Reconstructed Slice Thickness Research Articles

Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions.

The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use. The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs. MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners' reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA). The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions. The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening.

Assessing variability in non-contrast CT for the evaluation of stroke: The effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume.

To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume. The NCCT series image data of 87 patients imaged with a CT stroke protocol at our institution were reconstructed with 3 kernels (H10s-smooth, H40s-medium, H70h-sharp) and 2 slice thicknesses (1.5mm and 5mm) to create a reference condition (H40s/5mm) and 5 non-reference conditions. Each reconstruction for each patient was analyzed with the Brainomix e-Stroke software (Brainomix, Oxford, England) which yields an ASPECTS value and measure of total hypodense volume (mL). An ASPECTS value was returned for 74 of 87 cases in the reference condition (13 failures). ASPECTS in non-reference conditions changed from that measured in the reference condition for 59 cases, 7 of which changed above or below the clinical threshold of 7 for 3 non-reference conditions. ANOVA tests were performed to compare the differences in protocols, Dunnett's post-hoc tests were performed after ANOVA, and a significance level of p < 0.05 was defined. There was no significant effect of kernel (p = 0.91), a significant effect of slice thickness (p < 0.01) and no significant interaction between these factors (p = 0.91). Post-hoc tests indicated no significant difference between ASPECTS estimated in the reference and any non-reference conditions. There was a significant effect of kernel (p < 0.01) and slice thickness (p < 0.01) on hypodense volume, however there was no significant interaction between these factors (p = 0.79). Post-hoc tests indicated significantly different hypodense volume measurements for H10s/1.5mm (p = 0.03), H40s/1.5mm (p < 0.01), H70h/5mm (p < 0.01). No significant difference was found in hypodense volume measured in the H10s/5mm condition (p = 0.96). Automated ASPECTS and hypodense volume measurements can be significantly impacted by reconstruction kernel and slice thickness.

Development and Application of a Virtual Imaging Trial Framework for Longitudinal Quantification of Emphysema in CT.

Pulmonary emphysema is a progressive lung disease that requires accurate evaluation for optimal management. This task, possible using quantitative CT, is particularly challenging as scanner and patient attributes change over time, negatively impacting the CT-derived quantitative measures. Efforts to minimize such variations have been limited by the absence of ground truth in clinical data, thus necessitating reliance on clinical surrogates, which may not have one-to-one correspondence to CT-based findings. This study aimed to develop the first suite of human models with emphysema at multiple time points, enabling longitudinal assessment of disease progression with access to ground truth. A total of 14 virtual subjects were modeled across three time points. Each human model was virtually imaged using a validated imaging simulator (DukeSim), modeling an energy-integrating CT scanner. The models were scanned at two dose levels and reconstructed with two reconstruction kernels, slice thicknesses, and pixel sizes. The developed longitudinal models were further utilized to demonstrate utility in algorithm testing and development. Two previously developed image processing algorithms (CT-HARMONICA, EmphysemaSeg) were evaluated. The results demonstrated the efficacy of both algorithms in improving the accuracy and precision of longitudinal quantifications, from 6.1±6.3% to 1.1±1.1% and 1.6±2.2% across years 0-5. Further investigation in EmphysemaSeg identified that baseline emphysema severity, defined as >5% emphysema at year 0, contributed to its reduced performance. This finding highlights the value of virtual imaging trials in enhancing the explainability of algorithms. Overall, the developed longitudinal human models enabled ground-truth based assessment of image processing algorithms for lung quantifications.

Coronary artery calcium scoring assessment in ultra-low-dose chest computed tomography

ObjectivesTo investigate the effect of non-electrocardiogram (ECG) -triggered ultra-low-dose CT (ULD-CT) with different reconstruction protocols on coronary artery calcium (CAC) scoring assessment, compared with ECG-triggered CAC CT (CAC-CT). MethodsThis prospective study included 115 patients who underwent CAC-CT and ULD-CT scans under the same topogram images. CAC-CT adopted a prospective ECG-triggered sequential acquisition with a tube potential of 120 kV, and the reconstruction protocol was standard Qr36 + slice 3 mm (CACQr-3mm group). ULD-CT adopted a non-ECG-triggered high-pitch acquisition with a tube potential of Sn100 kV, and four groups of images (named ULDQr-3mm, ULDSa-3mm, ULDQr-1.5mm, and ULDSa-1.5mm) were reconstructed using different reconstruction algorithms (standard Qr36, kV-independent Sa36) and slice thicknesses (3 mm, 1.5 mm). The accuracy of CAC detection by ULD-CT was calculated. The agreement of the CAC score between ULD-CT and CAC-CT scans was assessed using intraclass correlation coefficients (ICC) and Bland-Altman plot, and the agreement of risk categorization was assessed using weighted kappa. ResultsThe sensitivity and specificity of the ULDSa-1.5mm group for detecting positive CAC were 100% and 97.4%, respectively (k = 0.980). The CAC score for the ULDSa-3mm and ULDSa-1.5mm groups demonstrated excellent agreement with the CACQr-3mm group (ICC = 0.992, 0.990, respectively), with a mean difference of −12.3 and − 12.4. The agreement of risk categorization based on absolute and percentile CAC score between the ULDSa-1.5mm and CACQr-3mm groups was excellent (weighted k = 0.954, 0.983, respectively), and risk reclassification rates were low (3.5%, 2.8%, respectively). The effective dose was reduced by approximately 77.2% for the ULD-CT compared to the CAC-CT (0.18 mSv vs. 0.79 mSv, p < 0.001). ConclusionReconstruction with a 1.5-mm slice thickness and kV-independent iterative algorithmic protocol in ULD-CT yielded excellent agreement in CAC score quantification and risk categorization compared with ECG-triggered CAC-CT.

The diagnosis performance of convolutional neural network in the detection of pulmonary nodules: a systematic review and meta-analysis.

Pulmonary nodules are an early imaging indication of lung cancer, and early detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the applications of machine learning, the convolutional neural network (CNN) applied to computed tomography (CT) imaging data improves the accuracy of diagnosis, but the results could be more consistent. To evaluate the diagnostic performance of CNN in assisting in detecting pulmonary nodules in CT images. PubMed, Cochrane Library, Web of Science, Elsevier, CNKI and Wanfang databases were systematically retrieved before 30 April 2023. Two reviewers searched and checked the full text of articles that might meet the criteria. The reference criteria are joint diagnoses by experienced physicians. The pooled sensitivity, specificity and the area under the summary receiver operating characteristic curve (AUC) were calculated by a random-effects model. Meta-regression analysis was performed to explore potential sources of heterogeneity. Twenty-six studies were included in this meta-analysis, involving 2,391,702 regions of interest, comprising segmented images with a few wide pixels. The combined sensitivity and specificity values of the CNN model in detecting pulmonary nodules were 0.93 and 0.95, respectively. The pooled diagnostic odds ratio was 291. The AUC was 0.98. There was heterogeneity in sensitivity and specificity among the studies. The results suggested that data sources, pretreatment methods, reconstruction slice thickness, population source and locality might contribute to the heterogeneity of these eligible studies. The CNN model can be a valuable diagnostic tool with high accuracy in detecting pulmonary nodules.

MSCT Thoracolumbal Kontras pada Suspek Metastasis Tulang Belakang: Studi Kasus di Rumah Sakit Hermina Pandanaran Semarang

Background: MSCT is a routine imaging modality for detecting bone metastases. The use of 1.25 mm slice thickness reconstruction on Thoracolumbar MSCT examination at Hermina Pandanaran Hospital is different from preliminary studies. This study aims to determine MSCT Thoracolumbar contrast with 1.25 mm slice thickness and 3D reconstruction to produce information on anatomical images in suspected cases of spinal metastases. Methods: This research was qualitative with a case study approach. The respondents were 3 radiographers, 2 radiologists, and 1 referring doctor. Data collection by observation, interviews, and document studies. Processing and data analysis used open coding and analysis to obtain conclusions. Results: The examination procedure was carried out by preparing the patient to fast for 6 hours before the examination, checking the results of ureum creatinine, filling out the informed consent and doing a skin test. Contrast media as much as 1.1-1.2 ml/kg per patient's body weight plus saline half of the amount of contrast, flow rate 2.5-3 mL/s, scan delay 25 s. DFOV 25-35cm, ASIR 40%. Reconstruction of slice thickness 1.25 mm to get more detailed results so that it could produce informative images that evaluated the spine and soft tissue. MPR and 3D reconstruction so that the image looked real bone and could be viewed 360ᵒ. Conclusions: The resulting slice thickness of 1.25mm and 3D reconstruction produced anatomical image information that could visualize bone to see destructive lytic lesions due to metastases, and visualize soft tissue such as paravertebral masses or discs.

Quantitative analysis of small coronary artery calcium detectability with an accurate simulation and validation on a clinical CT scanner.

The absence of coronary artery calcium (CAC) measured via CT is associated with very favorable prognosis, and current guidelines recommend low-density lipoprotein cholesterol (LDL-c) lowering therapy for individuals with any CAC. This motivates early detection of small granules of CAC; however, calcium scan sensitivity for detecting very low levels of calcium has not been quantified. In this work, the size limit of detectability of small calcium hydroxyapatite (CaHA) granules with clinical CAC scanning was assessed using validated simulations. CT projections of digital 3D mathematical phantoms containing small CaHA granules were simulated analytically; images were reconstructed using a filter designed to reproduce the point spread function of a specific commercial scanner, and a relationship of HU number versus diameter was derived. These simulation results were validated with experimental measurements of HU versus diameter from phantoms containing small granules of CaHA on a GE Revolution CT scanner in the clinic; ground truth measurements of the CaHA granule diameters were obtained using a Zeiss Xradia 510 Versa high-resolution 3D micro-CT imaging system. Using experimental measurements on the clinical CT scanner, detectability was quantified with a detectability index (d') using a non-prewhitened matched filter. The effect of changes to reconstruction slice thickness and reconstruction kernel on granule detectability was evaluated. Under typical clinical calcium scanning and reconstruction conditions, the minimum detectable diameter of a simulated spherical calcium granule with a clinically relevant CaHA density was 0.76mm. The minimum detectable volume was 2.4 times smaller on images reconstructed at a slice thickness of 0.625mm compared to 2.5mm. The detectability index d' increased by a factor of 1.7 when images were reconstructed with 0.625mm slices compared to 2.5mm slices. d' did not change when images were reconstructed with the high-resolution BONE filter compared to the less sharp STANDARD resolution filter on the GE Revolution CT. We have quantified detectability versus size of small calcium granules at the resolution limit of a widely available clinical CT scanner. Detectability increased significantly with reduced slice thickness and did not change with a sharper reconstruction kernel. The simulation can be used to calculate the trade-off between dose and CAC detectability.

Ultrahigh-Resolution Photon-Counting Detector CT of the Lungs: Association of Reconstruction Kernel and Slice Thickness With Image Quality.

BACKGROUND. Prior work has shown improved image quality for photon-counting detector (PCD) CT of the lungs compared with energy-integrating detector CT. A paucity of the literature has compared PCD CT of the lungs using different reconstruction parameters. OBJECTIVE. The purpose of this study is to the compare the image quality of ultra-high-resolution (UHR) PCD CT image sets of the lungs that were reconstructed using different kernels and slice thicknesses. METHODS. This retrospective study included 29 patients (17 women and 12 men; median age, 56 years) who underwent noncontrast chest CT from February 15, 2022, to March 15, 2022, by use of a commercially available PCD CT scanner. All acquisitions used UHR mode (1024 × 1024 matrix). Nine image sets were reconstructed for all combinations of three sharp kernels (BI56, BI60, and BI64) and three slice thicknesses (0.2, 0.4, and 1.0 mm). Three radiologists independently reviewed reconstructions for measures of visualization of pulmonary anatomic structures and pathologies; reader assessments were pooled. Reconstructions were compared with the clinical reference reconstruction (obtained using the BI64 kernel and a 1.0-mm slice thickness [BI641.0-mm]). RESULTS. The median difference in the number of bronchial divisions identified versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.5), BI600.4-mm (0.3), BI640.2-mm (0.5), and BI600.2-mm (0.2) (all p < .05). The median bronchial wall sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3) and BI640.2-mm (0.3) and was lower for BI561.0-mm (-0.7) and BI560.4-mm (-0.3) (all p < .05). Median pulmonary fissure sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3), BI600.4-mm (0.3), BI560.4-mm (0.5), BI640.2-mm (0.5), BI600.2-mm (0.5), and BI560.2-mm (0.3) (all p < .05). Median pulmonary vessel sharpness versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3), BI600.4-mm (-0.3), BI560.4-mm (-0.7), BI640.2-mm (-0.7), BI600.2-mm (-0.7), and BI560.2-mm (-0.7). Median lung nodule conspicuity versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3) and BI560.4-mm (-0.3) (both p < .05). Median conspicuity of all other pathologies versus the clinical reference reconstruction was lower for reconstructions with BI561.0 mm (-0.3), BI560.4-mm (-0.3), BI640.2-mm (-0.3), BI600.2-mm (-0.3), and BI560.2-mm (-0.3). Other comparisons among reconstructions were not significant (all p > .05). CONCLUSION. Only the reconstruction using BI640.4-mm yielded improved bronchial division identification and bronchial wall and pulmonary fissure sharpness without a loss in pulmonary vessel sharpness or conspicuity of nodules or other pathologies. CLINICAL IMPACT. The findings of this study may guide protocol optimization for UHR PCD CT of the lungs.

Computed tomography-based measurements of normative liver and spleen volumes in children.

Quantification of organ size has utility in clinical care and research for diagnostics, prognostics and surgical planning. Volumetry is regarded as the best measure of organ size and change in size over time. Scarce reference values exist for liver and spleen volumes in healthy children. To report liver and spleen volumes for a sample of children defined by manual segmentation of contrast-enhanced CT images with the goal of defining normal values and thresholds that might indicate disease. This retrospective study included clinically acquired contrast-enhanced CTs of the abdomen/pelvis for children and adolescents imaged between January 2018 and July 2021. Liver and spleen volumes were derived through manual segmentation of CTs reconstructed at 2.5-, 3- or 5-mm slice thickness. A subset of images (5%, n=16) was also segmented using 0.5-mm slice thickness reconstructions to define agreement based on image slice thickness. We used Pearson correlation and multivariable regression to assess associations between organ volumes and patient characteristics. We generated reference intervals for the 5th, 25th, 50th (median), 75th and 95th percentiles for organ volumes as a function of age and weight using quantile regression models. Finally, we calculated Bland-Altman plots and intraclass correlation coefficients (ICC) to quantify agreement. We included a total of 320 children (mean age ± standard deviation [SD] = 9±4.6years; mean weight 38.1±18.8kg; 160 female). Liver volume ranged from340-2,002mL, and spleen volume ranged from 28-480mL. Patient weight (kg) (β=12.5), age (months) (β=1.7) and sex (female) (β = -35.3) were independent predictors of liver volume, whereas patient weight (kg) (β=2.4) and age (months) (β=0.3) were independent predictors of spleen volume. There was excellent absolute agreement (ICC=0.99) and minimal absolute difference (4mL) in organ volumes based on reconstructed slice thickness. We report reference liver and spleen volumes for children without liver or spleen disease. These results provide reference ranges and potential thresholds to identify liver and spleen size abnormalities that might reflect disease in children.

Optimization of Ct-Spn Anatomical Image of Coronal Section With Slice Thickness Reconstruction Variations and Increment Reconstruction in Rhinosinusitis Cases Chronic at Rs Balimed

Background: One of the modalities used to diagnose rhinosinusitis is CT Scan. In CT Scan, there are several parameters, namely slice thickness and reconstruction increment. Objective: aims to determine the optimization of the CT-SPN anatomical image produced with variations in slice thickness and reconstruction Methods: This type of research is a quantitative study with an experimental approach conducted at Balimed Hospital from July to August 2022. Results: Friedman test results for the whole anatomy showed a significance p of 0.0001 (p value &lt;0.05) which means Ho is rejected and Ha is accepted which means that there is a significant difference in the anatomical image information of CT Scan of the paranasal sinuses in coronal sections with variations in slice thickness reconstruction and incremental reconstruction in chronic rhinosinusitis. And based on the Wilcoxon test, it also showed that each variation had a p value &lt;0.05, which means that there are differences in anatomical image information for each variation. Conslusion: The conclusion of this study, based on this study, shows that there are differences in information on the coronal CT-SPN anatomical image with variations in slice thickness and increment reconstruction in cases of chronic rhinosinusitis and variation 2 has the most optimal anatomical image information. Based on the Wilcoxon test, it also shows that each variation has different anatomical image information.

A comprehensive quality assurance procedure for 4D CT commissioning and periodic QA.

The 4D computed tomography (CT) simulation is an essential procedure for tumors exhibiting breathing-induced motion. However, to date there are no established guidelines to assess the characteristics of existing systems and to describe meaningful performance. We propose a commissioning quality assurance (QA) protocol consisting of measurements and acquisitions that assess the mechanical and computational operation for 4D CT with both phase and amplitude-based reconstructions, for regular and irregular respiratory patterns. The 4D CT scans of a QUASAR motion phantom were acquired for both regular and irregular breathing patterns. The hardware consisted of the Canon Aquilion Exceed LB CT scanner used in conjunction with the Anzai laser motion monitoring system. The nominal machine performance and reconstruction were demonstrated with measurements using regular breathing patterns. For irregular breathing patterns the performance was quantified through the analysis of the target motion in the superior and inferior directions, and the volume of the internal target volume (ITV). Acquisitions were performed using multiple pitches and the reconstructions were performed using both phase and amplitude-based binning. The target was accurately captured during regular breathing. For the irregular breathing, the measured ITV exceeded the nominal ITV parameters in all scenarios, but all deviations were less than the reconstructed slice thickness. The mismatch between the nominal pitch and the actual breathing rate did not affect markedly the size of the ITV. Phase and normalized amplitude binning performed similarly. We demonstrated a framework for measuring and quantifying the initial performance of 4D CT simulation scans that can also be applied during periodic QAs. The regular breathing provided confidence that the hardware and the software between the systems performs adequately. The irregular breathing data suggest that the system may be expected to capture in excess the target motion and geometry, but the deviation is expected to be within the slice thickness.

Thrombus Density in Acute Basilar Artery Occlusion Depends on Slice Thickness and the Method of Manual Thrombus Delineation.

Introduction: High thrombus attenuation on CT has been suggested as a predictor of successful recanalization. It is as well speculated that thrombi of different density may be susceptible to different methods of mechanical thrombectomy. In this study we sought to determine the effect of different methods of manual thrombus delineation and reconstructed slice thickness on thrombus density. Material and Methods: Fifty-six patients with acute occlusion of the basilar artery treated with endovascular therapy were retrospectively included. Clinical, demographic, radiological and outcome parameters were collected. Two raters measured absolute and relative thrombus density employing three different methods (one region of interest, three regions of interest, whole thrombus delineation) and using three different reconstructed slice thicknesses (0.625, 2.5 and 5 mm) of the original admission CT. Results: Thirty-nine patients were successfully recanalized (thrombolysis in cerebral infarction score ≥ 2b). Good clinical outcome (modified Rankin scale ≤ 2) occurred significantly more often in the recanalized group (36 vs. 6%, p = 0.023, Fisher’s exact test), in the non-recanalized group symptomatic intracranial hemorrhage occurred more often (9 vs. 29%, p = 0.001, Fisher’s exact test). Absolute and relative thrombus density were largely different between methods and slice thicknesses. Multiple regression showed a decrease of thrombus density with increasing slice thickness (β = −3.98, p < 0.001) and logistic regression showed a statistically significant but very small relation between density and recanalization (β = 0.006, odds ratio (95% confidence interval) = 1.006 (1.003–1.01), p < 0.001). Conclusions: The methods for manual thrombus delineation and reconstructed slice thickness had a significant influence on absolute and relative thrombus density. Density alone may be of limited value as a predictive marker for recanalization success in acute occlusion of the basilar artery. Standards for density measurements must be defined when comparing different studies and when evaluating different methods of mechanical thrombectomy.

Different CT slice thickness and contrast-enhancement phase in radiomics models on the differential performance of lung adenocarcinoma.

BackgroundTo investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma.MethodsA total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort (n = 149) and validation cohort (n = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance.ResultsFinally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness (p > 0.05).ConclusionsThe radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.

Accelerated Three-dimensional T2-Weighted Turbo-Spin-Echo Sequences with Inner-Volume Excitation and Iterative Denoising in the Setting of Pelvis MRI at 1.5T: Impact on Image Quality and Lesion Detection

To investigate image quality and rate of lesion detection in a novel three-dimensional T2-weighted turbo-spin-echo sequence with inner-volume excitation (zoomed imaging) and iterative denoising processing in pelvic MRI at 1.5T. Two-dimensional T2-weighted turbo-spin-echo sequences were used as the clinical reference standard (2D-T2-TSE). This is a prospective study of patients with various pelvic pathologies. Each patient underwent standard 2D-T2-TSE in three planes with two-fold acceleration as well as a single three-dimensional T2-TSE in the sagittal plane with four-fold acceleration known as Sampling-Perfection-with-Application-optimized-Contrast-using-different-flip-angle-Evolutions (3D-T2-SPACE). The 3D-T2-SPACE images were reconstructed in three orthogonal planes at a slice thickness of 2 mm (vs. 2D-T2-TSE at 4 mm). Two radiologists conducted a qualitative image analysis on standard 2D-T2-TSE and multiplanar reconstructed 3D-T2-SPACE images. These parameters were compared and inter-reader agreement was computed. Furthermore, each reader documented the observed lesions of various pelvic organs. The rate of lesion detection was compared between readers and sequences. Inter-reader and inter-sequence agreement were computed. Forty patients (25 females) were included. Mean patient age was 58 ± 13 years. 3D-T2-SPACE enabled an approximate 22% reduction of acquisition time and 50% of reconstructed slice thickness. 3D-T2-SPACE showed fewer artifacts than 2D-T2-TSE (p < 0.001). However, 2D-T2-TSE was rated to have significantly higher signal intensity than 3D-T2-SPACE (p < 0.001). There were no significant differences between the two sequences regarding all other parameters. Inter-reader agreement regarding image quality parameters was substantial (Kappa=0.772). For all analyzed pelvic anatomic structures, inter-reader and inter-sequence agreement for lesion detection was excellent (Kappa > 0.80). 3D-T2-SPACE with the inner-volume excitation and iterative denoising is clinically feasible at 1.5 T, enabling faster imaging, thinner slices, and significant reduction of artifacts. Despite that signal intensity was inferior in the SPACE images, overall image quality, diagnostic confidence and lesion detection were not compromised. This prospective study sets the stage for further clinical implementation and future investigations tailored to specific indications in pelvis MRI.

Performance of a deep learning‐based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness

Deep learning (DL) is rapidly finding applications in low-dose CT image denoising. While having the potential to improve the image quality (IQ) over the filtered back projection method (FBP) and produce images quickly, performance generalizability of the data-driven DL methods is not fully understood yet. The main purpose of this work is to investigate the performance generalizability of a low-dose CT image denoising neural network in data acquired under different scan conditions, particularly relating to these three parameters: reconstruction kernel, slice thickness, and dose (noise) level. A secondary goal is to identify any underlying data property associated with the CT scan settings that might help predict the generalizability of the denoising network. We select the residual encoder-decoder convolutional neural network (REDCNN) as an example of a low-dose CT image denoising technique in this work. To study how the network generalizes on the three imaging parameters, we grouped the CT volumes in the Low-Dose Grand Challenge (LDGC) data into three pairs of training datasets according to their imaging parameters, changing only one parameter in each pair. We trained REDCNN with them to obtain six denoising models. We test each denoising model on datasets of matching and mismatching parameters with respect to its training sets regarding dose, reconstruction kernel, and slice thickness, respectively, to evaluate the denoising performance changes. Denoising performances are evaluated on patient scans, simulated phantom scans, and physical phantom scans using IQ metrics including mean-squared error (MSE), contrast-dependent modulation transfer function (MTF), pixel-level noise power spectrum (pNPS), and low-contrast lesion detectability (LCD). REDCNN had larger MSE when the testing data were different from the training data in reconstruction kernel, but no significant MSE difference when varying slice thickness in the testing data. REDCNN trained with quarter-dose data had slightly worse MSE in denoising higher-dose images than that trained with mixed-dose data (17%-80%). The MTF tests showed that REDCNN trained with the two reconstruction kernels and slice thicknesses yielded images of similar image resolution. However, REDCNN trained with mixed-dose data preserved the low-contrast resolution better compared to REDCNN trained with quarter-dose data. In the pNPS test, it was found that REDCNN trained with smooth-kernel data could not remove high-frequency noise in the test data of sharp kernel, possibly because the lack of high-frequency noise in the smooth-kernel data limited the ability of the trained model in removing high-frequency noise. Finally, in the LCD test, REDCNN improved the lesion detectability over the original FBP images regardless of whether the training and testing data had matching reconstruction kernels. REDCNN is observed to be poorly generalizable between reconstruction kernels, more robust in denoising data of arbitrary dose levels when trained with mixed-dose data, and not highly sensitive to slice thickness. It is known that reconstruction kernel affects the in-plane pNPS shape of a CT image, whereas slice thickness and dose level do not, so it is possible that the generalizability performance of this CT image denoising network highly correlates to the pNPS similarity between the testing and training data.

Improved coronary calcium detection and quantification with low-dose full field-of-view photon-counting CT: a phantom study.

The aim of the current study was to systematically assess coronary artery calcium (CAC) detection and quantification for spectral photon-counting CT (SPCCT) in comparison to conventional CT and, in addition, to evaluate the possibility of radiation dose reduction. Routine clinical CAC CT protocols were used for data acquisition and reconstruction of two CAC containing cylindrical inserts which were positioned within an anthropomorphic thorax phantom. In addition, data was acquired at 50% lower radiation dose by reducing tube current, and slice thickness was decreased. Calcifications were considered detectable when three adjacent voxels exceeded the CAC scoring threshold of 130 Hounsfield units (HU). Quantification of CAC (as volume and mass score) was assessed by comparison with known physical quantities. In comparison with CT, SPCCT detected 33% and 7% more calcifications for the small and large phantoms, respectively. At reduced radiation dose and reduced slice thickness, small phantom CAC detection increased by 108% and 150% for CT and SPCCT, respectively. For the large phantom size, noise levels interfered with CAC detection. Although comparable between CT and SPCCT, routine protocols CAC quantification showed large deviations (up to 134%) from physical CAC volume. At reduced radiation dose and slice thickness, physical volume overestimations decreased to 96% and 72% for CT and SPCCT, respectively. In comparison with volume scores, mass score deviations from physical quantities were smaller. CAC detection on SPCCT is superior to CT, and was even preserved at a reduced radiation dose. Furthermore, SPCCT allows for improved physical volume estimation. • In comparison with conventional CT, increased coronary artery calcium detection (up to 156%) for spectral photon-counting CT was found, even at 50% radiation dose reduction. • Spectral photon-counting CT can more accurately measure physical volumes than conventional CT, especially at reduced slice thickness and for high-density coronary artery calcium. • For both conventional and spectral photon-counting CT, reduced slice thickness reconstructions result in more accurate physical mass approximation.

Uncertainty in organ delineation using low-dose computed tomography images with high-strength iterative reconstruction technique in radiotherapy for prostate cancer

Abstract Introduction This study aimed to investigate the uncertainty in organ delineation of low-dose computed tomography (CT) images using a high-strength iterative reconstruction (IR) during radiotherapy planning for the treatment of prostate cancer. Methods Two CT datasets were prepared with different dose levels by adjusting the reconstruction slice thickness. Two observers independently delineated the prostate, seminal vesicles, bladder and rectum on both images without referring to other modality images. The delineated organ volumes were compared between both images. Observer delineation variability was assessed using Dice similarity coefficient (DSC) and mean distance to agreement. Results No significant differences regarding the delineated organ volumes were observed between the low- and standard-dose images for all organs. Regarding inter-observer variability, the DSC was relatively high for both images, whereas mean distance to agreement was not significantly different between images (p &gt; 0·05 for all). Intra-observer variability for each observer showed high DSC (&gt;0·8 and &gt;0·9 for seminal vesicles and other organs, respectively) but no significant differences in the mean distance to agreement (p &gt; 0·05 for all). Conclusions Our results indicate that low-dose CT images with high-strength IR would be available for organ delineation in the radiotherapy treatment planning for prostate cancer.

Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings

Computed tomography in suspected urolithiasis provides information about the presence, location and size of stones. Particularly stone size is a key parameter in treatment decision; however, data on impact of reformatation and measurement strategies is sparse. This study aimed to investigate the influence of different image reformatations, slice thicknesses and window settings on stone size measurements. Reference stone sizes of 47 kidney stones representative for clinically encountered compositions were measured manually using a digital caliper (Man-M). Afterwards stones were placed in a 3D-printed, semi-anthropomorphic phantom, and scanned using a low dose protocol (CTDIvol 2 mGy). Images were reconstructed using hybrid-iterative and model-based iterative reconstruction algorithms (HIR, MBIR) with different slice thicknesses. Two independent readers measured largest stone diameter on axial (2 mm and 5 mm) and multiplanar reformatations (based upon 0.67 mm reconstructions) using different window settings (soft-tissue and bone). Statistics were conducted using ANOVA ± correction for multiple comparisons. Overall stone size in CT was underestimated compared to Man-M (8.8 ± 2.9 vs. 7.7 ± 2.7 mm, p < 0.05), yet closely correlated (r = 0.70). Reconstruction algorithm and slice thickness did not significantly impact measurements (p > 0.05), while image reformatations and window settings did (p < 0.05). CT measurements using multiplanar reformatation with a bone window setting showed closest agreement with Man-M (8.7 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05, r = 0.83). Manual CT-based stone size measurements are most accurate using multiplanar image reformatation with a bone window setting, while measurements on axial planes with different slice thicknesses underestimate true stone size. Therefore, this procedure is recommended when impacting treatment decision.

Influence Of Slice Thickness And Iterative Reconstruction On Coronary Artery Calcification Quantification

Introduction: Reconstruction parameters including iterative reconstruction algorithms and slice thickness influence the quantification of coronary calcification. Moreover, with modern scanner platforms and increasing use of iterative reconstruction algorithms, assessment of coronary calcium quantification will ultimately be affected. We sought to analyze the influence of slice thickness and iterative reconstruction on quantitative parameters of coronary calcification.

Reproducibility of lung nodule radiomic features: Multivariable and univariable investigations that account for interactions between CT acquisition and reconstruction parameters.

Recent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel. A cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer-aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6mm, 1mm, and 2mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well-known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC≥0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA. All included radiomic features lacked intercondition reproducibility (OCCC<0.9) among all the 36 conditions. Out of 226 radiomic features analyzed, only 17 and 18 features were considered reproducible (OCCC≥0.9) to dose and kernel variation, respectively, within the corresponding condition subsets. Slice thickness demonstrated the largest impact on radiomic feature values where only one to five features were reproducible at a few condition subsets. ANOVA revealed significant interactions (P<0.05) between CT parameters affecting the variability of >50% of radiomic features. We systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies.