Phantom On The Scan 005 (2021) (digital) (F) (S...
Purpose: The aim of the study was to determine a faster PET acquisition protocol for a total-body PET/CT scanner by assessing the image quality that is equivalent to a conventional digital PET/CT scanner from both a phantom and a clinical perspective.
Phantom on the Scan 005 (2021) (digital) (F) (S...
Methods: A phantom study using a NEMA/IEC NU-2 body phantom was first performed in both a total-body PET/CT (uEXPLORER) and a routine digital PET/CT (uMI 780), with a hot sphere to background activity concentration ratio of 4:1. The contrast recovery coefficient (CRC), background variability (BV), and recovery coefficient (RC: RCmax and RCmean) were assessed in the uEXPLORER with different scanning durations and reconstruction protocols, which were compared to those acquired from the uMI 780 with clinical acquisition settings. The coefficient of variation (COV) of the uMI 780 with clinical settings was calculated and used as a threshold reference to determine the optimized scanning duration and reconstruction protocol for the uEXPLORER. The obtained protocol from the phantom study was subsequently tested and validated in 30 oncology patients. Images acquired from the uMI 780 with 2-3 min per bed position were referred as G780 and served as the reference for comparison. All PET raw data from the uEXPLORER were reconstructed using the data-cutting technique to simulate a 30-s, 45-s, or 60-s acquisition duration, respectively. The iterations were 2 and 3 for the uEXPLORER, referred as G30s_3i, G45s_2i, G45s_3i, G60s_2i, and G60s_3i, respectively. A 5-point Likert scale was used in the qualitative analysis to assess the image quality. The image quality was also evaluated by the liver COV, the lesion target-to-background ratio (TBR), and the lesion signal-to-noise ratio (SNR).
Results: In the phantom study, CRC, BV, RCmax, and RCmean in the uEXPLORER with different scanning durations and reconstruction iterations were compared with those in the uMI 780 with clinical settings. A minor fluctuation was found among different scanning durations. COV of the uMI 780 with clinical settings was 11.6%, and a protocol with a 30-45-s scanning duration and 2 or 3 iterations for the uEXPLORER was found to provide an equivalent image quality as the uMI 780. An almost perfect agreement was shown with a kappa value of 0.875. The qualitative score of the G30s_3i in the uEXPLORER was inferior to the G780 reference (p = 0.001); however, the scores of other groups in the uEXPLORER with a 45-s and above acquisition time were higher than the G780 in the uMI 780. In quantitative analysis, the delay time between the two scans in the two orders was not significantly different. There was no significant difference of the liver COV between the G780 and G30s_3i (p = 0.162). A total of 33 lesions were analyzed in the clinical patient study. There was no significant difference in lesion TBR between the reference G780 and the G45s_2i obtained from the uEXPLORER (p = 0.072), while the latter showed a higher lesion SNR value compared to that in uMI 780 with clinical settings (p
Three resin model pairs reflecting different tooth (mal)positions were mounted in the phantom head of a dental simulation unit and scanned by three dentists and three non-graduate investigators using a confocal laser IO scanner (Trios 3). The tooth-crown surfaces of the IO scans and reference scans were superimposed by means of best-fit alignment. A novel method comprising the measurement of individual tooth positions was used to determine the deviations of each tooth in the six degrees of freedom, i.e., in terms of 3D translation and rotation. Deviations between IO and reference scans, among tooth-(mal)position models, and between dentists and non-graduate investigators were analyzed using linear mixed-effects models.
To compare the image quality of a different time resolution, the same image quality phantom was imaged in the Siemens Biograph mCT PET/CT scanner with 527 ps intrinsic time resolution  and the new Siemens Biograph Vision with 210 ps intrinsic time resolution [44, 61]. The phantom had six hot spheres (5, 8, 10, 13, 17, and 22 mm diameter) with a contrast of about 4:1, and a total activity in the background of about 50 MBq of 18F. The data were reconstructed TOF and nonTOF in both scanners. In Fig. 2, we show one transaxial slice of the image obtained with iterative reconstruction with resolution recovery and no post-reconstruction filter: a nonTOF image from the mCT, a corresponding TOF image from the mCT (527 ps resolution), and a TOF image from the Biograph Vision (210 ps resolution). One can observe the improving image quality with improving time resolution: the smallest sphere is visible only using 210 ps time resolution TOF PET reconstruction.
Transaxial slices of an image quality phantom, reconstructed with high-resolution iterative reconstruction with resolution recovery. The scans were performed on a Siemens Biograph mCT PET/CT scanner (527 ps intrinsic time resolution) and the new Siemens Biograph Vision (210 ps intrinsic time resolution). From left to right: a non TOF image from mCT, b TOF image from Biograph mCT, and c TOF image from Biograph Vision. The phantom had six hot spheres (5, 8, 10, 13, 17, 22 mm diameter) with a contrast of about 4:1, and a total activity in the background of about 50 MBq of 18F
There is a whole series of possibilities opened by a characteristic of TOF PET that is intrinsically tied to the extra time information available. The recent literature has pointed out that TOF PET reconstruction is able to better handle data that are inconsistent, incomplete, or incorrect [13, 54]: TOF PET reconstruction is less sensitive to mismatched attenuation correction [12, 34, 58], inaccurate normalization, and scatter correction [12, 63, 65]. To visually demonstrate this phenomenon, the same image quality phantom was scanned in the Siemens Biograph mCT PET/CT scanner (527 ps intrinsic time resolution) and the new Siemens Biograph Vision (210 ps intrinsic time resolution). The phantom had six hot spheres (5, 8, 10, 13, 17, and 22 mm diameter) with a contrast of about 4:1, and a total activity in the background of about 50 MBq of 18F. The data were reconstructed TOF and nonTOF in both scanners. To show the improving robustness of TOF reconstruction with improving time resolution, artifacts were intentionally introduced in the normalization, the attenuation correction, and scatter correction: we replaced the accurate corrections with inaccurate or artificially manipulated corrections. By doing so, artifacts appeared in the images. We observed that, while the inclusion of the TOF information dramatically decreases the artifacts compared to the corresponding nonTOF reconstruction, better time resolution produces images with even lower residual artifacts. Typical normalization ring artifacts are much reduced, attenuation artifacts almost disappear, and TOF images are less biased by inaccurate scatter correction. As an example, in Fig. 3, reconstruction was performed with no scatter correction, and one can observe that the typical hump in the center of the phantom is barely visible in the 210 ps TOF image.
Transaxial slices of an image quality phantom, scanned on a Siemens Biograph mCT PET/CT scanner (527 ps intrinsic time resolution), and the new Siemens Biograph Vision (210 ps intrinsic time resolution). a Non TOF image from mCT, b TOF image from mCT, c nonTOF image from Biograph Vision, and d TOF image from Biograph Vision. The scatter correction was replaced by no scatter correction, which results in a typical bump of activity towards the center of the phantom
Methods: CT images derived from a Catphan 500 phantom were acquired using manufacturer-specific iterative reconstruction (IR) algorithms and deep learning image reconstruction (DLIR) on CT scanners from 5 different manufacturers and compared using filtered back projection with 2 radiation doses of 0.25 and 0.75 mGy. Image high-contrast spatial resolution and image noise were objectively characterized by modulation transfer function (MTF) and noise power spectrum (NPS). Image high-contrast spatial resolution and image low-contrast detectability were compared directly by visual evaluation. CT number linearity and image uniformity were compared with intergroup differences using one-way analysis of variance (ANOVA).
While many studies have examined the use of LDCT screening for lung cancer and osteoporosis, few have compared the physical image quality obtained from the Catphan 500 using CT scanners from different manufacturers (3,9,13). Therefore, this study performed phantom experiment to systematically investigate the physical image quality of CT scanners from 5 different manufacturers using an LDCT scan protocol.
where N is the number of slices acquired by a single scan, T is the nominal thickness of one slice, and D (z) is a dose profile along the longitudinal axis, centered at z =0 (17). The measurement of CTDI100 in a phantom is made at the center and at 4 peripheral positions 1 cm below the phantom surface (15).
Different QCT scan protocols can change the CT values, which can then influence BMD distribution in the QCT images (31). In this study, a Catphan 500 phantom was scanned at a fixed tube voltage of 120 kV, which assured the accuracy of the BMD values. The CT attenuation numbers are only affected by bean energy (kVp), whereas the variable tube current mainly affects image noise; thus, changing the tube voltage will influence QCT measurements (32). The tube voltage of a standard lumbar spine scan is set to 120 kV. In patients, the BMD values derived from routine lumbar spine scans at 120 kV are more accurate than those at 140 kV (33). Many previous studies have shown that LDCT scans at 120 kV can accurately measure the BMD of the spine by QCT (3,9,10). At a given tube voltage of 120 kV, the image noise is mainly influenced by the tube current-exposure time product, reconstruction kernel, and slice thickness (34). Though an LDCT scan can reduce the radiation dose, its contribution to the image noise is greatly increased. To improve the image quality in LDCT, we adopted the manufacturer-specific IR algorithms for the CT scanners and compared it to FBP, including the DLIR algorithms (new-generation deep learning image reconstruction algorithms). The DLIR algorithm from GE Healthcare integrates image quality improvement knowledge into a DNN composed by layers of mathematical equations that comprise many parameters to represent the characteristics of high-quality images even when acquired CT data is degraded by lower dose or non-ideal scanning conditions. These algorithms could reduce the radiation dose significantly without altering the image noise and produced high diagnostic quality images at a low radiation dose compared to other IR algorithms and FBP (35-37). 041b061a72