Oncology-specific radiation dose and image noise reference levels in adult abdominal-pelvic CT

Optimizing CT Imaging Parameters: Implications for Diagnostic Accuracy in Nuclear Medicine

X-ray computed tomography (CT) is an important companion modality in molecular imaging, offering attenuation correction (AC) of single-photon emission computed tomography (SPECT) - and positron emission tomography (PET)-data, topographic information in scans as well as changes in morphology in serial follow-up studies. Image quality plays a critical role in delivering an acceptable diagnosis and in medical treatment planning. Variability in protocols can present a considerable challenge in achieving consistent image quality within departments. The differences in CT scanning protocol metrics established by various manufacturers and across different generations of scanners can contribute to this issue, making the standardization of image quality a complex task. This review aims to present relevant literature herein and provide an introduction of the CT imaging parameters, including acquisition factors, reconstruction algorithms, and relevant image quality metrics, and discuss possible ways to implement a robust CT protocol review process in a nuclear medicine department. We also evaluate the potential of iterative reconstruction (IR) and deep learning (DL) for enhancing image quality and minimizing exposure doses. This article points to the need for periodic audit of image quality to guarantee that CT protocols are suited for the intended purpose. Through the creation of local diagnostic reference levels and monitoring performance through protocol management, physicians may aim at delivering high quality imaging services consistently adhering to the principles of ALARA and reduction of dose for both patients and workers.

Ranking the Relative Importance of Image Quality Features in CT by Consensus Survey

OBJECTIVE

This study sought to determine consensus opinions from subspecialty radiologists and imaging physicists on the relative importance of image quality features in CT.

METHODS

A prospective survey of subspecialty radiologists and medical physicists was conducted to collect consensus opinions on the relative importance of 10 image quality features: axial sharpness, blooming, contrast, longitudinal sharpness, low-contrast axial sharpness, metal artifact, motion, noise magnitude, noise texture, and streaking. The survey was first sent to subspecialty radiologists in volunteer leadership roles in the ACR and RSNA, thereafter relying on snowball sampling. Surveyed subspecialties were abdominal, cardiac, emergency, musculoskeletal, neuroradiology, pediatric, and thoracic radiology and medical physics. Individual respondents' ratings were normalized for calculation of mean normalized ratings and priority rankings for each feature within subspecialties. Also calculated were intraclass correlation coefficients across image quality features within subspecialties and analysis of variance across subspecialties within each feature.

RESULTS

Most subspecialties had moderate to excellent intraclass agreement. For every radiology subspecialty except musculoskeletal, motion was the most important image quality feature. There was agreement across subspecialties that axial sharpness and contrast are only moderately important. There was disagreement across subspecialties on the relative importance of noise magnitude. Blooming was highly important to cardiac radiologists, and noise texture was highly important to musculoskeletal radiologists.

CONCLUSION

Image quality preferences differ based on clinical tasks and challenges in each anatomical radiology subspecialty. CT image analysis and development of quantitative measures of quality and protocol optimization-and related policy initiatives-should be specific to radiology subspecialty.

Quality assessment of abdominal CT images: an improved ResNet algorithm with dual-attention mechanism

OBJECTIVES

To enhance medical image classification using a Dual-attention ResNet model and investigate the impact of attention mechanisms on model performance in a clinical setting.

METHODS

We utilized a dataset of medical images and implemented a Dual-attention ResNet model, integrating self-attention and spatial attention mechanisms. The model was trained and evaluated using binary and five-level quality classification tasks, leveraging standard evaluation metrics.

RESULTS

Our findings demonstrated substantial performance improvements with the Dual-attention ResNet model in both classification tasks. In the binary classification task, the model achieved an accuracy of 0.940, outperforming the conventional ResNet model. Similarly, in the five-level quality classification task, the Dual-attention ResNet model attained an accuracy of 0.757, highlighting its efficacy in capturing nuanced distinctions in image quality.

CONCLUSIONS

The integration of attention mechanisms within the ResNet model resulted in significant performance enhancements, showcasing its potential for improving medical image classification tasks. These results underscore the promising role of attention mechanisms in facilitating more accurate and discriminative analysis of medical images, thus holding substantial promise for clinical applications in radiology and diagnostics.

An automated technique for global noise level measurement in CT image with a conjunction of image gradient

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.

Low pitch significantly reduces helical artifacts in abdominal CT

PURPOSE

High helical pitch scanning minimizes scan times in CT imaging, and thus also minimizes motion artifact and mis-synchronization with contrast bolus. However, high pitch produces helical artifacts that may adversely affect diagnostic image quality. This study aims to determine the severity and incidence of helical artifacts in abdominal CT imaging and their relation to the helical pitch scan parameter.

METHODS

To obtain a dataset with varying pitch values, we used CT exam data both internal and external to our center. A cohort of 59 consecutive adult patients receiving an abdomen CT examination at our center with an accompanying prior examination from an external center was selected for retrospective review. Two expert observers performed a blinded rating of helical artifact in each examination using a five-point Likert scale. The incidence of artifacts with respect to the helical pitch was assessed. A generalized linear mixed-effects regression (GLMER) model, with study arm (Internal or External to our center) and helical pitch as the fixed-effect predictor variables, was fit to the artifact ratings, and significance of the predictor variables was tested.

RESULTS

For a pitch of <0.75, the proportion of exams with mild or worse helical artifacts (Likert scores of 1-3) was <1%. The proportion increased to 16% for exams with pitch between 0.75 and 1.2, and further increased to 78% for exams with a pitch greater than 1.2. Pitch was significantly associated with helical artifact in the GLMER model (p = 2.8 × 10-9), while study arm was not a significant factor (p = 0.76).

CONCLUSION

The incidence and severity of helical artifact increased with helical pitch. This difference persisted even after accounting for the potential confounding factor of the center where the study was performed.

Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

OBJECTIVE

To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT.

METHODS

We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis.

RESULTS

Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths.

CONCLUSION

Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.

Making CT Dose Monitoring Meaningful: Augmenting Dose with Imaging Quality

Due to the concerns about radiation dose associated with medical imaging, radiation dose monitoring systems (RDMSs) are now utilized by many radiology providers to collect, process, analyze, and manage radiation dose-related information. Currently, most commercially available RDMSs focus only on radiation dose information and do not track any metrics related to image quality. However, to enable comprehensive patient-based imaging optimization, it is equally important to monitor image quality as well. This article describes how RDMS design can be extended beyond radiation dose to simultaneously monitor image quality. A newly designed interface was evaluated by different groups of radiology professionals (radiologists, technologists, and physicists) on a Likert scale. The results show that the new design is effective in assessing both image quality and safety in clinical practices, with an overall average score of 7.8 out of 10.0 and scores ranging from 5.5 to 10.0. Radiologists rated the interface highest at 8.4 out of 10.0, followed by technologists at 7.6 out of 10.0, and medical physicists at 7.5 out of 10.0. This work demonstrates how the assessment of the radiation dose can be performed in conjunction with the image quality using customizable user interfaces based on the clinical needs associated with different radiology professions.