Dataset Information

Improved image reconstruction of ⁸⁹Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm.

ABSTRACT:

Purpose

The aim of this study was to evaluate the use of a Bayesian penalized likelihood reconstruction algorithm (Q.Clear) for ⁸⁹Zr-immunoPET image reconstruction and its potential to improve image quality and reduce the administered activity of ⁸⁹Zr-immunoPET tracers.

Methods

Eight ⁸⁹Zr-immunoPET whole-body PET/CT scans from three ⁸⁹Zr-immunoPET clinical trials were selected for analysis. On average, patients were imaged 6.3?days (range 5.0-8.0?days) after administration of 69?MBq (range 65-76?MBq) of [⁸⁹Zr]Zr-DFO-daratumumab, [⁸⁹Zr]Zr-DFO-pertuzumab, or [⁸⁹Zr]Zr-DFO-trastuzumab. List-mode PET data was retrospectively reconstructed using Q.Clear with incremental ?-values from 150 to 7200, as well as standard ordered-subset expectation maximization (OSEM) reconstruction (2-iterations, 16-subsets, a 6.4-mm Gaussian transaxial filter, "heavy" z-axis filtering and all manufacturers' corrections active). Reduced activities were simulated by discarding 50% and 75% of original counts in each list mode stream. All reconstructed PET images were scored for image quality and lesion detectability using a 5-point scale. SUV_max for normal liver and sites of disease and liver signal-to-noise ratio were measured.

Results

Q.Clear reconstructions with ? = 3600 provided the highest scores for image quality. Images reconstructed with ?-values of 3600 or 5200 using only 50% or 25% of the original counts provided comparable or better image quality scores than standard OSEM reconstruction images using 100% of counts.

Conclusion

The Bayesian penalized likelihood reconstruction algorithm Q.Clear improved the quality of ⁸⁹Zr-immunoPET images. This could be used in future studies to improve image quality and/or decrease the administered activity of ⁸⁹Zr-immunoPET tracers.

SUBMITTER: Kirchner J

PROVIDER: S-EPMC7815860 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Improved image reconstruction of <sup>89</sup>Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm.

Kirchner Julian J O'Donoghue Joseph A JA Becker Anton S AS Ulaner Gary A GA

EJNMMI physics 20210119 1

<h4>Purpose</h4>The aim of this study was to evaluate the use of a Bayesian penalized likelihood reconstruction algorithm (Q.Clear) for <sup>89</sup>Zr-immunoPET image reconstruction and its potential to improve image quality and reduce the administered activity of <sup>89</sup>Zr-immunoPET tracers.<h4>Methods</h4>Eight <sup>89</sup>Zr-immunoPET whole-body PET/CT scans from three <sup>89</sup>Zr-immunoPET clinical trials were selected for analysis. On average, patients were imaged 6.3 days (rang ...[more]

PMID: 33469848

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Project description:PurposeMonoclonal antibody (mAb)-based PET (immunoPET) imaging can characterise tumour lesions non-invasively. It may be a valuable tool to determine which patients may benefit from treatment with a specific monoclonal antibody (mAb) and evaluate treatment response. For 89Zr immunoPET imaging, higher sensitivity of state-of-the art PET/CT systems equipped with silicon photomultiplier (SiPM)-based detector elements may be beneficial as the low positron abundance of 89Zr causes a low signal-to-noise level. Moreover, the long physical half-life limits the amount of activity that can be administered to the patients leading to poor image quality even when using long scan durations. Here, we investigated the difference in semiquantitative performance between the PMT-based Biograph mCT, our clinical reference system, and the SiPM-based Biograph Vision PET/CT in 89Zr immunoPET imaging. Furthermore, the effects of scan duration reduction using the Vision on semiquantitative imaging parameters and its influence on image quality assessment were evaluated.MethodsData were acquired on day 4 post 37 MBq 89Zr-labelled mAb injection. Five patients underwent a double scan protocol on both systems. Ten patients were scanned only on the Vision. For PET image reconstruction, three protocols were used, i.e. one camera-dependent protocol and European Association of Nuclear Medicine Research Limited (EARL) standards 1 and 2 compliant protocols. Vision data were acquired in listmode and were reprocessed to obtain images at shorter scan durations. Semiquantitative PET image parameters were derived from tumour lesions and healthy tissues to assess differences between systems and scan durations. Differently reconstructed images obtained using the Vision were visually scored regarding image quality by two nuclear medicine physicians.ResultsWhen images were reconstructed using 100% acquisition time on both systems following EARL standard 1 compliant reconstruction protocols, results regarding semiquantification were comparable. For Vision data, reconstructed images that conform to EARL1 standards still resulted in comparable semiquantification at shorter scan durations (75% and 50%) regarding 100% acquisition time.ConclusionScan duration of 89Zr immunoPET imaging using the Vision can be decreased up to 50% compared with using the mCT while maintaining image quality using the EARL1 compliant reconstruction protocol.

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Project description:UnlabelledQ.Clear, a Bayesian penalized-likelihood reconstruction algorithm for PET, was recently introduced by GE Healthcare on their PET scanners to improve clinical image quality and quantification. In this work, we determined the optimum penalization factor (beta) for clinical use of Q.Clear and compared Q.Clear with standard PET reconstructions.MethodsA National Electrical Manufacturers Association image-quality phantom was scanned on a time-of-flight PET/CT scanner and reconstructed using ordered-subset expectation maximization (OSEM), OSEM with point-spread function (PSF) modeling, and the Q.Clear algorithm (which also includes PSF modeling). Q.Clear was investigated for β (B) values of 100-1,000. Contrast recovery (CR) and background variability (BV) were measured from 3 repeated scans, reconstructed with the different algorithms. Fifteen oncology body (18)F-FDG PET/CT scans were reconstructed using OSEM, OSEM PSF, and Q.Clear using B values of 200, 300, 400, and 500. These were visually analyzed by 2 scorers and scored by rank against a panel of parameters (overall image quality; background liver, mediastinum, and marrow image quality; noise level; and lesion detectability).ResultsAs β is increased, the CR and BV decreases; Q.Clear generally gives a higher CR and lower BV than OSEM. For the smallest sphere reconstructed with Q.Clear B400, CR is 28.4% and BV 4.2%, with corresponding values for OSEM of 24.7% and 5.0%. For the largest hot sphere, Q.Clear B400 yields a CR of 75.2% and a BV of 3.8%, with corresponding values for OSEM of 64.4% and 4.0%. Scorer 1 and 2 ranked B400 as the preferred reconstruction in 13 of 15 (87%) and 10 of 15 (73%) cases. The least preferred reconstruction was OSEM PSF in all cases. In most cases, lesion detectability was highest ranked for B200, in 9 of 15 (67%) and 10 of 15 (73%), with OSEM PSF ranked lowest. Poor lesion detectability on OSEM PSF was seen in cases of mildly (18)F-FDG-avid mediastinal nodes in lung cancer and small liver metastases due to background noise. Conversely, OSEM PSF was ranked second highest for lesion detectability in most pulmonary nodule evaluation cases. The combined scores confirmed B400 to be the preferred reconstruction.ConclusionOur phantom measurement results demonstrate improved CR and reduced BV when using Q.Clear instead of OSEM. A β value of 400 is recommended for oncology body PET/CT using Q.Clear.

Dataset Information

Improved image reconstruction of ⁸⁹Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm.

Purpose

Methods

Results

Conclusion

Publications

Improved image reconstruction of <sup>89</sup>Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Improved image reconstruction of 89Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm.

Purpose

Methods

Results

Conclusion

Publications

Improved image reconstruction of <sup>89</sup>Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Improved image reconstruction of ⁸⁹Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm.