Influence of region-of-interest determination on measurement of signal-to-noise ratio in liver on PET images

Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study

PURPOSE

Radiomics features have been utilised as group metrics of image quality in many areas of diagnostic radiology. In this pilot study, the relationship between technical metrics used in image quality assurance and visual grading scores provided by a radiologist were evaluated. Image dataset harmonisation allowed comparison between the two and allowed trends to be extracted. We propose a reproducible technique to identify the metrics.

METHODS

A retrospective chart review of 30 [18F] FDG-PET/CT performed in a nuclear medicine referral centre was performed. Image datasets were reprocessed to correspond to a bed duration of 180, 120, 60, 30 s per bed position and were analysed according to a pre-set bank of semi-quantitative features by a radiology resident. The extraction of radiomic features in PET images was performed using SLICER-RADIOMICS Module version 5.2.2. To facilitate the comparison of radiomic features and radiologist scoring data, normalisation was performed on both data sets. Fréchet distance analysis, Mean Square Error and Mean Absolute Error display the level of agreement between features and radiologist following the rescale of the data.

RESULTS

Of the 120 reprocessed image datasets, 115 were included in the study. We focused on overall image quality score rather than individual radiomic metrics as this identified the most robust trend. A significant difference in the 30 s image dataset with respect to each group individually and combined for the radiologist overall score was observed.

CONCLUSION

Our results show that a large percentage change in certain features can indicate a significant change in quality in clinically processed images.

Detection of Liver Lesions in Colorectal Cancer Patients Using 18F-FDG PET/CT Dual-Time-Point Scan Imaging

OBJECTIVE

The aim of this study was to evaluate the diagnostic performance of dual-time-point fluorine-18-fluorodeoxyglucose positron emission computed tomography/computed tomography (18F-FDG PET/CT) compared to conventional early imaging for detecting colorectal liver metastases (CRLM) in colorectal cancer (CRC) patients.

METHODS

One hundred twenty-four consecutive CRC patients underwent dual-time-point imaging scans on a retrospective basis. Histopathological confirmation and/or clinical follow-up were accepted as the gold standard. Standard uptake values (SUV), signal-to-noise ratio (SNR), retention index (RI), tumor-to-normal liver ratio (TNR), and lesion sizes were measured for early and delayed PET scans. The diagnostic performance of early and delayed images was calculated on a per-patient basis and compared using McNemar's test.

RESULTS

Among the 124 patients, 57 (46%) had CRLM, 6 (4.8%) had benign lesions, and 61 (49.2%) had no concerning lesions detected. Smaller CRLM lesions (<5 cm3) showed significantly higher uptake in the delayed scans relative to early imaging (p < 0.001). The SUV and TNR increased significantly in delayed imaging of all metastatic lesions (p < 0.001). The retention index of all CRLM was high (40.8%), especially for small lesions (54.8%). A total of 177 lesions in delayed images and 124 in standard early images were identified. In a per-patient analysis, delayed imaging had significantly higher sensitivity (100% vs. 87.7%) and specificity (91.0% vs. 94.0%) compared to early imaging (p-value = 0.04).

CONCLUSIONS

The detection of liver lesions using dual-time-point PET/CT scan improves the sensitivity and specificity for the detection of colorectal liver metastasis.

Impact of Region-of-Interest Delineation on Stability and Reproducibility of Liver SNR Measurements in 68 Ga-PSMA PET/CT

Objective  This study aims to assess the impact of various regions of interest (ROIs) and volumes of interest (VOIs) delineations on the reproducibility of liver signal-to-noise-ratio (SNRliver) measurements, as well as to find the most reproducible way to estimate it in gallium-68 positron emission tomography ( ⁶⁸ Ga-PET) imaging. We also investigated the SNRliver-weight relationship for these ROIs and VOIs delineations. Methods  A cohort of 40 patients (40 males; mean weight: 76.5 kg [58-115 kg]) with prostate cancer were included. ⁶⁸ Ga-PET/CT imaging (mean injected activity: 91.4 MBq [51.2 MBq to 134.1 MBq] was performed on a 5-ring bismuth germanium oxide-based Discovery IQ PET/CT using ordered subset expectation maximization image reconstruction algorithm. Afterward, circular ROIs and spherical VOIs with two different diameters of 30 and 40 mm were drawn on the right lobe of the livers. The performance of the various defined regions was evaluated by the average standardized uptake value (SUV _mean ), standard deviation (SD) of the SUV (SUV _SD ), SNR _liver , and SD of the SNR _liver metrics. Results  There were no significant differences in SUV _mean among the various ROIs and VOIs ( p  > 0.05). On the other hand, the lower SUV _SD was obtained by spherical VOI with diameter of 30 mm. The largest SNR _liver was obtained by ROI (30 mm). The SD of SNR _liver with ROI (30 mm) was also the largest, while the lowest SD of SNR _liver was observed for VOI (40 mm). There is a higher correlation coefficient between the patient-dependent parameter of weight and the image quality parameter of SNRliver for both VOI (30 mm) and VOI (40 mm) compared to the ROIs. Conclusion  Our results indicate that SNR _liver measurements are affected by the size and shape of the respective ROIs and VOIs. The spherical VOI with a 40 mm diameter leads to more stable and reproducible SNR measurement in the liver.

Modified signal-to-noise ratio in the liver using the background-to-lung activity ratio to assess image quality of whole-body 18F-fluorodeoxyglucose positron emission tomography

The signal-to-noise ratio in the liver (SNR liver) is commonly used to assess the quality of positron emission tomography (PET) images; however, it is weakly correlated with visual assessments. Conversely, the noise equivalent count (NEC) density showed a strong correlation with visual assessment but did not consider the effects of image reconstruction conditions. Therefore, we propose a new indicator, the modified SNR liver, and plan to verify its usefulness by comparing it with conventional indicators. We retrospectively analyzed 103 patients who underwent whole-body PET/computed tomography (CT). Approximately 60 min after the intravenous injection of ¹⁸F-fluorodeoxyglucose (FDG), the participants were scanned for 2 min/bed. The SNR liver and NEC density were calculated according to the Japanese guidelines for oncology FDG-PET/CT. The modified SNR live was calculated by multiplying the background-to-lung activity ratio by the SNR liver. Patients were classified into groups based on body mass index (BMI) and visual scores. Subsequently, the relationships between these physical indicators, BMI, and visual scores were evaluated. Although the relationship between the modified SNR liver and BMI was inferior to that of NEC density and BMI, the modified SNR liver distinguished the BMI groups more clearly than the conventional SNR liver. Additionally, the modified SNR liver distinguished low visual scores from high scores more accurately than the conventional SNR liver and NEC density. Whether the modified SNR liver is more suitable than the NEC density remains equivocal; however, the modified SNR liver may be superior to the conventional SNR liver for image-quality assessment.

Comparison of Image Quality and Semi-quantitative Measurements with Digital PET/CT and Standard PET/CT from Different Vendors

Purpose

This study aimed to evaluate the concordance and equivalence of results between the newly acquired digital PET/CT(dPET) and the standard PET/CT (sPET) to investigate possible differences in visual and semi-quantitative analyses.

Methods

A total of 30 participants were enrolled and underwent a single ¹⁸F-FDG injection followed by dual PET/CT scans, by a dPET scan, and immediately after by the sPET scan or vice versa. Two readers reviewed overall image quality using a 5-point scale and counted the number of suggestive ¹⁸F-FDG avid lesions. The SUV values were measured in the background organs and in hypermetabolic target lesions. Additionally, we objectively evaluated image quality using the liver signal-to-noise ratio (SNR).

Results

The dPET identified 4 additional ¹⁸F-FDG avid lesions in 3 of 30 participants with improved visual image quality. The standard deviations of SUV of the background organs were significantly lower with Digital_PET than with sPET, and dPET could acquire images with better SNR (11.13 ± 2.01 vs. 8.71 ± 1.32, P < 0.001). The reliability of SUV values between scanners showed excellent agreement. Bland-Altman plot analysis of 81 lesions showed an acceptable agreement between scanners for most of the SUVmax and SUVpeak values. No relationship between the SUV values and time delays of dual PET/CT acquisition was found.

Conclusions

The dPET provides improved image quality and lesion detectability than the sPET. The semi-quantitative values of the two PET/CT systems of different vendors are comparable. This pilot study will be an important basis for possible interchangeable use of either system in clinical practice.

Image quality evaluation in a modern PET system: impact of new reconstructions methods and a radiomics approach

The present work investigates the influence of different biological and physical parameters on image quality (IQ) perception of the abdominal area in a modern PET scanner, using new reconstruction algorithms and testing the utility of a radiomics approach. Scans of 112 patients were retrospectively included. Images were reconstructed using both OSEM + PSF and BSRM methods, and IQ of the abdominal region was subjectively evaluated. First, 22 IQ related parameters were obtained (including count rate and biological or mixed parameters) and compared to the subjective IQ scores by means of correlations and logistic regression. Second, an additional set of radiomics features was extracted, and a model was constructed by means of an elastic-net regression. For the OSEM + PSF and especially for the BSRM reconstructions, IQ parameters presented only at best moderated correlations with the subjective IQ. None of the studied parameters presented a good predictive power for IQ, while a simple radiomics model increased the performance of the IQ prediction. These results suggest the necessity of changing the standard parameters to evaluate IQ, particularly when a BSRM algorithm is involved. Furthermore, it seems that a simple radiomics model can outperform the use of any single parameter to assess IQ.