Protocol Optimization for Functional Cardiac CT Imaging Using Noise Emulation in the Raw Data Domain

Background

Four-dimensional (4D) wide coverage computed tomography (CT) is an effective imaging modality for measuring the mechanical function of the myocardium. However, repeated CT measurement across a number of heartbeats is still a concern.

Purpose

A projection-domain noise emulation method is presented to generate accurate low-dose (mA modulated) 4D cardiac CT scans from high-dose scans, enabling protocol optimization to deliver sufficient image quality for functional cardiac analysis while using a dose level that is as low as reasonably achievable (ALARA).

Methods

Given a targeted low-dose mA modulation curve, the proposed noise emulation method injects both quantum and electronic noise of proper magnitude and correlation to the high-dose data in projection domain. A spatially varying (i.e., channel-dependent) detector gain term as well as its calibration method were proposed to further improve the noise emulation accuracy. To determine the ALARA dose threshold, a straightforward projection domain image quality (IQ) metric was proposed that is based on the number of projection rays that do not fall under the non-linear region of the detector response. Experiments were performed to validate the noise emulation method with both phantom and clinical data in terms of visual similarity, contrast-to-noise ratio (CNR), and noise-power spectrum (NPS).

Results

For both phantom and clinical data, the low-dose emulated images exhibited similar noise magnitude (CNR difference within 2%), artifacts, and texture to that of the real low-dose images. The proposed channel-dependent detector gain term resulted in additional increase in emulation accuracy. Using the proposed IQ metric, recommended kVp and mA settings were calculated for low dose 4D Cardiac CT acquisitions for patients of different sizes.

Conclusions

A detailed method to estimate system-dependent parameters for a raw-data based low dose emulation framework was described. The method produced realistic noise levels, artifacts, and texture with phantom and clinical studies. The proposed low-dose emulation method can be used to prospectively select patient-specific minimal-dose protocols for functional cardiac CT.

Qualitative and quantitative image quality of coronary CT angiography using photon-counting computed tomography: Standard and Ultra-high resolution protocols

PURPOSE

We aimed to identify the optimal reconstruction settings based on qualitative and quantitative image quality parameters on standard and ultra-high resolution (UHR) images using photon-counting CT (PCCT).

METHOD

We analysed 45 patients, 29 with standard and 16 with UHR acquisition, applying both smoother and sharper kernel settings. Coronary CT angiography images were performed on a dual-source PCCT system using standard (0.4/0.6 mm slice thickness, Bv40/Bv44 kernels, QIR levels 0-4) or UHR acquisition (0.2/0.4 mm slice thickness, Bv44/Bv56 kernels, QIR levels 0-4). Qualitative image quality was assessed using a 4-point Likert scale. Image noise (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in both the proximal and distal segments.

RESULTS

On standard resolution, larger slice thickness resulted in an average increase of 12.5 % in CNR, whereas sharper kernel led to an average 8.7 % decrease in CNR. Highest CNR was measured on 0.6 mm, Bv40, QIR4 images and lowest on 0.4 mm, Bv44, QIR0 images: 25.8 ± 4.1vs.8.3 ± 1.6 (p < 0.001). On UHR images, highest CNR was observed on 0.4 mm, Bv40, QIR4 and lowest on 0.2 mm, Bv56 and QIR0 images: 21.5 ± 3.9vs.3.6 ± 0.8 (p < 0.001). Highest qualitative image quality was found on images with Bv44 kernel and QIR level 3/4 with both slice thicknesses on standard reconstruction. Additionally, Bv56 with QIR4 on 0.2 mm slice thickness images showed highest subjective image quality. Preserved distal vessel visualization was detected using QIR 2-4, Bv56 and 0.2 mm slice thickness.

CONCLUSIONS

Photon-counting CT demonstrated high qualitative and quantitative image quality for the assessment of coronaries and stents.

Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study

PURPOSE

To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality.

METHOD

A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes.

RESULTS

The DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis.

CONCLUSION

Despite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn't favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics.

Effect of Laryngeal Mask Airway on Image Quality in Pediatric Patients Undergoing Brain Magnetic Resonance Imaging: A Randomized Controlled Trial

Background

Magnetic resonance imaging (MRI) is highly sensitive to motion, resulting in artifacts and lowering image quality. Laryngeal mask airway (LMA) provides numerous advantages over endotracheal tubes as it reduces laryngospasm, coughing, and the risk of postoperative desaturation.

Objectives

We aimed to compare LMA with oral airway for airway management during brain MRI in terms of reducing motion artifacts, which can improve image quality.

Methods

This randomized, controlled, double-blind trial was carried out on 40 pediatrics aged 1 - 18 years, American Society of Anesthesiologists (ASA) physical status І and П undergoing brain MRI. Patients were randomized into two equal groups according to the airway method, the control (Guedel oral airway) group and the LMA group. A compatible anesthesia machine was used to provide O2 and sevoflurane 2% - 4%.

Results

The mean MRI image quality score was significantly higher in the LMA group than in the control group (26.10 ± 3.97 versus 18.60 ± 5.30, P < 0.001). Mean arterial blood pressure and heart rate were significantly lower in the LMA group than in the control group at all study times except at baseline and immediate post-extubation (P < 0.05). Cough was significantly lower in LMA than in the control group (15% vs. 50%, P = 0.040). Airway complications (sore throat, laryngeal spasm, and bronchospasm), nausea, and vomiting did not have a significantly different between the two groups.

Conclusions

Compared to Guedel oral airway, using LMA for airway management in pediatrics undergoing MRI scans improved the image quality with less cough and better hemodynamics.

An assessment of the dose and image quality difference between AP and PA positioned adult radiographic knee examinations

INTRODUCTION

Knee X-rays are a standard examination to diagnose multiple conditions ranging from traumatic injuries, degeneration, and cancer. This study explores the differences between adult Anterior-Posterior (AP) and Posterior-Anterior (PA) weight-bearing knee examinations using absorbed radiation dose data and image quality.

METHODS

The study modelled and compared AP and PA knee X-ray radiation dose data using Monte-Carlo software, an Ion Chamber, and thermoluminescence dosemeters (TLDs) on a Rando phantom. Imaging parameters used were 66kVp, 4mAs, 100cm distance and 13 × 24cm collimation. The interval data analysis used a two-tailed t-test. The image quality of a sample of the AP and PA knee X-rays was assessed using Likert 5-point ordinal Image Quality Scoring (IQS) and the Wilcoxon matched pairs test.

RESULTS

Monte-Carlo modelling provided limited results; the Ion Chamber data for absorbed dose provided no variation between AP and PA positions but was similar to the AP TLD dose. The absorbed doses recorded with batches of TLDs demonstrated a 27.4% reduction (46.1μGy; p=0.01) in Skin Entrance Dose (ESD) and 9 - 58% dose reduction (1.6 - 16.4μGy; p=0.00-0.2) to the tissues and organs while maintaining diagnostic image quality (p=0.67).

CONCLUSION

The study has highlighted the various challenges of using different dosimetry approaches to measure absorbed radiation dose in extremity (knee) X-ray imaging. The Monte-Carlo simulated absorbed knee dose was overestimated, but the simulated body organ/tissue doses were lower than the actual TLD absorbed doses. The Ion Chamber absorbed doses did not differentiate between the positions. The TLD organ/tissue absorbed doses demonstrated a reduction in dose in the PA position compared to the AP position, without a detrimental effect on image quality. The study findings in laboratory conditions raise awareness of opportunities and potential to lower radiation dose, with further study replicated in a clinical site recommended.

Image Quality Classification for Automated Visual Evaluation of Cervical Precancer

Image quality control is a critical element in the process of data collection and cleaning. Both manual and automated analyses alike are adversely impacted by bad quality data. There are several factors that can degrade image quality and, correspondingly, there are many approaches to mitigate their negative impact. In this paper, we address image quality control toward our goal of improving the performance of automated visual evaluation (AVE) for cervical precancer screening. Specifically, we report efforts made toward classifying images into four quality categories ("unusable", "unsatisfactory", "limited", and "evaluable") and improving the quality classification performance by automatically identifying mislabeled and overly ambiguous images. The proposed new deep learning ensemble framework is an integration of several networks that consists of three main components: cervix detection, mislabel identification, and quality classification. We evaluated our method using a large dataset that comprises 87,420 images obtained from 14,183 patients through several cervical cancer studies conducted by different providers using different imaging devices in different geographic regions worldwide. The proposed ensemble approach achieved higher performance than the baseline approaches.

Artifacts in Optical Coherence Tomography Angiography

We performed a comprehensive search of the published literature in PubMed and Google Scholar to identify types, prevalence, etiology, clinical impact, and current methods for correction of various artifacts in optical coherence tomography angiography (OCTA) images. We found that the prevalence of OCTA image artifacts is fairly high. Artifacts associated with eye motion, misidentification of retinal layers, projections, and low optical coherence tomography signal are the most prevalent types. Artifacts in OCTA images are the major limitations of this diagnostic modality in clinical practice and identification of these artifacts and measures to mitigate them are essential for correct diagnosis and follow-up of patients.

An Improved Method of Automated Noise Measurement System in CT Images

Background:

It is necessary to have an automated noise measurement system working accurately to optimize dose in computerized tomography (CT) examinations.

Objective:

This study aims to develop an algorithm to automate noise measurement that can be implemented in CT images of all body regions.

Materials and Methods:

In this retrospective study, our automated noise measurement method consists of three steps as follows: the first is segmenting the image of the patient. The second is developing a standard deviation (SD) map by calculating the SD value for each pixel with a sliding window operation. The third step is estimating the noise as the smallest SD from the SD map. The proposed method was applied to the images of a homogenous phantom and a full body adult anthropomorphic phantom, and retrospectively applied to 27 abdominal images of patients.

Results:

For a homogeneous phantom, the noises calculated using our proposed and previous algorithms have a linear correlation with R² = 0.997. It is found that the noise magnitude closely follows the magnitude of the water equivalent diameter (D_w) in all body regions. The proposed algorithm is able to distinguish the noise magnitude due to variations in tube currents and different noise suppression techniques such as strong, standard, mild, and weak ones in a reconstructed image using the AIDR 3D algorithm.

Conclusion:

An automated noise calculation has been proposed and successfully implemented in all body regions. It is not only accurate and easy to implement but also not influenced by the subjectivity of user.

Super-Resolution in Digital Breast Tomosynthesis: Limitations of the Conventional System Design and Strategies for Optimization

Our previous work explored the use of super-resolution as a way to improve the visibility of calcifications in digital breast tomosynthesis. This paper demonstrates that there are anisotropies in super-resolution throughout the reconstruction, and investigates new motion paths for the x-ray tube to suppress these anisotropies. We used a theoretical model of a sinusoidal test object to demonstrate the existence of the anisotropies. In addition, high-frequency test objects were simulated with virtual clinical trial (VCT) software developed for breast imaging. The simulated objects include a lead bar pattern phantom as well as punctate calcifications in a breast-like background. In a conventional acquisition geometry in which the source motion is directed laterally, we found that super-resolution is not achievable if the frequency is oriented in the perpendicular direction (posteroanteriorly). Also, there are positions, corresponding to various slices above the breast support, at which super-resolution is inherently not achievable. The existence of these anisotropies was validated with VCT simulations. At locations predicted by theoretical modeling, the bar pattern phantom showed aliasing, and the spacing between individual calcifications was not properly resolved. To show that super-resolution can be optimized by re-designing the acquisition geometry, we applied our theoretical model to the analysis of new motion paths for the x-ray tube; specifically, motions with more degrees of freedom and with more rapid pulsing (submillimeter spacing) between source positions. These two strategies can be used in combination to suppress the anisotropies in super-resolution.

Proposing Rapid Source Pulsing for Improved Super-Resolution in Digital Breast Tomosynthesis

Our previous work showed that digital breast tomosynthesis (DBT) systems are capable of super-resolution, or subpixel resolution relative to the detector. Using a bar pattern phantom, it is possible to demonstrate that there are anisotropies in super-resolution throughout the reconstruction. These anisotropies are lessened in acquisition geometries with narrow spacing between source positions. This paper demonstrates that by re-arranging the source positions in the scan, the anisotropies can be minimized even further. To this end, a theoretical model of the reconstruction of a high-frequency sinusoidal test object was developed from first principles. We modeled the effect of clustering additional source positions around each conventional source position in fine increments (submillimeter). This design can be implemented by rapidly pulsing the source during a continuous sweep of the x-ray tube. It is shown that it is not possible to eliminate the anisotropies in a conventional DBT system with uniformly-spaced source positions, even if the increments of spacing are narrower than those used clinically. However, super-resolution can be achieved everywhere if the source positions are re-arranged in clusters with submillimeter spacing. Our previous work investigated a different approach for optimizing super-resolution through the use of detector motion perpendicular to the breast support. The advantage of introducing rapid source pulsing is that detector motion is no longer required; this mitigates the need for a thick detector housing, which may be cumbersome for patient positioning.

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and excluding outliers. By performing QA on large-scale, previously unlabeled testing data, categorical QA scores (e.g. "successful" versus "unsuccessful") can be generated. Unfortunately, the precious use of resources for human in-the-loop QA scores are not typically reused in medical image machine learning, especially to train a deep neural network for image segmentation. Herein, we perform a pilot study to investigate if the QA labels can be used as supplementary supervision to augment the training process in a semi-supervised fashion. In this paper, we propose a semi-supervised multi-organ segmentation deep neural network consisting of a traditional segmentation model generator and a QA involved discriminator. An existing 3-D abdominal segmentation network is employed, while the pre-trained ResNet-18 network is used as discriminator. A large-scale dataset of 2027 volumes are used to train the generator, whose 2-D montage images and segmentation mask with QA scores are used to train the discriminator. To generate the QA scores, the 2-D montage images were reviewed manually and coded 0 (success), 1 (errors consistent with published performance), and 2 (gross failure). Then, the ResNet-18 network was trained with 1623 montage images in equal distribution of all three code labels and achieved an accuracy 94% for classification predictions with 404 montage images withheld for the test cohort. To assess the performance of using the QA supervision, the discriminator was used as a loss function in a multi-organ segmentation pipeline. The inclusion of QA-loss function boosted performance on the unlabeled test dataset from 714 patients to 951 patients over the baseline model. Additionally, the number of failures decreased from 606 (29.90%) to 402 (19.83%). The contributions of the proposed method are three-fold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional "true/false", and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method. The use of QA-inspired loss functions represents a promising area of future research and may permit tighter integration of supervised and semi-supervised learning.

Optimization of exposure parameters and relationship between subjective and technical image quality in cone-beam computed tomography

Purpose

This study was performed to investigate the effect of exposure parameters on image quality obtained using a cone-beam computed tomography (CBCT) scanner and the relationship between physical factors and clinical image quality depending on the diagnostic task.

Materials and Methods

CBCT images of a SedentexCT IQ phantom and a real skull phantom were obtained under different combinations of tube voltage and tube current (Alphard 3030 CBCT scanner, 78–90 kVp and 2–8 mA). The images obtained using a SedentexCT IQ phantom were analyzed technically, and the physical factors of image noise, contrast resolution, spatial resolution, and metal artifacts were measured. The images obtained using a real skull phantom were evaluated for each diagnostic task by 6 oral and maxillofacial radiologists, and each setting was classified as acceptable or unacceptable based on those evaluations. A statistical analysis of the relationships of exposure parameters and physical factors with observer scores was conducted.

Results

For periapical diagnosis and implant planning, the tube current of the acceptable images was significantly higher than that of the unacceptable images. Image noise, the contrast-to-noise ratio (CNR), the line pair chart on the Z axis, and modulation transfer function (MTF) values showed statistically significant differences between the acceptable and unacceptable image groups. The cut-off values obtained using receiver operating characteristic curves for CNR and MTF 10 were useful for determining acceptability.

Conclusion

Tube current had a major influence on clinical image quality. CNR and MTF 10 were useful physical factors that showed significantly associations with clinical image quality.

Image quality optimization of narrow detector dental computed tomography for paediatric patients

OBJECTIVES

Dental CBCT exposure parameters should be optimized according to patient-specific indications, mainly for children that are most vulnerable to harmful effects of ionizing radiation. The aim of this study was to determine optimized kV settings for paediatric acquisitions for a dental CBCT device.

METHODS

Clinical and quantitative evaluations of image quality were performed using 5 and 10 years old (y/o) anthropomorphic phantoms. Technical evaluation was performed with the SEDENTEXCT-IQ phantom. Images were obtained using a PaX-i3D Green CBCT (Vatech, Korea) device, combining tube voltages ranging from 85 to 110 kV and 2 fields of view (FOVs: 21 × 19 and 12 × 9 cm), while maintaining the radiation dose fixed by adjusting the mA accordingly. Clinically, observers assessed images based on overall quality, sharpness, contrast, artefacts, and noise. For quantitative evaluation, mean grey value shift, % increase standard deviation, % beam-hardening and contrast-to-noise ratio were calculated. For technical evaluation, segmentation accuracy, contrast-to-noise ratio and full width at half maximum were measured. Biplot graphs were used to choose representative parameters, from which the best kV was selected for each protocol and evaluation. kV values that had no statistical differences (p > 0.05) with the best kV chosen were considered as having the same quality.

RESULTS

Clinically, 95 kV was found as a cut-off value. From the quantitative aspect, 85 kV (p < 0.05) showed the worst quality, except in 12 × 9 cm 5 y/o. Technically, 85 and 110 kV in the large FOV showed significantly worse quality for the large FOV.

CONCLUSION

For paediatric indications, 95 kV or higher (and correspondingly low mA values) was found as optimal.

Personalization of X-Ray Tube Motion in Digital Breast Tomosynthesis Using Virtual Defrise Phantoms

In digital breast tomosynthesis (DBT), projection images are acquired as the x-ray tube rotates in the plane of the chest wall. We constructed a prototype next-generation tomosynthesis (NGT) system that has an additional component of tube motion in the perpendicular direction (i.e., posteroanterior motion). Our previous work demonstrated the advantages of the NGT system using the Defrise phantom. The reconstruction shows higher contrast and fewer blurring artifacts. To expand upon that work, this paper analyzes how image quality can be further improved by customizing the motion path of the x-ray tube based on the object being imaged. In simulations, phantoms are created with realistic 3D breast outlines based on an established model of the breast under compression. The phantoms are given an internal structure similar to a Defrise phantom. Two tissue types (fibroglandular and adipose) are arranged in a square-wave pattern. The reconstruction is analyzed as a binary classification task using thresholding to segment the two tissue types. At various thresholds, the classification of each voxel in the reconstruction is compared against the phantom, and receiver operating characteristic (ROC) curves are calculated. It is shown that the area under the ROC curve (AUC) is dependent on the x-ray tube trajectory. The trajectory that maximizes AUC differs between phantoms. In conclusion, this paper demonstrates that the acquisition geometry in DBT should be personalized to the object being imaged in order to optimize the image quality.

Image Artifacts in Optical Coherence Tomography Angiography Among Patients With Multiple Sclerosis

PURPOSE

To evaluate artifacts in optical coherence tomography angiography (OCT-A) images of multiple sclerosis (MS) patients and healthy controls.

MATERIALS AND METHODS

This was a prospective cross-sectional study conducted at the Department of Neurology and the Wilmer Eye Institute at Johns Hopkins Hospital. Subjects included patients with an established diagnosis of MS and healthy volunteers. OCT-A was performed using Spectralis® OCT-A prototype, OCT2 (Heidelberg, Germany). The type and frequency of artifacts, the clinical factors associated with them, and their impact on vessel density measurements were assessed.

RESULTS

Overall, 385 images from 102 participants were analyzed. The majority of images (97.1%) had some degree of artifact. The most frequent was motion artifact (96.3%), followed by blinking (51.9%), and loss of focus (25.1%). MS patients were more likely to have any artifact vs. controls (OR [95% CI], 3.83 [1.12-12.92]), and were more likely to have motion artifacts with longer disease duration (OR [95% CI], 1.11 [1.03-1.20]) or history of optic neuritis (OR [95% CI], 4.24 [1.19-15.16]). The relative area occupied by the artifact was found to underestimate vessel density measurements in both MS patients and controls.

CONCLUSIONS

Artifacts are common with OCT-A imaging using this particular Spectralis® OCT-A prototype and can impact quantitative vascular density metrics. Future studies should review images for artifacts before drawing definitive conclusions.

An analysis of effective dose optimization and its impact on image quality and diagnostic efficacy relating to dental cone beam computed tomography (CBCT)

The potential of high resolution, three-dimensional (3D) images which overcome limitations such as superimposition and anatomical noise of two-dimensional (2D) conventional imaging, has made cone beam computed tomography (CBCT) an increasingly popular imaging modality in many dental applications. It is in light of the increasingly prevalent use of CBCT, particularly in a primary dental care setting, that the goal of this review is to investigate what evidence-based guidance is available to the clinician to justify and reduce radiation risk of this higher dose imaging modality while maintaining diagnostically acceptable images. To this end, the literature on radiation dose and related patient risk was comprehensively investigated, before an analysis of the ways in which dose can be optimized and the implications that optimization has on image quality was discussed. Finally, although it is accepted that CBCT has the potential to improve diagnosis, it is uncertain if its use has positive ramifications on issues of diagnostic efficacy, including clinical decision-making and patient outcome. In order to investigate these issues, the levels of evidence of the existing studies and their validity were assessed. On review of the available literature, it is evident that there is limited practical advice available to dentists regarding dose optimization and any existing protocols may not be readily transferable to every CBCT machine, the manufacturers' role is not often conducive to dose limitation and that the bulk of evidence is at lower levels of evidence. Furthermore, there is minimal supporting evidence to suggest an impact of CBCT on diagnostic thinking and consequent choice of treatment and no evidence of a positive effect of CBCT on patient outcome.

Analysis of Volume Overestimation Artifacts in the Breast Outline Segmentation in Tomosynthesis

In digital breast tomosynthesis (DBT), the reconstruction is calculated from x-ray projection images acquired over a small range of angles. One step in the reconstruction process is to identify the pixels that fall outside the shadow of the breast, to segment the breast from the background (air). In each projection, rays are back-projected from these pixels to the focal spot. All voxels along these rays are identified as air. By combining these results over all projections, a breast outline can be determined for the reconstruction. This paper quantifies the accuracy of this breast segmentation strategy in DBT. In this study, a physical phantom modeling a breast under compression was analyzed with a prototype next-generation tomosynthesis (NGT) system described in previous work. Multiple wires were wrapped around the phantom. Since the wires are thin and high contrast, their exact location can be determined from the reconstruction. Breast parenchyma was portrayed outside the outline defined by the wires. Specifically, the size of the phantom was overestimated along the posteroanterior (PA) direction; i.e., perpendicular to the plane of conventional source motion. To analyze how the acquisition geometry affects the accuracy of the breast outline segmentation, a computational phantom was also simulated. The simulation identified two ways to improve the segmentation accuracy; either by increasing the angular range of source motion laterally or by increasing the range in the PA direction. The latter approach is a unique feature of the NGT design; the advantage of this approach was validated with our prototype system.

Application of a newly developed software program for image quality assessment in cone-beam computed tomography

PURPOSE

The purpose of this study was to apply a newly developed free software program, at low cost and with minimal time, to evaluate the quality of dental and maxillofacial cone-beam computed tomography (CBCT) images.

MATERIALS AND METHODS

A polymethyl methacrylate (PMMA) phantom, CQP-IFBA, was scanned in 3 CBCT units with 7 protocols. A macro program was developed, using the free software ImageJ, to automatically evaluate the image quality parameters. The image quality evaluation was based on 8 parameters: uniformity, the signal-to-noise ratio (SNR), noise, the contrast-to-noise ratio (CNR), spatial resolution, the artifact index, geometric accuracy, and low-contrast resolution.

RESULTS

The image uniformity and noise depended on the protocol that was applied. Regarding the CNR, high-density structures were more sensitive to the effect of scanning parameters. There were no significant differences between SNR and CNR in centered and peripheral objects. The geometric accuracy assessment showed that all the distance measurements were lower than the real values. Low-contrast resolution was influenced by the scanning parameters, and the 1-mm rod present in the phantom was not depicted in any of the 3 CBCT units. Smaller voxel sizes presented higher spatial resolution. There were no significant differences among the protocols regarding artifact presence.

CONCLUSION

This software package provided a fast, low-cost, and feasible method for the evaluation of image quality parameters in CBCT.

Fundamental limits of image registration performance: Effects of image noise and resolution in CT-guided interventions

PURPOSE

In image-guided procedures, image acquisition is often performed primarily for the task of geometrically registering information from another image dataset, rather than detection / visualization of a particular feature. While the ability to detect a particular feature in an image has been studied extensively with respect to image quality characteristics (noise, resolution) and is an ongoing, active area of research, comparatively little has been accomplished to relate such image quality characteristics to registration performance.

METHODS

To establish such a framework, we derived Cramer-Rao lower bounds (CRLB) for registration accuracy, revealing the underlying dependencies on image variance and gradient strength. The CRLB was analyzed as a function of image quality factors (in particular, dose) for various similarity metrics and compared to registration accuracy using CT images of an anthropomorphic head phantom at various simulated dose levels. Performance was evaluated in terms of root mean square error (RMSE) of the registration parameters.

RESULTS

Analysis of the CRLB shows two primary dependencies: 1) noise variance (related to dose); and 2) sum of squared image gradients (related to spatial resolution and image content). Comparison of the measured RMSE to the CRLB showed that the best registration method, RMSE achieved the CRLB to within an efficiency factor of 0.21, and optimal estimators followed the predicted inverse proportionality between registration performance and radiation dose.

CONCLUSIONS

Analysis of the CRLB for image registration is an important step toward understanding and evaluating an intraoperative imaging system with respect to a registration task. While the CRLB is optimistic in absolute performance, it reveals a basis for relating the performance of registration estimators as a function of noise content and may be used to guide acquisition parameter selection (e.g., dose) for purposes of intraoperative registration.

Analysis of the priority of anatomic structures according to the diagnostic task in cone-beam computed tomographic images

Purpose

This study was designed to evaluate differences in the required visibility of anatomic structures according to the diagnostic tasks of implant planning and periapical diagnosis.

Materials and Methods

Images of a real skull phantom were acquired under 24 combinations of different exposure conditions in a cone-beam computed tomography scanner (60, 70, 80, 90, 100, and 110 kV and 4, 6, 8, and 10 mA). Five radiologists evaluated the visibility of anatomic structures and the image quality for diagnostic tasks using a 6-point scale.

Results

The visibility of the periodontal ligament space showed the closest association with the ability to use an image for periapical diagnosis in both jaws. The visibility of the sinus floor and canal wall showed the closest association with the ability to use an image for implant planning. Variations in tube voltage were associated with significant differences in image quality for all diagnostic tasks. However, tube current did not show significant associations with the ability to use an image for implant planning.

Conclusion

The required visibility of anatomic structures varied depending on the diagnostic task. Tube voltage was a more important exposure parameter for image quality than tube current. Different settings should be used for optimization and image quality evaluation depending on the diagnostic task.

Survey of Thoracic CT Protocols and Technical Parameters in Korean Hospitals: Changes before and after Establishment of Thoracic CT Guideline by Korean Society of Thoracic Radiology in 2008

We retrospectively reviewed the thoracic CT scan protocols and technical parameters obtained from hospitals in Korea, one group during May 2007 (n = 100) and the other group during January 2012 (n = 173), before and after the establishment of the thoracic CT Guideline in 2008. Each group was also divided into two subgroups according to the health care delivery level, i.e. the "A" subgroup from primary and the "B" subgroup from secondary and tertiary care hospitals. When comparing the data from 2007 and 2012, the tube current decreased from 179.1 mAs to 137.2 mAs. The scan interval decreased from 6.4 mm to 4.8 mm. Also, the insufficient scan range decreased from 19.0% to 8.7%, and the suboptimal quality scans decreased from 33.0% to 5.2%. Between groups A and B, group B had lower tube voltages, smaller scan thicknesses, and smaller scan intervals. However, group B had more phase numbers. In terms of the suboptimal quality scans, a decrease was seen in both groups. In conclusion, during the five-year time period between 2007 and 2012, a reduction in the tube current values was seen. And the overall image quality improved over the same time period. We assume that these changes are attributed to the implementation of the thoracic CT guideline in 2008.