Quantitative Image Quality Comparison of Reduced- and Standard-Dose Dual-Energy Multiphase Chest, Abdomen, and Pelvis CT.
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SUBMITTER: Buty M
PROVIDER: S-EPMC5573232 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeOrgan autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging.Acquisition and validation methodsThe dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise.Data format and usage notesThe data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2.Potential applicationsThis dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.
| S-EPMC9090951 | biostudies-literature
Project description:ObjectivesThis prospective study tested the diagnostic accuracy, and absolute agreement with MRI of a low-dose CT protocol for left ventricular ejection fraction (LVEF) measurement. Furthermore we assessed its potential for combining it with Chest-Abdomen-Pelvis CT (CAP-CT) for a one-stop examination.Materials & methodsEighty-two patients underwent helical low-dose CT. Cardiac magnetic resonance imaging (MRI) was the reference standard. In fifty patients, CAP-CT was performed concurrently, using a modified injection protocol. In these, LVEF was measured with radioisotope cardiography (MUGA). Patients >18 years, without contrast media or MRI contraindications, were included. Bias was measured with Bland-Altman analysis, classification accuracy with Receiver Operating Characteristics, and inter-reader agreement with Intra-Class Correlation Coefficient (ICC). Correlation was examined using Pearson's correlation coefficients. CAP image quality was compared to previous scans with visual grading characteristics.ResultsThe mean CT dose-length-product (DLP) was 51.8 mGycm, for an estimated effective dose of 1.4 mSv, compared to 5.7 mSv for MUGA. CT LVEF bias was between 2 % and 10 %, overestimating end-diastolic volume. When corrected for bias, sensitivity and specificity of 100 and 98.5 % for classifying reduced LVEF (50 % MRI value) was achieved. ICC for MUGA was significantly lower than MRI and CT. Distinction of renal medulla and cortex was reduced in the CAP scan, but proportion of diagnostic scans was not significantly different from standard protocol.ConclusionWhen corrected for inter-modality bias, CT classifies patients with reduced LVEF with high accuracy at a quarter of MUGA dose and can be combined with CAP-CT without loss of diagnostic quality.
| S-EPMC11255516 | biostudies-literature
Project description:ObjectivesTo evaluate correlations between DRL quantities (DRLq) stratified into patient size groups for non-contrast chest and abdomen-pelvis CT examinations in adult patients and the corresponding organ doses.MethodsThis study presents correlations between DRLq (CTDIvol, DLP and SSDE) stratified into patient size ranges and corresponding organ doses shared in four groups: inside, peripheral, distributed and outside. The demographic, technical and dosimetric parameters were used to identify the influence of these quantities in organ doses. A robust statistical method was implemented in order to establish these correlations and its statistical significance.ResultsMedian values of the grouped organ doses are presented according to the effective diameter ranges. Organ doses in the regions inside the imaged area are higher than the organ doses in peripheral, distributed and outside regions, excepted to the peripheral doses associated with chest examinations. Different levels of statistical significance between organ doses and the DRLq were presented.ConclusionsCorrelations between DRLq and target-organ doses associated with clinical practice can support guidance's to the establishment of optimization criteria. SSDE demonstrated to be significant in the evaluation of organ doses is also highlighted. The proposed model allows the design of optimization actions with specific risk-reduction results.
| S-EPMC10079797 | biostudies-literature
Project description:BackgroundEfforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.MethodsThis retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1-18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.ResultsDLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture.ConclusionCompared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.
| S-EPMC8503996 | biostudies-literature
Project description:PurposeTo assess the spectrum of computed tomography angiogram (CTA) abdominal and pelvic findings in critically ill COVID-19 patients and investigate correlation with CT chest scores.MethodsAn IRB approved retrospective study of CTA of the chest, abdomen and pelvis between dates March 1st to September 15th, 2020 was performed in the hospitalized COVID-19 positive patients. CTA studies of solely the chest were excluded. Medical record review was performed to note patient demographics, CTA scan details and coagulation profile. CTA findings were reviewed to record vascular and non-vascular findings. CT chest was reviewed to calculate CT chest score. Logistic regression analyses were performed to correlate CT chest scores with odds of vascular and other abdomen-pelvis findings. A p < 0.05 was considered statistically significant.ResultsA total of 45 consecutive hospitalized COVID-19 positive patients with 61 years mean age and M:F (2:1) gender ratio were evaluated, out of which majority 68.9% (n = 31) had CTA chest, abdomen and pelvis. The most common vascular findings were hematoma 46.7% (n = 21), active extravasation 24.4% (n = 11) and vascular occlusion 17.8% (n = 8). Higher CT chest scores were significantly associated with hematoma/extravasation (OR 1.19, 95% CI 1.07-1.34, p < 0.01). The most common non-vascular abdomen-pelvis findings were seen in organs gallbladder 20% (n = 9), liver 20% (n = 9) followed by kidney 15.6% (n = 7). Higher CT chest scores were significantly associated with bowel findings (OR 1.28, 95% CI 1.01-1.63, p < 0.05) and cholestasis (OR 13.3, 95% CI 1.28-138.9, p < 0.05).ConclusionPatients with moderate to severe COVID-19 pneumonia have significantly higher rate of vascular complications in the abdomen and pelvis.
| S-EPMC8193959 | biostudies-literature
Project description:ObjectivesLate contrast enhancement CT (LCE-CT) visualizes the presence of myocardial infarcts. Differentiation of the contrast-enhanced infarct from blood pool is challenging. We developed a novel method using data from first pass CT angiography (CTA) imaging to enable automatic infarct detection.Materials and methodsA canine model of myocardial infarction was produced in 11 animals. Two months later, first pass CTA (90 kVp) and LCE-CT (dual energy 90 kVp/150 kVp tin filtered) were performed. Late gadolinium enhancement MRI was used as reference standard. The CTA and LCE-CT were co-registered using a fully automatic non-rigid method based on curved B-splines. The method allowed for limited elastic deformation and the considerable differences in attenuation between first-pass and delayed image. The blood pool was easily identified on the CTA image by high attenuation. Because CTA and LCE-CT were registered, the blood pool segmentation can be directly transferred to the LCE-CT - thereby solving the key problem of infarct/blood pool differentiation. The remaining segmentation of infarcted vs. noninfarcted myocardium was performed using a threshold. Automatic and MRI-guided expert segmentations of LCE-CT infarcts were compared to each other on volume and area basis (intraclass correlation coefficient, ICC) and on voxel basis (dice similarity coefficient, DSC between automatic and expert CT segmentation). CT infarct volumes were compared with the reference standard MRI.ResultsThe infarcts were mainly subendocardial (81%) and relatively small (median MRI infarct mass 7.4 g). The automatic segmentation showed excellent agreement with expert segmentation on volume and area measurements (ICC = 0.96 and 0.87, respectively). DSC showed moderately good agreement (DSC = 0.47). Compared to MRI there was modest agreement (ICC = 0.62) and excellent correlation (R = 0.9). Manual interaction was less than 1 min per exam.ConclusionWe propose an automatic method for infarct segmentation on LCE-CT using multiphase CT information, which showed excellent agreement with expert readers and favorable correlation with MRI.
| S-EPMC5493020 | biostudies-literature
Project description: Not available
| S-EPMC7267478 | biostudies-literature
Project description:This paper addresses 123I and 125I dual isotope SPECT imaging, which can be challenging because of spectrum overlap in the low energy spectrums of these isotopes. We first quantify the contribution of low-energy photons from each isotope using GATE-based Monte Carlo simulations for the MOBY mouse phantom. We then describe and analyze a simple, but effective method that uses the ratio of detected low and high energy 123I activity to separate the mixed low energy 123I and 125I activities. Performance is compared with correction methods used in conventional tissue biodistribution techniques. The results indicate that the spectrum overlap effects can be significantly reduced, if not entirely eliminated, when attenuation and scatter is either absent or corrected for using standard methods. In particular, we show that relative activity levels of the two isotopes can be accurately estimated for a wide range of organs and provide quantitative validation that standard methods for spectrum overlap correction provide reasonable estimates for reasonable corrections in small-animal SPECT/CT imaging.
| S-EPMC4388517 | biostudies-literature
Project description:To evaluate the image quality of virtual monochromatic images synthesized from dual-source dual-energy computed tomography (CT) in comparison with conventional polychromatic single-energy CT for the same radiation dose.In dual-energy CT, besides the material-specific information, one may also synthesize monochromatic images at different energies, which can be used for routine diagnosis similar to conventional polychromatic single-energy images. In this work, the authors assessed whether virtual monochromatic images generated from dual-source CT scanners had an image quality similar to that of polychromatic single-energy images for the same radiation dose. First, the authors provided a theoretical analysis of the optimal monochromatic energy for either the minimum noise level or the highest iodine contrast to noise ratio (CNR) for a given patient size and dose partitioning between the low- and high-energy scans. Second, the authors performed an experimental study on a dual-source CT scanner to evaluate the noise and iodine CNR in monochromatic images. A thoracic phantom with three sizes of attenuating rings was used to represent four adult sizes. For each phantom size, three dose partitionings between the low-energy (80 kV) and the high-energy (140 kV) scans were used in the dual-energy scan. Monochromatic images at eight energies (40 to 110 keV) were generated for each scan. Phantoms were also scanned at each of the four polychromatic single energy (80, 100, 120, and 140 kV) with the same radiation dose.The optimal virtual monochromatic energy depends on several factors: phantom size, partitioning of the radiation dose between low- and high-energy scans, and the image quality metrics to be optimized. With the increase of phantom size, the optimal monochromatic energy increased. With the increased percentage of radiation dose on the low energy scan, the optimal monochromatic energy decreased. When maximizing the iodine CNR in monochromatic images, the optimal energy was lower than that when minimizing noise level. When the total radiation dose was equally distributed between low and high energy in dual-energy scans, for minimum noise, the optimal energies were 68, 71, 74, and 77 keV for small, medium, large, and extra-large (xlarge) phantoms, respectively; for maximum iodine CNR, the optimal energies were 66, 68, 70, 72 keV. With the optimal monochromatic energy, the noise level was similar to and the CNR was better than that in a single-energy scan at 120 kV for the same radiation dose. Compared to an 80 kV scan, however, the iodine CNR in monochromatic images was lower for the small, medium, and large phantoms.In dual-source dual-energy CT, optimal virtual monochromatic energy depends on patient size, dose partitioning, and the image quality metric optimized. With the optimal monochromatic energy, the noise level was similar to and the iodine CNR was better than that in 120 kV images for the same radiation dose. Compared to single-energy 80 kV images, the iodine CNR in virtual monochromatic images was lower for small to large phantom sizes.
| S-EPMC3230639 | biostudies-other
Project description:BackgroundThere is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation.MethodsWe used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithms beyond pre-defined keywords, attention-guided recurrent neural networks (RNN) were trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for each organ system. Alternative effects on disease classification performance were evaluated using random initialization or pre-trained embedding as well as different sizes of training datasets. The RBA was tested on a subset of 2158 manually labeled reports and performance was reported as accuracy and F-score. The RNN was tested against a test set of 48,758 reports labeled by RBA and performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method.ResultsManual validation of the RBA confirmed 91-99% accuracy across the 15 different labels. Our models extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random initialization across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with a relatively small number of cases. Pre-trained classification AUCs reached > 0.95 for all four disease outcomes and normality across all three organ systems.ConclusionsOur label-extracting pipeline was able to encompass a variety of cases and diseases in body CT reports by generalizing beyond strict rules with exceptional accuracy. The method described can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.
| S-EPMC9011942 | biostudies-literature