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 parameterßwas 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, variousßvalues (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 increasingß, peaking atß= 550. The CNR for allß, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images withß= 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.

The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection on 18F-FDG images using a BGO-based PET/CT scanner: a phantom and clinical study.

BACKGROUND: The Q.Clear algorithm is a fully convergent iterative image reconstruction technique. We hypothesize that different PET/CT scanners with distinct crystal properties will require different optimal settings for the Q.Clear algorithm. Many studies have investigated the improvement of the Q.Clear reconstruction algorithm on PET/CT scanner with LYSO crystals and SiPM detectors. We propose an optimum penalization factor (ß) for the detection of rectal cancer and its metastases using a BGO-based detector PET/CT system which obtained via accurate and comprehensive phantom and clinical studies. METHODS: 18F-FDG PET-CT scans were acquired from NEMA phantom with lesion-to-background ratio (LBR) of 2:1, 4:1, 8:1, and 15 patients with rectal cancer. Clinical lesions were classified into two size groups. OSEM and Q.Clear (ß value of 100-500) reconstruction was applied. In Q.Clear, background variability (BV), contrast recovery (CR), signal-to-noise ratio (SNR), SUVmax, and signal-to-background ratio (SBR) were evaluated and compared to OSEM. RESULTS: OSEM had 11.5-18.6% higher BV than Q.Clear using ß value of 500. Conversely, RC from OSEM to Q.Clear using ß value of 500 decreased by 3.3-7.7% for a sphere with a diameter of 10 mm and 2.5-5.1% for a sphere with a diameter of 37 mm. Furthermore, the increment of contrast using a ß value of 500 was 5.2-8.1% in the smallest spheres compared to OSEM. When the ß value was increased from 100 to 500, the SNR increased by 49.1% and 30.8% in the smallest and largest spheres at LBR 2:1, respectively. At LBR of 8:1, the relative difference of SNR between ß value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as ß increased from 100 to 500, the SUVmax decreased by 47.7% in small and 31.1% in large lesions. OSEM demonstrated the least SUVmax, SBR, and contrast. The decrement of SBR and contrast using OSEM were 13.6% and 12.9% in small and 4.2% and 3.4%, respectively, in large lesions. CONCLUSIONS: Implementing Q.Clear enhances quantitative accuracies through a fully convergent voxel-based image approach, employing a penalization factor. In the BGO-based scanner, the optimal ß value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal ß value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended ß value of 300 for small lesions and ß value of 500 for large lesions in clinical study.

Synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET radiomic features in non-small cell lung cancer: Phantom and clinical studies.

OBJECTIVES: This study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET image radiomic features in non-small cell lung cancer (NSCLC) patients, and investigating the robustness of features performance in differentiating NSCLC histopathology subtypes. METHODS: An in-house developed thoracic phantom incorporating lesions with different sizes was used with different reconstruction settings, including various reconstruction algorithms, number of subsets and iterations, full-width at half-maximum of post-reconstruction smoothing filter and acquisition parameters, including injected activity and test-retest with and without motion simulation. To simulate motion, a special motor was manufactured to simulate respiratory motion based on a normal patient in two directions. The lesions were delineated semi-automatically to extract 174 radiomic features. All radiomic features were categorized according to the coefficient of variation (COV) to select robust features. A cohort consisting of 40 NSCLC patients with adenocarcinoma (n = 20) and squamous cell carcinoma (n = 20) was retrospectively analyzed. Statistical analysis was performed to discriminate robust features in differentiating histopathology subtypes of NSCLC lesions. RESULTS: Overall, 29% of radiomic features showed a COV ≤5% against motion. Forty-five percent and 76% of the features showed a COV ≤ 5% against the test-retest with and without motion in large lesions, respectively. Thirty-three percent and 45% of the features showed a COV ≤ 5% against different reconstruction parameters with and without motion, respectively. For NSCLC histopathological subtype differentiation, statistical analysis showed that 31 features were significant (p-value < 0.05). Two out of the 31 significant features, namely, the joint entropy of GLCM (AUC = 0.71, COV = 0.019) and median absolute deviation of intensity histogram (AUC = 0.7, COV = 0.046), were robust against the motion (same reconstruction setting). CONCLUSIONS: Motion, acquisition, and reconstruction parameters significantly impact radiomic features, just as their synergies. Radiomic features with high predictive performance (statistically significant) in differentiating histopathological subtype of NSCLC may be eliminated due to non-reproducibility.

Tumor volume-adapted SUVN as an alternative to SUVpeak for quantification of small lesions in PET/CT imaging: a proof-of-concept study.

PURPOSE: SUVpeak is a recommended quantification metric except for small lesions. We aimed to assess the averaged standard uptake value (SUVN) as an alternative to SUVpeak for small-lesion quantification. MATERIALS AND METHODS: NEMA-like phantom images were reconstructed using OSEM, OSEM + PSF, OSEM + TOF and OSEM + TOF + PSF with two post-smoothing Gaussian filters for different background activity levels. SUVmax, SUVN (N = 5, 10, 15, 20, 25, 30, 35 or 40 hottest voxels), and SUVpeak, relative percent error, contrast recovery, and volume recovery coefficients were quantified and assessed. RESULTS: SUVN did not have the limitations of SUVpeak for smaller lesions. In the smallest insert at 2.68 kBq/ml, optimum N values for OSEM, OSEM + PSF, OSEM + TOF and OSEM + TOF + PSF were 10, 5, 15, and 10 for SUVN, respectively. The same N values were obtained for metabolic tumor volumes (MTVs) for all reconstruction algorithms. At 5.30 kBq/ml, N = 5 was optimum for SUVN and MTVs. For the larger inserts, the optimum N increased and tended towards the maximum (similar to SUVpeak). CONCLUSIONS: SUVN is more accurate than SUVmax or SUVpeak for small lesions, while being as accurate in larger ones. This harmonizing capacity of SUVN can be beneficial for the quantitative analysis of small tumor volumes.

Performance evaluation of developed dedicated breast PET scanner and improvement of the spatial resolution by wobbling: a Monte Carlo study.

PURPOSE: Molecular imaging, particularly PET scanning, has become an important cancer diagnostic tool. Whole-body PET is not effective for local staging of cancer because of their declining efficiency in detecting small lesions. The preliminary results of the performance evaluation of designed dedicated breast PET scanner presented. METHODS AND MATERIALS: A new scanner is based on LYSO crystals coupled with SiPM, and it consists of 14 compact modules with a transaxial FOV of 180 mm in diameter. In this study, initial GATE simulation studies were performed to predict the spatial resolution, absolute sensitivity, noise equivalent count rate (NECR) and scatter fraction (SF) of the new design. Spatial wobbling acquisitions were also implemented. Finally, the obtained projections were reconstructed using analytical and iterative algorithms. RESULTS: The simulation results indicate that absolute sensitivity is 1.42% which is appropriate than other commercial breast PET systems. The calculated SF and NECR in our design are 20.6% and 21.8 kcps. The initial simulation results demonstrate the potential of this design for breast cancer detection. A small wobble motion to improve spatial resolution and contrast. CONCLUSION: The performance of the dedicated breast PET scanner is considered to be reasonable enough to support its use in breast cancer imaging.

Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging.

OBJECTIVES: We aim to develop and rigorously evaluate an image-based deconvolution method to jointly compensate respiratory motion and partial volume effects (PVEs) for quantitative oncologic PET imaging, including studying the impact of various reconstruction algorithms on quantification performance. PROCEDURES: An image-based deconvolution method that incorporated wavelet-based denoising within the Lucy-Richardson algorithm was implemented and assessed. The method was evaluated using phantom studies with signal-to-background ratios (SBR) of 4 and 8, and clinical data of 10 patients with 42 lung lesions ≤30 mm in diameter. In each study, PET images were reconstructed using four different algorithms: OSEM-basic, PSF, TOF, and TOFPSF. The performance was quantified using contrast recovery (CR), coefficient of variation (COV) and contrast-to-noise-ratio (CNR) metrics. Further, in each study, variabilities arising due to the four different reconstruction algorithms were assessed. RESULTS: In phantom studies, incorporation of wavelet-based denoising improved COV in all cases. Processing images using proposed method yielded significantly higher CR and CNR particularly in small spheres, for all reconstruction algorithms and all SBRs (P < 0.05). In patient studies, processing images using the proposed method yielded significantly higher CR and CNR (P < 0.05). The choice of the reconstruction algorithm impacted quantification performance for changes in motion amplitude, tumor size and SBRs. CONCLUSIONS: Our results provide strong evidence that the proposed joint-compensation method can yield improved PET quantification. The choice of the reconstruction algorithm led to changes in quantitative accuracy, emphasizing the need to carefully select the right combination of reconstruction-image-based compensation methods.

Impact of image reconstruction methods on quantitative accuracy and variability of FDG-PET volumetric and textural measures in solid tumors.

OBJECTIVE: This study aims to assess the impact of different image reconstruction methods on PET/CT quantitative volumetric and textural parameters and the inter-reconstruction variability of these measurements. METHODS: A total of 25 oncology patients with 65 lesions (between 2017 and 2018) and a phantom with signal-to-background ratios (SBR) of 2 and 4 were included. All images were retrospectively reconstructed using OSEM, PSF only, TOF only, and TOFPSF with 3-, 5-, and 6.4-mm Gaussian filters. The metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. The relative percent error (ΔMTV and ΔTLG) with respect to true values, volume recovery coefficients, and Dice similarity coefficient, as well as inter-reconstruction variabilities were quantified and assessed. In clinical scans, textural features (coefficient of variation, skewness, and kurtosis) were determined. RESULTS: Among reconstruction methods, mean ΔMTV differed by -163.5 ± 14.1% to 6.3 ± 6.2% at SBR2 and -42.7 ± 36.7% to 8.6 ± 3.1 at SBR4. Dice similarity coefficient significantly increased by increasing SBR from 2 to 4, ranging from 25.7 to 83.4% between reconstruction methods. Mean ΔTLG was -12.0 ± 1.7 for diameters > 17 mm and -17.8 ± 7.8 for diameters ≤ 17 mm at SBR4. It was -31.7 ± 4.3 for diameters > 17 mm and -14.2 ± 5.8 for diameters ≤ 17 mm at SBR2. Textural features were prone to variations by reconstruction methods (p < 0.05). CONCLUSIONS: Inter-reconstruction variability was significantly affected by the target size, SBR, and cut-off threshold value. In small tumors, inter-reconstruction variability was noteworthy, and quantitative parameters were strongly affected. TOFPSF reconstruction with small filter size produced greater improvements in performance and accuracy in quantitative PET/CT imaging. KEY POINTS: • Quantitative volumetric PET evaluation is critical for the analysis of tumors. • However, volumetric and textural evaluation is prone to important variations according to different image reconstruction settings. • TOFPSF reconstruction with small filter size improves quantitative analysis.

Quantification and reduction of respiratory induced artifacts in positron emission tomography/computed tomography using the time-of-flight technique.

OBJECTIVE: The aim of this study was to investigate the impact of time-of-flight (TOF) on quantification and reduction of respiratory artifacts. PATIENTS AND METHODS: The National Electrical Manufacturers Association phantom was used for optimization of reconstruction parameters. Twenty seven patients with lesions located in the diaphragmatic region were evaluated. The PET images were retrospectively reconstructed using non-TOF (routine protocol in our department) and TOF algorithms with different reconstruction parameters. Maximum standardized uptake value, estimated maximum tumor diameter, coefficient of variation, signal-to-noise ratio, and lesion-to-background-ratio were also evaluated. RESULTS: On the basis of phantom experiments, TOF algorithms with two iterations, 18 subsets, and 5.4 mm and 6.4 mm postsmoothing filter reduced the noise by 3.1 and 12.6% in phantom with 2 : 1 activity ratio, and 3.0 and 13.1% in phantom with 4 : 1 activity ratio. The TOF algorithm with two iterations, 18 subsets, and 6.4 mm postsmoothing filter had the highest signal-to-noise value, and was selected as the optimal TOF reconstruction. Mean relative difference for signal-to-noise between non-TOF and optimal TOF in phantom with 2 : 1 and 4 : 1 activity ratio were 11.6 and 18.7%, respectively. In clinical data, the mean relative difference for estimated maximum tumor diameter and maximum standardized uptake value between routine protocol and optimal TOF algorithm were -6.3% (range: -20.4 to -0.6%) and 13.2% (range: 0.3-57.6%), respectively. CONCLUSION: Integration of TOF in reconstruction algorithm remarkably improved the white band artifact in the diaphragmatic region. This technique affected the quantification accuracy and resulted in smaller tumor size and higher standardized uptake value in tumors located in/near the diaphragmatic region.

Design and development of a dedicated portable gamma camera system for intra-operative imaging.

PURPOSE: We developed a high performance portable gamma camera platform dedicated to identification of sentinel lymph nodes (SLNs) and radio-guided surgery for cancer patients. In this work, we present the performance characteristics of SURGEOSIGHT-I, the first version of this platform that can intra-operatively provide high-resolution images of the surveyed areas. METHODS: At the heart of this camera, there is a 43×43 array of pixelated sodium-activated cesium iodide (CsI(Na)) scintillation crystal with 1×1mm(2) pixel size and 5mm thickness coupled to a Hamamatsu H8500 flat-panel multi-anode (64 channels) photomultiplier tube. The probe is equipped with a hexagonal parallel-hole lead collimator with 1.2mm holes. The detector, collimator, and the associated front-end electronics are encapsulated in a common housing referred to as head. RESULTS: Our results show a count rate of ∼41kcps for 20% count loss. The extrinsic energy resolution was measured as 20.6% at 140keV. The spatial resolution and the sensitivity of the system on the collimator surface was measured as 2.2mm and 142cps/MBq, respectively. In addition, the integral and differential uniformity, after uniformity correction, in useful field-of-view (UFOV) were measured 4.5% and 4.6%, respectively. CONCLUSIONS: This system can be used for a number of clinical applications including SLN biopsy and radiopharmaceutical-guided surgery.

Respiratory-induced errors in tumor quantification and delineation in CT attenuation-corrected PET images: effects of tumor size, tumor location, and respiratory trace: a simulation study using the 4D XCAT phantom.

PURPOSE: We investigated the magnitude of respiratory-induced errors in tumor maximum standardized uptake value (SUVmax), localization, and volume for different respiratory motion traces and various lesion sizes in different locations of the thorax and abdomen in positron emission tomography (PET) images. PROCEDURES: Respiratory motion traces were simulated based on the common patient breathing cycle and three diaphragm motions used to drive the 4D XCAT phantom. Lesions with different diameters were simulated in different locations of lungs and liver. The generated PET sinograms were subsequently corrected using computed tomography attenuation correction involving the end exhalation, end inhalation, and average of the respiratory cycle. By considering respiration-averaged computed tomography as a true value, the lesion volume, displacement, and SUVmax were measured and analyzed for different respiratory motions. RESULTS: Respiration with 35-mm diaphragm motion results in a mean lesion SUVmax error of 24 %, a mean superior inferior displacement of 7.6 mm and a mean lesion volume overestimation of 129 % for a 9-mm lesion in the liver. Respiratory motion results in lesion volume overestimation of 50 % for a 9-mm lower lung lesion near the liver with just 15-mm diaphragm motion. Although there are larger errors in lesion SUVmax and volume for 35-mm motion amplitudes, respiration-averaged computed tomography results in smaller errors than the other two phases, except for the lower lung region. CONCLUSIONS: The respiratory motion-induced errors in tumor quantification and delineation are highly dependent upon the motion amplitude, tumor location, tumor size, and choice of the attenuation map for PET image attenuation correction.

Qualitative and quantitative assessment of metal artifacts arising from implantable cardiac pacing devices in oncological PET/CT studies: a phantom study.

PURPOSE: We evaluate the magnitude of metallic artifacts caused by various implantable cardiac pacing devices (without leads) on both attenuation maps (µ-maps) and positron emission tomography (PET) images using experimental phantom studies. We also assess the efficacy of a metal artifact reduction (MAR) algorithm along with the severity of artifacts in the presence of misalignment between µ-maps and PET images. METHODS: Four pacing devices including two pacemakers (pacemakers 1 and 2) and two cardiac resynchronization therapy (CRT) devices of pacemaker (CRT-P) and defibrillator (CRT-D) type were placed in three phantoms including a cylindrical Ge-68 phantom, a water-bath phantom and an anthropomorphic heart/thorax phantom. The µ-maps were derived from computed tomography (CT) images reconstructed using the standard method supplied by the manufacturer and those reconstructed using the MAR algorithm. In addition, the standard reconstructed CT images of the last two phantoms were manually misaligned by 10 mm along the patient's axis to simulate misalignment between CT and PET images. RESULTS: The least and severest artifacts produced on both µ-maps and PET images of the Ge-68 phantom were induced by CRT-P and pacemaker 1 devices, respectively. In the water-bath phantom, CRT-P induced 17.5% over- and 9.2% underestimation of tracer uptake whereas pacemaker 1 induced 69.6% over- and 65.7% underestimation. In the heart/thorax phantom representing a pacemaker-bearing patient, pacemaker 1 induced 41.8% increase and 36.6% decrease in tracer uptake and attenuation coefficients on average in regions corresponding to bright and dark streak artifacts, respectively. Statistical analysis revealed that the MAR algorithm was successful in reducing bright streak artifacts, yet unsuccessful for dark ones. In the heart/thorax phantom, the MAR algorithm reduced the overestimations to 4.4% and the underestimations to 35.5% on average. Misalignment between µ-maps and PET images increased the peak of pseudo-uptake by approximately 20%. CONCLUSIONS: This study demonstrated that, depending on their elemental composition, different implantable cardiac pacing devices result in varying magnitudes of metal artifacts and thus pseudo-uptake on PET images. The MAR algorithm was not successful in compensating for underestimations which calls for a more efficient algorithm. The results showed that misalignments between PET and CT images render metal-related pseudo-uptake more severe.