Dose Optimization in <sup>18</sup>F-FDG PET Based on Noise-Equivalent Count Rate Measurement and Image Quality Assessment

Rana, Nivedita; Kaur, Manpreet; Singh, Harmandeep; Mittal, Bhagwant Rai

doi:10.2967/jnmt.120.250282

Cited by 3 publications

(10 citation statements)

References 12 publications

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“…Based on the excellent intra-and inter-agreement agreement (all kappa > 0.85), the mean threshold SNR L was 11.2 for acceptable image quality of 68 Ga-DOTA-TATE total-body PET/CT. Compared with 18 F-FDG images we previously analyzed, the threshold SNR of 14.0 was slightly higher. The difference was consistent with a previous study in which the acceptable SNR L was 6.2 for whole-body 68 Ga-DOTATATE PET/CT, but 18 F-FDG studies have revealed a higher SNR of 10 [19].…”

Section: Discussionmentioning

confidence: 70%

“…Image quality is commonly evaluated by the metric SNR or noise equivalent count rate (NECR). The SNR is calculated as the value of the square root of the product of system sensitivity, injected activity, and acquisition time [ 13 ], whereas the NECR is calculated as the ratio of the square of the true events to the total of true events, random events, and scatter coincidences [ 18 ]. A constant value of SNR and NECR can overcome the limitation of patient-specific attenuation to render uniform image quality.…”

Section: Discussionmentioning

confidence: 99%

“…Although that recommendation provides a reference for clinical practice, such a broad injection range creates certain challenges with respect to the comparability and repeatability of images. In previous research, we established a convenient patient-specific injection regimen of 18 F-FDG for repeatable and constant imaging. Thus, considering the influence of the total-body PET system and body mass on image quality, the present study proposes a personal variable acquisition time regimen to gain constant image quality and avoid extending acquisition time based on objective and subjective image quality evaluation in the development of 68 Ga-DOTATATE PET/CT imaging.…”

Section: Discussionmentioning

confidence: 99%

“…Total-body PET detector crystals with a size of 2.76 × 2.76 × 18.0 mm 3 coupled to silicon photomultipliers (SIPMs) shows ultrahigh sensitivity and spatial resolution [5]. Recently, a series of studies reported that low injection activity of 18 F-FDG or a shorter scan time of total-body PET/CT imaging could still be feasible for good image quality [6][7][8][9]. Additionally, influence factors of image quality were varied and complicated, such as PET equipment, activity of the tracer, acquisition time, and patient-specific photon attenuation, particularly for large body mass.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding constant and acceptable image quality, we previously investigated the influence of patient size on image quality, and proposed a dose regimen based on body mass index (BMI, kg/m 2 ), demonstrating the feasibility of constant image quality for 18 F-FDG total-body PET/CT [11]. In addition, adjusting the duration time per bed based on scanner sensitivity and patient-specific attenuation might acquire uniform image noise or homogenous image [12].…”

Section: Introductionmentioning

confidence: 99%

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A personal acquisition time regimen of 68Ga-DOTATATE total-body PET/CT in patients with neuroendocrine tumor (NET): a feasibility study

Xiao

Sui

et al. 2022

Cancer Imaging

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Background The injection activity of tracer, acquisition time, patient-specific photon attenuation, and large body mass, can influence on image quality. Fixed acquisition time and body mass related injection activity in clinical practice results in a large difference in image quality. Thus, this study proposes a patient-specific acquisition time regimen of 68 Ga-DOTATATE total-body positron emission tomography-computed tomography (PET/CT) to counteract the influence of body mass (BM, kg) on image quality, and acquire an acceptable and constant image of patients with neuroendocrine tumors (NETs). Methods The development cohort consisting of 19 consecutive patients with full activity (88.7–204.9 MBq, 2.0 ± 0.1 MBq/kg) was to establish the acquisition time regimen. The liver SNR (signal-to-noise ratio, SNRL) was normalized (SNRnorm) by the product of injected activity (MBq) and acquisition time (min). Fitting of SNRnorm against body mass (BM, kg) in linear correlation was performed. Subjective assessment of image quality was performed using a 5-point Likert scale to determine the acceptable threshold of SNRL, and an optimized acquisition regimen based on BM was proposed, and validated its feasibility through the validation cohort of 57 consecutive NET patients with half activity (66.9 ± 11.3 MBq, 1.0 ± 0.1 MBq/kg) and a fixed acquisition time regimen. Results The linear correlation (R2 = 0.63) between SNRnorm and BM (kg) was SNRnorm = -0.01*BM + 1.50. The threshold SNRL of acceptable image quality was 11.2. The patient-specific variable acquisition time regimen was determined as: t (min) = 125.4/(injective activity)*(-0.01*BM + 1.50)2. Based on that proposed regimen, the average acquisition time for acceptable image quality in the validation cohort was 2.99 ± 0.91 min, ranging from 2.18 to 6.35 min, which was reduced by 36.50% ~ 78.20% compared with the fixed acquisition time of 10 min. Subjective evaluation showed that acceptable image quality could be obtained at 3.00 min in the validation group, with an average subjective score of 3.44 ± 0.53 (kappa = 0.97, 95% CI: 0.96 ~ 0.98). Bland–Altman analysis revealed good agreement between the proposed regimen and the fixed acquisition time cohort. Conclusion A patient-specific acquisition time regimen was proposed in NET patients in development cohort and validated its feasibility in patients with NETs in validation cohort by 68 Ga-DOTATATE total-body PET/CT imaging. Based on the proposed regimen, the homogenous image quality with optimal acquisition time was available independent of body mass.

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Section: Discussionmentioning

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A personal acquisition time regimen of 68Ga-DOTATATE total-body PET/CT in patients with neuroendocrine tumor (NET): a feasibility study

Xiao

Sui

et al. 2022

Cancer Imaging

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Fully automated computational measurement of noise in positron emission tomography

Sartoretti,

Skawran,

Gennari

et al. 2023

Eur Radiol

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Objectives To introduce an automated computational algorithm that estimates the global noise level across the whole imaging volume of PET datasets. Methods [18F]FDG PET images of 38 patients were reconstructed with simulated decreasing acquisition times (15–120 s) resulting in increasing noise levels, and with block sequential regularized expectation maximization with beta values of 450 and 600 (Q.Clear 450 and 600). One reader performed manual volume-of-interest (VOI) based noise measurements in liver and lung parenchyma and two readers graded subjective image quality as sufficient or insufficient. An automated computational noise measurement algorithm was developed and deployed on the whole imaging volume of each reconstruction, delivering a single value representing the global image noise (Global Noise Index, GNI). Manual noise measurement values and subjective image quality gradings were compared with the GNI. Results Irrespective of the absolute noise values, there was no significant difference between the GNI and manual liver measurements in terms of the distribution of noise values (p = 0.84 for Q.Clear 450, and p = 0.51 for Q.Clear 600). The GNI showed a fair to moderately strong correlation with manual noise measurements in liver parenchyma (r = 0.6 in Q.Clear 450, r = 0.54 in Q.Clear 600, all p < 0.001), and a fair correlation with manual noise measurements in lung parenchyma (r = 0.52 in Q.Clear 450, r = 0.33 in Q.Clear 600, all p < 0.001). Classification performance of the GNI for subjective image quality was AUC 0.898 for Q.Clear 450 and 0.919 for Q.Clear 600. Conclusion An algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets. Clinical relevance statement An automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking of clinical PET imaging within and across institutions. Key Points • Noise is an important quantitative marker that strongly impacts image quality of PET images. • An automated computational noise measurement algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets. • An automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking as well as protocol harmonization.

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Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging

Azimi,

Kamali-Asl,

et al. 2024

Quant Imaging Med Surg

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Background Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images. Methods We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target. These six PVC approaches include geometric transfer matrix (GTM), multi-target correction (MTC), region-based voxel-wise correction (RBV), iterative Yang (IY), reblurred Van-Cittert (RVC), and Richardson-Lucy (RL). The proposed models were evaluated using standard criteria, such as peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), relative bias, and absolute relative bias. Results Different levels of error were observed for these partial volume correction methods, which were relatively smaller for GTM with a SSIM of 0.63 for LD and 0.29 for FD, IY with an SSIM of 0.63 for LD and 0.67 for FD, RBV with an SSIM of 0.57 for LD and 0.65 for FD, and RVC with an SSIM of 0.89 for LD and 0.94 for FD PVC approaches. However, large quantitative errors were observed for multi-target MTC with an RMSE of 2.71 for LD and 2.45 for FD and RL with an RMSE of 5 for LD and 3.27 for FD PVC approaches. Conclusions We found that the proposed framework could effectively perform joint de-noising and partial volume correction for PET images with LD and FD input PET data (LD vs . FD). When no magnetic resonance imaging (MRI) images are available, the developed deep learning models could be used for partial volume correction on LD or standard PET-computed tomography (PET-CT) scans as an image quality enhancement technique.

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Dose Optimization in ¹⁸F-FDG PET Based on Noise-Equivalent Count Rate Measurement and Image Quality Assessment

Cited by 3 publications

References 12 publications

A personal acquisition time regimen of 68Ga-DOTATATE total-body PET/CT in patients with neuroendocrine tumor (NET): a feasibility study

A personal acquisition time regimen of 68Ga-DOTATATE total-body PET/CT in patients with neuroendocrine tumor (NET): a feasibility study

Fully automated computational measurement of noise in positron emission tomography

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging

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Dose Optimization in 18F-FDG PET Based on Noise-Equivalent Count Rate Measurement and Image Quality Assessment

Cited by 3 publications

References 12 publications

A personal acquisition time regimen of 68Ga-DOTATATE total-body PET/CT in patients with neuroendocrine tumor (NET): a feasibility study

A personal acquisition time regimen of 68Ga-DOTATATE total-body PET/CT in patients with neuroendocrine tumor (NET): a feasibility study

Fully automated computational measurement of noise in positron emission tomography

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging

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Dose Optimization in ¹⁸F-FDG PET Based on Noise-Equivalent Count Rate Measurement and Image Quality Assessment