSEM. Two-minute emission data from the pelvic region of 25 patients who underwent 68Ga-PSMA PET/MR were retrospectively reconstructed. Reference OSEM reconstructions had 28 subsets and 2 iterations. BSREM reconstructions were performed with 15 ? values between 150 and 1200. Regions of interest (ROIs) were drawn around lesions and in uniform background. Background SUVmean (average) and SUVstd (standard deviation), and lesion SUVmax (average of 5 hottest voxels) were calculated. Differences were analyzed using the Wilcoxon matched pairs signed-rank test.A total of 40 lesions were identified in the pelvic region. Background noise (SUVstd) and lesions SUVmax decreased with increasing ?. Image reconstructions with ? values lower than 400 have higher (p?<?0.01) background noise, compared to the reference OSEM reconstructions, and are therefore less useful. Lesions with low activity on images reconstructed with ? values higher than 600 have a lower (p?<?0.05) SUVmax compared to the reference. These reconstructions are likely visually appealing due to the lower background noise, but the lower SUVmax could possibly render small low-uptake lesions invisible.In our study, we showed that PET images reconstructed with TOF BSREM in combination with the 68Ga-PSMA tracer result in lower background noise and higher SUVmax values in lesions compared to TOF OSEM. Our study indicates that a ? value between 400 and 550 might be the optimal compromise between high SUVmax and low background noise.

Dataset Information

Optimal Tikhonov regularization for DEER spectroscopy.

Publications

Optimal Tikhonov regularization for DEER spectroscopy.

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