Optimization of Image Reconstruction for 90Y Selective Internal Radiotherapy on a Lutetium Yttrium Orthosilicate PET/CT System Using a Bayesian Penalized Likelihood Reconstruction Algorithm

Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm

Positron Emission Tomography (PET) imaging after90 Y liver radioembolization is used for both lesion identification and dosimetry. Bayesian penalized likelihood (BPL) reconstruction algorithms are an alternative to ordered subset expectation maximization (OSEM) with improved image quality and lesion detectability. The investigation of optimal parameters for90 Y image reconstruction of Q.Clear, a commercial BPL algorithm developed by General Electric (GE), in PET/MR is a field of interest and the subject of this study. The NEMA phantom was filled at an 8:1 sphere-to-background ratio. Acquisitions were performed on a PET/MR scanner for clinically relevant activities between 0.7 and 3.3 MBq/ml. Reconstructions with Q.Clear were performed varying the β penalty parameter between 20 and 6000, the acquisition time between 5 and 20 min and pixel size between 1.56 and 4.69 mm. OSEM reconstructions of 28 subsets with 2 and 4 iterations with and without Time-of-Flight (TOF) were compared to Q.Clear with β = 4000. Recovery coefficients (RC), their coefficient of variation (COV), background variability (BV), contrast-to-noise ratio (CNR) and residual activity in the cold insert were evaluated. Increasing β parameter lowered RC, COV and BV, while CNR was maximized at β = 4000; further increase resulted in oversmoothing. For quantification purposes, β = 1000-2000 could be more appropriate. Longer acquisition times resulted in larger CNR due to reduced image noise. Q.Clear reconstructions led to higher CNR than OSEM. A β of 4000 was obtained for optimal image quality, although lower values could be considered for quantification purposes. An optimal acquisition time of 15 min was proposed considering its clinical use.

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameterβwas varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration.Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, variousβvalues (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV).Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasingβ, peaking atβ= 550. The CNR for allβ, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images withβ= 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration.Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

Optimization of Bayesian penalized likelihood reconstruction for 68 Ga-prostate-specific membrane antigen-11 PET/computed tomography

OBJECTIVE

The objective of this study is to determine the optimal β value for clinical use in digital 68 Ga-prostate-specific membrane antigen (PSMA-11) PET/computed tomography (CT) imaging.

METHODS

68 Ga PSMA PET/CT of 21 patients with prostate cancer were reconstructed using block-sequential regularized expectation maximization ( β value of 400-1600) and ordered subsets expectation maximization. Nine independent blinded readers evaluated each reconstruction for overall image quality, noise level and lesion detectability. Maximum standardized uptake value (SUVmax) of the most intense lesion, liver SUVmean and liver SUV SD were recorded. Lesions were then subdivided according to uptake and size; the SUVmax of these lesions were analyzed.

RESULTS

There is a statistically significant correlation between improvement in image quality and β value, with the best being β 1400. This trend was also seen in image noise ( P  < 0.001), with the least image noise reported with β 1400. Lesion detectability was not significantly different between the different β values ( P  =  0.6452). There was no statistically significant difference in SUVmax of the most intense lesion ( P  = 0.9966) and SUVmean of liver background between the different β values ( P  = 0.9999); however, the SUV SD of the liver background showed a clear trend, with the lowest with β 1400 ( P  = 0.0008). There was a decreasing trend observed in SUVmax when β values increased from 800 to 1400 for all four subgroups, and this decrease was greatest in small and low uptake lesions.

CONCLUSION

Bayesian penalized likelihood reconstruction algorithms improve image quality without affecting lesion detectability. A β value of 1400 is optimal.

Triple modality image reconstruction of PET data using SPECT, PET, CT information increases lesion uptake in images of patients treated with radioembolization with [Formula: see text] micro-spheres

PURPOSE

Nuclear medicine imaging modalities like computed tomography (CT), single photon emission CT (SPECT) and positron emission tomography (PET) are employed in the field of theranostics to estimate and plan the dose delivered to tumors and the surrounding tissues and to monitor the effect of the therapy. However, therapeutic radionuclides often provide poor images, which translate to inaccurate treatment planning and inadequate monitoring images. Multimodality information can be exploited in the reconstruction to enhance image quality. Triple modality PET/SPECT/CT scanners are particularly useful in this context due to the easier registration process between images. In this study, we propose to include PET, SPECT and CT information in the reconstruction of PET data. The method is applied to Yttrium-90 ([Formula: see text]Y) data.

METHODS

Data from a NEMA phantom filled with [Formula: see text]Y were used for validation. PET, SPECT and CT data from 10 patients treated with Selective Internal Radiation Therapy (SIRT) were used. Different combinations of prior images using the Hybrid kernelized expectation maximization were investigated in terms of VOI activity and noise suppression.

RESULTS

Our results show that triple modality PET reconstruction provides significantly higher uptake when compared to the method used as standard in the hospital and OSEM. In particular, using CT-guided SPECT images, as guiding information in the PET reconstruction significantly increases uptake quantification on tumoral lesions.

CONCLUSION

This work proposes the first triple modality reconstruction method and demonstrates up to 69% lesion uptake increase over standard methods with SIRT [Formula: see text]Y patient data. Promising results are expected for other radionuclide combination used in theranostic applications using PET and SPECT.

A novel figure of merit to investigate 68Ga PET/CT image quality based on patient weight and lesion size using Q.Clear reconstruction algorithm: A phantom study

INTRODUCTION

Q.Clear is a Bayesian penalised-likelihood algorithm that uses a β-value for positron emission tomography(PET)/computed tomography(CT) image reconstruction(IR). Our study proposes a novel figure of merit, named CRBV, to compare the Q.Clear performances using 68Ga PET/CT image with the ordered-subset-expectation-maximization(OSEM) algorithm and to identify the optimal β-values for these images using two phantoms mimicking normal and overweight patients.

METHODS

NEMA IQ phantom with or without a ring of water-filled plastic bags (NEMAstd and NEMAow, respectively) was acquired and reconstructed with OSEM and Q.Clear at various β-values and minutes/bed position(min/bp). Contrast recovery(CR), background variability(BV) and CRBV were calculated. Highest CRBV values were used to identify optimal β-value ranges.

RESULTS

Q.Clear with 250 ≤ β ≤ 800 improved CRBV compared to OSEM for all the investigated spheres and acquisition setups. Outside of this range, Q.Clear still outperformed OSEM with few exceptions depending on spheres diameters and phantoms(e.g.,β-value = 1600 for diameters ≤ 17 mm using the NEMAow phantom). Regarding the CRBV performance for IR optimization, for the 4 min/bp NEMAstd IR, β-values = 300 ÷ 350 allowed to simultaneously optimize all diameters(except for the 10 mm); for the NEMAow IR, β-values = 350 ÷ 500 were needed for diameters > 20 mm, while β-values = 200 ÷ 250 were selected for the remaining diameters. For the 2 min/bp, β-value = 500 was suitable for diameters > 17 mm in both NEMAstd and NEMAow IR, while for smaller diameters β-value = 200 and β-values = 250 ÷ 350 were obtained for NEMAstd and NEMAow, respectively.

CONCLUSION

Almost all tested β-values of Q.Clear improved the CRBV compared to OSEM. In both phantoms, simulating normal and over-weight patients, optimal β-values were found according to lesion sizes and investigated acquisition times.

Analysis of image quality by regulating beta function of BSREM reconstruction algorithm and comparison with conventional reconstructions in carcinoma breast studies of PET CT with BGO detector

BACKGROUND

The study aimed to evaluate the beta penalization factor of the BSREM reconstruction algorithm on a five-ring BGO-based PET CT system and compared it with conventional reconstructions.

METHODS

Retrospective study involves 30 breast cancer patient data of 18F-fluorodeoxyglucose ( 18 F-FDG) PET CT for reconstruction with OSEM, OSEM + PSF, and BSREM under variable β factors ranging from 200 to 600 in the steps of 50. Liver noise, lesion SUVmax, SBR, and SNR for each reconstruction were calculated. Quantitative parameters of each beta factor of BSREM were compared with OSEM and OSEM + PSF, using the Wilcoxon sign rank test with Bonferroni correction, a value of P < 0.002 was considered statistically significant. Visual scoring by two readers was also evaluated.

RESULTS

Thirty lesions of mean size 1.91 ± 0.58 cm range (0.7-3.6 cm) were identified. Liver noise and SBR were reduced, whereas SNR was increased with an increasing β value of BSREM. In comparison with OSEM, liver noise was not significantly different from β200 and β250. SNR of OSEM was significantly lower than any other β factors and SBR of β factor less than 500 was significantly higher than OSEM. In comparison with OSEM + PSF, liver noise was not significantly different from β400 and β350-500 do not show a significant difference in SNR and SBR compared with OSEM + PSF. β350 scored highest under visual scoring with a moderate agreement.

CONCLUSION

The study quantitatively indicates the optimum beta range of β250-450 and the qualitative evaluation indicates that β350 is an optimum beta factor of BSREM in breast cancer cases for 18 F-FDG WB-PET CT.

Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [18 F]FDG and [11 C]PiB

PURPOSE

The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain PET images acquired using ¹⁸ F-fluoro-2-deoxy-D-glucose ([¹⁸ F]FDG) and ¹¹ C-labeled Pittsburg compound B ([¹¹ C]PiB).

METHODS

Images generated from Hoffman 3D brain and cylindrical phantoms were acquired using a Discovery PET/CT 710 and reconstructed using OSEM + time-of-flight (TOF) under clinical conditions and BPL + TOF (β = 20-1,000). Contrast was calculated from images generated by the Hoffman 3D brain phantom, and noise and uniformity were calculated from those generated by the cylindrical phantom. Five cognitively healthy controls and five patients with Alzheimer disease were assessed using [¹⁸ F]FDG and [¹¹ C]PiB PET to validate the findings from the phantom study. The β values were restricted by the findings of the phantom study, then one certified nuclear medicine physician and two certified nuclear medicine technologists visually determined optimal β values by scoring the quality parameters of image contrast, image noise, cerebellar stability, and overall image quality of PET images from 1 (poor) to 5 (excellent).

RESULTS

The contrast in BPL satisfied the Japanese Society of Nuclear Medicine (JSNM) criterion of ≥ 55% and exceeded that of OSEM at ranges of β = 20-450 and 20-600 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively. The image noise in BPL satisfied the JSNM criterion of ≤ 15% and was below that in OSEM when β = 150-1000 and 400-1,000 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively. The phantom study restricted the ranges of β values to 100-300 and 300-500 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively. The BPL scores for grey-white matter contrast and image noise, exceeded those of OSEM in [¹⁸ F]FDG and [¹¹ C]PiB images regardless of β values. Visual evaluation confirmed that the optimal β values were 200 and 450 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively.

CONCLUSIONS

The BPL achieved better image contrast and less image noise than OSEM, while maintaining quantitative SUVR due to full convergence, more rigorous noise control and edge preservation. The optimal β values for [¹⁸ F]FDG and [¹¹ C]PiB brain PET were apparently 200 and 450, respectively. The present study provides useful information about how to determine optimal β values in BPL for brain PET imaging. This article is protected by copyright. All rights reserved.

Effects of Respiratory Motion on Y-90 PET Dosimetry for SIRT

Respiratory motion degrades the quantification accuracy of PET imaging by blurring the radioactivity distribution. In the case of post-SIRT PET-CT verification imaging, respiratory motion can lead to inaccuracies in dosimetric measures. Using an anthropomorphic phantom filled with ⁹⁰Y at a range of clinically relevant activities, together with a respiratory motion platform performing realistic motions (10–15 mm amplitude), we assessed the impact of respiratory motion on PET-derived post-SIRT dosimetry. Two PET scanners at two sites were included in the assessment. The phantom experiments showed that device-driven quiescent period respiratory motion correction improved the accuracy of the quantification with statistically significant increases in both the mean contrast recovery (+5%, p = 0.003) and the threshold activities corresponding to the dose to 80% of the volume of interest (+6%, p < 0.001). Although quiescent period gating also reduces the number of counts and hence increases the noise in the PET image, its use is encouraged where accurate quantification of the above metrics is desired.

Yttrium-90 quantitative phantom study using digital photon counting PET

BACKGROUND

PET imaging of 90Y-microsphere distribution following radioembolisation is challenging due to the count-starved statistics from the low branching ratio of e+/e- pair production during 90Y decay. PET systems using silicon photo-multipliers have shown better 90Y image quality compared to conventional photo-multiplier tubes. The main goal of the present study was to evaluate reconstruction parameters for different phantom configurations and varying listmode acquisition lengths to improve quantitative accuracy in 90Y dosimetry, using digital photon counting PET/CT.

METHODS

Quantitative PET and dosimetry accuracy were evaluated using two uniform cylindrical phantoms specific for PET calibration validation. A third body phantom with a 9:1 hot sphere-to-background ratio was scanned at different activity concentrations of 90Y. Reconstructions were performed using OSEM algorithm with varying parameters. Time-of-flight and point-spread function modellings were included in all reconstructions. Absorbed dose calculations were carried out using voxel S-values convolution and were compared to reference Monte Carlo simulations. Dose-volume histograms and root-mean-square deviations were used to evaluate reconstruction parameter sets. Using listmode data, phantom and patient datasets were rebinned into various lengths of time to assess the influence of count statistics on the calculation of absorbed dose. Comparisons between the local energy deposition method and the absorbed dose calculations were performed.

RESULTS

Using a 2-mm full width at half maximum post-reconstruction Gaussian filter, the dosimetric accuracy was found to be similar to that found with no filter applied but also reduced noise. Larger filter sizes should not be used. An acquisition length of more than 10 min/bed reduces image noise but has no significant impact in the quantification of phantom or patient data for the digital photon counting PET. 3 iterations with 10 subsets were found suitable for large spheres whereas 1 iteration with 30 subsets could improve dosimetry for smaller spheres.

CONCLUSION

The best choice of the combination of iterations and subsets depends on the size of the spheres. However, one should be careful on this choice, depending on the imaging conditions and setup. This study can be useful in this choice for future studies for more accurate 90Y post-dosimetry using a digital photon counting PET/CT.

Impact of the Noise Penalty Factor on Quantification in Bayesian Penalized Likelihood (Q.Clear) Reconstructions of 68Ga-PSMA PET/CT Scans

Functional imaging with ⁶⁸Ga prostate-specific membrane antigen (PSMA) and positron emission tomography (PET) can fulfill an important role in treatment selection and adjustment in prostate cancer. This article focusses on quantitative assessment of ⁶⁸Ga-PSMA-PET. The effect of various parameters on standardized uptake values (SUVs) is explored, and an optimal Bayesian penalized likelihood (BPL) reconstruction is suggested. PET acquisitions of two phantoms consisting of a background compartment and spheres with diameter 4 mm to 37 mm, both filled with solutions of ⁶⁸Ga in water, were performed with a GE Discovery 710 PET/CT scanner. Recovery coefficients (RCs) in multiple reconstructions with varying noise penalty factors and acquisition times were determined and analyzed. Apparent recovery coefficients of spheres with a diameter smaller than 17 mm were significantly lower than those of spheres with a diameter of 17 mm and bigger (p < 0.001) for a tumor-to-background (T/B) ratio of 10:1 and a scan time of 10 min per bed position. With a T/B ratio of 10:1, the four largest spheres exhibit significantly higher RCs than those with a T/B ratio of 20:1 (p < 0.0001). For spheres with a diameter of 8 mm and less, alignment with the voxel grid potentially affects the RC. Evaluation of PET/CT scans using (semi-)quantitative measures such as SUVs should be performed with great caution, as SUVs are influenced by scanning and reconstruction parameters. Based on the evaluation of multiple reconstructions with different β of phantom scans, an intermediate β (600) is suggested as the optimal value for the reconstruction of clinical ⁶⁸Ga-PSMA PET/CT scans, considering that both detectability and reproducibility are relevant.

Retrospective SPECT/CT dosimetry following transarterial radioembolization

Transarterial radioembolization (TARE) effectively treats unresectable primary and metastatic liver tumors through intra‐arterial injection of Yttrium‐90 (⁹⁰Y) beta particle emitting microspheres which implant around the tumor. Current dosimetry models are highly simplistic and there is a large need for an image‐based dosimetry post‐TARE, which would improve treatment safety and efficacy. Current post‐TARE imaging is ⁹⁰Y bremsstrahlung SPECT/CT and we study the use of these images for dosimetry. Retrospective image review of ten patients having a Philips Healthcare^TM SPECT/CT following TARE SIR‐Spheres® implantation. Emission series with attenuation correction were resampled to 3 mm resolution and used to create image‐based dose distributions. Dose distributions and analysis were performed in MIM Software SurePlan^TM utilizing SurePlan^TM Local Deposition Method (LDM) and a dose convolution method (WFBH). We sought to implement a patient‐specific background subtraction prior to dose calculation to make these noisy bremsstrahlung SPECT images suitable for post‐TARE dosimetry. On average the percentage of mean background counts to maximum count in the image across all patients was 9.4 ± 4.9% (maximum = 7.6%, minimum = 2.3%). Absolute dose increased and profile line width decreased as background subtraction value increased. The average value of the LDM and WFBH dose methods was statistically the same. As background subtraction value increased, the DVH curves become unrealistic and distorted. Background subtraction on bremsstrahlung SPECT image has a large effect on post‐TARE dosimetry. The background contour defined provides a systematic estimate to the activity background that accounts for the scanner and patient conditions at the time of the image study and is easily implemented using commercially available software. Using the mean count in the background contour as a subtraction across the entire image gave the most realistic dose distributions. This methodology is independent of microsphere and software manufacturer allowing for use with any available products or tools.

Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography?

Purpose

This study aims to provide a detailed investigation on the noise penalization factor in Bayesian penalized likelihood (BPL)‐based algorithm, with the utilization of partial volume effect correction (PVC), so as to offer the suitable beta value and optimum standardized uptake value (SUV) parameters in clinical practice for small pulmonary nodules.

Methods

A National Electrical Manufacturers Association (NEMA) image‐quality phantom was scanned and images were reconstructed using BPL with beta values ranged from 100 to 1000. The recovery coefficient (RC), contrast recovery (CR), and background variability (BV) were measured to assess the quantification accuracy and image quality. In the clinical assessment, lesions were categorized into sub‐centimeter (<10 mm, n = 7) group and medium size (10–30 mm, n = 16) group. Signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) were measured to evaluate the image quality and lesion detectability. With PVC was performed, the impact of beta values on SUVs (SUVmax, SUVmean, SUVpeak) of small pulmonary nodules was evaluated. Subjective image analysis was performed by two experienced readers.

Results

With the increasing of beta values, RC, CR, and BV decreased gradually in the phantom work. In the clinical study, SNR and CNR of both groups increased with the beta values (P < 0.001), although the sub‐centimeter group showed increases after the beta value reached over 700. In addition, highly significant negative correlations were observed between SUVs and beta values for both lesion‐size groups before the PVC (P < 0.001 for all). After the PVC, SUVpeak measured from the sub‐centimeter group was no significantly different among different beta values (P = 0.830).

Conclusion

Our study suggests using SUVpeak as the quantification parameter with PVC performed to mitigate the effects of beta regularization. Beta values between 300 and 400 were preferred for pulmonary nodules smaller than 30 mm.

Quantitative and Qualitative Improvement of Low-Count [68Ga]Citrate and [90Y]Microspheres PET Image Reconstructions Using Block Sequential Regularized Expectation Maximization Algorithm

PURPOSE

There are several important positron emission tomography (PET) imaging scenarios that require imaging with very low photon statistics, for which both quantitative accuracy and visual quality should not be neglected. For example, PET imaging with the low photon statistics is closely related to active efforts to significantly reduce radiation exposure from radiopharmaceuticals. We investigated two examples of low-count PET imaging: (a) imaging [⁹⁰Y]microsphere radioembolization that suffers the very small positron emission fraction of Y-90's decay processes, and (b) cancer imaging with [⁶⁸Ga]citrate with uptake time of 3-4 half-lives, necessary for visualizing tumors. In particular, we investigated a type of penalized likelihood reconstruction algorithm, block sequential regularized expectation maximization (BSREM), for improving both image quality and quantitative accuracy of these low-count PET imaging cases.

PROCEDURES

The NEMA/IEC Body phantom filled with aqueous solution of Y-90 or Ga-68 was scanned to mimic the low-count scenarios of corresponding patient data acquisitions on a time-of-flight (TOF) PET/magnetic resonance imaging system. Contrast recovery, background variation, and signal-to-noise ratio were evaluated in different sets of count densities using both conventional TOF ordered subset expectation (TOF-OSEM) and TOF-BSREM algorithms. The regularization parameter, beta, in BSREM that controls the tradeoff between image noise and resolution was evaluated to find a value for improved confidence in image interpretation. Visual quality assessment of the images obtained from patients administered with [⁶⁸Ga]citrate (n = 6) was performed. We also made preliminary visual image quality assessment for one patient with [⁹⁰Y]microspheres. In Y-90 imaging, the effect of 511-keV energy window selection for minimizing the number of random events was also evaluated.

RESULTS

Quantitatively, phantom images reconstructed with TOF-BSREM showed improved contrast recovery, background variation, and signal-to-noise ratio values over images reconstructed with TOF-OSEM. Both phantom and patient studies of delayed imaging of [⁶⁸Ga]citrate show that TOF-BSREM with beta = 500 gives the best tradeoff between image noise and image resolution based on visual assessment by the readers. The NEMA-IQ phantom study with [⁹⁰Y]microspheres shows that the narrow energy window (460-562 keV) recovers activity concentrations in small spheres better than the regular energy window (425-650 keV) with the beta value of 2000 using the TOF-BSREM algorithm. For the images obtained from patients with [⁶⁸Ga]citrate using TOF-BSREM with beta = 500, the visual analogue scale (VAS) was improved by 17 % and the Likert score was increased by 1 point on average, both in comparison to corresponding scores for images reconstructed using TOF-OSEM.

CONCLUSION

Our investigation shows that the TOF-BSREM algorithm improves the image quality and quantitative accuracy in low-count PET imaging scenarios. However, the beta value in this algorithm needed to be adjusted for each radiopharmaceutical and counting statistics at the time of scans.

Impact of image reconstruction method on dose distributions derived from 90Y PET images: phantom and liver radioembolization patient studies

PET images acquired after liver ⁹⁰Y radioembolization therapies are typically very noisy, which significantly challenges both visualization and quantification of activity distributions. To improve their noise characteristics, regularized iterative reconstruction algorithms such as block sequential regularized expectation maximization (Q.Clear for GE Healthcare, USA) have been proposed. In this study, we aimed to investigate the effects which different reconstruction algorithms may have on patient images, with reconstruction parameters initially narrowed down using phantom studies. Moreover, we evaluated the impact of these reconstruction methods on voxel-based dose distribution in phantom and patient studies (lesions and healthy livers). The International Electrotechnical Commission (IEC)/NEMA phantom, containing six spheres, was filled with ⁹⁰Y and imaged using a GE Discovery 690 PET/CT scanner with time-of-flight enabled. The images were reconstructed using Q.Clear (with β parameter ranging from 0 to 8000) and ordered subsets expectation maximization. The image quality and quantification accuracy were evaluated by computing the hot ([Formula: see text]) and cold ([Formula: see text]) contrast recovery coefficients, background variability (BV) and activity bias. Next, dose distributions and dose volume histograms were generated using MIM® software's SurePlan LiverY90 toolbox. Subsequently, parameters optimized in these phantom studies were applied to five patient datasets. Dose parameters, such as D_max, D_mean, D₇₀, and V_100Gy, were estimated, and their variability for different reconstruction methods was investigated. Based on phantom studies, the β parameter values optimized for image quality and quantification accuracy were 2500 and 300, respectively. When all investigated reconstructions were applied to patient studies, D_mean, D₅₀, D₇₀, and V_100Gy showed coefficients of variation below 8%; whereas the variability of D_max was up to 30% for both phantom and patient images. Although β = 300-1000 would provide accurate activity quantification for a region of interest, when considering activity/dose voxelized distribution, higher β value (e.g. 4000-5000) would provide the greatest accuracy for dose distributions. In this ⁹⁰Y radioembolization PET/CT study, the β parameter in regularized iterative (Q.Clear) reconstruction was investigated for image quality, accurate quantification and dose distributions based on phantom experiments and then applied to patient studies. Our results indicate that more accurate dose distribution can be achieved from smoother PET images, reconstructed with larger β values than those yielding the best activity quantifications but noisy images. Most importantly, these results suggest that quantitative measures, which are commonly used in clinics, such as SUV_max or SUV_peak( equivalent of D_max), should not be employed for ⁹⁰Y PET images, since their values would highly depend on the image reconstruction.

Evaluation of block-sequential regularized expectation maximization reconstruction of 68Ga-DOTATOC, 18F-fluoride, and 11C-acetate whole-body examinations acquired on a digital time-of-flight PET/CT scanner

Background

Block-sequential regularized expectation maximization (BSREM) is a fully convergent iterative image reconstruction algorithm. We hypothesize that tracers with different distribution patterns will result in different optimal settings for the BSREM algorithm. The aim of this study was to evaluate the image quality with variations in the applied β-value and acquisition time for three positron emission tomography (PET) tracers. NEMA image quality phantom measurements and clinical whole-body digital time-of-flight (TOF) PET/computed tomography (CT) examinations with ⁶⁸Ga-DOTATOC (n = 13), ¹⁸F-fluoride (n = 10), and ¹¹C-acetate (n = 13) were included. Each scan was reconstructed using BSREM with β-values of 133, 267, 400, and 533, and ordered subsets expectation maximization (OSEM; 3 iterations, 16 subsets, and 5-mm Gaussian post-processing filter). Both reconstruction methods included TOF and point spread function (PSF) recovery. Quantitative measures of noise, signal-to-noise ratio (SNR), and signal-to-background ratio (SBR) were analysed for various acquisition times per bed position (bp).

Results

The highest β-value resulted in the lowest level of noise, which in turn resulted in the highest SNR and lowest SBR. Noise levels equal to or lower than those of OSEM were found with β-values equal to or higher than 400, 533, and 267 for ⁶⁸Ga-DOTATOC, ¹⁸F-fluoride, and ¹¹C-acetate, respectively. The specified β-ranges resulted in increased SNR at a minimum of 25% (P < 0.0001) and SBR at a maximum of 23% (P < 0.0001) as compared to OSEM. At a reduced acquisition time by 25% for ⁶⁸Ga-DOTATOC and ¹⁸F-fluoride, and 67% for ¹¹C-acetate, BSREM with β-values equal to or higher than 533 resulted in noise equal to or lower than that of OSEM at full acquisition duration (2 min/bp for ⁶⁸Ga-DOTATOC and ¹⁸F-fluoride, 3 min/bp for ¹¹C-acetate). The reduced acquisition time with β 533 resulted in increased SNR (16–26%, P < 0.003) and SBR (6–18%, P < 0.0001 (P = 0.07 for ¹¹C-acetate)) compared to the full acquisition OSEM.

Conclusions

Within tracer-specific ranges of β-values, BSREM reconstruction resulted in increased SNR and SBR with respect to conventional OSEM reconstruction. Similar SNR, SBR, and noise levels could be attained with BSREM at relatively shorter acquisition times or, alternatively, lower administered dosages, compared to those attained with OSEM.

Evaluation of a Bayesian penalized likelihood reconstruction algorithm for low-count clinical 18F-FDG PET/CT

Background

Recently, a Bayesian penalized likelihood (BPL) reconstruction algorithm was introduced for a commercial PET/CT with the potential to improve image quality. We compared the performance of this BPL algorithm with conventional reconstruction algorithms under realistic clinical conditions such as daily practiced at many European sites, i.e. low ¹⁸F-FDG dose and short acquisition times.

Results

To study the performance of the BPL algorithm, regular clinical ¹⁸F-FDG whole body PET scans were made. In addition, two types of phantoms were scanned with 4-37 mm sized spheres filled with ¹⁸F-FDG at sphere-to-background ratios of 10-to-1, 4-to-1, and 2-to-1. Images were reconstructed using standard ordered-subset expectation maximization (OSEM), OSEM with point spread function (PSF), and the BPL algorithm using β-values of 450, 550 and 700. To quantify the image quality, the lesion detectability, activity recovery, and the coefficient of variation (COV) within a single bed position (BP) were determined. We found that when applying the BPL algorithm both smaller lesions in clinical studies as well as spheres in phantom studies can be detected more easily due to a higher SUV recovery, especially for higher contrast ratios. Under standard clinical scanning conditions, i.e. low number of counts, the COV is higher for the BPL (β=450) than the OSEM+PSF algorithm. Increase of the β-value to 550 or 700 results in a COV comparable to OSEM+PSF, however, at the cost of contrast, though still better than OSEM+PSF. At the edges of the axial field of view (FOV) where BPs overlap, COV can increase to levels at which bands become visible in clinical images, related to the lower local axial sensitivity of the PET/CT, which is due to the limited bed overlap of 23% such as advised by the manufacturer.

Conclusions

The BPL algorithm performs better than the standard OSEM+PSF algorithm on small lesion detectability, SUV recovery, and noise suppression. Increase of the percentage of bed overlap, time per BP, administered activity, or the β-value, all have a direct positive impact on image quality, though the latter with some loss of small lesion detectability. Thus, BPL algorithms are very interesting for improving image quality, especially in small lesion detectability.

Optimising quantitative 90Y PET imaging: an investigation into the effects of scan length and Bayesian penalised likelihood reconstruction

Background

Positron emission tomography (PET) imaging of ⁹⁰Y following selective internal radiation therapy (SIRT) is possible, but image quality is poor, and therefore, accurate quantification and dosimetry are challenging. This study aimed to quantitatively optimise ⁹⁰Y PET imaging using a new Bayesian penalised likelihood (BPL) reconstruction algorithm (Q.Clear, GE Healthcare). The length of time per bed was also investigated to study its impact on quantification accuracy.

Methods

A NEMA IQ phantom with an 8:1 sphere-to-background ratio was scanned overnight on a GE Discovery 710 PET/CT scanner. Datasets were rebinned into varying lengths of time (5–60 min); the 15-min rebins were reconstructed using BPL reconstruction with a range of noise penalisation weighting factors (beta values). The metrics of contrast recovery (CR), background variability (BV), and recovered activity percentage (RAP) were calculated in order to identify the optimum beta value. Reconstructions were then carried out on the rest of the timing datasets using the optimised beta value; the same metrics were used to assess the quantification accuracy of the reconstructed images.

Results

A beta value of 1000 produced the highest CR and RAP (76% and 73%, 37 mm sphere) without overly accentuating the noise (BV) in the image. There was no statistically significant increase (p < 0.05) in either the CR or RAP for scan times of > 15 min. For the 5-min acquisitions, there was a statistically significant decrease in RAP (28 mm sphere, p < 0.01) when compared to the 15-min acquisition.

Conclusion

Our results indicate that an acquisition length of 15 min and beta value of 1000 (when using Q.Clear reconstruction) are optimum for quantitative ⁹⁰Y PET imaging. Increasing the acquisition time to more than 15 min reduces the image noise but has no significant impact on image quantification.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0512-y) contains supplementary material, which is available to authorized users.

Performance characteristics of silicon photomultiplier based 15-cm AFOV TOF PET/CT

Background

This paper describes the National Electrical Manufacturers Association (NEMA) system performance of the Discovery MI 3-ring PET/CT (GE Healthcare) installed in Bruges, Belgium. This time-of-flight (TOF) PET camera is based on silicon photomultipliers instead of photomultiplier tubes.

Methods

The NEMA NU2-2012 standard was used to evaluate spatial resolution, sensitivity, image quality (IQ) and count rate curves of the system. Timing and energy resolution were determined.

Results

Full width at half maximum (FWHM) of spatial resolution in radial, tangential and axial direction was 4.69, 4.08 and 4.68 mm at 1 cm; 5.58, 4.64 and 5.83 mm at 10 cm; and 7.53, 5.08 and 5.47 mm at 20 cm from the centre of the field of view (FOV) for the filtered backprojection reconstruction. For non-TOF ordered subset expectation maximization (OSEM) reconstruction without point spread function (PSF) correction, FWHM was 3.87, 3.69 and 4.15 mm at 1 cm; 4.80, 3.81 and 4.87 mm at 10 cm; and 7.38, 4.16 and 3.98 mm at 20 cm. Sensitivity was 7.258 cps/kBq at the centre of the FOV and 7.117 cps/kBq at 10-cm radial offset. Contrast recovery (CR) using the IQ phantom for the TOF OSEM reconstruction without PSF correction was 47.4, 59.3, 67.0 and 77.0% for the 10-, 13-, 17- and 22-mm radioactive spheres and 82.5 and 85.1% for the 28- and 37-mm non-radioactive spheres. Background variability (BV) was 16.4, 12.1, 9.1, 6.6, 5.1 and 3.8% for the 10-, 13-, 17-, 22-, 28- and 37-mm spheres. Lung error was 8.5%. Peak noise equivalent count rate (NECR) was 102.3 kcps at 23.0 kBq/ml with a scatter fraction of 41.2%. Maximum accuracy error was 3.88%. Coincidence timing resolution was 375.6 ps FWHM. Energy resolution was 9.3% FWHM. Q.Clear reconstruction significantly improved CR and reduced BV compared with OSEM.

Conclusion

System sensitivity and NECR are lower and IQ phantom’s BV is higher compared with larger axial FOV (AFOV) scanners like the 4-ring discovery MI, as expected from the smaller solid angle of the 3-ring system. The other NEMA performance parameters are all comparable with those of the larger AFOV scanners.

How Do the More Recent Reconstruction Algorithms Affect the Interpretation Criteria of PET/CT Images?

Purpose

Recently, a new Bayesian Penalized Likelihood (BPL) Reconstruction Algorithm was introduced by GE Healthcare, Q.Clear; it promises to provide better PET image resolution compared to the widely used Ordered Subset Expectation Maximization (OSEM). The aim of this study is to compare the performance of these two algorithms on several types of findings, in terms of image quality, lesion detectability, sensitivity, and specificity.

Methods

Between September 6th 2017 and July 31st 2018, 663 whole body 18F-FDG PET/CT scans were performed at the Nuclear Medicine Department of S. Martino Hospital (Belluno, Italy). Based on the availability of clinical/radiological follow-up data, 240 scans were retrospectively reviewed. For each scan, a hypermetabolic finding was selected, reporting both for OSEM and Q.Clear: SUVmax and SUVmean values of the finding, the liver and the background close to the finding; size of the finding; percentage variations of SUVmax and SUVmean. Each finding was subsequently correlated with clinical and radiological follow-up, to define its benign/malignant nature.

Results

Overall, Q.Clear improved the SUV values in each scan, especially in small findings (< 10 mm), high SUVmax values (≥ 10), and medium/low backgrounds. Furthermore, Q.Clear amplifies the signal of hypermetabolic findings without modifying the background signal, which leads to an increase in signal-to-noise ratio, improving overall image quality. Finally, Q.Clear did not affect PET sensitivity or specificity, in terms of number of reported findings and characterization of their nature.

Conclusions

Q.Clear is an iterative algorithm that improves significantly the quality of PET images compared to OSEM, increasing the SUVmax of findings (in particular for small findings) and the signal-to-noise ratio. However, due to the intrinsic characteristics of this algorithm, it will be necessary to adapt and/or modify the current interpretative criteria based of quantitative evaluation, to avoid an overestimation of the disease burden.

Design and fabrication of a personalized anthropomorphic phantom using 3D printing and tissue equivalent materials

To fabricate an individualized anthropomorphic lung phantom with tissue-equivalent radiation attenuation properties using a cost-effective three-dimensional (3D) printing technique. Based on anonymized human chest CT images, the phantom contained a 3D-printed skin shell, filled with tissue equivalent materials with similar radiation attenuation characteristics. The filling materials were a mixture of CaCO₃, MgO, agarose, NaCl, pearl powder and silica gel. The dose calculation accuracy of different treatment planning system (TPS) algorithms was validated and compared with the ion chamber measurements in the phantom, including tumor and surrounding normal tissues. The chest phantom was shown to represent a human's chest in terms of radiation attenuation property and human anatomy. The Hounsfield unit ranges were -60 to -100, 20 to 60, and 120 to 300 for fat, muscle, and bone, respectively. The actual measured values of the ionization chamber were 213.7 cGy for the tumor, 53.85 cGy for normal lung tissue, and 4.1 cGy for the spinal cord, compared to 214.1, 55.2, and 4.5 cGy, respectively, with use of the Monte Carlo algorithm in TPS. The application of 3D printing in anthropomorphic phantoms can improve personalized medical need and efficiency with reduce costs thus, can be used for radiation dose verification.

Phantom and clinical evaluation of the effect of full Monte Carlo collimator modelling in post-SIRT yttrium-90 Bremsstrahlung SPECT imaging

Background

Post-therapy SPECT/CT imaging of ⁹⁰Y microspheres delivered to hepatic malignancies is difficult, owing to the continuous, high-energy Bremsstrahlung spectrum emitted by ⁹⁰Y. This study aimed to evaluate the utility of a commercially available software package (HybridRecon, Hermes Medical Solutions AB) which incorporates full Monte Carlo collimator modelling. Analysis of image quality was performed on both phantom and clinical images in order to ultimately provide a recommendation of an optimum reconstruction for post-therapy ⁹⁰Y microsphere SPECT/CT imaging.

A 3D-printed anthropomorphic liver phantom was filled with ⁹⁰Y with a sphere-to-background ratio of 4:1 and imaged on a GE Discovery 670 SPECT/CT camera. Datasets were reconstructed using ordered-subsets expectation maximization (OSEM) 1–7 iterations in order to identify the optimal OSEM reconstruction (5 iterations, 15 subsets). Quantitative analysis was subsequently carried out on phantom datasets obtained using four reconstruction algorithms: the default OSEM protocol (2 iterations, 10 subsets) and the optimised OSEM protocol, both with and without full Monte Carlo collimator modelling. The quantitative metrics contrast recovery (CR) and background variability (BV) were calculated.

The four algorithms were then used to retrospectively reconstruct 10 selective internal radiation therapy (SIRT) patient datasets which were subsequently blind scored for image quality by a consultant radiologist.

Results

The optimised OSEM reconstruction (5 iterations, 15 subsets with full MC collimator modelling) increased the CR by 42% (p < 0.001) compared to the default OSEM protocol (2 iterations, 10 subsets). The use of full Monte Carlo collimator modelling was shown to further improve CR by 14% (30 mm sphere, CR = 90%, p < 0.05).

The consultant radiologist had a significant preference for the optimised OSEM over the default OSEM protocol (p < 0.001), with the optimised OSEM being the favoured reconstruction in every one of the 10 clinical cases presented.

Conclusions

OSEM (5 iterations, 15 subsets) with full Monte Carlo collimator modelling is quantitatively the optimal image reconstruction for post-SIRT ⁹⁰Y Bremsstrahlung SPECT/CT imaging. The use of full Monte Carlo collimator modelling for correction of image-degrading effects significantly increases contrast recovery without degrading clinical image quality.

Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET

In recent years, there have been multiple advances in positron emission tomography/computed tomography (PET/CT) that improve cancer imaging. The present generation of PET/CT scanners introduces new hardware, software, and acquisition methods. This review describes these new developments, which include time-of-flight (TOF), point-spread-function (PSF), maximum-a-posteriori (MAP) based reconstruction, smaller voxels, respiratory gating, metal artefact reduction, and administration of quadratic weight-dependent 18F-fluorodeoxyglucose (FDG) activity. Also, hardware developments such as continuous bed motion (CBM), (digital) solid-state photodetectors and combined PET and magnetic resonance (MR) systems are explained. These novel techniques have a significant impact on cancer imaging, as they result in better image quality, improved small lesion detectability, and more accurate quantification of radiopharmaceutical uptake. This influences cancer diagnosis and staging, as well as therapy response monitoring and radiotherapy planning. Finally, the possible impact of these developments on the European Association of Nuclear Medicine (EANM) guidelines and EANM Research Ltd. (EARL) accreditation for FDG-PET/CT tumor imaging is discussed.