Radiomic features of primary tumor by lung cancer stage: analysis in BRAF mutated non-small cell lung cancer.

BACKGROUND: The clinical features and traditional semantic imaging characteristics of BRAF-mutated non-small cell lung cancer (NSCLC) have been previously reported. The radiomic features of BRAF-mutated NSCLC and their role in predicting cancer stage, however, have yet to be investigated. This study's goal is to assess the differences in CT radiomic features of primary NSCLC driven by BRAF mutation and stratified by tumor-node-metastasis (TNM) staging. METHODS: Our IRB approved study included 62 patients with BRAF mutations (V600 in 27 and non-V600 in 35 patients), who underwent contrast-enhanced chest CT. Tumor stage was determined based on the 8th edition of TNM staging. Two thoracic radiologists assessed the primary tumor imaging features such, including tumor size (maximum and minimum dimensions) and density (Hounsfield units, HU). De-identified transverse CT images (DICOM) were processed with 3D slicer (Version 4.7) for manual lesion segmentation and estimation of radiomic features. Descriptive statistics, multivariate logistic regression, and receiver operating characteristics (ROC) were performed. RESULTS: There were significant differences in the radiomic features based on cancer stages I-IV with the most significant differences between stage IV and stage I lesions [AUC 0.94 (95% CI: 0.86-0.99), P<0.04]. There were also significant differences in radiomic features between stage IV and combined stages I-III [40/113 radiomic features; AUC 0.71 (95% CI: 0.59-0.85); P<0.04-0.0001]. None of the clinical (0/6) or imaging (0/3) features were significantly different between stage IV and combined stages I-III. CONCLUSIONS: The radiomic features of primary tumor in BRAF driven NSCLC significantly vary with cancer stage, independent of standard imaging and clinical features.

Development and validation of image quality scoring criteria (IQSC) for pediatric CT: a preliminary study.

OBJECTIVE: To develop and assess the value and limitations of an image quality scoring criteria (IQSC) for pediatric CT exams. METHODS: IQSC was developed for subjective assessment of image quality using the scoring scale from 0 to 4, with 0 indicating desired anatomy or features not seen, 3 for adequate image quality, and 4 depicting higher than needed image quality. Pediatric CT examinations from 30 separate patients were selected, five each for routine chest, routine abdomen, kidney stone, appendicitis, craniosynostosis, and ventriculoperitoneal (VP) shunt. Five board-certified pediatric radiologists independently performed image quality evaluation using the proposed IQSC. The kappa statistics were used to assess the interobserver variability. RESULTS: All five radiologists gave a score of 3 to two-third (67%) of all CT exams, followed by a score of 4 for 29% of CT exams, and 2 for 4% exams. The median image quality scores for all exams were 3 and the interobserver agreement among five readers (acceptable image quality [scores 3 or 4] vs sub-optimal image quality ([scores 1 and 2]) was moderate to very good (kappa 0.4-1). For all five radiologists, the lesion detection was adequate for all CT exams. CONCLUSIONS: The image quality scoring criteria covering routine and some clinical indication-based imaging scenarios for pediatric CT examinations has potential to offer a simple and practical tool for assessing image quality with a reasonable degree of interobserver agreement. A more extensive and multi-centric study is recommended to establish wider usefulness of these criteria.

Diffuse Lung Metastases in EGFR-Mutant Non-Small Cell Lung Cancer.

Diffuse lung metastases have been reported in non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) mutations. The purpose of our study was to compare the incidence of diffuse lung metastases in EGFR-mutant NSCLC and EGFR-wild type NSCLC and to assess other imaging features that may be associated with diffuse lung metastases in EGFR-mutant NSCLC. Two radiologists retrospectively reviewed pre-treatment imaging of metastatic NSCLC cases with known EGFR mutation status. We assessed the imaging features of the primary tumor and patterns of metastases. The cohort consisted of 217 patients (117 EGFR-mutant, 100 EGFR wild-type). Diffuse lung metastasis was significantly more common in EGFR-mutant NSCLC compared with wild-type (18% vs. 3%, p < 0.01). Among the EGFR-mutant group, diffuse lung metastases were inversely correlated with the presence of a nodule greater than 6 mm other than the primary lung lesion (OR: 0.13, 95% CI: 0.04-0.41, p < 0.01). EGFR mutations in NSCLC are associated with increased frequency of diffuse lung metastases. The presence of diffuse lung metastases in EGFR-mutant NSCLC is also associated with a decreased presence of other larger discrete lung metastases. EGFR mutations in NSCLC should be suspected in the setting of a dominant primary lung mass associated with diffuse lung metastases.

Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?

BACKGROUND: To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. MATERIALS AND METHODS: The IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software. RESULTS: Only 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT (P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505-0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605-0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001). CONCLUSIONS: Temporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT.

Imaging characteristics of BRAF-mutant non-small cell lung cancer by functional class.

OBJECTIVES: Mutations in the BRAF gene have emerged as a validated molecular target in the treatment of non-small cell lung cancer (NSCLC). These mutations can be classified into three functional classes based on their mechanisms of oncogenesis. The relationship between these functional classes and their imaging features has not been systematically investigated. The goal of this work is to determine if imaging features of the primary tumor and the pattern of metastasis correlate with the functional class of BRAF mutation. METHODS: We reviewed pre-treatment computed tomography (CT) images of patients with BRAF-mutated NSCLC with known functional class. We assessed and recorded the features of the primary tumor and the patterns of lymphadenopathy and distant metastasis. Wilcoxon rank-sum test and Kruskal-Wallis test were performed to compare continuous characteristics, and Fisher's exact test was used to compare categorical features between groups. RESULTS AND CONCLUSIONS: 105 patients with BRAF-mutant NSCLC had pre-treatment imaging available for review (n = 43 class I, n = 40 class II, and n = 22 class III). Approximately half of the primary tumors were considered masses (n = 54/105, 51%) and most were solid (n = 81/105, 77%). There were no statistically significant differences in imaging features of the primary tumor among the three functional classes. Intrathoracic metastases occurred more frequently in class I tumors compared to tumors with class II and III mutations (p = 0.03). The odds of class I mutation were higher among tumors involving the pleural space (OR: 4.39, 95% CI: 1.11-17.4) and lower among tumors disseminating to the abdomen (OR: 0.25, 95% CI: 0.07-0.92). Our findings suggest that class I (V600) mutated NSCLC may be more likely to have intrathoracic metastases, while classes II and III (non-V600) mutated NSCLC may be more likely to have intra-abdominal metastases at the time of presentation.

Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?

To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ±â€Š11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686-0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646-0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656-0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.

Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction.

Commercial iterative reconstruction techniques help to reduce CT radiation dose but altered image appearance and artifacts limit their adoptability and potential use. Deep learning has been investigated for low-dose CT (LDCT). Here we design a modularized neural network for LDCT and compared it with commercial iterative reconstruction methods from three leading CT vendors. While popular networks are trained for an end-to-end mapping, our network performs an end-to-process mapping so that intermediate denoised images are obtained with associated noise reduction directions towards a final denoised image. The learned workflow allows radiologists-in-the-loop to optimize the denoising depth in a task-specific fashion. Our network was trained with the Mayo LDCT Dataset, and tested on separate chest and abdominal CT exams from Massachusetts General Hospital. The best deep learning reconstructions were systematically compared to the best iterative reconstructions in a double-blinded reader study. This study confirms that our deep learning approach performed either favorably or comparably in terms of noise suppression and structural fidelity, and is much faster than the commercial iterative reconstruction algorithms.

Deep learning in chest radiography: Detection of findings and presence of change.

BACKGROUND: Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs. METHODS AND FINDINGS: We processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis. RESULTS: About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2-0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837-0.929 and 0.693-0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities. CONCLUSIONS: DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings.

CT texture analysis of histologically proven benign and malignant lung lesions.

The purpose of our study was to determine accuracy of CT texture analysis (CTTA) for differentiating benign from malignant pulmonary nodules, and well-differentiated from poorly differentiated lung cancers, with histology as the standard of reference.In this IRB-approved study, 175 adult patients (average age 66 ±â€Š12 years; age range 27-89 years, male 82: female 93) who underwent a noncontrast chest CT examination prior to CT-guided biopsy of pulmonary nodules were included. There were 57 benign (24 tumors or tumor-like lesions; 33 inflammatory conditions) and 120 malignant (29 well-differentiated adenocarcinomas, 48 poorly differentiated adenocarcinomas, and 43 squamous cell carcinomas) diagnoses on pathology. CTTA was performed on the prebiopsy noncontrast CT images using a commercially available software (TexRAD limited, UK). The CTCA features analyzed included mean HU values, percent positive pixels (PPP), mean value of positive pixels (MPP), standard deviation (SD), normalized SD, skewness, kurtosis, and entropy.The ROC analyses showed that normalized SD [AUC: 0.63, (CI: 0.55-72), P = .003] had moderate accuracy for differentiating between benign and malignant lesions. For differentiating among well-differentiated and poorly differentiated tumors, the ROC analysis showed that except skewness all other parameters were statistically significant The AUC values of other CTTA parameters were: mean (AUC: 0.73-0.76, P = .001- < .0001).CT texture analyses can reliably predict well- and poorly differentiated lung malignancies. However, inflammatory lung lesions with tissue heterogeneity negatively affect the performance of CTTA when it comes to differentiation between benign and malignant pulmonary nodules.

Dual-Energy Computed Tomographic Applications for Differentiation of Intracranial Hemorrhage, Calcium, and Iodine.

This article reviews the physical principles of dual-energy material decomposition and its current implementation. Clinical applications of dual-energy material decomposition including differentiation of calcification from hemorrhage and iodinated contrast from hemorrhage are highlighted, and their applications to neuroimaging are reviewed.

Assessment of chest CT at CTDIvol less than 1 mGy with iterative reconstruction techniques.

OBJECTIVE: To assess the image quality of chest CT reconstructed with image-based iterative reconstruction (SafeCT; MedicVision®, Tirat Carmel, Israel), adaptive statistical iterative reconstruction (ASIR; GE Healthcare, Waukesha, WI) and model-based iterative reconstruction (MBIR; GE Healthcare, Waukesha, WI) techniques at CT dose index volume (CTDIvol) <1 mGy. METHODS: In an institutional review board-approved study, 25 patients gave written informed consent for acquisition of three reduced dose (0.25-, 0.4- and 0.8-mGy) chest CT after standard of care CT (8 mGy) on a 64-channel multidetector CT (MDCT) and reconstructed with SafeCT, ASIR and MBIR. Two board-certified thoracic radiologists evaluated images from the lowest to the highest dose of the reduced dose CT series and subsequently for standard of care CT. RESULTS: Out of the 182 detected lesions, the missed lesions were 35 at 0.25, 24 at 0.4 and 9 at 0.8 mGy with SafeCT, ASIR and MBIR, respectively. The most missed lesions were non-calcified lung nodules (NCLNs) 25/112 (<5 mm) at 0.25, 18/112 (<5 mm) at 0.4 and 3/112 (<4 mm) at 0.8 mGy. There were 78%, 84% and 97% lung nodules detected at 0.25, 0.4 and 0.8 mGy, respectively regardless of iterative reconstruction techniques (IRTs), Most mediastinum structures were not sufficiently seen at 0.25-0.8 mGy. CONCLUSION: NCLNs can be missed in chest CT at CTDIvol of <1 mGy (0.25, 0.4 and 0.8 mGy) regardless of IRTs. The most lung nodules (97%) were detected at CTDIvol of 0.8 mGy. The most mediastinum structures were not sufficiently seen at 0.25-0.8 mGy. Advances in knowledge: NCLNs can be missed regardless of IRTs in chest CT at CTDIvol of <1 mGy. The performance of ASIR, SafeCT and MBIR was similar for lung nodule detection at 0.25, 0.4 and 0.8 mGy.

Point Organ Radiation Dose in Abdominal CT: Effect of Patient Off-Centering in an Experimental Human Cadaver Study.

To determine the effect of patient off-centering on point organ radiation dose measurements in a human cadaver scanned with routine abdominal CT protocol. A human cadaver (88 years, body-mass-index 20 kg/m2) was scanned with routine abdominal CT protocol on 128-slice dual source MDCT (Definition Flash, Siemens). A total of 18 scans were performed using two scan protocols (a) 120 kV-200 mAs fixed-mA (CTDIvol 14 mGy) (b) 120 kV-125 ref mAs (7 mGy) with automatic exposure control (AEC, CareDose 4D) at three different positions (a) gantry isocenter, (b) upward off-centering and (c) downward off-centering. Scanning was repeated three times at each position. Six thimble (in liver, stomach, kidney, pancreas, colon and urinary bladder) and four MOSFET dosimeters (on cornea, thyroid, testicle and breast) were placed for calculation of measured point organ doses. Organ dose estimations were retrieved from dose-tracking software (eXposure, Radimetrics). Statistical analysis was performed using analysis of variance. There was a significant difference between the trends of point organ doses with AEC and fixed-mA at all three positions (p < 0.01). Variation in point doses between fixed-mA and AEC protocols were statistically significant across all organs at all Table positions (p < 0.001). There was up to 5-6% decrease in point doses with upward off-centering and in downward off-centering. There were statistical significant differences in point doses from dosimeters and dose-tracking software (mean difference for internal organs, 5-36% for fixed-mA & 7-48% for AEC protocols; p < 0.001; mean difference for surface organs, >92% for both protocols; p < 0.0001). For both protocols, the highest mean difference in point doses was found for stomach and lowest for colon. Measured absorbed point doses in abdominal CT vary with patient-centering in the gantry isocenter. Due to lack of consideration of patient positioning in the dose estimation on automatic software-over estimation of the doses up to 92% was reported.

Assessment of sub-milli-sievert abdominal computed tomography with iterative reconstruction techniques of different vendors.

AIM: To assess diagnostic image quality of reduced dose (RD) abdominal computed tomography (CT) with 9 iterative reconstruction techniques (IRTs) from 4 different vendors to the standard of care (SD) CT. METHODS: In an Institutional Review Board approved study, 66 patients (mean age 60 ± 13 years, 44 men, and 22 women) undergoing routine abdomen CT on multi-detector CT (MDCT) scanners from vendors A, B, and C (≥ 64 row CT scanners) (22 patients each) gave written informed consent for acquisition of an additional RD CT series. Sinogram data of RD CT was reconstructed with two vendor-specific and a vendor-neutral IRTs (A-1, A-2, A-3; B-1, B-2, B-3; and C-1, C-2, C-3) and SD CT series with filtered back projection. Subjective image evaluation was performed by two radiologists for each SD and RD CT series blinded and independently. All RD CT series (198) were assessed first followed by SD CT series (66). Objective image noise was measured for SD and RD CT series. Data were analyzed by Wilcoxon signed rank, kappa, and analysis of variance tests. RESULTS: There were 13/50, 18/57 and 9/40 missed lesions (size 2-7 mm) on RD CT for vendor A, B, and C, respectively. Missed lesions includes liver cysts, kidney cysts and stone, gall stone, fatty liver, and pancreatitis. There were also 5, 4, and 4 pseudo lesions (size 2-3 mm) on RD CT for vendor A, B, and C, respectively. Lesions conspicuity was sufficient for clinical diagnostic performance for 6/24 (RD-A-1), 10/24 (RD-A-2), and 7/24 (RD-A-3) lesions for vendor A; 5/26 (RD-B-1), 6/26 (RD-B-2), and 7/26 (RD-B-3) lesions for vendor B; and 4/20 (RD-C-1) 6/20 (RD-C-2), and 10/20 (RD-C-3) lesions for vendor C (P = 0.9). Mean objective image noise in liver was significantly lower for RD A-1 compared to both RD A-2 and RD A-3 images (P < 0.001). Similarly, mean objective image noise lower for RD B-2 (compared to RD B-1, RD B-3) and RD C-3 (compared to RD C-1 and C-2) (P = 0.016). CONCLUSION: Regardless of IRTs and MDCT vendors, abdominal CT acquired at mean CT dose index volume 1.3 mGy is not sufficient to retain clinical diagnostic performance.

Comparison of Measured and Estimated CT Organ Doses for Modulated and Fixed Tube Current:: A Human Cadaver Study.

RATIONALE AND OBJECTIVES: The aim of this study was to compare the directly measured and the estimated computed tomography (CT) organ doses obtained from commercial radiation dose-tracking (RDT) software for CT performed with modulated tube current or automatic exposure control (AEC) technique and fixed tube current (mAs). MATERIALS AND METHODS: With the institutional review board (IRB) approval, the ionization chambers were surgically implanted in a human cadaver (88 years old, male, 68 kg) in six locations such as liver, stomach, colon, left kidney, small intestine, and urinary bladder. The cadaver was scanned with routine abdomen pelvis protocol on a 128-slice, dual-source multidetector computed tomography (MDCT) scanner using both AEC and fixed mAs. The effective and quality reference mAs of 100, 200, and 300 were used for AEC and fixed mAs, respectively. Scanning was repeated three times for each setting, and measured and estimated organ doses (from RDT software) were recorded (N = 3*3*2 = 18). RESULTS: Mean CTDIvol for AEC and fixed mAs were 4, 8, 13 mGy and 7, 14, 21 mGy, respectively. The most estimated organ doses were significantly greater (P < 0.01) than the measured organ doses for both AEC and fixed mAs. At AEC, the mean estimated organ doses (for six organs) were 14.7 mGy compared to mean measured organ doses of 12.3 mGy. Similarly, at fixed mAs, the mean estimated organ doses (for six organs) were 24 mGy compared to measured organ doses of 22.3 mGy. The differences among the measured and estimated organ doses were higher for AEC technique compared to the fixed mAs for most organs (P < 0.01). CONCLUSIONS: The most CT organ doses estimated from RDT software are greater compared to directly measured organ doses, particularly when AEC technique is used for CT scanning.

A new technique to characterize CT scanner bow-tie filter attenuation and applications in human cadaver dosimetry simulations.

PURPOSE: To present a noninvasive technique for directly measuring the CT bow-tie filter attenuation with a linear array x-ray detector. METHODS: A scintillator based x-ray detector of 384 pixels, 307 mm active length, and fast data acquisition (model X-Scan 0.8c4-307, Detection Technology, FI-91100 Ii, Finland) was used to simultaneously detect radiation levels across a scan field-of-view. The sampling time was as short as 0.24 ms. To measure the body bow-tie attenuation on a GE Lightspeed Pro 16 CT scanner, the x-ray tube was parked at the 12 o'clock position, and the detector was centered in the scan field at the isocenter height. Two radiation exposures were made with and without the bow-tie in the beam path. Each readout signal was corrected for the detector background offset and signal-level related nonlinear gain, and the ratio of the two exposures gave the bow-tie attenuation. The results were used in the geant4 based simulations of the point doses measured using six thimble chambers placed in a human cadaver with abdomen/pelvis CT scans at 100 or 120 kV, helical pitch at 1.375, constant or variable tube current, and distinct x-ray tube starting angles. RESULTS: Absolute attenuation was measured with the body bow-tie scanned at 80-140 kV. For 24 doses measured in six organs of the cadaver, the median or maximum difference between the simulation results and the measurements on the CT scanner was 8.9% or 25.9%, respectively. CONCLUSIONS: The described method allows fast and accurate bow-tie filter characterization.

Quantification of interstitial fluid on whole body CT: comparison with whole body autopsy.

PURPOSE: Interstitial fluid accumulation can occur in pleural, pericardial, and peritoneal spaces, and subcutaneous tissue planes. The purpose of the study was to assess if whole body CT examination in a postmortem setting could help determine the presence and severity of third space fluid accumulation in the body. MATERIALS AND METHODS: Our study included 41 human cadavers (mean age 61 years, 25 males and 16 females) who had whole-body postmortem CT prior to autopsy. All bodies were maintained in the morgue in the time interval between death and autopsy. Two radiologists reviewed the whole-body CT examinations independently to grade third space fluid in the pleura, pericardium, peritoneum, and subcutaneous space using a 5-point grading system. Qualitative CT grading for third space fluid was correlated with the amount of fluid found on autopsy and the quantitative CT fluid volume, estimated using a dedicated software program (Volume, Syngo Explorer, Siemens Healthcare). RESULTS: Moderate and severe peripheral edema was seen in 16/41 and 7/41 cadavers respectively. It is not possible to quantify anasarca at autopsy. Correlation between imaging data for third space fluid and the quantity of fluid found during autopsy was 0.83 for pleural effusion, 0.4 for pericardial effusion and 0.9 for ascites. The degree of anasarca was significantly correlated with the severity of ascites (p < 0.0001) but not with pleural or pericardial effusion. There was strong correlation between volumetric estimation and qualitative grading for anasarca (p < 0.0001) and pleural effusion (p < 0.0001). CONCLUSION: Postmortem CT can help in accurate detection and quantification of third space fluid accumulation. The quantity of ascitic fluid on postmortem CT can predict the extent of anasarca.

Ultralow-Dose Abdominal Computed Tomography: Comparison of 2 Iterative Reconstruction Techniques in a Prospective Clinical Study.

PURPOSE: To assess lesion detection and image quality of ultralow-dose (ULD) abdominal computed tomography (CT) reconstructed with filtered back projection (FBP) and 2 iterative reconstruction techniques: hybrid-based iDose, and image-based SafeCT. MATERIALS AND METHODS: In this institutional review board-approved ongoing prospective clinical study, 41 adult patients provided written informed consent for an additional ULD abdominal CT examination immediately after standard dose (SD) CT exam on a 256-slice multidetector computed tomography (iCT, Philips-Healthcare). The SD examination (size-specific dose estimate, 10 ± 3 mGy) was performed at 120 kV with automatic exposure control, and reconstructed with FBP. The ULD examination (1.5 ± 0.4 mGy) was performed at 120 kV and fixed tube current of 17 to 20 mAs/slice to achieve ULD radiation dose, with the rest of the scan parameters same as SD examination. The ULD data were reconstructed with (a) FBP, (b) iDose, and (c) SafeCT. Lesions were detected on ULD FBP series and compared to SD FBP "reference-standard" series. True lesions, pseudolesions, and missed lesions were recorded. Four abdominal radiologists independently blindly performed subjective image quality. Objective image quality included image noise calculation and noise spectral density plots. RESULTS: All true lesions (n, 52: liver metastases, renal cysts, diverticulosis) in SD FBP images were detected in ULD images. Although there were no missed or pseudolesions on ULD iDose and ULD SafeCT images, appearance of small low-contrast hepatic lesions was suboptimal. The ULD FBP images were unacceptable across all patients for both lesion detection and image quality. In patients with a body mass index (BMI) of 25 kg/m or less, ULD iDose and ULD SafeCT images were acceptable for image quality that was close to SD FBP for both normal and abnormal abdominal and pelvic structures. With increasing BMI, the image quality of ULD images was deemed unacceptable due to photo starvation. Evaluation of kidney stones with ULD iDose/SafeCT images was found acceptable regardless of patient size. Image noise levels were significantly lower in ULD iDose and ULD SafeCT images compared to ULD FBP (P < 0.01). CONCLUSIONS: Preliminary results show that ULD abdominal CT reconstructed with iterative reconstruction techniques is achievable in smaller patients (BMI ≤ 25 kg/m) but remains a challenge for overweight to obese patients. Lesion detection is similar in full-dose SD FBP and ULD iDose/SafeCT images, with suboptimal visibility of low-contrast lesions in ULD images.

Assessment of Filtered Back Projection, Adaptive Statistical, and Model-Based Iterative Reconstruction for Reduced Dose Abdominal Computed Tomography.

PURPOSE: To compare standard of care and reduced dose (RD) abdominal computed tomography (CT) images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), model-based iterative reconstruction (MBIR) techniques. MATERIALS AND METHODS: In an Institutional Review Board-approved, prospective clinical study, 28 patients (mean age 59 ± 13 years ), undergoing clinically indicated routine abdominal CT on a 64-channel multi-detector CT scanner, gave written informed consent for acquisition of an additional RD (<1 milli-Sievert) abdomen CT series. Sinogram data of RD series were reconstructed with FBP, ASIR, and MBIR and compared with FBP images of standard dose abdomen CT. Two radiologists performed randomized, independent, and blinded comparison for lesion detection, lesion margin, visibility of normal structures, and diagnostic confidence. RESULTS: Mean CT dose index volume was 10 ± 3.4 mGy and 1.3 ± 0.3 mGy for standard and RD CT, respectively. There were 73 "true positive" lesions detected on standard of care CT. Nine lesions (<8 mm in size) were missed on RD abdominal CT images which included liver lesions, liver cysts, kidney cysts, and paracolonic abscess. These lesions were missed regardless of patient size and types of iterative reconstruction techniques used for reconstruction of RD data sets. The visibility of lesion margin was suboptimal in (23/28) patients with RD FBP, (15/28) patients with RD ASIR, and (14/28) patients with RD MBIR compared to standard of care FBP images (P < 0.001). Diagnostic confidence for the assessment of lesions on RD images was suboptimal in most patients regardless of iterative reconstruction techniques. CONCLUSIONS: Clinically significant lesions (< 8 mm) can be missed on abdominal CT examinations acquired at a CT dose index volume of 1.3 mGy regardless of patients' size and reconstruction techniques (FBP, ASIR, and MBIR).

CT radiation dose and iterative reconstruction techniques.

1. CT radiation dose optimization is one of the major concerns for the scientific community. 2. CT image quality is dependent on the selected image reconstruction algorithm. 3. Iterative reconstruction algorithms have reemerged with the potential of radiation dose optimization by lowering image noise. 4. Tube current is the most common parameter used to reduce radiation dose along with iterative reconstruction. 5. Tube potential (kV) is also used for dose optimization with iterative reconstruction in CT angiography protocols and small patients.