A Comparison of Accuracy of Planar and Evolution SPECT/CT Bone Imaging in Differentiating Benign from Metastatic Bone Lesions.

Objective: To compare sensitivity, specificity, accuracy and diagnostic confidence in the differentiation between benign and metastatic bone lesions on whole body planar bone scintigraphy and Evolution SPECT/CT. Material and Method: Eighty diagnosed or suspected cancer patients with indeterminate lesions on planar scintigraphy were recruited in the present prospective study. Additional whole body Evolution SPECT/CT was performed after whole body planar scintigraphy. All lesions on both imagings were categorized into 5 categories; definitely metastasis, probably metastasis, indeterminate, probably benign and definitely benign. The diagnosis of each lesion was confirmed by follow-up imaging, pathological findings or clinical follow-up for at least 6 months. Results: Detected lesions on planar scintigraphy and Evolution SPECT/CT imaging were 442 and 477 lesions, respectively.The sensitivity, specificity and accuracy of planar scintigraphy and Evolution SPECT/CT imaging in the diagnosis of metastatic lesions were 27% (95% CI: 13.8, 44.1), 63.2% (95% CI: 58.5, 67.7), 60%, and 97.3% (95% CI: 85.8, 99.9), 100% (95% CI: 96.4, 100) and 99.8%, respectively. Indeterminate lesions on planar scintigraphy were 34.2% (151 lesions from total 442 lesions, which 135 of these 151 indeterminate lesions or 89.4% were located in axial skeleton). Evolution SPECT/CT images were able to characterize all indeterminate lesions. Conclusion: Differentiation of benign and metastatic lesions by Evolution SPECT/CT images has superior diagnostic performance and diagnostic confidence over the planar scintigraphy. Thus, Evolution SPECT/CT images should be considered in characterization of indeterminate lesions on planar scintigraphy, especially in the axial skeleton.

Automated segmentation of lung, liver, and liver tumors from Tc-99m MAA SPECT/CT images for Y-90 radioembolization using convolutional neural networks.

PURPOSE: 90 Y selective internal radiation therapy (SIRT) has become a safe and effective treatment option for liver cancer. However, segmentation of target and organ-at-risks is labor-intensive and time-consuming in 90 Y SIRT planning. In this study, we developed a convolutional neural network (CNN)-based method for automated lungs, liver, and tumor segmentation on 99m Tc-MAA SPECT/CT images for 90 Y SIRT planning. METHODS: 99m Tc-MAA SPECT/CT images and corresponding clinical segmentations were retrospectively collected from 56 patients who underwent 90 Y SIRT. The collected data were used to train three CNN-based segmentation algorithms for lungs, liver, and tumor segmentation. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), surface DSC, and average symmetric surface distance (ASSD). Dosimetric parameters (volume, counts, and lung shunt fraction) were measured from the segmentation results and were compared with clinical reference segmentations. RESULTS: The evaluation results show that the method can accurately segment lungs, liver, and tumor with median [interquartile range] DSCs of 0.98 [0.97-0.98], 0.91 [0.83-0.93], and 0.85 [0.71-0.88]; surface DSCs of 0.99 [0.97-0.99], 0.86 [0.77-0.93], and 0.85 [0.62-0.93], and ASSDs of 0.91 [0.69-1.5], 4.8 [2.6-8.4], and 4.7 [3.5-9.2] mm, respectively. Dosimetric parameters from the three segmentation networks show relationship with those from the reference segmentations. The overall segmentation took about 1 min per patient on an NVIDIA RTX-2080Ti GPU. CONCLUSION: This work presents CNN-based algorithms to segment lungs, liver, and tumor from 99m Tc-MAA SPECT/CT images. The results demonstrated the potential of the proposed CNN-based segmentation method for assisting 90 Y SIRT planning while drastically reducing operator time.

Factors affecting standardized uptake value (SUV) of positron emission tomography (PET) imaging with l8F-FDG.

OBJECTIVE: The purpose of the study was to study factors affecting SUV of PET imaging with 18F-FDG. MATERIAL AND METHOD: PET/CT Biograph 64 was used to acquire the data. A NEMA PET phantom with 6 spheres varying in diameter from 10 to 37 mm was used to mimic the human body and tumors. Background activity of 18F in the phantom was 0.14 microCi/ml and tumor-to-background ratios (TBR) of 2:1, 5:1 and 10:1 were studied. For each TBR, thirty sinograms were acquired with 3-min scan durations. Different scan durations varying from 3 to 20 min using a TBR of 5:1 were studied and three datasets of each scan time were collected Sinograms were reconstructed using the Ordered Subset Expectation Maximization (OSEM) algorithm with 5 mm Full-Width-at-Half-Maximum (FWHM) Gaussian filtering. Sinograms at TBR of 5:1 were reconstructed by varying the number of iterative updates of OSEM (N) from 8 to 168 and SUVavg and SUVmax were measured. The percentage of underestimation of SUVs was used to study the effect of tumor size and TBR. Intraclass correlation coefficient (ICC) was used to test the reliability of SUVmax with different scan durations. RESULTS: The results showed that both the SUVavg and SUVmax rapidly increased when N was <48 and slightly increased afterwards. At TBRs ranging from 2:l to 10:1, the percentages of underestimation of SUVmax ranged from 8.17 to 22.46 and that of SUVavg were ranged from 41.44 to 52.33 for 37-mm sphere and from 40.38 to 54.52 and from 48.97 to 67.73 for 10-mm sphere respectively. Different scan durations gave reliable SUVs(max) with ICC of 0.996. CONCLUSION: SUVs increased as N increased The percentage of underestimation of the SUV depended on tumor size and TBR. Scan duration did not affect SUVs.

Optimization of imaging protocols for myocardial blood flow (MBF) quantification with 18F-flurpiridaz PET.

The new PET tracer, 18F-flurpiridaz, with high myocardial extraction allows quantitative myocardial blood flow (MBF) estimation from dynamic PET data and tracer kinetic modeling. The goal of this study is to determine the optimal imaging protocols and parameters using a realistic simulation study. The time activity curves (TACs) of different tissue organs from a 30-s infusion time (IT) of 18F-flurpiridaz in a dynamic PET study were extracted from a previous study. The TACs at different time points were incorporated in a series of realistic 3D XCAT phantoms from which the parameters of a 2-compartment model and the 'true' MBF of 18F-flurpiridaz were determined. The compartmental model was used to generate TACs from 7 additional ITs. PET projection data from the XCAT phantoms were generated using Monte Carlo simulation. They were reconstructed using an OS-EM reconstruction algorithm with different update number (N) to obtain dynamic PET images. The blood and myocardial TACs were derived from the dynamic images from which the MBF and %MBF error was estimated. The %MBF error decreases with increasing N of the OS-EM and levels off after ∼42. The 30-s IT gave the smallest %MBF error that decreases from ∼0.57% to ∼19.40%. The MBF for 2-min, 4-min, 8-min and 16-min IT were statistically significant different from the MBF for 30-s IT (P<0.05). Too fast or too slow infusion time gave higher %MBF error. The optimal imaging protocol in dynamic 18F-flurpiridaz PET for accurate quantitative MBF estimation was 30-s IT and N of ∼42 for the OS-EM.

Effect of attenuation correction on lesion detection using a hybrid PET system.

OBJECTIVE: The purpose of this study was to investigate the effect of attenuation correction (AC) on lesion detection for a hybrid PET system. MATERIAL AND METHOD: Experimental list-mode data were acquired from hot spheres inside a uniform cylindrical phantom with an elliptical cross-section using a Siemens E. CAM+ dual-camera hybrid PET system. Spheres with inner diameters of 0.8- and 1-cm and the cylindrical phantom were filled with F-18 to simulate lesions with lesion-to-background (L/B) ratios of 14:1 and 8:1, respectively, found in clinical PET studies. The list-mode data of each sphere size were regrouped into sinograms with peak-to-peak energy window settings at 30% and 20% for the 0.8- and 1-cm diameter lesion, respectively. They were then rebinned using the single slice rebinning method. Attenuation correction was applied assuming uniform attenuation. The sinograms with and without AC were reconstructed using 5 iterations of OS-EM algorithm with 8 angles/ subset and postfiltered with a Butterworth filter with n = 5 and fc = 0.52 cycles/cm. Human observer performance study and localization receiver operating characteristic (LROC) analysis were used to evaluate the reconstructed images for maximum lesion detection. Average areas under the LROC curves (A(LROC)) across 8 observers obtained with and without AC were determined. The null hypothesis that there was no difference between with AC and without AC was tested using a two-tailed t-test with 95% confidence interval. RESULTS: The results indicated that for the 0. 8-cm lesion with 14:1 L/B ratio, the A(LROC) decreases from 0.66 to 0.62 when AC is applied as compared to without AC andfrom 0.69 to 0.63 for the 1.0-cm lesion with 8:1 L/ B ratio, but no statistical significant difference (p > 0. 05). CONCLUSION: The authors conclude that for a phantom with hot lesions embedded in a uniform background, AC decreases lesion detectability compared to without AC using a hybrid PET system for small lesion sizes.

Optimization and evaluation of reconstruction-based compensation methods and reconstruction parameters for Tc-99m MIBI parathyroid SPECT.

The value of Tc-99m MIBI parathyroid SPECT for localizing parathyroid hyperplasia in chronic renal failure patients remains inconclusive due to limited image quality. Advanced reconstruction methods to improve image quality have been developed but require optimization and evaluation. The goal of this study was to optimize and evaluate compensation methods and reconstruction parameters for Tc-99m MIBI parathyroid SPECT. A phantom population and projection data that modelled clinically realistic variations found in patients were simulated. The 3D OS-EM reconstruction with compensation for attenuation, detector response and scatter in various combinations were studied. For each compensation, the number of updates for OS-EM and the cutoff frequency of a 3D Butterworth filter were optimized and evaluated using anthropomorphic model observer. With optimal parameters, the method with compensation for attenuation and detector response, with or without the addition of scatter compensation, provided the highest lesion detectability for Tc-99m MIBI parathyroid SPECT.