Radiation dose in cardiac SPECT/CT: An estimation of SSDE and effective dose

Pathology-based radiation dose in computed tomography: investigation of the effect of lung lesions on water-equivalent diameter, CTDIVol and SSDE in COVID-19 patients

Lung lesions can increase the CT number and affect the water-equivalent diameter (Dw), Dw-based conversion factor (CFw), and Dw-based size-specific dose estimate (SSDEw). We evaluated the effect of COVID-19 lesions and total severity score (TSS) on radiation dose considering the effect of automatic tube current modulation (ATCM) and fixed tube current (FTC). A total of 186 chest CT scans were categorised into five TSS groups, including healthy, minimal, mild, moderate and severe. The effective diameter (Deff), Dw, CFw, Deff-based conversion factor (CFeff), volume computed tomography dose index (CTDIVol), pathological dose impact factor (PDIF) 1 and SSDEw were calculated. TSS was correlated with Dw (r = 0.29, p-value = 0.001), CTDIVol (ATCM) (r = 0.23, p = 0.001) and PDIF (r = - 0.51, p-value = 0.001). $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (FTC) was significantly different among all groups. $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (ATCM) was greater for moderate (13%) and mild (14%) groups. Increasing TSS increase the Dw and causes a decrease in CFw and $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (FTC), and can increase $\overline{{\mathrm{SSDE}}_{\mathrm{w}}}$ (ATCM) in some Dw ranges.

Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT

BACKGROUND

Deep learning (DL)-based attenuation correction (AC) is promising to improve myocardial perfusion (MP) SPECT. We aimed to optimize and compare the DL-based direct and indirect AC methods, with and without SPECT and CT mismatch.

METHODS

One hundred patients with different 99mTc-sestamibi activity distributions and anatomical variations were simulated by a population of XCAT phantoms. Additionally, 34 patients 99mTc-sestamibi stress/rest SPECT/CT scans were retrospectively recruited. Projections were reconstructed by OS-EM method with or without AC. Mismatch between SPECT and CT images was modeled. A 3D conditional generative adversarial network (cGAN) was optimized for two DL-based AC methods: (i) indirect approach, i.e., non-attenuation corrected (NAC) SPECT paired with the corresponding attenuation map for training. The projections were reconstructed with the DL-generated attenuation map for AC; (ii) direct approach, i.e., NAC SPECT paired with the corresponding AC SPECT for training to perform direct AC.

RESULTS

Mismatch between SPECT and CT degraded DL-based AC performance. The indirect approach is superior to direct approach for various physical and clinical indices, even with mismatch modeled.

CONCLUSION

DL-based estimation of attenuation map for AC is superior and more robust to direct generation of AC SPECT.

A systematic review and meta-analysis on the radiation dose of computed tomography in hybrid nuclear medicine imaging

BACKGROUND

While diagnostic reference levels (DRLs) are well-established for the radiopharmaceutical part, published DRLs for the CT component of positron emission tomography/computed tomography (PET/CT) and single photon emission computed tomography/computed tomography (SPECT/CT) are limited. This systematic review and meta-analysis provides an overview of the different objectives of CT in hybrid imaging and summarizes reported CT dose values for the most common PET/CT and SPECT/CT examinations. Also, an overview of already proposed national DRLs is given.

METHODS

A systematic literature search was performed to identify original articles reporting CT dose index volume (CTDIvol), dose-length product (DLP) and/or national DRLs for the most frequently performed PET/CT and/or SPECT/CT examinations. Data were grouped according to the clinical objective: diagnostic (D-CT), anatomical localisation (AL-CT) or attenuation correction (AC-CT) CT. Random-effects meta-analyses were conducted.

RESULTS

Twenty-seven articles were identified of which twelve reported national DRLs. For brain and tumour PET/CT imaging, CTDIvol and DLP values were higher for a D-CT (brain: 26.7 mGy, 483 mGy cm; tumour: 8.8 mGy, 697 mGy cm) than for an AC/AL-CT (brain: 11.3 mGy, 216 mGy cm; tumour: 4.3 mGy, 419 mGy cm). Similar conclusions were found for bone and parathyroid SPECT/CT studies: D-CT (bone: 6.5 mGy, 339 mGy cm; parathyroid: 15.1 mGy, 347 mGy cm) results in higher doses than AL-CT (bone: 3.8 mGy, 156 mGy cm; parathyroid: 4.9 mGy, 166 mGy cm). For cardiac (AC-CT), mIBG/octreotide, thyroid and post-thyroid ablation (AC/AL-CT) SPECT/CT pooled mean CTDIvol (DLP) values were 1.8 mGy (33 mGy cm), 4.6 mGy (208 mGy cm), 3.1 mGy (105 mGy cm) and 4.6 mGy (145 mGy cm), respectively. For all examinations, high variability in nuclear medicine practice was observed.

CONCLUSION

The large variation in CT dose values and national DRLs highlights the need for optimisation in hybrid imaging and justifies the clinical implementation for nuclear medicine specific DRLs.

Attenuation Correction for Dedicated Cardiac SPECT Imaging Without Using Transmission Data

Objectives

Attenuation correction (AC) using transmission scanning-like computed tomography (CT) is the standard method to increase the accuracy of cardiac single-photon emission computed tomography (SPECT) images. Recently developed dedicated cardiac SPECT do not support CT, and thus, scans on these systems are vulnerable to attenuation artifacts. This study presented a new method for generating an attenuation map directly from emission data by segmentation of precisely non-rigid registration extended cardiac-torso (XCAT)-digital phantom with cardiac SPECT images.

Methods

In-house developed non-rigid registration algorithm automatically aligns the XCAT- phantom with cardiac SPECT image to precisely segment the contour of organs. Pre-defined attenuation coefficients for given photon energies were assigned to generate attenuation maps. The CT-based attenuation maps were used for validation with which cardiac SPECT/CT data of 38 patients were included. Segmental myocardial counts of a 17-segment model from these databases were compared based on the basis of the paired t-test.

Results

The mean, and standard deviation of the mean square error and structural similarity index measure of the female stress phase between the proposed attenuation maps and the CT attenuation maps were 6.99±1.23% and 92±2.0%, of the male stress were 6.87±3.8% and 96±1.0%. Proposed attenuation correction and computed tomography based attenuation correction average myocardial perfusion count was significantly higher than that in non-AC in the mid-inferior, mid-lateral, basal-inferior, and lateral regions (p<0.001).

Conclusion

The proposed attenuation maps showed good agreement with the CT-based attenuation map. Therefore, it is feasible to enable AC for a dedicated cardiac SPECT or SPECT standalone scanners.

Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study

BACKGROUND

The aim of this work was to assess the robustness of cardiac SPECT radiomic features against changes in imaging settings, including acquisition, and reconstruction parameters.

METHODS

Four commercial SPECT and SPECT/CT cameras were used to acquire images of a static cardiac phantom mimicking typical myorcardial perfusion imaging using 185 MBq of 99mTc. The effects of different image acquisition and reconstruction parameters, including number of views, view matrix size, attenuation correction, as well as image reconstruction related parameters (algorithm, number of iterations, number of subsets, type of post-reconstruction filter, and its associated parameters, including filter order and cut-off frequency) were studied. In total, 5,063 transverse views were reconstructed by varying the aforementioned factors. Eighty-seven radiomic features including first-, second-, and high-order textures were extracted from these images. To assess reproducibility and repeatability, the coefficient of variation (COV), as a widely adopted metric, was measured for each of the radiomic features over the different imaging settings.

RESULTS

The Inverse Difference Moment Normalized (IDMN) and Inverse Difference Normalized (IDN) features from the Gray Level Co-occurrence Matrix (GLCM), Run Percentage (RP) from the Gray Level Co-occurrence Matrix (GLRLM), Zone Entropy (ZE) from the Gray Level Size Zone Matrix (GLSZM), and Dependence Entropy (DE) from the Gray Level Dependence Matrix (GLDM) feature sets were the only features that exhibited high reproducibility (COV ≤ 5%) against changes in all imaging settings. In addition, Large Area Low Gray Level Emphasis (LALGLE), Small Area Low Gray Level Emphasis (SALGLE) and Low Gray Level Zone Emphasis (LGLZE) from GLSZM, and Small Dependence Low Gray Level Emphasis (SDLGLE) from GLDM feature sets turned out to be less reproducible (COV > 20%) against changes in imaging settings. The GLRLM (31.88%) and GLDM feature set (54.2%) had the highest (COV < 5%) and lowest (COV > 20%) number of the reproducible features, respectively. Matrix size had the largest impact on feature variability as most of the features were not repeatable when matrix size was modified with 82.8% of them having a COV > 20%.

CONCLUSION

The repeatability and reproducibility of SPECT/CT cardiac radiomic features under different imaging settings is feature-dependent. Different image acquisition and reconstruction protocols have variable effects on radiomic features. The radiomic features exhibiting low COV are potential candidates for future clinical studies.

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

OBJECTIVES

The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.

METHODS

In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE).

RESULTS

The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8.

CONCLUSIONS

The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction.

KEY POINTS

• Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.

Deep learning-based attenuation map generation for myocardial perfusion SPECT

PURPOSE

Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission scanning and therefore scans on these systems are susceptible to attenuation artifacts. Moreover, the use of CT scans also increases radiation dose to patients and significant artifacts can occur due to the misregistration between the SPECT and CT scans as a result of patient motion. The purpose of this study is to develop an approach to estimate attenuation maps directly from SPECT emission data using deep learning methods.

METHODS

Both photopeak window and scatter window SPECT images were used as inputs to better utilize the underlying attenuation information embedded in the emission data. The CT-based attenuation maps were used as labels with which cardiac SPECT/CT images of 65 patients were included for training and testing. We implemented and evaluated deep fully convolutional neural networks using both standard training and training using an adversarial strategy.

RESULTS

The synthetic attenuation maps were qualitatively and quantitatively consistent with the CT-based attenuation map. The globally normalized mean absolute error (NMAE) between the synthetic and CT-based attenuation maps were 3.60% ± 0.85% among the 25 testing subjects. The SPECT reconstructed images corrected using the CT-based attenuation map and synthetic attenuation map are highly consistent. The NMAE between the reconstructed SPECT images that were corrected using the synthetic and CT-based attenuation maps was 0.26% ± 0.15%, whereas the localized absolute percentage error was 1.33% ± 3.80% in the left ventricle (LV) myocardium and 1.07% ± 2.58% in the LV blood pool.

CONCLUSION

We developed a deep convolutional neural network to estimate attenuation maps for SPECT directly from the emission data. The proposed method is capable of generating highly reliable attenuation maps to facilitate attenuation correction for SPECT-only scanners for myocardial perfusion imaging.

Size specific dose estimate (SSDE) for estimating patient dose from CT used in myocardial perfusion SPECT/CT

Objectives

Size specific dose estimate (SSDE) is a new parameter that includes patient size factor in its calculation. Recent studies have produced mixed results on the utility of SSDE, especially when automatic exposure control (AEC) was used. The objective of the study was to find out if there is a relationship between patient size and each of the parameters, SSDE and CTDIvol, when AEC is used.

Methods

CT data of consecutively selected 111 patients were included for analysis. CTDIvol values of the CT scans were extracted for each patient. Effective diameter of each patient was calculated as geometric mean of anteroposterior and lateral diameters measured on axial CT images. Corresponding conversion factors for effective diameters were obtained from American Association of Physicists in Medicine (AAPM) report 204. SSDE was obtained as the product of CTDIvol and conversion factor values. Linear regression model was used to evaluate the relationship between patient size and the parameters SSDE and CTDIvol.

Results

Mean weight was 62 (11.5) and range was 34 - 103 kg. Median CTDIvol (mGy) on AEC mode was 7.27(IQ range 7.27, 7.65) and mean effective diameter was 26.2 cm (2.4). Mean SSDE (mGy) was 10.6 (0.84). Good positive correlation was obtained between CTDIvol and effective diameter (r=0.536; p<0.0005). Strong inverse correlation was noted between SSDE and effective diameter (r=-0.777; p<0.0005). Linear regression model for establishing relationship between CTDIvol and effective diameter showed slope of 0.314mGy/cm (R=0.561; R2=0.314; P<0.0005) whereas between effective diameter and SSDE slope was -0.23mGy/cm (R=0.676; R2=0.457; P< 0.0005).

Conclusion

The study shows that CTDIvol and SSDE vary but divergently, with patient size. SSDE is a better estimate of patient radiation dose from CT of MPI SPECT/CT than CTDIvol in systems that use automated exposure control.

Reject analysis in digital radiography: A local study on radiographers and students' attitude in Iran

Reject analysis is as a quality indicator and critical tool for dose and image quality optimization in radiology departments. By reducing image rejection rate (RR), radiation dose to patients can be reduced effectively, yielding increased total cost-effectiveness. The aims of this study were to assess the rate of image rejection at 2 direct digital radiography (DR) departments to find the sources of rejection and to observe how radiology students and radiographers deal with image rejection.

Two radiology departments were surveyed during a 3-month period for all imaging procedures. Type of examination, numbers, and reasons for digital image rejection were obtained by systems and questionnaire. A predefined questionnaire, including 13 causes for rejection, was filled by radiographers and students.

Out of the 14 022 acquired images, 1116 were rejected, yielding an overall RR of 8%. Highest RRs were found for examination of cervical spine and lumbosacral. Positioning errors and improper patient preparation were the main reasons for digital image rejection.

The image RR was small, but there is a need for optimizing radiographic practice, and enhancing radiographer’s knowledge may enhance the performance.

Dose Optimization of Hybrid Imaging

Hybrid imaging combines the functional and molecular imaging of positron emission computed tomography and single-photon emission computed tomography with the anatomical information available from computed tomography and magnetic resonance imaging. As a result, the clinical utility of positron emission computed tomography/computed tomography and single-photon emission computed tomography/computed tomography has been clearly established in the past 17 y. In addition, the use of positron emission computed tomography/magnetic resonance, which was introduced to the clinic in the past decade, has continued to grow. These multimodality approaches to medical imaging have substantial dosimetric aspects associated with their practice in both adults and children. For positron emission computed tomography/computed tomography and single-photon emission computed tomography/computed tomography, one must consider the radiation dose delivered from both the radiopharmaceutical and the computed tomography portion of the hybrid scan. Whether the computed tomography is to be used solely for attenuation correction, anatomical correlation of patient, or full diagnosis must be taken into account when deciding on the computed tomography acquisition parameters. Even after 17 y, the most appropriate approach to the acquisition of these modalities is not fully established. When appropriately used, positron emission computed tomography/magnetic resonance provides the opportunity for notable dose reduction. In addition to the elimination of the radiation dose from the computed tomography, one may consider the higher sensitivity of the positron emission computed tomography component relative to that used in positron emission computed tomography/computed tomography and the longer acquisition time to reduce the amount of administered activity of the radiopharmaceutical. However, one must realize that magnetic resonance presents a different set of safety concerns outside of those associated with ionizing radiation. As with all medical procedures, the benefits as well as the potential risks of the procedure need to be evaluated in the context of choosing the most appropriate procedure to be performed and the optimization of acquisition protocol to assure high-quality clinical information with the least potential for risk possible.

Tailoring CT Dose to Patient Size: Implementation of the Updated 2017 ACR Size-specific Diagnostic Reference Levels

RATIONALE AND OBJECTIVES

To use an automatic computed tomography (CT) dose monitoring system to analyze the institutional chest and abdominopelvic CT dose data as regards the updated 2017 American College of Radiology (ACR) diagnostic reference levels (DRLs) based on water-equivalent diameter (Dw) and size-specific dose estimates (SSDE) to detect patient-size subgroups in which CT dose can be optimized.

MATERIALS AND METHODS

All chest CT examinations performed between July 2016 and April 2017 with and without contrast material, CT of the pulmonary arteries, and abdominopelvic CT with and without contrast material were included in this retrospective study. Dw and SSDE were automatically calculated for all scans using a previously validated in-house developed Matlab software and stored into our CT dose monitoring system. CT dose data were analyzed as regards the updated ACR DRLs (size groups: 21-25 cm, 25-29 cm, 29-33 cm, 33-37 cm, 37-41 cm). SSDE and volumetric computed tomography dose index (CTDIvol) were used as CT dose parameter.

RESULTS

Overall, 30,002 CT examinations were performed in the study period, 3860 of which were included in the analysis (mean age 62.1 ± 16.4 years, Dw 29.0 ± 3.3 cm; n = 577 chest CT without contrast material, n = 628 chest CT with contrast material, n = 346 CT of chest pulmonary, n = 563 abdominopelvic CT without contrast material, n = 1746 abdominopelvic CT with contrast material). Mean SSDE and CTDIvol relative to the updated DRLs were 43.3 ± 26.4 and 45.1 ± 27.9% for noncontrast chest CT, 52.3 ± 23.1 and 52.0 ± 23.1% for contrast-enhanced chest CT, 68.8 ± 29.5 and 70.0 ± 31.0% for CT of pulmonary arteries, 41.9 ± 29.2 and 43.3 ± 31.3% for noncontrast abdominopelvic CT, and 56.8 ± 22.2 and 58.8 ± 24.4% for contrast-enhanced abdominopelvic CT. Lowest dose compared to the DRLs was found for the Dw group of 21-25 cm in noncontrast abdominopelvic CT (SSDE 30.4 ± 21.8%, CTDIvol 30.8 ± 21.4%). Solely the group of patients with a Dw of 37-41 cm undergoing noncontrast abdominopelvic CT exceeded the ACR DRL (SSDE 100.3 ± 59.0%, CTDIvol 107.1 ± 63.5%).

CONCLUSIONS

On average, mean SSDE and CTDIvol of our institutional chest and abdominopelvic CT protocols were lower than the updated 2017 ACR DRLs. Size-specific subgroup analysis revealed a wide variability of SSDE and CTDIvol across CT protocols and patient size groups with a transgression of DRLs in noncontrast abdominopelvic CT of large patients (Dw 37-41 cm).

Common atrium and the associated malformations: Evaluation by low-dose dual-source computed tomography

Common atrium (CA) is a rare complex congenital heart disease. The studies of CA are mostly case reports, while few have been done regarding its morphological characteristics. We aimed to determine CA characteristics and diagnostic accuracy in assessing associated malformations in these patients with low-dose dual-source computed tomography (DSCT).Twenty-one pediatric and adolescent CA patients underwent low-dose DSCT. Different ventricular types and associated malformations were assessed. The diagnostic accuracy of DSCT and transthoracic echocardiography (TTE) in evaluating associated malformations were assessed. The effective doses of DSCT were calculated.Patients (n = 21) were divided into CA with biventricular physiology (n = 7) and CA with single ventricle (SV) (n = 14). There were 3 types of SV morphology: single left ventricle (n = 5), single right ventricle (n = 6), and undifferentiated ventricle (n = 3). In all, 22 associated malformations were seen in CA and 56 in CA with SV. DSCT was superior to TTE for detecting intracardiac anomalies (sensitivity: DSCT, 92.31% vs TTE, 76.92%), great vessels anomalies (sensitivity: DSCT, 100.00% vs TTE, 77.50%), and of collateral vessels (sensitivity: DSCT, 100% vs TTE, 20.00%). The estimated mean effective dose was 0.95 ± 0.44 mSv (<1 mSv).This study indicated that low-dose DSCT is an ideal alternative for pediatric and adolescent patients with CA, providing morphological details of CA and associated malformations with high accuracy.