The ALARA concept in pediatric CT: myth or reality?

An efficient dual-domain deep learning network for sparse-view CT reconstruction

BACKGROUND AND OBJECTIVE

We develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners.

METHODS

We designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively. The proposed network has small training parameters and comparable running time among dual-domain based reconstruction networks and is easy to train (end-to-end). We prospectively collected clinical thoraco-abdominal CT projection data acquired on a Siemens Biograph 128 Edge CT scanner to train and validate the proposed network. Further, we quantitatively evaluated the CT Hounsfield unit (HU) values on 21 organs and anatomic structures, such as the liver, aorta, and ribcage. We also analyzed the noise properties and compared the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of the reconstructed images. Besides, two radiologists conducted the subjective qualitative evaluation including the confidence and conspicuity of anatomic structures, and the overall image quality using a 1-5 likert scoring system.

RESULTS

Objective and subjective evaluation showed that the proposed algorithm achieves competitive results in eliminating noise and artifacts, restoring fine structure details, and recovering edges and contours of anatomic structures using 384 views (1/6 sparse rate). The proposed method exhibited good computational cost performance on clinical projection data.

CONCLUSION

This work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.

Deep learning-based low-dose CT simulator for non-linear reconstruction methods

BACKGROUND

Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image-domain noise simulation methods useful. However, the introduction of non-linear reconstruction methods, such as iterative and deep learning-based reconstruction, makes noise insertion in the image domain intractable, as it is not possible to determine the noise textures analytically.

PURPOSE

To develop a deep learning-based image-domain method to generate low-dose CT images from clinical-dose CT (CDCT) images for non-linear reconstruction methods.

METHODS

We propose a fully image domain-based method, utilizing a series of three convolutional neural networks (CNNs), which, respectively, denoise CDCT images, predict the standard deviation map of the low-dose image, and generate the noise power spectra (NPS) of local patches throughout the low-dose image. All three models have U-net-based architectures and are partly or fully three-dimensional. As a use case for this study and with no loss of generality, we use paired low-dose and clinical-dose brain CT scans. A dataset of326 $\hskip.001pt 326$ paired scans was retrospectively obtained. All images were acquired with a wide-area detector clinical system and reconstructed using its standard clinical iterative algorithm. Each pair was registered using rigid registration to correct for motion between acquisitions. The data was randomly partitioned into training (251 $\hskip.001pt 251$ samples), validation (25 $\hskip.001pt 25$ samples), and test (50 $\hskip.001pt 50$ samples) sets. The performance of each of these three CNNs was validated separately. For the denoising CNN, the local standard deviation decrease, and bias were determined. For the standard deviation map CNN, the real and estimated standard deviations were compared locally. Finally, for the NPS CNN, the NPS of the synthetic and real low-dose noise were compared inside and outside the skull. Two proof-of-concept denoising studies were performed to determine if the performance of a CNN- or a gradient-based denoising filter on the synthetic low-dose data versus real data differed.

RESULTS

The denoising network had a median decrease in noise in the cerebrospinal fluid by a factor of1.71 $1.71$ and introduced a median bias of+ 0.7 $ + 0.7$ HU. The network for standard deviation map estimation had a median error of+ 0.1 $ + 0.1$ HU. The noise power spectrum estimation network was able to capture the anisotropic and shift-variant nature of the noise structure by showing good agreement between the synthetic and real low-dose noise and their corresponding power spectra. The two proof of concept denoising studies showed only minimal difference in standard deviation improvement ratio between the synthetic and real low-dose CT images with the median difference between the two being 0.0 and +0.05 for the CNN- and gradient-based filter, respectively.

CONCLUSION

The proposed method demonstrated good performance in generating synthetic low-dose brain CT scans without access to the projection data or to the reconstruction method. This method can generate multiple low-dose image realizations from one clinical-dose image, so it is useful for validation, optimization, and repeatability studies of image-processing algorithms.

Morphometric and clinical comparison of MRI-based synthetic CT to conventional CT of the hip in children

BACKGROUND

MRI-based synthetic CT (sCT) generates CT-like images from MRI data.

OBJECTIVE

To evaluate equivalence, inter- and intraobserver reliability, and image quality of sCT compared to conventional (cCT) for assessing hip morphology and maturity in pediatric patients.

MATERIALS AND METHODS

We prospectively enrolled patients <21 years old with cCT and 3T MRI of the hips/pelvis. A dual-echo gradient-echo sequence was used to generate sCT via a commercially available post-processing software (BoneMRI v1.5 research version, MRIguidance BV, Utrecht, NL). Two pediatric musculoskeletal radiologists measured seven morphologic hip parameters. 3D surface distances between cCT and sCT were computed. Physeal status was established at seven locations with cCT as reference standard. Images were qualitatively scored on a 5-point Likert scale regarding diagnostic quality, signal-to-noise ratio, clarity of bony margin, corticomedullary differentiation, and presence and severity of artifacts. Quantitative evaluation of Hounsfield units (HU) was performed in bone, muscle, and fat tissue. Inter- and intraobserver reliability were measured by intraclass correlation coefficients. The cCT-to-sCT intermodal agreement was assessed via Bland-Altman analysis. The equivalence between modalities was tested using paired two one-sided tests. The quality parameter scores of each imaging modality were compared via Wilcoxon signed-rank test. For tissue-specific HU measurements, mean absolute error and mean percentage error values were calculated using the cCT as the reference standard.

RESULTS

Thirty-eight hips in 19 patients were included (16.6 ± 3 years, range 9.9-20.9; male = 5). cCT- and sCT-based morphologic measurements demonstrated good to excellent inter- and intraobserver correlation (0.77

CONCLUSION

sCT is equivalent to cCT for the assessment of hip morphology, physeal status, and radiodensity assessment in pediatric patients.

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Key Words

• Hip imaging
• Hip morphology
• Hounsfield units
• MRI based
• Quality
• Synthetic CT

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MESH Headings

• Humans
• Male
• Child
• Female
• Magnetic Resonance Imaging/methods
• Prospective Studies
• Reproducibility of Results
• Adolescent
• Tomography, X-Ray Computed/methods
• Hip Joint/diagnostic imaging
• Child, Preschool
• Imaging, Three-Dimensional/methods

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Affiliation(s)

Jade Iwasaka-Neder Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA.
M Alejandra Bedoya Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA
James Connors Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA
Simon Warfield Computational Radiology Laboratory, Boston Children's Hospital, 401 Park Drive, Boston, MA, 02215, USA
Sarah D Bixby Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA

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MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works.

MDST: multi-domain sparse-view CT reconstruction based on convolution and swin transformer

Objective.Sparse-view computed tomography (SVCT), which can reduce the radiation doses administered to patients and hasten data acquisition, has become an area of particular interest to researchers. Most existing deep learning-based image reconstruction methods are based on convolutional neural networks (CNNs). Due to the locality of convolution and continuous sampling operations, existing approaches cannot fully model global context feature dependencies, which makes the CNN-based approaches less efficient in modeling the computed tomography (CT) images with various structural information.Approach.To overcome the above challenges, this paper develops a novel multi-domain optimization network based on convolution and swin transformer (MDST). MDST uses swin transformer block as the main building block in both projection (residual) domain and image (residual) domain sub-networks, which models global and local features of the projections and reconstructed images. MDST consists of two modules for initial reconstruction and residual-assisted reconstruction, respectively. The sparse sinogram is first expanded in the initial reconstruction module with a projection domain sub-network. Then, the sparse-view artifacts are effectively suppressed by an image domain sub-network. Finally, the residual assisted reconstruction module to correct the inconsistency of the initial reconstruction, further preserving image details.Main results. Extensive experiments on CT lymph node datasets and real walnut datasets show that MDST can effectively alleviate the loss of fine details caused by information attenuation and improve the reconstruction quality of medical images.Significance.MDST network is robust and can effectively reconstruct images with different noise level projections. Different from the current prevalent CNN-based networks, MDST uses transformer as the main backbone, which proves the potential of transformer in SVCT reconstruction.

MLF-IOSC: Multi-Level Fusion Network With Independent Operation Search Cell for Low-Dose CT Denoising

Computed tomography (CT) is widely used in clinical medicine, and low-dose CT (LDCT) has become popular to reduce potential patient harm during CT acquisition. However, LDCT aggravates the problem of noise and artifacts in CT images, increasing diagnosis difficulty. Through deep learning, denoising CT images by artificial neural network has aroused great interest for medical imaging and has been hugely successful. We propose a framework to achieve excellent LDCT noise reduction using independent operation search cells, inspired by neural architecture search, and introduce the Laplacian to further improve image quality. Employing patch-based training, the proposed method can effectively eliminate CT image noise while retaining the original structures and details, hence significantly improving diagnosis efficiency and promoting LDCT clinical applications.

Classification of Skull Shape Deformities Related to Craniosynostosis on 3D Photogrammetry

Implementation of the Utrecht Cranial Shape Quantificator (UCSQ) classification method on 3D photogrammetry in patients with different types of craniosynostosis is the aim of the present study. Five children (age <1 year) of every group of the common craniosynostoses (scaphocephaly, brachycephaly, trigonocephaly, right-sided and left-sided anterior plagiocephaly) were randomly included. The program 3-Matic (v13.0) was used to import and analyze the included 3dMD photos. Three external landmarks were placed. Using the landmarks, a base plane was created, as well as a plane 4 cm superior to the base plane. Using UCSQ, we created sinusoid curves of the patients, the resulting curves were analyzed and values were extracted for calculations. Results per patient were run through a diagnostic flowchart in order to determine correctness of the flowchart when using 3D photogrammetry. Each of the patients (n=25) of the different craniosynostosis subgroups is diagnosed correctly based on the different steps in the flowchart. This study proposes and implements a diagnostic approach of craniosynostosis based on 3D photogrammetry. By using a diagnostic flowchart based on specific characteristics for every type of craniosynostosis related to specific skull deformities, diagnosis can be established. All variables are expressed in number and are therefore objective.

The Actual Role of Iterative Reconstruction Algorithm Methods in Several Saudi Hospitals As A Tool For Radiation Dose Minimization of Ct Scan Examinations

Background

Iterative reconstruction algorithm (IR) techniques were developed to maintain a lower radiation dose for patients as much as possible while achieving the required image quality and medical benefits. The main purpose of the current research was to assess the level and usage extent of IR techniques in computed tomographic (CT) scan exams. Also, the obligation of practitioners in several hospitals in Saudi Arabia to implement IR in CT exams was assessed.

Material and Methodology

The recent research was based on two studies: data collection and a survey study. Data on the CT scan examinations were retrospectively collected from CT scanners. The survey was conducted using a questionnaire to evaluate radiographers’ and radiologists’ perceptions about IR and their practices with IR techniques. The statistical analysis results were performed to measure the usage strength level of IR methods.

Results and Discussions

The IR strength level of 50% was selected for nearly 80% of different CT examinations and patients of different ages and weights. About 46% of the participants had not learned about IR methods during their college studies, and 54% had not received formal training in applying IR techniques. Only 32% of the participants had adequate experience with IR. Half of the participants were not involved in the updating process of the CT protocol.

Conclusion

The results indicate that the majority of radiographer and radiologist at four different hospitals in Saudi Arabia have no explicit or understandable knowledge of selecting IR strength levels during the CT examination of patients. There is a need for more training in IR applications for both radiologists and radiographers. Training sessions were suggested to support radiographers and radiologists to efficiently utilize IR techniques to optimize image quality. Further studies are required to adjust CT exam protocols effectively to utilize the IR technique.

Neuroimaging of Headache: Indications and Controversies

Headache is a common presenting symptom in the ambulatory setting that often prompts imaging. The increased use and associated health care money spent in the setting of headache have raised questions about the cost-effectiveness of neuroimaging in this setting. Neuroimaging for headache in most cases is unlikely to reveal significant abnormality or impact patient management. In this article, reasons behind an observed increase in neuroimaging and its impact on health care expenditures are discussed. The typical imaging modalities available and various imaging guidelines for common clinical headache scenarios are presented, including recommendations from the American College of Radiology.

ADMM-SVNet: An ADMM-Based Sparse-View CT Reconstruction Network

In clinical medical applications, sparse-view computed tomography (CT) imaging is an effective method for reducing radiation doses. The iterative reconstruction method is usually adopted for sparse-view CT. In the process of optimizing the iterative model, the approach of directly solving the quadratic penalty function of the objective function can be expected to perform poorly. Compared with the direct solution method, the alternating direction method of multipliers (ADMM) algorithm can avoid the ill-posed problem associated with the quadratic penalty function. However, the regularization items, sparsity transform, and parameters in the traditional ADMM iterative model need to be manually adjusted. In this paper, we propose a data-driven ADMM reconstruction method that can automatically optimize the above terms that are difficult to choose within an iterative framework. The main contribution of this paper is that a modified U-net represents the sparse transformation, and the prior information and related parameters are automatically trained by the network. Based on a comparison with other state-of-the-art reconstruction algorithms, the qualitative and quantitative results show the effectiveness of our method for sparse-view CT image reconstruction. The experimental results show that the proposed method performs well in streak artifact elimination and detail structure preservation. The proposed network can deal with a wide range of noise levels and has exceptional performance in low-dose reconstruction tasks.

A Limited-View CT Reconstruction Framework Based on Hybrid Domains and Spatial Correlation

Limited-view Computed Tomography (CT) can be used to efficaciously reduce radiation dose in clinical diagnosis, it is also adopted when encountering inevitable mechanical and physical limitation in industrial inspection. Nevertheless, limited-view CT leads to severe artifacts in its imaging, which turns out to be a major issue in the low dose protocol. Thus, how to exploit the limited prior information to obtain high-quality CT images becomes a crucial issue. We notice that almost all existing methods solely focus on a single CT image while neglecting the solid fact that, the scanned objects are always highly spatially correlated. Consequently, there lies bountiful spatial information between these acquired consecutive CT images, which is still largely left to be exploited. In this paper, we propose a novel hybrid-domain structure composed of fully convolutional networks that groundbreakingly explores the three-dimensional neighborhood and works in a “coarse-to-fine” manner. We first conduct data completion in the Radon domain, and transform the obtained full-view Radon data into images through FBP. Subsequently, we employ the spatial correlation between continuous CT images to productively restore them and then refine the image texture to finally receive the ideal high-quality CT images, achieving PSNR of 40.209 and SSIM of 0.943. Besides, unlike other current limited-view CT reconstruction methods, we adopt FBP (and implement it on GPUs) instead of SART-TV to significantly accelerate the overall procedure and realize it in an end-to-end manner.

Generalized deep iterative reconstruction for sparse-view CT imaging

Sparse-view CT is a promising approach in reducing the X-ray radiation dose in clinical CT imaging. However, the CT images reconstructed from the conventional filtered backprojection (FBP) algorithm suffer from severe streaking artifacts. Iterative reconstruction (IR) algorithms have been widely adopted to mitigate these streaking artifacts, but they may prolong the CT imaging time due to the intense data-specific computations. Recently, model-driven deep learning (DL) CT image reconstruction method, which unrolls the iterative optimization procedures into the deep neural network, has shown exciting prospect in improving the image quality and shortening the reconstruction time. In this work, we explore the generalized unrolling scheme for such iterative model to further enhance its performance on sparse-view CT imaging. By using it, the iteration parameters, regularizer term, data-fidelity term and even the mathematical operations are all assumed to be learned and optimized via the network training. Results from the numerical and experimental sparse-view CT imaging demonstrate that the newly proposed network with the maximum generalization provides the best reconstruction performance.

CT Reconstruction With PDF: Parameter-Dependent Framework for Data From Multiple Geometries and Dose Levels

The current mainstream computed tomography (CT) reconstruction methods based on deep learning usually need to fix the scanning geometry and dose level, which significantly aggravates the training costs and requires more training data for real clinical applications. In this paper, we propose a parameter-dependent framework (PDF) that trains a reconstruction network with data originating from multiple alternative geometries and dose levels simultaneously. In the proposed PDF, the geometry and dose level are parameterized and fed into two multilayer perceptrons (MLPs). The outputs of the MLPs are used to modulate the feature maps of the CT reconstruction network, which condition the network outputs on different geometries and dose levels. The experiments show that our proposed method can obtain competitive performance compared to the original network trained with either specific or mixed geometry and dose level, which can efficiently save extra training costs for multiple geometries and dose levels.

What Can We Learn From COVID-19 Protocols With Regard to Management of Nonoperative Pediatric Orthopaedic Injuries?

INTRODUCTION

The COVID-19 pandemic has resulted in significant changes to normal practice in pediatric outpatient orthopaedics, with the instigation of telephone fracture clinic appointments, and the use of self-removable casting. We aim to determine any beneficial or detrimental short-term effects of these changes.

METHODS

All patients referred to fracture clinic from the emergency department during the period March 24, 2020 to May 10, 2020 (national lockdown) were assessed for number of face to face and telephone appointments, number of radiographs performed, time to discharge, use of a removable cast, any cast complications, other complications, reattendance or re-referral after discharge. They were compared with patients referred in the same period in 2019. Follow-up was to 6 months for every patient.

RESULTS

In 2019, 240 patients were reviewed and 110 in 2020. Changes in practice resulted in significant differences in the number of face to face appointments per patient [2 (1 to 6) 2019 vs. 1 (0 to 5) 2020 (P<0.00001)] and increase in telephone appointments [0 (0 to 1) 2019 vs. 1 (0 to 2) 2020]. Number of radiographs per patient [1 (1 to 7) 2019 vs. 1 (1 to ) 2020 (P=0.0178)] and time to discharge [29 d (0 to 483) 2019 vs. 16 d (0 to 216) 2020 (P<0.00001)] also reduced significantly. Use of a self-removable casting technique increased significantly (2.4% of casts in 2019 vs. 91.8% in 2020 (P<0.00001). There were no significant differences in complications related to cast or otherwise, unplanned attendance or reattendance after discharge. Use of self-removable casts for supracondylar fractures and for simple injuries (including distal radius, forearm, Toddler's, and ankle fractures) also demonstrated no change in complication rate. Significant potential cost savings of >£185 000 per annum could be demonstrated through clinic appointment and cast removal reductions.

DISCUSSION

Changes to the normal management of pediatric orthopaedic trauma brought about by the COVID-19 pandemic have been demonstrated to be safe in the short term with no increase in complications demonstrated. Potential cost savings are possible both to the health care provider and also to the patient because of reduced hospital attendance. It is feasible to continue these practices for the potential benefits as they appear safe in the short term.

LEVEL OF EVIDENCE

Level III-therapeutic study-retrospective comparative study.

Can Common Lead Apron in Testes Region Cause Radiation Dose Reduction during Chest CT Scan? A Patient Study

BACKGROUND

Computed tomography (CT) is a routine procedure for diagnosing using ionization radiation which has hazardous effects especially on sensitive organs.

OBJECTIVE

The aim of this study was to quantify the dose reduction effect of lead apron shielding on the testicular region during routine chest CT scans.

MATERIAL AND METHODS

In this measurement study, the routine chest CT examinations were performed for 30 male patients with common lead aprons folded and positioned in testis regions. The patient's mean body mass index (BMI) was 26.2 ± 4.6 kg/m2. To calculate the doses at testis region, three thermoluminescent dosimeters (TLD-100) were attached at the top surface of the apron as an indicator of the doses without shielding, and three TLDs under the apron for doses with shielding. The TLD readouts were compared using SPSS software (Wilcoxon test) version 16.

RESULTS

The radiation dose in the testicular regions was reduced from 0.46 ± 0.04 to 0.20 ± 0.04 mGy in the presence of lead apron shielding (p < 0.001), the reduction was equal to 56%. Furthermore, the heritable risk probability was obtained at 2.0 ×10-5 % and 4.6 ×10-5 % for the patients using the lead apron shield versus without shield, respectively.

CONCLUSION

Applying common lead aprons as shielding in the testis regions of male patients undergoing chest CT scans can reduce the radiation doses significantly. Therefore, this shield can be recommended for routine chest CT examinations.

Clinical concordance with Image Gently guidelines for pediatric computed tomography: a study across 663,417 CT scans at 53 clinical facilities

BACKGROUND

Managing patient radiation dose in pediatric computed tomography (CT) examinations is essential. Some organizations, most notably Image Gently, have suggested techniques to lower dose to pediatric patients and mitigate risk while maintaining image quality.

OBJECTIVE

We sought to validate whether institutions are observing Image Gently guidelines in practice.

MATERIALS AND METHODS

Dose-relevant data from 663,417 abdomen-pelvis and chest CT scans were obtained from 53 facilities. Patients were assigned arbitrary age cohorts with a minimum size of n=12 patients in each age group, for statistical purposes. All pediatric (<19 years old) cohorts at a given facility were compared to the adult cohort by a Kruskal-Wallis test for each of the four scan parameters - (1) x-ray tube kilovoltage (kV), (2) tube-current-by-exposure-time product (tube mAs), (3) scan pitch and (4) tube rotation time - to assess whether the distribution of values in the pediatric cohorts differed from the adult cohort. The same was repeated with volume CT dose index (CTDI_vol) and size-specific dose estimate (SSDE) to assess whether pediatric cohorts received less dose than adult cohorts. A P-value of <0.05 was deemed significant.

RESULTS

Across the 150 pediatric cohorts, 134 had scan parameters that were more child-sized than their adult counterparts. In 128 of these 134 pediatric cohorts, the CTDI_vol was less than the adult counterpart. In 111 of these 128 pediatric cohorts, the SSDE was less than the adult counterpart.

CONCLUSION

The study reaffirms that in practice, Image Gently's suggestions of lowering tube mAs and peak kilovoltage are commonly employed and effective at reducing pediatric CT dose.

Pediatric Thrombectomy: Design and Workflow Lessons From Two Experienced Centers

Endovascular thrombectomy has played a major role in advancing adult stroke care and may serve a similar role in pediatric stroke care. However, there is a need to develop better evidence and infrastructure for pediatric stroke care. In this work, we review 2 experienced pediatric endovascular thrombectomy programs and examine key design features in both care environments, including a formalized protocol and workflow, integration with an adult endovascular thrombectomy workflow, simplification and automation of workflow steps, pediatric adaptations of stroke imaging, advocacy of pediatric stroke care, and collaboration between providers, among others. These essential features transcend any single hospital environment and may provide an important foundation for other pediatric centers that aim to enhance the care of children with stroke.

DaNet: dose-aware network embedded with dose-level estimation for low-dose CT imaging

Many deep learning (DL)-based image restoration methods for low-dose CT (LDCT) problems directly employ the end-to-end networks on low-dose training data without considering dose differences. However, the radiation dose difference has a great impact on the ultimate results, and lower doses increase the difficulty of restoration. Moreover, there is increasing demand to design and estimate acceptable scanning doses for patients in clinical practice, necessitating dose-aware networks embedded with adaptive dose estimation. In this paper, we consider these dose differences of input LDCT images and propose an adaptive dose-aware network. First, considering a large dose distribution range for simulation convenience, we coarsely define five dose levels in advance as lowest, lower, mild, higher and highest radiation dose levels. Instead of directly building the end-to-end mapping function between LDCT images and high-dose CT counterparts, the dose level is primarily estimated in the first stage. In the second stage, the adaptively learned low-dose level is used to guide the image restoration process as the pattern of prior information through the channel feature transform. We conduct experiments on a simulated dataset based on original high dose parts of American Association of Physicists in Medicine challenge datasets from the Mayo Clinic. Ablation studies validate the effectiveness of the dose-level estimation, and the experimental results show that our method is superior to several other DL-based methods. Specifically, our method provides obviously better performance in terms of the peak signal-to-noise ratio and visual quality reflected in subjective scores. Due to the dual-stage process, our method may suffer limitations under more parameters and coarse dose-level definitions, and thus, further improvements in clinical practical applications with different CT equipment vendors are planned in future work.

Are Continued Efforts to Reduce Radiation Exposures from X-Rays Warranted?

There are pressures to avoid use of radiological imaging throughout all healthcare due to the notion that all radiation is carcinogenic. This perception stems from the long-standing use of the linear no-threshold (LNT) assumption of risk associated with radiation exposures. This societal perception has led to relentless efforts to avoid and reduce radiation exposures to patients at great costs. Many radiation reduction campaigns have been launched to dissuade doctors from using radiation imaging. Lower-dose imaging techniques and practices are being advocated. Alternate imaging procedures are encouraged. Are these efforts warranted? Based on recent evidence, LNT ideology is shown to be defunct for risk assessment at low-dose exposure ranges which includes X-rays and CT scans. In fact, the best evidence that was once used to support LNT ideology, including the Life Span Study data, now indicates thresholds for cancer induction are high; therefore, low-dose X-rays cannot cause harm. Current practices are safe as exposures currently encountered are orders of magnitude below threshold levels shown to be harmful. As long as imaging is medically warranted, it is shown that efforts to reduce exposures that are within background radiation levels and that are also shown to enhance health by upregulating natural adaptive protection systems are definitively wasted resources.

Sparse-view CT reconstruction based on multi-level wavelet convolution neural network

Sparse-view computed tomography (CT) is a recent approach to reducing the radiation dose in patients and speeding up the data acquisition. Consequently, sparse-view CT has been of particular interest among researchers within the CT community. Advanced reconstruction algorithms for sparse-view CT, such as iterative algorithms with total-variation (TV), have been studied along with the problem of increasing computational burden and the blurring of artifacts in the reconstructed images. Studies on deep-learning-based approaches applying U-NET have recently achieved remarkable outcomes in various domains including low-dose CT. In this study, we propose a new method for sparse-view CT reconstruction based on a multi-level wavelet convolutional neural network (MWCNN). First, a filtered backprojection (FBP) was used to reconstruct a sparsely sampled sinogram from 60, 120, and 180 projections. Subsequently, the sparse-view data obtained from FBP were fed to a deep-learning network, i.e., the MWCNN. Our network architecture combines a wavelet transform and modified U-NET without pooling. By replacing the pooling function with the wavelet transform, the receptive field is enlarged to improve the performance. We qualitatively and quantitatively evaluated the interpolation, iterative TV method, and standard U-NET in terms of a reduction in the streaking artifacts and a preservation of the anatomical structures. When compared with other methods, the proposed method showed the highest performance based on various evaluation parameters such as the structural similarity, root mean square error, and resolution. These results indicate that the MWCNN possesses a powerful potential for achieving a sparse-view CT reconstruction.

Fluoroscopic imaging optimization in children during percutaneous nephrolithotrispy

INTRODUCTION AND OBJECTIVES

Radiation protection management recommends radiation exposures that are as low as reasonably achievable (ALARA), while still maintaining image quality. The aim of the study is to compare radiation exposure during pediatric percutaneous nephrolithotomy (PCNL) before and after implementation of strategy for optimization of fluoroscopic imaging by measuring the Dose Area Product (DAP) and the Fluoroscopy time (FT) and study its effect on surgical outcomes.

PATIENTS & METHODS

We prospectively observed 56 children (group 1) undergoing PCNL for kidney stones in whom a radiation dose reduction strategy was adopted. The strategy included several intraoperative measures, including: optimizing position by keeping the fluoroscopy table as far from the X-ray tube as possible and the image intensifier close to the patient, preventing use of fluoroscopy for positioning, use of pulsed mode with last image hold technique, beam collimation and use of a designated fluoroscopy technician. Outcomes were compared to those in 42 children (group 2) before implementing dose reduction strategy.

RESULTS

DAP was decreased by 44% from 2.46 in group 2 to 1.38 mGy m2 in group 1 (p < 0.04). Total fluoroscopy time was significantly reduced by 55% from 100.8 s in group 2-45 s in group 1 (p < 0.002) after protocol implementation with very little loss of image quality.

CONCLUSIONS

Radiation exposure in children undergoing PCNL can be reduced significantly after optimization of fluoroscopy imaging. A reduced radiation protocol did not increase surgical complexity, operative time, or complication rates while reducing radiation exposure in a population vulnerable to its hazardous effects.

Development of a computational phantom for validation of automated noise measurement in CT images

The purpose of this study was to develop a computational phantom for validation of automatic noise calculations applied to all parts of the body, to investigate kernel size in determining noise, and to validate the accuracy of automatic noise calculation for several noise levels. The phantom consisted of objects with a very wide range of HU values, from -1000 to +950. The incremental value for each object was 10 HU. Each object had a size of 15 × 15 pixels separated by a distance of 5 pixels. There was no dominant homogeneous part in the phantom. The image of the phantom was then degraded to mimic the real image quality of CT by convolving it with a point spread function (PSF) and by addition of Gaussian noise. The magnitude of the Gaussian noises was varied (5, 10, 25, 50, 75 and 100 HUs), and they were considered as the ground truth noise (N_G). We also used a computational phantom with added actual noise from a CT scanner. The phantom was used to validate the automated noise measurement based on the average of the ten smallest standard deviations (SD) from the standard deviation map (SDM). Kernel sizes from 3 × 3 up to 27 × 27 pixels were examined in this study. A computational phantom for automated noise calculations validation has been successfully developed. It was found that the measured noise (N_M) was influenced by the kernel size. For kernels of 15 × 15 pixels or smaller, the N_Mvalue was much smaller than the N_G. For kernel sizes from 17 × 17 to 21 × 21 pixels, the N_Mvalue was about 90% of N_G. And for kernel sizes of 23 × 23 pixels and above, N_Mis greater than N_G. It was also found that even with small kernel sizes the relationship between N_Mand N_Gis linear with R²more than 0.995. Thus accurate noise levels can be automatically obtained even with small kernel sizes without any concern regarding the inhomogeneity of the object.

Low-dose CT of paediatric paranasal sinus using an ultra-low tube voltage (70 kVp) combined with the flash technique

AIM

To evaluate the radiation dose and diagnostic image quality of low-dose computed tomography (CT) of the paranasal sinus in children, with acquisition at an ultra-low tube voltage (70 kVp) combined with the Flash technique.

MATERIALS AND METHODS

Eighty paediatric patients underwent CT of the paranasal sinus and were divided into two groups according to different protocols (group A: 80 kVp protocol with conventional spiral mode [n=40] and group B: 70 kVp protocol with Flash scan mode [n=40]). For each examination, the CT dose index (CTDIvol), dose-length product (DLP), and effective dose (ED) were estimated. The image noise, signal-to-noise ratio (SNR), and overall subjective diagnostic image quality were also evaluated.

RESULTS

For radiation dose, the CTDIvol (mGy), DLP (mGy·cm), and ED (mSv) values of the 70 kVp protocol were significantly lower than those of the 80 kVp protocol (CTDIvol: 1.57±0.009 versus 0.39±0.004 mGy, p<0.001; DLP: 19.88±2.01 versus 6.31±0.52 mGy·cm, p<0.001; ED: 0.079±0.016 versus 0.024±0.005 mSv, p<0.001). Compared with those of the 80-kVp protocol, the image noise increased by 40.7% (p=0.113), the SNR_soft-tissue decreased by 48.9%, and the SNR_bone increased by 10.1% with the 70-kVp protocol (p=0.176 and 0.227, respectively). There was no significant difference in the overall subjective image quality grades between these two groups (p=0.15).

CONCLUSION

When imaging the paranasal sinus in children, an ultra-low tube voltage (70 kVp) combined with the Flash CT technique can reduce the radiation dose significantly while maintaining diagnostic image quality with clinically acceptable image noise.

Reducing the radiation dose of pediatric paranasal sinus CT using an ultralow tube voltage (70 kVp) combined with iterative reconstruction: Feasibility and image quality

BACKGROUND

As the gold standard for imaging sinus disease, the main disadvantage of computed tomography (CT) of the pediatric paranasal sinus is radiation exposure. Because of this, 1 protocol for CT should reduce radiation dose while maintaining image quality. The aim of this study is to evaluate the image quality of dose-reduced paranasal sinus computed tomography (CT) using an ultralow tube voltage (70 kVp) combined with iterative reconstruction (IR) in children.

METHODS

CT scans of the paranasal sinus were performed using different protocols [70 kVp protocols with IR, Group A, n = 80; 80 kVp protocols with a filtered back projection algorithm, Group B, n = 80] in 160 pediatric patients. Then, the volume-weighted CT dose index, dose-length product, and effective dose were estimated. Image noise, the signal-to-noise ratio and the diagnostic image quality were also evaluated.

RESULTS

For the radiation dose, the volume-weighted CT dose index, dose-length product and effective dose values were significantly lower for the 70 kVp protocols than for the 80 kVp protocols (P < .001). Compared with the 80 kVp protocols, the 70 kVp protocols had significantly higher levels of image noise (P = .001) and a lower signal-to-noise ratio (P = .002). No significant difference in the overall subjective image quality grades was observed between these 2 groups (P = .098).

CONCLUSION

The ultralow tube voltage (70 kVp) technique combined with IR enabled a significant dose reduction in CT examinations performed in the pediatric paranasal sinus while maintaining diagnostic image quality with clinically acceptable image noise.

Optimization of low-dose scan parameters in dual-energy computed tomography for displaying the anterior cruciate ligament

Objective

This study was performed to assess low-dose scan parameters in dual-energy computed tomography (CT) for displaying the anterior cruciate ligament.

Methods

Dual-energy CT scans with low and standard dose parameters, respectively, were performed in nine human knee joint specimens. Eighteen imaging data sets for cruciate ligament specimens were obtained and processed. Statistical analysis was performed for signal-to-noise ratios of the CT images and subjective scores.

Results

Comparable signal-to-noise ratios and subjective image quality scores by evaluators in dual-energy CT anterior cruciate ligament images between the low and standard-dose groups were observed.

Conclusion

Low-dose scan parameters do not compromise the outcomes of anterior cruciate ligament imaging.

Diagnosis of coronary artery disease in patients with atrial fibrillation using low tube voltage coronary CT angiography with isotonic low-concentration contrast agent

This prospective study evaluated the image quality and accuracy of coronary computed tomography angiography (CCTA) for diagnosing coronary artery disease (CAD) in patients with atrial fibrillation (AF), in which CCTA used adaptive iterative dose reduction (AIDR) with a low tube voltage and low concentration of isotonic contrast agent. Sixty-eight consecutive patients with AF and suspected CAD were equally and randomly apportioned to two groups and underwent CCTA. In the experimental group, the contrast agent was iodixanol (270 mg I/mL), patients were scanned with 100 kV, and reconstruction was by AIDR. In the conventional scanning (control) group, the contrast agent was iopromide (370 mg I/mL), patients were scanned with 120 kV, and reconstruction was by filtered back projection. The image quality, effective radiation dose (E), and total iodine intake of the groups were compared. Thirty-nine patients with coronary artery stenosis later were given invasive coronary angiography (ICA). The groups were similar with regard to mean CT value, noise, and signal-to-noise and contrast-to-noise ratios. The figure of merit of the experimental group was significantly higher than that of the control group, while the E and total iodine were significantly lower. Using ICA as the diagnostic reference, the groups shared similar sensitivity, specificity, and false positive and false negative rates for diagnosing coronary artery stenosis. For determining CAD in patients with AF, CCTA with isotonic low-concentration contrast agent and low-voltage scanning is a feasible alternative that improves accuracy and reduces radiation dose and iodine intake.

Reduction in Head Computed Tomography Ordering in Pediatric Emergency Patients: Effect of National Publication and Local Availability of Urgent Neurology Appointments

OBJECTIVE

The objective of this study was to evaluate the effect of the Pediatric Emergency Care Applied Research Network (PECARN) blunt head trauma guidelines and implementation of urgent neurology follow-up (UNF) appointments on an observed decline in head computed tomography (CT) use for pediatric emergency department (PED) patients presenting with headache, seizure, and trauma.

METHODS

Patients ages 0 to 18 years presenting to and discharged from an urban tertiary care PED with chief complaint of trauma, headache, and seizure between 2007 and 2013 were retrospectively included. The total number of head CTs obtained in the trauma, headache, and seizure groups was compared before and after the publication of the PECARN guidelines in 2009 and the implementation of urgent UNF within a week from PED discharge in 2011, respectively.

RESULTS

Between 2007 and 2013, 24,434 encounters were identified with 2762 head CTs performed. Analysis demonstrated a decline in pediatric head CTs for trauma (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.8-2.2) after the publication of the PECARN study on blunt head trauma, for headache (OR, 1.4; 95% CI, 1.1-1.8) and seizure (OR, 1.9; 95% CI, 1.4-2.6) with UNF. However, cross comparison (headache and seizure with PECARN and trauma with UNF) also demonstrated similar significant declines.

CONCLUSIONS

The decline in head CTs observed at our institution demonstrated a strong linear relationship, yet cannot be solely attributed to the PECARN blunt head trauma study or the implementation of UNF.

Coronary CT angiography radiation dose trends: A 10-year analysis to develop institutional diagnostic reference levels

PURPOSE

To develop institutional diagnostic reference levels (IDRL) for coronary CT angiography (CCTA) according to patient size by analyzing radiation dose changes over the past 10 years.

MATERIALS AND METHODS

This IRB approved retrospective investigation analyzed radiation dose data from CCTA between 2007 and 2016 at our institution. Annual trends in radiation dose were described for each scanner type and scanning mode. Radiation levels were analyzed for normorhythmic patients, patients with prior coronary artery bypass grafting (CABG), arrhythmia, and according to patient size and tube voltage. Median, and quartile values for volume CT dose index (CTDIvol), dose-length product (DLP), and size-specific dose estimate (SSDE) were calculated. Wilcoxon rank-sum test and Kruskal Wallis test were performed to assess the significance of quantitative data.

RESULTS

35,375 examinations from 33,317 patients (median age, 58 [50-66] years; male patients, 21,087 [58.7%]) were analyzed. CTDIvol, DLP, and SSDE significantly decreased by 9.0%, 30.8%, and 40.1% (all P < 0.05) for all examinations, respectively. All radiation dose metrics progressively decreased across scanning modes (especially retrospectively ECG-gated spiral and prospectively ECG-triggered high-pitch spiral acquisition mode), but did not significantly change across scanners in the last 6 years. CTDIvol and DLP increased with patient size when water-equivalent diameters were >19 cm for normorhythmic and CABG patients. In arrhythmic patients, CTDIvol increased progressively with water-equivalent diameters across all groups.

CONCLUSION

CCTA radiation dose has progressively decreased in the past decade except in patients with prior CABG and arrhythmia. Size-specific IDRLs may optimize radiation utilization in these patients going forward.

Examination of Pediatric Radiation Dose Delivered After Cervical Spine Trauma

OBJECTIVES

Pediatric cervical spine injuries (CSIs) are rare but potentially fatal injuries. Plain radiographs (x-rays) and computed tomography (CT) are used to diagnose CSIs. Given concerns related to radiation exposure, the utility of x-rays in diagnosing CSIs compared with other forms of imaging must be examined.

METHODS

Patients younger than 19 years presenting with possible CSI to an urban tertiary care hospital who received imaging for possible CSI between January 1, 2011, and December 31, 2013, were included. The dose-length product was abstracted from the PACS system. Test performance for x-ray, CT, and MRI were calculated and effective radiation dose by age group was analyzed using the Kruskal-Wallis Test.

RESULTS

A total of 671 patient charts were reviewed, 574 children were included in the study cohort. Median age of enrolled children was 9.70 (interquartile range, 4.78-13.83) years; 42.5% were female. Test performance of x-ray, CT, and MRI to detect CSI were calculated. Cervical x-rays performed only slightly inferior to CT. Sensitivity was 83% (95% confidence interval [CI], 36-99%), and specificity was 97% (95% CI, 96%-99%) versus 100% (95% CI, 96%-100%) for CT. Median effective dose of radiation for cervical CTs was 4.51 mSv (interquartile range, 3.84-5.59 mSv). Median dose significantly increased with age (2.94-5.10 mSv, P < 0.001).

CONCLUSIONS

Plain radiographs were largely sufficient to screen for CSIs, indicating their utility as a screening tool for CSIs. The incidence of CSIs in our sample was similar to prior reports. The effective radiation dose delivered during pediatric head and cervical CTs were lower than previously published estimates.

Derivation of new diagnostic reference levels for neuro-paediatric computed tomography examinations in Switzerland

PURPOSE

Definition of new national diagnostic reference levels (DRLs) for volume computed tomography dose index (CTDI_vol) and dose length product (DLP) for neuro-paediatric CT examinations depending on the medical indication.

METHODS

Paediatric cranial CT data sets acquired between January 2013 and December 2016 were retrospectively collected between July 2016 and March 2017 from eight of the largest university and cantonal hospitals that perform most of the neuro-paediatric CTs in Switzerland. A consensus review of CTDI_vol and DLP was undertaken for three defined anatomical regions: brain, facial bone, and petrous bone, each with and without contrast medium application. All indications for cranial CT imaging in paediatrics were assigned to one of these three regions. Descriptive statistical analysis of the distribution of the median values for CTDI_vol and DLP yielded values in the minimum, maximum, 25th percentile (1st quartile), median (2nd quartile), and 75th percentile (3rd quartile). New DRLs for neuro-paediatric CT examinations in Switzerland were based on the 75th percentiles of the distributions of the median values of all eight centres. Where appropriate, values were rounded such that the DRLs increase or at least remain constant as the age of the patient increases.

RESULTS

Our results revealed DRLs for CTDI_vol and DLP up to 20% lower than the DRLs used so far in Switzerland and elsewhere in Europe.

CONCLUSIONS

This study provides Swiss neuro-paediatric CT DRL values to establish optimum conditions for paediatric cranial CT examinations. Periodic national updates of DRLs, following international comparisons, are essential.

A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.

Ionizing radiation exposure: hazards, prevention, and biomarker screening

Radiation is a form of energy derived from a source that is propagated through material in space. It consists of ionizing radiation or nonionizing radiation. Ionizing radiation is a feature of the environment and an important tool in medical treatment, but it can cause serious damage to organisms. A number of protective measures and standards of protection have been proposed to protect against radiation. There is also a need for biomarkers to rapidly assess individual doses of radiation, which can not only estimate the dose of radiation but also determine its effects on health. Proteomics, genomics, metabolomics, and lipidomics have been widely used in the search for such biomarkers. These topics are discussed in depth in this review.

Ionizing radiation from computed tomography versus anesthesia for magnetic resonance imaging in infants and children: patient safety considerations

In the context of health care, risk assessment is the identification, evaluation and estimation of risk related to a particular clinical situation or intervention compared to accepted medical practice standards. The goal of risk assessment is to determine an acceptable level of risk for a given clinical treatment or intervention in association with the provided clinical circumstances for a patient or group of patients. In spite of the inherent challenges related to risk assessment in pediatric cross-sectional imaging, the potential risks of ionizing radiation and sedation/anesthesia in the pediatric population are thought to be quite small. Nevertheless both issues continue to be topics of discussion concerning risk and generate significant anxiety and concern for patients, parents and practicing pediatricians. Recent advances in CT technology allow for more rapid imaging with substantially lower radiation exposures, obviating the need for anesthesia for many indications and potentially mitigating concerns related to radiation exposure. In this review, we compare and contrast the potential risks of CT without anesthesia against the potential risks of MRI with anesthesia, and discuss the implications of this analysis on exam selection, providing specific examples related to neuroblastoma surveillance imaging.

Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI

Positron emission tomography (PET) is an essential technique in many clinical applications such as tumor detection and brain disorder diagnosis. In order to obtain high-quality PET images, a standard-dose radioactive tracer is needed, which inevitably causes the risk of radiation exposure damage. For reducing the patient's exposure to radiation and maintaining the high quality of PET images, in this paper, we propose a deep learning architecture to estimate the high-quality standard-dose PET (SPET) image from the combination of the low-quality low-dose PET (LPET) image and the accompanying T1-weighted acquisition from magnetic resonance imaging (MRI). Specifically, we adapt the convolutional neural network (CNN) to account for the two channel inputs of LPET and T1, and directly learn the end-to-end mapping between the inputs and the SPET output. Then, we integrate multiple CNN modules following the auto-context strategy, such that the tentatively estimated SPET of an early CNN can be iteratively refined by subsequent CNNs. Validations on real human brain PET/MRI data show that our proposed method can provide competitive estimation quality of the PET images, compared to the state-of-the-art methods. Meanwhile, our method is highly efficient to test on a new subject, e.g., spending ~2 seconds for estimating an entire SPET image in contrast to ~16 minutes by the state-of-the-art method. The results above demonstrate the potential of our method in real clinical applications.

Pediatric providers and radiology examinations: knowledge and comfort levels regarding ionizing radiation and potential complications of imaging

BACKGROUND

Pediatric providers should understand the basic risks of the diagnostic imaging tests they order and comfortably discuss those risks with parents. Appreciating providers' level of understanding is important to guide discussions and enhance relationships between radiologists and pediatric referrers.

OBJECTIVE

To assess pediatric provider knowledge of diagnostic imaging modalities that use ionizing radiation and to understand provider concerns about risks of imaging.

MATERIALS AND METHODS

A 6-question survey was sent via email to 390 pediatric providers (faculty, trainees and midlevel providers) from a single academic institution. A knowledge-based question asked providers to identify which radiology modalities use ionizing radiation. Subjective questions asked providers about discussions with parents, consultations with radiologists, and complications of imaging studies.

RESULTS

One hundred sixty-nine pediatric providers (43.3% response rate) completed the survey. Greater than 90% of responding providers correctly identified computed tomography (CT), fluoroscopy and radiography as modalities that use ionizing radiation, and ultrasound and magnetic resonance imaging (MRI) as modalities that do not. Fewer (66.9% correct, P<0.001) knew that nuclear medicine utilizes ionizing radiation. A majority of providers (82.2%) believed that discussions with radiologists regarding ionizing radiation were helpful, but 39.6% said they rarely had time to do so. Providers were more concerned with complications of sedation and cost than they were with radiation-induced cancer, renal failure or anaphylaxis.

CONCLUSION

Providers at our academic referral center have a high level of basic knowledge regarding modalities that use ionizing radiation, but they are less aware of ionizing radiation use in nuclear medicine studies. They find discussions with radiologists helpful and are concerned about complications of sedation and cost.

Image quality evaluation for CARE kV technique combined with iterative reconstruction for chest computed tomography scanning

BACKGROUND

To investigate the radiation dose and image quality for iterative reconstruction combined with the CARE kV technique in chest computed tomography (CT) scanning for physical examination.

METHODS

A total of 130 patients who underwent chest CT scanning were randomly chosen and the quality reference value was set as 80 mAs. The scanning scheme was set and the patients were randomly divided into groups according to the scanning scheme. Sixty patients underwent a chest scan with 100 kV using the CARE kV technique and SAFIRE reconstruction (value=3) (experimental group) and the other 70 patients underwent chest scanning with 120 kV (control group). The mean CT value, image noise (SD), and signal-to-noise ratio (SNR) of the apex of the lung, the level of the descending aorta bifurcation of the trachea, and the middle area of the left atrium were measured. The image quality was assessed on a 5-point scale by two radiologists and results of the two groups were compared. The CT dose index of the volume (CTDIvol), dose length product (DLP), and effective dose (ED) were compared.

RESULTS

All the images for both groups satisfied the diagnosis requirement. There was no statistical difference in the image quality between the two methods (P > 0.05). The mean CT value of the apex of the lung, the level of the descending aorta bifurcation of the trachea, and the middle area of the left atrium were not significantly different for both groups (P > 0.05), while the image noise (SD) and the signal-to-noise ratio (SNR) of the apex of the lung, the level of the descending aorta bifurcation of the trachea, and the middle area of the left atrium were statistically different for both groups (P < 0.05). The CTDIvol was 3.29 ± 1.17 mGy for the experimental group and 5.30 ± 1.53 mGy for the control group. The DLP was 114.9 ± 43.73 mGy cm for the low-dose group and 167.6 ± 44.59 mGy cm for the control group. The ED was 1.61 ± 0.61 mSv for the low-dose group and 2.35 ± 0.62 mSv for the control group (P  < 0.05).

CONCLUSION

The CARE kV technique combined with iterative reconstruction for chest CT scanning for physical examination could reduce the radiation dosage and improve CT image quality, which has a potential clinical value for imaging the thorax.

How accurate is size-specific dose estimate in pediatric body CT examinations?

BACKGROUND

Size-specific dose estimate is gaining increased acceptance as the preferred index of CT dose in children. However it was developed based on non-clinical data.

OBJECTIVE

To compare the accuracy of size-specific dose estimate (SSDE) based on geometric and body weight measures in pediatric chest and abdomen CT scans, versus the more accurate [Formula: see text] (mean SSDE based on water-equivalent diameter).

MATERIALS AND METHODS

We retrospectively identified 50 consecutive children (age <18 years) who underwent chest CT examination and 50 children who underwent abdomen CT. We measured anteroposterior diameter (DAP) and lateral diameter (DLAT) at the central slice (of scan length) of each patient and calculated DAP+LAT (anteroposterior diameter plus lateral diameter) and DED (effective diameter) for each patient. We calculated the following in each child: (1) SSDEs based on DAP, DLAT, DAP+LAT, DED, and body weight, and (2) SSDE based on software calculation of mean water-equivalent diameter ([Formula: see text] adopted standard within our study). We used intraclass correlation coefficient (ICC) and Bland-Altman analysis to compare agreement between the SSDEs and [Formula: see text].

RESULTS

Gender and age distribution were similar between chest and abdomen CT groups; mean body weight was 37 kg for both groups, with ranges of 6-130 kg (chest) and 8-107 kg (abdomen). SSDEs had very strong agreement (ICC>0.9) with [Formula: see text]. SSDEs based on DLAT had 95% limits of agreement of up to 43% with [Formula: see text]. SSDEs based on other parameters (body weight, DAP, DAP+LAT, DED) had 95% limits of agreement of up to 25%.

CONCLUSION

Differences between SSDEs calculated using various indications of patient size (geometric indices and patient weight) and the more accurate [Formula: see text] calculated using proprietary software were generally small, with the possible exception for lateral diameter, and provide acceptable dose estimates for body CT in children.

An Imaging Diagnostic Protocol in Children with Clinically Suspected Acute Appendicitis

The objective of the present study is to evaluate a new diagnostic strategy using clinical findings followed by ultrasound (US) and, in selected cases, MRI. This study included 166 children presenting signs and symptoms suggesting acute appendicitis. Cases classified as suggesting appendicitis according to clinical exams had to be referred to surgery, whereas the other cases were discharged. Unclear cases were evaluated using US. If the US results were considered inconclusive, patients underwent MRI. Of the 166 patients, 78 (47%) had acute appendicitis and 88 (53%) had other diseases. The strategy under study had a sensitivity of 96 per cent, specificity of 100 per cent, positive predictive value of 100 per cent, negative predictive value of 97 per cent, and accuracy of 98 per cent. Eight patients remained undiagnosed and underwent MRI. After MRI two girls presented normal appendixes and were discharged. One girl had an enlarged appendix on MRI and appendicitis could have been confirmed by surgery. In the other five patients, no other sign of the disease was detected by MRI such as an inflammatory mass, free fluid or an abscess in the right iliac fossa. All of them were discharged after clinical observation. In the vast majority of cases the correct diagnosis was reached by clinical and US examinations. When clinical assessment and US findings were inconclusive, MRI was useful to detect normal and abnormal appendixes and valuable to rule out other abdominal pathologies that mimic appendicitis.

Coronary Computed Tomographic Angiography at Low Concentration of Contrast Agent and Low Tube Voltage in Patients with Obesity:: A Feasibility Study

RATIONALE AND OBJECTIVES

Using lower tube voltage can reduce the exposure to radiation and the dose of contrast agent. However, lower tube voltage is often linked to more noise and poor image quality, which create a need for more effective technology to resolve this problem. To explore the feasibility of coronary computed tomographic angiography (CCTA) in patients with obesity at low tube voltage (100 kV) and low contrast agent concentration (270 mg/mL) using iterative reconstruction.

MATERIALS AND METHODS

A total of 48 patients with body mass index greater than 30 kg/m(2) were included and randomly divided into two groups. Group A received a traditional protocol (iopromide 370 mg/mL + 120 kV); group B received a protocol with low tube voltage (100 kV), low contrast agent concentration (270 mg/mL), and iterative reconstruction. The effective dose (ED), average attenuation values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the figure of merit (FOM), image quality scores, and the total iodine intake were compared.

RESULTS

No significant differences in average CT attenuations, SNR, CNR, and subjective scores were noticed between the two groups (P > 0.05), whereas the FOM of group B was significantly higher than that of group A. Effective radiation dose, total iodine, and iodine injection rate in group B were lower than those of group A (P <0.01).

CONCLUSIONS

In patients with obesity, isotonic contrast agent with low iodine concentration and low-dose CCTA were feasible. Substantial reduction in radiation dose and the iodine intake could be achieved without compromising the image quality.

Pediatric Head Trauma

Traumatic brain injury (TBI) refers to a spectrum of brain injury that can result in significant morbidity and mortality in pediatric patients. Pediatric head trauma is distinct from adult TBI. The purpose of this review article is to discuss pediatric TBI and current treatment modalities available.

Optimizing diagnostic imaging in the emergency department

While emergency diagnostic imaging use has increased significantly, there is a lack of evidence for corresponding improvements in patient outcomes. Optimizing emergency department (ED) diagnostic imaging has the potential to improve the quality, safety, and outcomes of ED patients, but to date, there have not been any coordinated efforts to further our evidence-based knowledge in this area. The objective of this article is to discuss six aspects of diagnostic imaging to provide background information on the underlying framework for the 2015 Academic Emergency Medicine consensus conference, "Diagnostic Imaging in the Emergency Department: A Research Agenda to Optimize Utilization." The consensus conference aims to generate a high priority research agenda for emergency diagnostic imaging that will inform the design of future investigations. The six components herein will serve as the group topics for the conference: 1) patient-centered outcomes research; 2) clinical decision rules; 3) training, education, and competency; 4) knowledge translation and barriers to image optimization; 5) use of administrative data; and 6) comparative effectiveness research: alternatives to traditional CT use.