Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography

OBJECTIVE

Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging.

METHODS

Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics.

RESULTS

For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics.

CONCLUSION

The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.

First experience of evaluation of the impact of high-matrix size reconstruction in image quality in arterial CT runoff studies of the lower extremities

OBJECTIVES

To determine whether image reconstruction with a higher matrix size improves image quality for lower extremity CTA studies.

METHODS

Raw data from 50 consecutive lower extremity CTA studies acquired on two MDCT scanners (SOMATOM Flash, Force) in patients evaluated for peripheral arterial disease (PAD) were retrospectively collected and reconstructed with standard (512 × 512) and higher resolution (768 × 768, 1024 × 1024) matrix sizes. Five blinded readers reviewed representative transverse images in randomized order (150 total). Readers graded image quality (0 (worst)-100 (best)) for vascular wall definition, image noise, and confidence in stenosis grading. Ten patients' stenosis scores on CTA images were compared to invasive angiography. Scores were compared using mixed effects linear regression.

RESULTS

Reconstructions with 1024 × 1024 matrix were ranked significantly better for wall definition (mean score 72, 95% CI = 61-84), noise (74, CI = 59-88), and confidence (70, CI = 59-80) compared to 512 × 512 (wall = 65, CI = 53 × 77; noise = 67, CI = 52 × 81; confidence = 62, CI = 52 × 73; p = 0.003, p = 0.01, and p = 0.004, respectively). Compared to 512 × 512, the 768 × 768 and 1024 × 1024 matrix improved image quality in the tibial arteries (wall = 51 vs 57 and 59, p < 0.05; noise = 65 vs 69 and 68, p = 0.06; confidence = 48 vs 57 and 55, p < 0.05) to a greater degree than the femoral-popliteal arteries (wall = 78 vs 78 and 85; noise = 81 vs 81 and 84; confidence = 76 vs 77 and 81, all p > 0.05), though for the 10 patients with angiography accuracy of stenosis grading was not significantly different. Inter-reader agreement was moderate (rho = 0.5).

CONCLUSION

Higher matrix reconstructions of 768 × 768 and 1024 × 1024 improved image quality and may enable more confident assessment of PAD.

CLINICAL RELEVANCE STATEMENT

Higher matrix reconstructions of the vessels in the lower extremities can improve perceived image quality and reader confidence in making diagnostic decisions based on CTA imaging.

KEY POINTS

• Higher than standard matrix sizes improve perceived image quality of the arteries in the lower extremities. • Image noise is not perceived as increased even at a matrix size of 1024 × 1024 pixels. • Gains from higher matrix reconstructions are higher in smaller, more distal tibial and peroneal vessels than in femoropopliteal vessels.

Liver fat quantification in photon counting CT in head to head comparison with clinical MRI - First experience

PURPOSE

To compare liver fat quantification between MRI and photon-counting CT (PCCT).

METHOD

A cylindrical phantom with inserts containing six concentrations of oil (0, 10, 20, 30, 50 and 100%) and oil-iodine mixtures (0, 10, 20, 30 and 50% fat +3 mg/mL iodine) was imaged with a PCCT (NAEOTOM Alpha) and a 1.5 T MRI system (MR 450w, IDEAL-IQ sequence), using clinical parameters. An IRB-approved prospective clinical evaluation included 12 obese adult patients with known fatty liver disease (seven women, mean age: 61.5 ± 13 years, mean BMI: 30.3 ± 4.7 kg/m²). Patients underwent a same-day clinical MRI and PCCT of the abdomen. Liver fat fractions were calculated for four segments (I, II, IVa and VII) using in- and opposed-phase on MRI ((Mean_in - Mean_opp)/2*Mean_in) and iodine-fat, tissue decomposition analysis in PCCT (Syngo.Via VB60A). CT and MRI Fat fractions were compared using two-sample t-tests with equal variance. Statistical analysis was performed using RStudio (Version1.4.1717).

RESULTS

Phantom results showed no significant differences between the known fat fractions (P = 0.32) or iodine (P = 0.6) in comparison to PCCT-measured concentrations, and no statistically significant difference between known and MRI-measured fat fractions (P = 0.363). In patients, the mean fat signal fraction measured on MRI and PCCT was 13.1 ± 9.9% and 12.0 ± 9.0%, respectively, with an average difference of 1.1 ± 1.9% between the modalities (P = 0.138).

CONCLUSION

First experience shows promising accuracy of liver fat fraction quantification for PCCT in obese patients. This method may improve opportunistic screening for CT in the future.

Flat-panel dual-energy head computed tomography in the angiography suite after thrombectomy for acute stroke: A clinical feasibility study

BACKGROUND

Management of large vessel occlusion (LVO) patients after thrombectomy is affected by the presence of intracranial hemorrhage (ICH) on post-procedure imaging. Differentiating contrast staining from hemorrhage on post-procedural imaging has been facilitated by dual-energy computed tomography (DECT), traditionally performed in dedicated computed tomography (CT) scanners with subsequent delays in treatment. We employed a novel method of DECT using the Siemens cone beam CT (DE-CBCT) in the angiography suite to evaluate for post-procedure ICH and contrast extravasation.

METHODS

After endovascular treatment for LVO was performed and before the patient was removed from the operating table, DE-CBCT was performed using the Siemens Q-biplane system, with two separate 20-second CBCT scans at two energy levels: 70 keV (standard) and 125 keV with tin filtration (nonstandard). Post-procedurally, patients also underwent a standard DECT using Siemens SOMATOM Force CT scanner. Two independent reviewers blindly evaluated the DE-CBCT and DECT for hemorrhage and contrast extravasation.

RESULTS

We successfully performed intra-procedural DE-CBCT in 10 subjects with no technical failure. The images were high-quality and subjectively useful to differentiate contrast from hemorrhage. The one hemorrhage seen on standard DECT was very small and clinically silent. The interrater reliability was 100% for both contrast and hemorrhage detection.

CONCLUSION

We demonstrate that intra-procedural DE-CBCT after thrombectomy is feasible and provides clinically meaningful images. There was close agreement between findings on DE-CBCT and standard DECT. Our findings suggest that DE-CBCT could be used in the future to improve stroke thrombectomy patient workflow and to more efficiently guide the postoperative management of these patients.

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

BACKGROUND

The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown.

PURPOSE

To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions.

MATERIAL AND METHODS

A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited. Cohort A included all lesions, while cohort B included Bosniak II-IV and solid enhancing renal lesions. Radiomic models were built with features selected by the least absolute shrinkage and selection operator regression (LASSO). ROC analyses were performed to compare the diagnostic accuracy among conventional and radiomic models for predicting enhancing renal lesions.

RESULTS

The individual iodine concentration (IC), normalized IC, mean attenuation on 75-keV images, radiomic model of iodine images, 75-keV images and a combined model integrating all the above-mentioned features all demonstrated high AUCs for predicting renal lesion enhancement in cohort A (AUCs = 0.934-0.979) as well as in the test dataset (AUCs = 0.892-0.962) of cohort B (P values with Bonferroni correction >0.003). The AUC (0.864) of mean attenuation on 75-keV images was significantly lower than those of other models (all P values ≤0.001) except the radiomic model of 75-keV images (P = 0.038) in the training dataset of cohort B.

CONCLUSION

No incremental value was found by adding radiomic and machine learning analyses to iodine images for differentiating enhancing from non-enhancing renal lesions.

Time-resolved, Cardiac-gated Computed Tomography after Endovascular Ascending Aortic and Arch Repair

PURPOSE

Better time-resolved imaging of stent grafts in ascending aorta and arch accounting for cardiac motion is necessary to understand device-related complications and endoleaks. We describe a novel dynamic time-resolved computed tomography-angiography (d-CTA) and its combination with electrocardiography-gating (d-gated-CTA) to image stent grafts in ascending aorta and to better characterize endoleaks.

DESCRIPTION

d-CTA involves multiple scans acquired at different timepoints along contrast enhancement curve. d-gated-CTA involves concomitant electrocardiography-gating in a pre-defined cardiac phase minimizing motion induced artifacts.

EVALUATION

We illustrate the utility of d-CTA and d-gated-CTA in two clinical scenarios. d-CTA demonstrated type 1A endoleak in a patient with aortic arch aneurysm treated by total arch debranching and thoracic stent graft. d-gated-CTA demonstrated type 1A endoleak in a patient with ascending aortic pseudo-aneurysm treated by aortic cuff placement.

CONCLUSIONS

Dynamic, cardiac-gated CTA enables time-resolved angiographic imaging of ascending aorta and arch without any cardiac motion related artifacts. Such advanced imaging techniques help with better characterization of endoleaks after stent-graft deployment in the ascending aorta and arch.

Inter-operator reliability of an onsite machine learning-based prototype to estimate CT angiography-derived fractional flow reserve

Funding Acknowledgements

Type of funding sources: None.

Background

Advances in computed tomography (CT) and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (ML-FFRCT). However, reproducibility of measurements across operators is not well demonstrated.

Purpose

This study was designed to measure the inter-operator variability and reproducibility of Coronary CT Angiography–derived fractional flow reserve values using a post-processing prototype based on a machine learning algorithm (ML-FFRCT).

Methods

We included 60 symptomatic patients who underwent coronary CT angiography. FFRCT was calculated by 2 independent operators after training using a machine learning based on-site prototype. FFRCT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present.  Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate inter-operator variability effect in FFRCT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality.

Results

A total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI: 0.977 - 0.992) and 0.972 per segment (95% CI: 0.967 - 0.977). The absolute mean difference in FFRCT estimates was 0.012 per patient (95% CI for limits of agreement: -0.035 - 0.039) and 0.02 per segment (95% CI for limits of agreement: -0.077 - 0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared to proximal/mid segments (absolute mean difference 0.011 vs 0.025, p &lt; 0.001). Sensitivity analysis showed similar results across degrees of stenosis, image quality and those with cardiac risk factors such as hypertension, diabetes and dyslipidemia.

Conclusion

A high degree of inter-operator reproducibility can be achieved by onsite machine learning based ML-FFRCT assessment. Future research is required to evaluate the physiological relevance and prognostic value of ML-FFRCT.

Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study

PURPOSE

Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish a deep learning-based approach to DE extrapolation by estimating missing image data using data from both tubes to evaluate renal lesions.

METHOD

A DE extrapolation deep-learning (DEEDL) algorithm had been trained on DECT data of 50 patients using a DSCT with DE-FoV = 33 cm (Somatom Flash). Data from 128 patients with known renal lesions falling within DE-FoV was retrospectively collected (100/140 kVp; reference dataset 1). A smaller DE-FoV = 20 cm was simulated excluding the renal lesion of interest (dataset 2) and the DEEDL was applied to this dataset. Output from the DEEDL algorithm was evaluated using ReconCT v14.1 and Syngo.via. Mean attenuation values in lesions on mixed images (HU) were compared calculating the root-mean-squared-error (RMSE) between the datasets using MATLAB R2019a.

RESULTS

The DEEDL algorithm performed well reproducing the image data of the kidney lesions (Bosniak 1 and 2: 125, Bosniak 2F: 6, Bosniak 3: 1 and Bosniak 4/(partially) solid: 32) with RSME values of 10.59 HU, 15.7 HU for attenuation, virtual non-contrast, respectively. The measurements performed in dataset 1 and 2 showed strong correlation with linear regression (r²: attenuation = 0.89, VNC = 0.63, iodine = 0.75), lesions were classified as enhancing with an accuracy of 0.91.

CONCLUSION

This DEEDL algorithm can be used to reconstruct a full dual-energy FoV from restricted data, enabling reliable HU value measurements in areas not covered by the smaller FoV and evaluation of renal lesions.

A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks

Photon-counting CT detectors are the next step in advancing CT system development and will replace the current energy integrating detectors (EID) in CT systems in the near future. In this context, the performance of PCCT was compared to EID CT for three clinically relevant tasks: abdominal soft tissue imaging, where differentiating low contrast features is important; vascular imaging, where iodine detectability is critical; and, high-resolution skeletal and lung imaging. A multi-tiered phantom was imaged on an investigational clinical PCCT system (Siemens Healthineers) across different doses using three imaging modes: macro and ultra-high resolution (UHR) PCCT modes and EID CT. Images were reconstructed using filtered backprojection and soft tissue (B30f), vascular (B46f), or high-resolution (B70f; U70f for UHR) kernels. Noise power spectra, task transfer functions, and detectability index were evaluated. For a soft tissue task, PCCT modes showed comparable noise and resolution with improved contrast-to-noise ratio. For a vascular task, PCCT modes showed lower noise and improved iodine detectability. For a high resolution task, macro mode showed lower noise and comparable resolution while UHR mode showed higher noise but improved spatial resolution for both air and bone. PCCT offers competitive advantages to EID CT for clinical tasks.

Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings

Background Results of recent phantom studies show that variation in CT acquisition parameters and reconstruction techniques may make radiomic features largely nonreproduceable and of limited use for prognostic clinical studies. Purpose To investigate the effect of CT radiation dose and reconstruction settings on the reproducibility of radiomic features, as well as to identify correction factors for mitigating these sources of variability. Materials and Methods This was a secondary analysis of a prospective study of metastatic liver lesions in patients who underwent staging with single-energy dual-source contrast material-enhanced staging CT between September 2011 and April 2012. Technique parameters were altered, resulting in 28 CT data sets per patient that included different dose levels, section thicknesses, kernels, and reconstruction algorithm settings. By using a training data set (n = 76), reproducible intensity, shape, and texture radiomic features (reproducibility threshold, R2 ≥ 0.95) were selected and correction factors were calculated by using a linear model to convert each radiomic feature to its estimated value in a reference technique. By using a test data set (n = 75), the reproducibility of hierarchical clustering based on 106 radiomic features measured with different CT techniques was assessed. Results Data in 78 patients (mean age, 60 years ± 10; 33 women) with 151 liver lesions were included. The percentage of radiomic features deemed reproducible for any variation of the different technical parameters was 11% (12 of 106). Of all technical parameters, reconstructed section thickness had the largest impact on the reproducibility of radiomic features (12.3% [13 of 106]) if only one technical parameter was changed while all other technical parameters were kept constant. The results of the hierarchical cluster analysis showed improved clustering reproducibility when reproducible radiomic features with dedicated correction factors were used (ρ = 0.39-0.71 vs ρ = 0.14-0.47). Conclusion Most radiomic features are highly affected by CT acquisition and reconstruction settings, to the point of being nonreproducible. Selecting reproducible radiomic features along with study-specific correction factors offers improved clustering reproducibility. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Dual-Energy CT in Children: Imaging Algorithms and Clinical Applications

Dual-energy CT enables the simultaneous acquisition of CT images at two different x-ray energy spectra. By acquiring high- and low-energy spectral data, dual-energy CT can provide unique qualitative and quantitative information about tissue composition, allowing differentiation of multiple materials including iodinated contrast agents. The two dual-energy CT postprocessing techniques that best exploit the advantages of dual-energy CT in children are the material-decomposition images (which include virtual nonenhanced, iodine, perfused lung blood volume, lung vessel, automated bone removal, and renal stone characterization images) and virtual monoenergetic images. Clinical applications include assessment of the arterial system, lung perfusion, neoplasm, bowel diseases, renal calculi, tumor response to treatment, and metal implants. Of importance, the radiation exposure level of dual-energy CT is equivalent to or less than that of conventional single-energy CT. In this review, the authors discuss the basic principles of the dual-energy CT technologies and postprocessing techniques and review current clinical applications in the pediatric chest and abdomen.

Quantification of uncertainty in the assessment of coronary plaque in CCTA through a dynamic cardiac phantom and 3D-printed plaque model

The purpose of this study was to develop a dynamic physical cardiac phantom with a realistic coronary plaque to investigate stenosis measurement accuracy under clinically relevant heart-rates. The coronary plaque model (5 mm diameter, 50% stenosis, and 32 mm long) was designed and 3D-printed with tissue equivalent materials (calcified plaque with iodine-enhanced lumen). Realistic cardiac motion was modeled by converting computational cardiac motion vectors into compression and rotation profiles executed by a commercial base cardiac phantom. The phantom was imaged on a dual-source CT system applying a retrospective gated coronary CT angiography (CCTA) protocol using synthesized motion-synchronized electrocardiogram (ECG) waveforms. Multiplanar reformatted images were reconstructed along vessel centerlines. Enhanced lumens were segmented by five independent operators. On average, stenosis measurement accuracy was 0.9% positively biased for the motion-free condition. Average measurement accuracy monotonically decreased from 0.9% positive bias for the motion-free condition to 18.5% negative bias at 90 beats per minute. Contrast-to-noise ratio, lumen circularity, and segmentation conformity also decreased monotonically with increasing heart-rate. These results demonstrate successful implementation of a base cardiac phantom with a 3D-printed coronary plaque model, relevant motion profile, and coordinated ECG waveform. They further show the utility of the model to ascertain metrics of CCTA accuracy and image quality under realistic plaque, motion, and acquisition conditions.

Optimizing window settings for improved presentation of virtual monoenergetic images in dual-energy computed tomography

PURPOSE

Dual-energy computed tomography virtual monoenergetic imaging (VMI) at 40 keV exhibits superior contrast-to-noise ratio (CNR), although practicing radiologists do not consistently prefer it over VMI at 70 keV due to high perceivable noise. We hypothesize that the presentation of 40 keV VMI may be compromised using window settings (i.e., window-and-level values [W-L values]) designed for conventional single-energy CT. This study aimed to devise optimum window settings that reduce the apparent noise and utilize the high CNR of 40 keV VMI, in order to improve the conspicuity of hypervascular liver lesions.

MATERIALS AND METHODS

Three W-L value adjustment methods were investigated to alter the presentation of 40 keV VMI. To harness the high CNR of 40 keV VMI, the methods were designed to achieve (a) liver histogram distribution, (b) lesion-to-liver contrast, or (c) liver background noise comparable to those perceived in 70 keV VMI. This IRB-approved study included 18 patient abdominal datasets reconstructed at 40 and 70 keV. For each patient, the W-L values were determined using the three methods. For each of the images with default or adjusted W-L values, the noise, contrast, and CNR were calculated in terms of both display space and native CT number (referred to as HU) space. An observer study was performed to compare the 40 keV images with the three adjusted W-L values, and 40 and 70 keV images with default W-L values in terms of noise, contrast, and diagnostic preference. A comparison was also made in terms of the applicability of using patient-specific or patient-averaged W-L values.

RESULTS

Using the default W-L values, 40 keV VMI exhibited higher HU CNR than 70 keV VMI by 24.6 ± 14.9% (P < 0.001) but lower display CNR by 38.0 ± 16.4% (P < 0.001). Using adjusted W-L values, 40 keV images showed increased display CNR as compared to 70 keV images, by 21.2 ± 13.1%, 17.4 ± 13.6%, and 24.2 ± 15.9% (P < 0.001) for histogram-, noise-, and contrast equalization methods, respectively. The 40 keV images with all three W-L value adjustment methods showed improved perceived conspicuity (CNR) of liver presentation by 103-120% (P < 0.001), as compared to default W-L values. The qualitative observer study revealed that 40 keV images with noise- and histogram-equalized W-L values were the most preferred, followed by 40 keV images with contrast-equalized W-L values and 70 keV images with default W-L values. The 40 keV images with default W-L values were the least preferred. Patient-specific W-L values offered similar results to those of patient-averaged W-L values.

CONCLUSION

The adjusted W-L values can significantly improve the perception of VMI dataset image quality by improving the actual display CNR.

Dual-Source Single-Energy Multidetector CT Used to Obtain Multiple Radiation Exposure Levels within the Same Patient: Phantom Development and Clinical Validation

Purpose To develop, in a phantom environment, a method to obtain multidetector computed tomographic (CT) data sets at multiple radiation exposure levels within the same patient and to validate its use for potential dose reduction by using different image reconstruction algorithms for the detection of liver metastases. Materials and Methods The American College of Radiology CT accreditation phantom was scanned by using a dual-source multidetector CT platform. By adjusting the radiation output of each tube, data sets at six radiation exposure levels (100%, 75%, 50%, 37.5%, 25%, and 12.5%) were reconstructed from two consecutive dual-source single-energy (DSSE) acquisitions, as well as a conventional single-source acquisition. A prospective, HIPAA-compliant, institutional review board-approved study was performed by using the same DSSE strategy in 19 patients who underwent multidetector CT of the liver for metastatic colorectal cancer. All images were reconstructed by using conventional weighted filtered back projection (FBP) and sinogram-affirmed iterative reconstruction with strength level of 3 (SAFIRE-3). Objective image quality metrics were compared in the phantom experiment by using multiple linear regression analysis. Generalized linear mixed-effects models were used to analyze image quality metrics and diagnostic performance for lesion detection by readers. Results The phantom experiment showed comparable image quality between DSSE and conventional single-source acquisition. In the patient study, the mean size-specific dose estimates for the six radiation exposure levels were 13.0, 9.8, 5.8, 4.4, 3.2, and 1.4 mGy. For each radiation exposure level, readers' perception of image quality and lesion conspicuity was consistently ranked superior with SAFIRE-3 when compared with FBP (P ≤ .05 for all comparisons). Reduction of up to 62.5% in radiation exposure by using SAFIRE-3 yielded similar reader rankings of image quality and lesion conspicuity when compared with routine-dose FBP. Conclusion A method was developed and validated to synthesize multidetector CT data sets at multiple radiation exposure levels within the same patient. This technique may provide a foundation for future clinical trials aimed at estimating potential radiation dose reduction by using iterative reconstructions. ^© RSNA, 2016 Online supplemental material is available for this article.

Evaluation of the use of automatic exposure control and automatic tube potential selection in low-dose cerebrospinal fluid shunt head CT

INTRODUCTION

Cerebrospinal fluid shunts are primarily used for the treatment of hydrocephalus. Shunt complications may necessitate multiple non-contrast head CT scans resulting in potentially high levels of radiation dose starting at an early age. A new head CT protocol using automatic exposure control and automated tube potential selection has been implemented at our institution to reduce radiation exposure. The purpose of this study was to evaluate the reduction in radiation dose achieved by this protocol compared with a protocol with fixed parameters.

METHODS

A retrospective sample of 60 non-contrast head CT scans assessing for cerebrospinal fluid shunt malfunction was identified, 30 of which were performed with each protocol. The radiation doses of the two protocols were compared using the volume CT dose index and dose length product. The diagnostic acceptability and quality of each scan were evaluated by three independent readers.

RESULTS

The new protocol lowered the average volume CT dose index from 15.2 to 9.2 mGy representing a 39 % reduction (P < 0.01; 95 % CI 35-44 %) and lowered the dose length product from 259.5 to 151.2 mGy/cm representing a 42 % reduction (P < 0.01; 95 % CI 34-50 %). The new protocol produced diagnostically acceptable scans with comparable image quality to the fixed parameter protocol.

CONCLUSION

A pediatric shunt non-contrast head CT protocol using automatic exposure control and automated tube potential selection reduced patient radiation dose compared with a fixed parameter protocol while producing diagnostic images of comparable quality.

Diagnostic Performance of an Advanced Modeled Iterative Reconstruction Algorithm for Low-Contrast Detectability with a Third-Generation Dual-Source Multidetector CT Scanner: Potential for Radiation Dose Reduction in a Multireader Study

PURPOSE

To assess the effect of radiation dose reduction on low-contrast detectability by using an advanced modeled iterative reconstruction (ADMIRE; Siemens Healthcare, Forchheim, Germany) algorithm in a contrast-detail phantom with a third-generation dual-source multidetector computed tomography (CT) scanner.

MATERIALS AND METHODS

A proprietary phantom with a range of low-contrast cylindrical objects, representing five contrast levels (range, 5-20 HU) and three sizes (range, 2-6 mm) was fabricated with a three-dimensional printer and imaged with a third-generation dual-source CT scanner at various radiation dose index levels (range, 0.74-5.8 mGy). Image data sets were reconstructed by using different section thicknesses (range, 0.6-5.0 mm) and reconstruction algorithms (filtered back projection [FBP] and ADMIRE with a strength range of three to five). Eleven independent readers blinded to technique and reconstruction method assessed all data sets in two reading sessions by measuring detection accuracy with a two-alternative forced choice approach (first session) and by scoring the total number of visible object groups (second session). Dose reduction potentials based on both reading sessions were estimated. Results between FBP and ADMIRE were compared by using both paired t tests and analysis of variance tests at the 95% significance level.

RESULTS

During the first session, detection accuracy increased with increasing contrast, size, and dose index (diagnostic accuracy range, 50%-87%; interobserver variability, ±7%). When compared with FBP, ADMIRE improved detection accuracy by 5.2% on average across the investigated variables (P < .001). During the second session, a significantly increased number of visible objects was noted with increasing radiation dose index, section thickness, and ADMIRE strength over FBP (up to 80% more visible objects, P < .001). Radiation dose reduction potential ranged from 56% to 60% and from 4% to 80% during the two sessions, respectively.

CONCLUSION

Low-contrast detectability performance increased with increasing object size, object contrast, dose index, section thickness, and ADMIRE strength. Compared with FBP, ADMIRE allows a substantial radiation dose reduction while preserving low-contrast detectability. Online supplemental material is available for this article.

Iodine quantification to distinguish clear cell from papillary renal cell carcinoma at dual-energy multidetector CT: a multireader diagnostic performance study

PURPOSE

To investigate whether dual-energy multidetector row computed tomographic (CT) imaging with iodine quantification is able to distinguish between clear cell and papillary renal cell carcinoma ( RCC renal cell carcinoma ) subtypes.

MATERIALS AND METHODS

In this retrospective, HIPAA-compliant, institutional review board-approved study, 88 patients (57 men, 31 women) with diagnosis of either clear cell or papillary RCC renal cell carcinoma at pathologic analysis, who underwent contrast material-enhanced dual-energy nephrographic phase study between December 2007 and June 2013, were included. Five readers, blinded to pathologic diagnosis, independently evaluated all cases by determining the lesion iodine concentration on color-coded iodine maps. The receiving operating characteristic curve analysis was adopted to estimate the optimal threshold for discriminating between clear cell and papillary RCC renal cell carcinoma , and results were validated by using a leave-one-out cross-validation. Interobserver agreement was assessed by using an intraclass correlation coefficient. The correlation between tumor iodine concentration and tumor grade was investigated.

RESULTS

A tumor iodine concentration of 0.9 mg/mL represented the optimal threshold to discriminate between clear cell and papillary RCC renal cell carcinoma , and it yielded the following: sensitivity, 98.2% (987 of 1005 [95% confidence interval: 97.7%, 98.7%]); specificity, 86.3% (272 of 315 [95% confidence interval: 85.0%, 87.7%]); positive predictive value, 95.8% (987 of 1030 [95% confidence interval: 95.0%, 96.6%]); negative predictive value, 93.7% (272 of 290 [95% confidence interval: 92.8%, 94.7%]); overall accuracy of 95.3% (1259 of 1320 [95% confidence interval: 94.6%, 96.2%]), with an area under the curve of 0.923 (95% confidence interval: 0.913, 0.933). An excellent agreement was found among the five readers in measured tumor iodine concentration (intraclass correlation coefficient, 0.9990 [95% confidence interval: 0. 9987, 0.9993). A significant correlation was found between tumor iodine concentration and tumor grade for both clear cell (τ = 0.85; P < .001) and papillary RCC renal cell carcinoma (τ = 0.53; P < .001).

CONCLUSION

Dual-energy multidetector CT with iodine quantification can be used to distinguish between clear cell and papillary RCC renal cell carcinoma , and it provides insights regarding the tumor grade.

Differentiating calcium oxalate and hydroxyapatite stones in vivo using dual-energy CT and urine supersaturation and pH values

RATIONALE AND OBJECTIVES

Knowledge of urinary stone composition can guide therapeutic intervention for patients with calcium oxalate (CaOx) or hydroxyapatite (HA) stones. In this study, we determined the accuracy of noninvasive differentiation of these two stone types using dual-energy CT (DECT) and urine supersaturation (SS) and pH values.

MATERIALS AND METHODS

Patients who underwent clinically indicated DECT scanning for stone disease and subsequent surgical intervention were enrolled. Stone composition was determined using infrared spectroscopy. DECT images were processed using custom-developed software that evaluated the ratio of CT numbers between low- and high-energy images. Clinical information, including patient age, gender, and urine pH and supersaturation profile, was obtained from electronic medical records. Simple and multiple logistic regressions were used to determine if the ratio of CT numbers could discriminate CaOx from HA stones alone or in conjunction with urine supersaturation and pH.

RESULTS

Urinary stones (CaOx n = 43, HA n = 18) from 61 patients were included in this study. In a univariate model, DECT data, urine SS-HA, and urine pH had an area under the receiver operating characteristic curve of 0.78 (95% confidence interval [CI] 0.66-0.91, P = .016), 0.76 (95% CI 0.61-0.91, P = .003), and 0.60 (95% CI 0.44-0.75, P = .20), respectively, for predicting stone composition. The combination of CT data and the urinary SS-HA had an area under the receiver operating characteristic curve of 0.79 (95% CI 0.66-0.92, P = .007) for correctly differentiating these two stone types.

CONCLUSIONS

DECT differentiated between CaOx and HA stones similarly to SS-HA, whereas pH was a poor discriminator. The combination of DECT and urine SS or pH data did not improve this performance.

Nonconvex prior image constrained compressed sensing (NCPICCS): theory and simulations on perfusion CT

PURPOSE

To present and evaluate a new image reconstruction method for dynamic CT based on a nonconvex prior image constrained compressed sensing (NCPICCS) algorithm. The authors systematically compared the undersampling potential, functional information recovery, and solution convergence speed of four compressed sensing (CS) based image reconstruction methods using perfusion CT data: Standard l1-based CS, nonconvex CS (NCCS), and l1-based and nonconvex CS, including an additional constraint based on a prior image (PICCS and NCPICCS, respectively).

METHODS

The Shepp-Logan phantom was modified such that its uppermost ellipses changed attenuation through time, simulating both an arterial input function (AIF) and a homogeneous tissue perfusion region. Data were simulated with and without Poisson noise added to the projection data and subsequently reconstructed with all four CS-based methods at four levels of undersampling: 20, 12, 6, and 4 projections. Root mean squared (RMS) error of reconstructed images and recovered time attenuation curves (TACs) were assessed as well as convergence speed. The performance of both PICCS and NCPICCS methods were also evaluated using a kidney perfusion animal experiment data set.

RESULTS

All four CS-based methods were able to reconstruct the phantoms with 20 projections, with similar results on the RMS error of the recovered TACs. NCCS allowed accurate reconstructions with as few as 12 projections, PICCS with as few as six projections, and NCPICCS with as few as four projections. These results were consistent for noise-free and noisy data. NCPICCS required the fewest iterations to converge across all simulation conditions, followed by PICCS, NCCS, and then CS. On animal data, at the lowest level of undersampling tested (16 projections), the image quality of NCPICCS was better than PICCS with fewer streaking artifacts, while the TAC accuracy on the selected region of interest was comparable.

CONCLUSIONS

The authors have presented a novel method for image reconstruction using highly undersampled dynamic CT data. The NCPICCS method takes advantage of the information provided by a prior image, as in PICCS, but employs a more general nonconvex sparsity measure [such as the l(p)-norm (0 < p < or = 1)] rather than the conventional convex l1-norm. Despite the lack of guarantees of a globally optimal solution, the proposed nonconvex extension of PICCS consistently allowed for image reconstruction from fewer samples than the analogous l1-based PICCS method. Both nonconvex sparsity measures as well as prior image information (when available) significantly reduced the number of iterations required for convergence, potentially providing computational advantages for practical implementation of CS-based image reconstruction techniques.

Feasibility of dose reduction using novel denoising techniques for low kV (80 kV) CT enterography: optimization and validation

RATIONALE AND OBJECTIVES

The aim of this study was to optimize and validate projection-space denoising (PSDN) strategies for application to 80-kV computed tomographic (CT) data to achieve 50% dose reduction.

MATERIALS AND METHODS

Image data obtained at 80 kV (mean CT dose index volume, 7.9 mGy) from dual-source, dual-energy CT enterographic (CTE) exams in 42 patients were used. For each exam, nine 80 kV image data sets were reconstructed using PSDN (three levels of intensity) with or without image-based denoising and compared to commercial reconstruction kernels. For optimization, qualitative analysis selected optimal denoising strategies, with quantitative analysis measuring image contrast, noise, and sharpness (full width at half maximum bowel wall thickness, maximum CT number gradient). For validation, two radiologists examined image quality, comparing low-dose 80-kV optimally denoised images to full-dose mixed-voltage images.

RESULTS

PSDN algorithms generated the best 80-kV image quality (41 of 42 patients), while the commercial kernels produced the worst (39 of 42) (P < .001). Overall, 80-kV PSDN approaches resulted in higher contrast (mean, 332 vs 290 Hounsfield units), slightly less noise (mean, 20 vs 26 Hounsfield units), but slightly decreased image sharpness (relative bowel wall thickness, 1.069 vs 1.000) compared to full-dose mixed-voltage images. Mean image quality scores for full-dose CTE images were 4.9 compared to 4.5 for optimally denoised half-dose 80-kV CTE images and 3.1 for nondenoised 80-kV CTE images (P < .001).

CONCLUSION

Optimized denoising strategies improve the quality of 80-kV CTE images such that CT data obtained at 50% of routine dose levels approaches the image quality of full-dose exams.