Computed tomography and patient risk: Facts, perceptions and uncertainties

Size-specific dose estimates calculated using patient size measurements from scanned projection radiograph in high-resolution chest computed tomography

INTRODUCTION

Size-specific dose estimates (SSDE) are used to assess patient-specific radiation exposure in Computed Tomography (CT), complementing the volume CT dose index (CTDIvol). This study compared SSDE calculated using patient's lateral size from scan projection radiograph (SPR) with SSDE calculated using water equivalent diameter (Dw) from tomographic images in adult chest high-resolution CT (HRCT).

METHODS

In a single-centre study, the CTDIvol and dose-length product (DLP) were recorded from HRCT dose reports of adult patients. Lateral width (SLat), at the centre of the scan range, from the SPR was measured and the SSDE (SSDER) was calculated using conversion factors related to SLat. Average CT number, area of the slice, and lateral size of the patient (AxLat) were measured on the middle slice. The Dw and SSDE from Dw (SSDEW) were calculated. SSDER and SSDEW were compared using Wilcoxon signed rank test. Correlation between patient size and dosimetry parameters were investigated using Spearman Correlation test with statistical significance at P < 0.05. Bland-Altman plot was also used to test agreement between the two SSDE values.

RESULTS

Median CTDIvol, DLP, SSDER and SSDEW were 11.0 mGy, 372 mGy.cm, 11.6 mGy and 12.9 mGy, respectively. Small but statistically significant differences (P < 0.03) were found between SLat and AxLat as well as between SSDER and SSDEW. Bland-Altman analysis resulted in borderline agreement between SSDE values. Moderate correlations were observed between dosimetry quantities and patient size measurements (ρ > 0.640; P < 0.001). SSDEw showed statistically significant correlation (ρ = 0.587 and P < 0.001) with SSDER.

CONCLUSION

SSDER may be used to assess patients' absorbed radiation dose, before the scan, in adult chest HRCT. The median value of SSDER was about 10% lower than the median value SSDEW. However, the SSDEW should be used after the scan to establish effective dose and radiation risk to the patient.

Exploring communication gaps and parental needs during paediatric CT scan risk-benefit dialogue in resource-constrained facilities

BACKGROUND

Clear communication during informed consent is crucial in paediatric computed tomography (CT) procedures, particularly in resource-constrained settings. CT offers valuable diagnostic information but carries potential radiation risks, especially for paediatric patients. Parents play a critical role in decision-making, necessitating thorough risk-benefit discussions. This study aimed to explore parental experiences regarding risk-benefit communication during their children's CT scans in under-resourced healthcare facilities.

METHODS

A qualitative approach with a descriptive design was employed. Semi-structured interviews were conducted with 13 purposefully selected and consenting parents accompanying paediatric patients for CT scans at two public hospitals. Data were analysed using Tesch's eight-step method and ATLAS.ti software.

RESULTS

Participants were parents of children aged 0-10 years (8 males, 5 females), with 11 making their first visit to the CT department. Three main themes emerged: (1) Compromised consenting process, characterised by inadequate explanation of consent and limited risk-benefit communication; (2) Procedural information deficiency, including minimal communication about the procedure and lack of information on examination results; and (3) Preference for improved communication, with parents expressing a desire for comprehensive information and varied opinions on who should disseminate this information. Parents reported feeling uninformed, anxious, and unable to make well-informed decisions due to communication gaps.

CONCLUSIONS

Significant improvements are needed in risk-benefit communication during paediatric CT scans. Healthcare providers should use simplified language, visual aids, and patient-centred discussions to enhance understanding and reduce parental anxiety. Radiographers should allocate sufficient time for discussions, involve referring physicians when necessary, and document the informed consent process thoroughly. Addressing these issues can improve patient experiences and contribute to positive health outcomes in resource-constrained settings.

Assessment of organ dose for adult undergoing CT examinations: Comparison of three software applications using Monte Carlo simulation

Understanding organ dose during CT scans is crucial due to cancer risks from low-level radiation exposure. This study aims to analyze and compare different methods for estimating CT organ doses in adult male and female patients, assessing the compatibility of NCICT with standard phantoms and NCICT with body size adjustment with GEANT4 simulations. It also evaluates the impact of different CT manufacturers on organ dose calculations. Previous research used various phantoms to represent organ doses across age groups. This study utilizes DICOM images from real adult patients undergoing CT scans to evaluate organ dose using the GEANT4 simulation toolkit. A retrospective analysis of 240 CT scans (head, chest, and abdomen-pelvis) compared GEANT4 dose estimates to the software tool NCICT. Data from Siemens and Philips CT scanners were included. Organ doses for 34 organs were calculated using Siemens patient DICOM data, while Philips estimates made using only NCICT with body size adjustment. Statistical analysis assessed differences in organ doses by gender and scanner type. Organ doses for the brain, spinal cord, and liver were higher in females (48.1, 4.9, and 6.7 mGy) compared to males (42.5, 4.4, and 6.3 mGy). NCICT with body size adjustment estimates were more consistent with GEANT4 (differences up to 18%) compared to NCICT with standard phantoms (differences up to 46%). Notable variations were found between Siemens and Philips scanners, despite identical detector rows. Accurate models and scanner-specific differences are critical for reliable radiation dose assessments, emphasizing the need for tailored dosimetry to enhance patient safety.

Design and Characterisation of a Novel Z-Shaped Inductor-Based Wireless Implantable Sensor for Surveillance of Abdominal Aortic Aneurysm Post-Endovascular Repair

PURPOSE

An abdominal aortic aneurysm (AAA) is a dilation of the aorta over its normal diameter (> 3 cm). The minimally invasive treatment adopted uses a stent graft to be deployed into the aneurysm by a catheter to flow blood through it. However, this approach demands frequent monitoring using imaging modalities that involve radiation and contrast agents. Moreover, the multiple follow-ups are expensive, time-consuming, and resource-demanding for healthcare systems. This study proposes a novel wireless implantable medical sensor (WIMS) to measure the aneurysm growth after the endovascular repair.

METHODS

The proposed sensor is composed of a Z-shaped inductor, similar to a stent ring. The proposed design of the sensor is explored by investigating the inductance, resistance, and quality factor of different possible geometries related to a Z-shaped configuration, such as the height and number of struts. The study is conducted through a combination of numerical simulations and experimental tests, with the assessment being carried out at a frequency of 13.56 MHz.

RESULTS

The results show that a higher number of struts result in higher values of inductance and resistance. On the other hand, the increase in the number of struts decreases the quality factor of the Z-shaped inductor due to the presence of high resistance from the inductor. Moreover, it is observed that the influence of the number of struts present in the Z-shaped inductor tends to decrease for larger radii.

CONCLUSIONS

The numerical and experimental evaluation concludes the ability of the proposed sensor to measure the size of the aneurysm.

Quality of referrals and adherence to guidelines for adult patients with minimal to moderate head injuries in a selection of Norwegian hospitals

PURPOSE

This study aimed to assess adherence to the Scandinavian guidelines, the justification of referrals, and the quality of referrals of patients with mild, minimal, and moderate head injuries in a selection of Norwegian hospitals.

METHODS

We collected 283 head CT referrals for head trauma patients at one hospital trust in Norway in 2022. The data included the patients' sex, age, and the referral text. Six radiologists independently assessed all referrals using a registration form developed based on the Scandinavian guidelines for patients with mild, minimal, and moderate head injuries and general referral guidelines. Descriptive statistics was used to analyze data on adherence to guidelines, while Gwet's AC1/2 was used to test the agreement between the raters.

RESULTS

This study found that 65% of referrals were assessed to be justified according to the guideline by at least one rater, while 17% were rated justified outside the guideline. In 52%, at least one rater required more information. There was good to moderate interrater agreement.

CONCLUSIONS

Adherence to the Scandinavian guidelines and the quality of referrals of patients with mild, minimal, and moderate head injuries are low. Training and using S100B is recommended to improve the justification rate and quality of patient care.

Diagnostic value of CT scans in pediatric patients with acute non-traumatic altered mental status: a systematic review and meta-analysis

BACKGROUND

Computed tomography (CT) scans are widely used for evaluating children with acute atraumatic altered mental status (AMS) despite concerns about radiation exposure and limited diagnostic yield. This study aims to assess the efficacy of CT scans in this population and provide evidence-based recommendations.

METHODS

A systematic review was conducted according to PRISMA guidelines. Comprehensive searches were performed in PubMed, Embase, Cochrane Library, Scopus, and Web of Science for studies involving pediatric patients with acute atraumatic AMS undergoing head CT scans. Two independent reviewers conducted the literature search, extracted data, and assessed study quality.

RESULTS

From 4,739 identified studies, 13 met the inclusion criteria. The overall positive diagnostic yield of head CT scans was 35.9% (95% CI: 6.1%-65.7%). Subgroup analyses revealed that the diagnostic yield varied by clinical setting, age group, and presenting symptoms.

CONCLUSION

Head CT scans are frequently performed in pediatric patients with AMS, but their diagnostic usefulness is limited. Evidence-based guidelines and risk stratification methods are necessary to improve imaging utilization and minimize radiation exposure risks. What is Known • Computed tomography (CT) scans are commonly used to evaluate pediatric patients with acute atraumatic altered mental status (AMS). • There are concerns about radiation exposure from CT scans, especially in children due to their increased sensitivity and longer life expectancy. • Previous studies suggest a low diagnostic yield of CT scans in certain pediatric conditions, indicating potential overuse. What is New • This systematic review and meta-analysis specifically assess the diagnostic value of CT scans in pediatric patients with acute atraumatic AMS. • Findings reveal a relatively low positive diagnostic yield, indicating that CT scans may be overutilized in this population. • Subgroup analyses highlight variability in outcomes based on clinical setting, patient age, and presenting symptoms. • The study underscores the need for evidence-based guidelines and risk stratification tools to optimize imaging decisions and reduce unnecessary radiation exposure in children.

Artificial Intelligence in Pancreatic Intraductal Papillary Mucinous Neoplasm Imaging: A Systematic Review

Background

Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy.

Methods

Based on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field.

Results

Research on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n=11,44%) and included less than 250 patients (n=18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n=9,36%) or risk stratification (n=10,40%) rather than IPMN detection (n=5,20%) or IPMN segmentation (n=2,8%).

Conclusion

This systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation.

Beyond strong labels: Weakly-supervised learning based on Gaussian pseudo labels for the segmentation of ellipse-like vascular structures in non-contrast CTs

Deep learning-based automated segmentation of vascular structures in preoperative CT angiography (CTA) images contributes to computer-assisted diagnosis and interventions. While CTA is the common standard, non-contrast CT imaging has the advantage of avoiding complications associated with contrast agents. However, the challenges of labor-intensive labeling and high labeling variability due to the ambiguity of vascular boundaries hinder conventional strong-label-based, fully-supervised learning in non-contrast CTs. This paper introduces a novel weakly-supervised framework using the elliptical topology nature of vascular structures in CT slices. It includes an efficient annotation process based on our proposed standards, an approach of generating 2D Gaussian heatmaps serving as pseudo labels, and a training process through a combination of voxel reconstruction loss and distribution loss with the pseudo labels. We assess the effectiveness of the proposed method on one local and two public datasets comprising non-contrast CT scans, particularly focusing on the abdominal aorta. On the local dataset, our weakly-supervised learning approach based on pseudo labels outperforms strong-label-based fully-supervised learning (1.54% of Dice score on average), reducing labeling time by around 82.0%. The efficiency in generating pseudo labels allows the inclusion of label-agnostic external data in the training set, leading to an additional improvement in performance (2.74% of Dice score on average) with a reduction of 66.3% labeling time, where the labeling time remains considerably less than that of strong labels. On the public dataset, the pseudo labels achieve an overall improvement of 1.95% in Dice score for 2D models with a reduction of 68% of the Hausdorff distance for 3D model.

Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) applied to CAC scans can predict non-CHD events, including heart failure, atrial fibrillation, and stroke. We applied AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) of 5830 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) in the multi-ethnic study of atherosclerosis during 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow-up for AI-CAC vs. Agatston score was (0.784 vs. 0.701), (0.771 vs. 0.709), (0.789 vs. 0.712) and (0.816 vs. 0.729) (p < 0.0001 for all), respectively. AI-CAC plaque characteristics, including number, location, density, plus number of vessels, significantly improved CHD prediction in the CAC 1-100 cohort vs. Agatston Score. AI-CAC significantly improved the Agatston score for predicting all CVD events.

Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment

Currently, eight million people in the United States suffer from cancer and it is a major global health concern. Early detection and interventions are urgently needed for all cancers, including colorectal cancer. Colorectal cancer is the third most common type of cancer worldwide. Based on the diagnostic efforts to general awareness and lifestyle choices, it is understandable why colorectal cancer is so prevalent today. There is a notable lack of awareness concerning the impact of this cancer and its connection to lifestyle elements, as well as people sometimes mistaking symptoms for a different gastrointestinal condition. Artificial intelligence (AI) may assist in the early detection of all cancers, including colorectal cancer. The usage of AI has exponentially grown in healthcare through extensive research, and since clinical implementation, it has succeeded in improving patient lifestyles, modernizing diagnostic processes, and innovating current treatment strategies. Numerous challenges arise for patients with colorectal cancer and oncologists alike during treatment. For initial screening phases, conventional methods often result in misdiagnosis. Moreover, after detection, determining the course of which colorectal cancer can sometimes contribute to treatment delays. This article touches on recent advancements in AI and its clinical application while shedding light on why this disease is so common today.

Advancements and Challenges in the Image-Based Diagnosis of Lung and Colon Cancer: A Comprehensive Review

Image-based diagnosis has become a crucial tool in the identification and management of various cancers, particularly lung and colon cancer. This review delves into the latest advancements and ongoing challenges in the field, with a focus on deep learning, machine learning, and image processing techniques applied to X-rays, CT scans, and histopathological images. Significant progress has been made in imaging technologies like computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), which, when combined with machine learning and artificial intelligence (AI) methodologies, have greatly enhanced the accuracy of cancer detection and characterization. These advances have enabled early detection, more precise tumor localization, personalized treatment plans, and overall improved patient outcomes. However, despite these improvements, challenges persist. Variability in image interpretation, the lack of standardized diagnostic protocols, unequal access to advanced imaging technologies, and concerns over data privacy and security within AI-based systems remain major obstacles. Furthermore, integrating imaging data with broader clinical information is crucial to achieving a more comprehensive approach to cancer diagnosis and treatment. This review provides valuable insights into the recent developments and challenges in image-based diagnosis for lung and colon cancers, underscoring both the remarkable progress and the hurdles that still need to be overcome to optimize cancer care.

Traumatic brain injuries: a neuropsychological review

The best predictor of functional outcome in victims of traumatic brain injury (TBI) is a neuropsychological evaluation. An exponential growth of research into TBI has focused on diagnosis and treatment. Extant literature lacks a comprehensive neuropsychological review that is simultaneously scholarly and practical. In response, our group included, and went beyond a general overview of TBI's, which commonly include definition, types, severity, and pathophysiology. We incorporate reasons behind the use of particular neuroimaging techniques, as well as the most recent findings on common neuropsychological assessments conducted in TBI cases, and their relationship to outcome. In addition, we include tables outlining estimated recovery trajectories of different age groups, their risk factors and we encompass phenomenological studies, further covering the range of existing-promising tools for cognitive rehabilitation/remediation purposes. Finally, we highlight gaps in current research and directions that would be beneficial to pursue.

Localiser radiographs in CT: Current practice, radiation dose, image quality and clinical applications

INTRODUCTION

Survey or localiser radiographs are integral to CT imaging. However, the diverse functions and roles of the localiser radiograph are often obscure to radiographers and radiologists. This scoping review reports the full scope of localiser radiograph use and function in contemporary CT imaging.

METHODS

A scoping review was performed. A systematic literature search was conducted using four databases: MEDLINE, CINAHL, Emcare and Scopus from January 2013 to December 2023. Data extraction was conducted by two review authors and validated by a third reviewer. Thirty-six studies were included in this review.

RESULTS

Three major themes emerged: radiation dose management, image quality considerations and clinical protocol applications. Specifically, the number, order of selection and directions of localiser radiographs significantly impact patient dose and image quality; which are additionally impacted by off-centre patient positioning, which can influence the accuracy of body size estimates and CT numbers. Finally, the optimal selection of localiser radiographs, including exposure parameters (kVp, mAs), can be a part of clinical task-based imaging protocol optimisation.

CONCLUSIONS

The utilities of localiser radiographs in CT imaging are varied. It is salient that radiographers and radiologists understand their role and the impacts of poor application to ensure that radiation dose is minimised and image quality maximised through correct use. Radiographers and radiologists should also be aware of the impact of poor patient positioning on ACTM function, dose and image quality. Additionally, localiser radiographs should be used for clinical task-based protocol optimisation.

IMPLICATIONS FOR PRACTICE

The number, order of selection, direction, patient off-centring, and exposure parameters must be considered when utilising localiser radiographs as they impact dose, image quality, and protocol applications. It is essential for radiographers and radiologists to understand these impacts.

A comparative analysis of different augmentations for brain images

Deep learning (DL) requires a large amount of training data to improve performance and prevent overfitting. To overcome these difficulties, we need to increase the size of the training dataset. This can be done by augmentation on a small dataset. The augmentation approaches must enhance the model's performance during the learning period. There are several types of transformations that can be applied to medical images. These transformations can be applied to the entire dataset or to a subset of the data, depending on the desired outcome. In this study, we categorize data augmentation methods into four groups: Absent augmentation, where no modifications are made; basic augmentation, which includes brightness and contrast adjustments; intermediate augmentation, encompassing a wider array of transformations like rotation, flipping, and shifting in addition to brightness and contrast adjustments; and advanced augmentation, where all transformation layers are employed. We plan to conduct a comprehensive analysis to determine which group performs best when applied to brain CT images. This evaluation aims to identify the augmentation group that produces the most favorable results in terms of improving model accuracy, minimizing diagnostic errors, and ensuring the robustness of the model in the context of brain CT image analysis.

Exploring Patient Preferences for Information About CT Radiation Exposure: Bridging the Gap Between Patient Preference and Physician Practice

Background

CT scan utilizes ionizing radiation poses a danger to the patient's health. Thus, telling the patient about ionizing radiation would be critical in promoting shared decision-making and improving patient-doctor communication. However, few studies have examined this topic broadly.

Objective

The study was conducted to identify the frequency of physicians informing patients about the radiation risk before ordering a CT scan, as well as to examine the association between patients' demographic characteristics and their awareness of the radiation risks associated with CT scans.

Methods

A cross-sectional study was conducted among 387 patients who had undergone CT scans at a tertiary hospital in Riyadh, Saudi Arabia. Data were collected via phone interviews using a structured questionnaire. Chi-squared tests were employed to assess associations between patients' demographic characteristics and their awareness of CT scan radiation risks.

Results

When examining knowledge, 58% of patients knew that CT involves harmful radiation. This knowledge was significantly associated with higher education level and previous experience with CT scans. Regarding doctors' practice of providing information to patients about the scan, 344 (88.9%) patients indicated that their doctor had explained to them why they needed the scan. Only 28 (7.2%) patients stated that their doctor had mentioned the amount of radiation, and 74 (19.1%) patients indicated that doctors mentioned the risks associated with the radiation of the scan. Almost all patients (96.9%) preferred to be told about why they needed a CT scan.

Conclusion

The vast majority of patients who underwent CT scans did not receive enough information about the harm of the scans. However, most of them preferred to know about this harm.

Seeing the unseen: The role of bioimaging techniques for the diagnostic interventions in intervertebral disc degeneration

Intervertebral Disc Degeneration is a pathophysiological condition that primarily affects the spinal discs, causing back pain and neurological deficits. It is caused by the contribution of several factors such as genetic predisposition, age-related degeneration, and lifestyle choices like obesity and physical activity. Even though there are medications to treat pain, there is a lack of medicines for a complete cure. The main difficulty lies in poor diagnosis of the morphological and functional changes in the disc. With the ever-increasing research on bioimaging techniques, new techniques are being developed and repurposed to evaluate disc shape and composition, and their defects like thinning or deformities on the disc, leading to the proper diagnostic intervention in intervertebral disc degeneration. In this review, we aim to present a comprehensive overview of the imaging techniques used in the pre-clinical and clinical stages for the diagnosis of intervertebral disc degeneration. First, we will discuss about patho-anatomy and the pathophysiology of degenerative disc disease with the significance and a brief description of various dyes and tracers utilized for bioimaging. Then we will shed light on the latest advancements in diagnostic modalities in intervertebral disc degeneration; concluded by an analysis of the repercussions of the methodologies and experimental systems employed in identifying mechanisms and developing therapeutic strategies in intervertebral disc degeneration.

Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network

OBJECTIVE

To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve the effectiveness and precision of EC automatic segmentation and TN stage diagnosis.

METHODS

Attention U-Net, UperNet Swin, UNet++ and UNet were used to train the EC segmentation model to automatically segment the EC, esophagus, pericardium, aorta and MLN from CTA images of 182 patients with postoperative pathologically proven EC. The Dice similarity coefficient (DSC), sensitivity, and positive predictive value (PPV) were used to assess their segmentation effectiveness. The volume of EC was calculated using the segmentation results, and the outcomes and times of automatic and human segmentation were compared. All statistical analyses were completed using SPSS 25.0 software.

RESULTS

Among the four EC autosegmentation models, the UperNet Swin had the best autosegmentation results with a DSC of 0.7820 and the highest values of EC sensitivity and PPV. The esophagus, pericardium, aorta and MLN had DSCs of 0.7298, 0.9664, 0.9496 and 0.5091. The DSCs of the UperNet Swin were 0.6164, 0.7842, 0.8190, and 0.7259 for T1-4 EC. The volume of EC and its adjacent structures between the ground truth and UperNet Swin model were not significantly different.

CONCLUSIONS

The UperNet Swin showed excellent efficiency in autosegmentation and volume measurement of EC, MLN and its adjacent structures in different T stage, which can help to T and N stage diagnose EC and will save clinicians time and energy.

Incidence and Mortality Life-Attributable Risks for Patients Subjected to Recurrent CT Examinations and Cumulative Effective Dose Exceeding 100 mSv

Computed tomography (CT) multi-detector array has been heavily utilized over the past decade. While transforming an individual's diagnosis, the risk of developing pathogenesis as a result remains a concern. The main aim of this institutional cumulative effective dose (CED) review is to highlight the number of adult individuals with a record of CED ≥ 100 mSv over a time span of 5 years. Further, we aim to roughly estimate both incidence and mortality life-attributable risks (LARs) for the shortlisted individuals. CT studies performed over one year, in one dedicated trauma and emergency facility, were retrospectively retrieved and analyzed. Individuals with historical radiological CED ≥ 100 mSv were short-listed. LARs were defined and established based on organ, age and gender. Out of the 4406 CT studies reviewed, 22 individuals were found with CED ≥ 100 mSv. CED varied amongst the short-listed individuals, with the highest CED registered being 223.0 mSv, for a 57-year-old male, cumulated over an average study interval of 46.3 days. The highest median mortality risk was for females, 214 per 100,000 registered for the age group 51-60 years. While certain clinical indications and diseases require close follow-up using radiological examinations, the benefit-to-risk ratio should be carefully considered, particularly when CT is requested.

Artificial Intelligence Chatbots' Understanding of the Risks and Benefits of Computed Tomography and Magnetic Resonance Imaging Scenarios

PURPOSE

Patients may seek online information to better understand medical imaging procedures. The purpose of this study was to assess the accuracy of information provided by 2 popular artificial intelligence (AI) chatbots pertaining to common imaging scenarios' risks, benefits, and alternatives.

METHODS

Fourteen imaging-related scenarios pertaining to computed tomography (CT) or magnetic resonance imaging (MRI) were used. Factors including the use of intravenous contrast, the presence of renal disease, and whether the patient was pregnant were included in the analysis. For each scenario, 3 prompts for outlining the (1) risks, (2) benefits, and (3) alternative imaging choices or potential implications of not using contrast were inputted into ChatGPT and Bard. A grading rubric and a 5-point Likert scale was used by 2 independent reviewers to grade responses. Prompt variability and chatbot context dependency were also assessed.

RESULTS

ChatGPT's performance was superior to Bard's in accurately responding to prompts per Likert grading (4.36 ± 0.63 vs 3.25 ± 1.03 seconds, P < .0001). There was substantial agreement between independent reviewer grading for ChatGPT (κ = 0.621) and Bard (κ = 0.684). Response text length was not statistically different between ChatGPT and Bard (2087 ± 256 characters vs 2162 ± 369 characters, P = .24). Response time was longer for ChatGPT (34 ± 2 vs 8 ± 1 seconds, P < .0001).

CONCLUSIONS

ChatGPT performed superior to Bard at outlining risks, benefits, and alternatives to common imaging scenarios. Generally, context dependency and prompt variability did not change chatbot response content. Due to the lack of detailed scientific reasoning and inability to provide patient-specific information, both AI chatbots have limitations as a patient information resource.

Optimal image quality and radiation doses with optimal tube voltages/currents for pediatric anthropomorphic phantom brains

OBJECTIVE

Using pediatric anthropomorphic phantoms (APs), we aimed to determine the scanning tube voltage/current combinations that could achieve optimal image quality and avoid excessive radiation exposure in pediatric patients.

MATERIALS AND METHODS

A 64-slice scanner was used to scan a standard test phantom to determine the volume CT dose indices (CTDIvol), and three pediatric anthropomorphic phantoms (APs) with highly accurate anatomy and tissue-equivalent materials were studied. These specialized APs represented the average 1-year-old, 5-year-old, and 10-year-old children, respectively. The physical phantoms were constructed with brain tissue-equivalent materials having a density of ρ = 1.07 g/cm3, comprising 22 numbered 2.54-cm-thick sections for the 1-year-old, 26 sections for the 5-year-old, and 32 sections for the 10-year-old. They were scanned to acquire brain CT images and determine the standard deviations (SDs), effective doses (EDs), and contrast-to noise ratios (CNRs). The APs were scanned by 21 combinations of tube voltages/currents (80, 100, or 120 kVp/10, 40, 80, 120, 150, 200, or 250 mA) and rotation time/pitch settings of 1 s/0.984:1.

RESULTS

The optimal tube voltage/current combinations yielding optimal image quality were 80 kVp/80 mA for the 1-year-old AP; 80 kVp/120 mA for the 5-year-old AP; and 80 kVp/150 mA for the 10-year-old AP. Because these scanning tube voltages/currents yielded SDs, respectively, of 12.81, 13.09, and 12.26 HU, along with small EDs of 0.31, 0.34, and 0.31 mSv, these parameters and the induced values were expediently defined as optimal.

CONCLUSIONS

The optimal tube voltages/currents that yielded optimal brain image quality, SDs, CNRs, and EDs herein are novel and essentially important. Clinical translation of these optimal values may allow CT diagnosis with low radiation doses to children's heads.

Development of clinical prediction model to guide the use of CT head scans for non-traumatic Thai patient with seizure: A cross-sectional study

The aim of this study was to develop clinical predictor tools for guiding the use of computed tomography (CT) head scans in non-traumatic Thai patients presented with seizure. A prediction model using a retrospective cross-sectional design was conducted. We recruited adult patients (aged ≥ 18 years) who had been diagnosed with seizures by their physicians and had undergone CT head scans for further investigation. Positive CT head defined as the presence of any new lesion that related to the patient's presented seizure officially reported by radiologist. A total of 9 candidate predictors were preselected. The prediction model was developed using a full multivariable logistic regression with backward stepwise elimination. We evaluated the model's predictive performance in terms of its discriminative ability and calibration via AuROC and calibration plot. The application was then constructed based on final model. A total of 362 patients were included into the analysis which comprising of 71 patients with positive CT head findings and 291 patients with normal results. Six final predictors were identified including: Glasgow coma scale, the presence of focal neurological deficit, history of malignancy, history of CVA, Epilepsy, and the presence of alcohol withdrawal symptom. In terms of discriminative ability, the final model demonstrated excellent performance (AuROC of 0.82 (95% CI: 0.76-0.87)). The calibration plot illustrated a good agreement between observed and predicted risks. This prediction model offers a reliable tool for effectively reduce unnecessary use and instill confidence in supporting physicians in determining the need for CT head scans in non-traumatic patients with seizures.

Demonstrating the use of population level data to investigate trends in the rate, radiation dose and cost of Computed Tomography across clinical groups: Are there any areas of concern?

INTRODUCTION

Increases in computed tomography (CT) use may not always reflect clinical need or improve outcomes. This study aimed to demonstrate how population level data can be used to identify variations in care between patient groups, by analysing system-level changes in CT use around the diagnosis of new conditions.

METHODS

Retrospective repeated cross-sectional observational study using West Australian linked administrative records, including 504,723 adults diagnosed with different conditions in 2006, 2012 and 2015. For 90 days pre/post diagnosis, CT use (any and 2+ scans), effective dose (mSv), lifetime attributable risk (LAR) of cancer incidence and mortality from CT, and costs were assessed.

RESULTS

CT use increased from 209.4 per 1000 new diagnoses in 2006 to 258.0 in 2015; increases were observed for all conditions except neoplasms. Healthcare system costs increased for all conditions but neoplasms and mental disorders. Effective dose increased substantially for respiratory (+2.5 mSv, +23.1%, P < 0.001) and circulatory conditions (+2.1 mSv, +15.4%, P < 0.001). The LAR of cancer incidence and mortality from CT increased for endocrine (incidence +23.4%, mortality +18.0%) and respiratory disorders (+21.7%, +23.3%). Mortality LAR increased for circulatory (+12.1%) and nervous system (+11.0%) disorders. The LAR of cancer incidence and mortality reduced for musculoskeletal system disorders, despite an increase in repeated CT in this group.

CONCLUSIONS

Use and costs increased for most conditions except neoplasms and mental and behavioural disorders. More strategic CT use may have occurred in musculoskeletal conditions, while use and radiation burden increased for respiratory, circulatory and nervous system conditions. Using this high-level approach we flag areas requiring deeper investigation into appropriateness and value of care.

Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB

Background

Recent innovations are making radiology more advanced for patient and patient services. Under the immense burden of radiology practice, Artificial Intelligence (AI) assists in obtaining Computed Tomography (CT) images with less scan time, proper patient placement, low radiation dose (RD), and improved image quality (IQ). Hence, the aim of this study was to evaluate and compare the positioning accuracy, RD, and IQ of AI-based automatic and manual positioning techniques for CT kidney ureters and bladder (CT KUB).

Methods

This prospective study included 143 patients in each group who were referred for computed tomography (CT) KUB examination. Group 1 patients underwent manual positioning (MP), and group 2 patients underwent AI-based automatic positioning (AP) for CT KUB examination. The scanning protocol was kept constant for both the groups. The off-center distance, RD, and quantitative and qualitative IQ of each group were evaluated and compared.

Results

The AP group (9.66±6.361 mm) had significantly less patient off-center distance than the MP group (15.12±9.55 mm). There was a significant reduction in RD in the AP group compared with that in the MP group. The quantitative image noise (IN) was lower, with a higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the AP group than in the MP group (p<0.05). Qualitative IQ parameters such as IN, sharpness, and overall IQ also showed significant differences (p< 0.05), with higher scores in the AP group than in the MP group.

Conclusions

The AI-based AP showed higher positioning accuracy with less off-center distance (44%), which resulted in 12% reduction in RD and improved IQ for CT KUB imaging compared with MP.

Ultrasound quantification of pleural effusion volume in supine position: comparison of three model formulae

BACKGROUND

To investigate the accuracy of three model formulae for ultrasound quantification of pleural effusion (PE) volume in patients in supine position.

METHODS

A prospective study including 100 patients with thoracentesis and drainage of PE was conducted. Three model formulae (single section model, two section model and multi-section model) were used to calculate the PE volume. The correlation and consistency analyses between calculated volumes derived from three models and actual PE volume were performed.

RESULTS

PE volumes calculated by three models all showed significant linear correlations with actual PE volume in supine position (all p < 0.001). The reliability of multi-section model in predicting PE volume was significantly higher than that of single section model and slightly higher than that of two section model. When compared with actual drainage volume, the intra-class correlation coefficients (ICCs) of single section model, two section model and multi-section model were 0.72, 0.97 and 0.99, respectively. Significant consistency between calculated PE volumes by using two section model and multi-section model existed for full PE volume range (ICC 0.98).

CONCLUSION

Based on the convenience and accuracy of ultrasound quantification of PE volume, two section model is recommended for pleural effusion assessment in routine clinic, though different model formulae can be selected according to clinical needs.

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis

INTRODUCTION

Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).

METHODS

We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.

RESULTS

Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​< ​0.0001), and comparable to clinical risk factors (0.85, p ​= ​0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p ​< ​0.0001).

CONCLUSION

AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis

Background

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths.

Methods

We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score.

Results

During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342).

Conclusion

In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

Urgent Direct Access to Diagnostic Services for General Practitioners: Bridging the Gap in Cancer Diagnosis

Urgent direct access to diagnostic services for general practitioners (GPs) is a new pathway to capture any cancer diagnoses that may have been missed due to vague symptom presentations. Hence, GPs should look out for the key symptoms mentioned by NHS England that should prompt urgent direct access referrals for chest X-ray (CXR), computed tomography (CT) chest, MRI brain, ultrasound (US) abdomen and pelvis, and CT abdomen and pelvis. By implementing this approach, we can significantly reduce the time to diagnosis, while minimizing the number of visits to GP and specialist appointments prior to initiating investigations. However, the use of this pathway can only improve if access to diagnostic scans is improved. This needs to be done by ensuring all GPs in the country have access to directly request MRI brains, CT chest, abdomen, and pelvis. Further research into the impact of the urgent direct access pathway as well as investigating the number of GPs without access to these vital diagnostic services is required to fully improve and measure the progress of this referral pathway.

Preoperative and Postoperative Imaging and Outcome Scores for Osteochondral Lesion Repair of the Ankle

Cartilage lesions to the ankle joint are common and can result in pain and functional limitations. Surgical treatment aims to restore the damaged cartilage's integrity and quality. However, the current evidence for establishing best practices in ankle cartilage repair is characterized by limited quality and a low level of evidence. One of the contributing factors is the lack of standardized preoperative and postoperative assessment methods to evaluate treatment effectiveness and visualize repaired cartilage. This review article seeks to examine the importance of preoperative imaging, classification systems, patient-reported outcome measures, and radiological evaluation techniques for cartilage repair surgeries.

Implantable Pressure-Sensing Devices for Monitoring Abdominal Aortic Aneurysms in Post-Endovascular Aneurysm Repair

Over the past two decades, there has been extensive research into surveillance methods for the post-endovascular repair of abdominal aortic aneurysms, highlighting the importance of these technologies in supplementing or even replacing conventional image-screening modalities. This review aims to provide an overview of the current status of alternative surveillance solutions for endovascular aneurysm repair, while also identifying potential aneurysm features that could be used to develop novel monitoring technologies. It offers a comprehensive review of these recent clinical advances, comparing new and standard clinical practices. After introducing the clinical understanding of abdominal aortic aneurysms and exploring current treatment procedures, the paper discusses the current surveillance methods for endovascular repair, contrasting them with recent pressure-sensing technologies. The literature on three commercial pressure-sensing devices for post-endovascular repair surveillance is analyzed. Various pre-clinical and clinical studies assessing the safety and efficacy of these devices are reviewed, providing a comparative summary of their outcomes. The review of the results from pre-clinical and clinical studies suggests a consistent trend of decreased blood pressure in the excluded aneurysm sac post-repair. However, despite successful pressure readings from the aneurysm sac, no strong link has been established to translate these measurements into the presence or absence of endoleaks. Furthermore, the results do not allow for a conclusive determination of ongoing aneurysm sac growth. Consequently, a strong clinical need persists for monitoring endoleaks and aneurysm growth following endovascular repair.

Design and Characterisation of a Read-Out System for Wireless Monitoring of a Novel Implantable Sensor for Abdominal Aortic Aneurysm Monitoring

Abdominal aortic aneurysm (AAA) is a dilation of the aorta artery larger than its normal diameter (>3 cm). Endovascular aneurysm repair (EVAR) is a minimally invasive treatment option that involves the placement of a graft in the aneurysmal portion of the aorta artery. This treatment requires multiple follow-ups with medical imaging, which is expensive, time-consuming, and resource-demanding for healthcare systems. An alternative solution is the use of wireless implantable sensors (WIMSs) to monitor the growth of the aneurysm. A WIMS capable of monitoring aneurysm size longitudinally could serve as an alternative monitoring approach for post-EVAR patients. This study has developed and characterised a three-coil inductive read-out system to detect variations in the resonance frequency of the novel Z-shaped WIMS implanted within the AAA sac. Specifically, the spacing between the transmitter and the repeater inductors was optimised to maximise the detection of the sensor by the transmitter inductor. Moreover, an experimental evaluation was also performed for different orientations of the transmitter coil with reference to the WIMS. Finally, the FDA-approved material nitinol was used to develop the WIMS, the transmitter, and repeater inductors as a proof of concept for further studies. The findings of the characterisation from the air medium suggest that the read-out system can detect the WIMS up to 5 cm, regardless of the orientation of the Z-shape WIMS, with the detection range increasing as the orientation approaches 0°. This study provides sufficient evidence that the proposed WIMS and the read-out system can be used for AAA expansion over time.

Establishment of local diagnostic reference levels for computed tomography with cloud-based automated dose-tracking software in Türkiye

PURPOSE

The purpose of this study is to establish local diagnostic reference levels (LDRLs) for computed tomography (CT) procedures using cloud-based automated dose-tracking software.

METHODS

The study includes the dose data obtained from a total of 104,272 examinations performed on adult patients (>18 years) using 8 CT scanners over 12 months. The protocols included in our study were as follows: head CT without contrast, cervical spine CT without contrast, neck CT with contrast, chest CT without contrast, abdomen-pelvis CT without contrast, lumbar spine CT without contrast, high-resolution computed tomography (HRCT) of the chest, and coronary CT angiography (CTA). Dose data were collected using cloud-based automatic dose-tracking software. The 75th percentiles of the distributions of the median volume CT dose index (CTDIvol) and dose length product (DLP) values were used to determine the LDRLs for each protocol. The LDRLs were compared with national DRLs (NDRLs) and DRLs set in other countries. Inter-CT scanner variability, which is a measure of how well clinical practices are standardized, was determined for each protocol. Median values for each protocol were compared with the LDRLs for dose optimization in each CT scanner.

RESULTS

The LDRLs (for DLP and CTDIvol, respectively) were 839 mGy.cm and 41.2 mGy for head CT without contrast, 530.6 mGy.cm and 19.8 mGy for cervical spine CT without contrast, 431.9 mGy.cm and 15.5 mGy for neck CT with contrast, 364.8 mGy.cm and 9.3 mGy for chest CT without contrast, 588.9 mGy. cm and 11.2 mGy for abdomen-pelvis CT without contrast, 713 mGy.cm and 24.3 mGy for lumbar spine CT without contrast, 326 mGy.cm and 9.5 mGy for HRCT, and 642.3 mGy.cm and 33.4 mGy for coronary CTA. The LDRLs were comparable to or lower than NDRLs and DRLs set in other countries for most protocols. The comparisons revealed the need for immediate initiation of an optimization process for CT protocols with higher dose distributions. Furthermore, protocols with high inter-CT scanner variability revealed the need for standardization.

CONCLUSION

There is a need to update the NDRLs for CT protocols in Turkey. Until new NDRLs are established, local institutions in Turkey can initiate the optimization process by comparing their dose distributions to the LDRLs established in our study. Automated dose-tracking software can play an important role in establishing DRLs by facilitating the collection and analysis of large datasets.

Patient perspectives on ionising radiation exposure from computed tomography in Saudi Arabia: a knowledge and perception study

The objective of this study was to evaluate patient knowledge and understanding of ionising radiation and dosage, as well as the accompanying risks related to computed tomography scans. A total of 412 outpatients who underwent computed tomography (CT) scans were surveyed to assess their understanding of radiation dose and exposure risks. CT was correctly classified as an ionising radiation by 56.8% of the respondents. More than half of the patients reported that a CT scan increases the probability of inducing cancer. Awareness of varying radiation doses in different CT exams was noted in 75.2% of patients, but only 21.4% reported having discussions with their physician about radiation dose. Gender, age and employment were significantly correlated with knowledge levels. The survey findings indicate a limited understanding of the hazards associated with ionising radiation used in CT scans, highlighting a need for increased awareness and education on radiation protection to ensure informed consent.

Optimizing CT Abdomen-Pelvis Scan Radiation Dose: Examining the Role of Body Metrics (Waist Circumference, Hip Circumference, Abdominal Fat, and Body Mass Index) in Dose Efficiency

Objective: This study investigates the correlation between patient body metrics and radiation dose in abdominopelvic CT scans, aiming to identify significant predictors of radiation exposure. Methods: Employing a cross-sectional analysis of patient data, including BMI, abdominal fat, waist, abdomen, and hip circumference, we analyzed their relationship with the following dose metrics: the CTDIvol, DLP, and SSDE. Results: Results from the analysis of various body measurements revealed that BMI, abdominal fat, and waist circumference are strongly correlated with increased radiation doses. Notably, the SSDE, as a more patient-centric dose metric, showed significant positive correlations, especially with waist circumference, suggesting its potential as a key predictor for optimizing radiation doses. Conclusions: The findings suggest that incorporating patient-specific body metrics into CT dosimetry could enhance personalized care and radiation safety. Conclusively, this study highlights the necessity for tailored imaging protocols based on individual body metrics to optimize radiation exposure, encouraging further research into predictive models and the integration of these metrics into clinical practice for improved patient management.

COVID-19 and cancer risk arising from ionizing radiation exposure through CT scans: a cross-sectional study

BACKGROUND

The surge in the utilization of CT scans for COVID-19 diagnosis and monitoring during the pandemic is undeniable. This increase has brought to the forefront concerns about the potential long-term health consequences, especially radiation-induced cancer risk. This study aimed to quantify the potential cancer risk associated with CT scans performed for COVID-19 detection.

METHODS

In this cross-sectional study data from a total of 561 patients, who were referred to the radiology center at Imam Hossein Hospital in Shahroud, was collected. CT scan reports were categorized into three groups based on the radiologist's interpretation. The BEIR VII model was employed to estimate the risk of radiation-induced cancer.

RESULTS

Among the 561 patients, 299 (53.3%) were males and the average age of the patients was 49.61 ± 18.73 years. Of the CT scans, 408 (72.7%) were reported as normal. The average age of patients with normal, abnormal, and potentially abnormal CT scans was 47.57 ± 19.06, 54.80 ± 16.70, and 58.14 ± 16.60 years, respectively (p-value < 0.001). The average effective dose was 1.89 ± 0.21 mSv, with 1.76 ± 0.11 mSv for males and 2.05 ± 0.29 mSv for females (p-value < 0.001). The average risk of lung cancer was 3.84 ± 1.19 and 9.73 ± 3.27 cases per 100,000 patients for males and females, respectively. The average LAR for all cancer types was 10.30 ± 6.03 cases per 100,000 patients.

CONCLUSIONS

This study highlights the critical issue of increased CT scan usage for COVID-19 diagnosis and the potential long-term consequences, especially the risk of cancer incidence. Healthcare policies should be prepared to address this potential rise in cancer incidence and the utilization of CT scans should be restricted to cases where laboratory tests are not readily available or when clinical symptoms are severe.

Low Dose Pediatric CT Head Protocol using Iterative Reconstruction Techniques: A Comparison with Standard Dose Protocol

PURPOSE

Pediatric computed tomography (CT) head examination has also increased in recent years with the advancement in CT technology; however, children exposed to radiation at the youngest age are more vulnerable to the risks of radiation. The aim of the study is to evaluate radiation dose and image quality of low dose pediatric CT head protocol compared to standard dose pediatric CT head protocol.

METHODS

This was a prospective study. Group 1 included 73 patients aged < 1 year and 70 patients in the 1-5 years age group and had undergone CT head examination using the standard dose protocol. Group 2 included 31 patients aged < 1 year and 40 patients in the 1-5 years age group and had undergone CT head examination using the low dose protocol. The radiation dose was measured and image quality was assessed quantitatively and qualitatively.

RESULTS

There was a significant difference in radiation dose between the standard and low dose protocols (p > 0.05) with lower radiation dose for low dose group. The qualitative analysis did not show a significant difference between the standard and low dose protocols. The gray-white matter differentiation (GWMD), attenuation, contrast to noise ratio (CNR) and figure of merit (FOM) were higher in the low dose protocol compared to the standard dose with a significant difference (p > 0.05).

CONCLUSION

The study concludes that a low dose protocol at 80 kV tube voltage/150 mAs tube current exposure time product/iterative reconstruction-iDose4 (level 3) for < 1 year age group and 100 kV/200m As/iDose4 (level 3) for 1-5 years age group provides ultra-low effective dose with diagnostically acceptable image quality for pediatric CT head examination compared with standard dose protocol.

Significant variation in computed tomography imaging of pregnant trauma patients: a retrospective multicenter study

PURPOSE

Following motor vehicle collisions (MVCs), patients often undergo extensive computed tomography (CT) imaging. However, pregnant trauma patients (PTPs) represent a unique population where the risk of fetal radiation may supersede the benefits of liberal CT imaging. This study sought to evaluate imaging practices for PTPs, hypothesizing variability in CT imaging among trauma centers. If demonstrated, this might suggest the need to develop specific guidelines to standardize practice.

METHODS

A multicenter retrospective study (2016-2021) was performed at 12 Level-I/II trauma centers. Adult (≥18 years old) PTPs involved in MVCs were included, with no patients excluded. The primary outcome was the frequency of CT. Chi-square tests were used to compare categorical variables, and ANOVA was used to compare the means of normally distributed continuous variables.

RESULTS

A total of 729 PTPs sustained MVCs (73% at high speed of ≥ 25 miles per hour). Most patients were mildly injured but a small variation of injury severity score (range 1.1-4.6, p < 0.001) among centers was observed. There was a variation of imaging rates for CT head (range 11.8-62.5%, p < 0.001), cervical spine (11.8-75%, p < 0.001), chest (4.4-50.2%, p < 0.001), and abdomen/pelvis (0-57.3%, p < 0.001). In high-speed MVCs, there was variation for CT head (12.5-64.3%, p < 0.001), cervical spine (16.7-75%, p < 0.001), chest (5.9-83.3%, p < 0.001), and abdomen/pelvis (0-60%, p < 0.001). There was no difference in mortality (0-2.9%, p =0.19).

CONCLUSION

Significant variability of CT imaging in PTPs after MVCs was demonstrated across 12 trauma centers, supporting the need for standardization of CT imaging for PTPs to reduce unnecessary radiation exposure while ensuring optimal injury identification is achieved.

A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images

Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages. Prior detection of these lumps or masses can reduce the risk of death and treat bone cancer early. The goal of this current study is to utilize image processing techniques and deep learning-based Convolution neural network (CNN) to classify normal and cancerous bone images. Medical image processing techniques, like pre-processing (e.g., median filter), K-means clustering segmentation, and, canny edge detection were used to detect the cancer region in Computer Tomography (CT) images for parosteal osteosarcoma, enchondroma and osteochondroma types of bone cancer. After segmentation, the normal and cancerous affected images were classified using various existing CNN-based models. The results revealed that AlexNet model showed a better performance with a training accuracy of 98%, validation accuracy of 98%, and testing accuracy of 100%.

A Practical Method for Slice Spacing Measurement Using the American Association of Physicists in Medicine Computed Tomography Performance Phantom

Background

The slice spacing has a crucial role in the accuracy of computed tomography (CT) images in sagittal and coronal planes. However, there is no practical method for measuring the accuracy of the slice spacing.

Purpose

This study proposes a novel method to automatically measure the slice spacing using the American Association of Physicists in Medicine (AAPM) CT performance phantom.

Methods

The AAPM CT performance phantom module 610-04 was used to measure slice spacing. The process of slice spacing measurement involves a pair of axial images of the module containing ramp aluminum objects located at adjacent slice positions. The middle aluminum plate of each image was automatically segmented. Next, the two segmented images were combined to produce one image with two stair objects. The centroid coordinates of two stair objects were automatically determined. Subsequently, the distance between these two centroids was measured to directly indicate the slice spacing. For comparison, the slice spacing was calculated by accessing the slice position attributes from the DICOM header of both images. The proposed method was tested on phantom images with variations in slice spacing and field of view (FOV).

Results

The results showed that the automatic measurement of slice spacing was quite accurate for all variations of slice spacing and FOV, with average differences of 9.0% and 9.3%, respectively.

Conclusion

A new automated method for measuring the slice spacing using the AAPM CT phantom was successfully demonstrated and tested for variations of slice spacing and FOV. Slice spacing measurement may be considered an additional parameter to be checked in addition to other established parameters.

Evaluation of organ and effective doses using anthropomorphic phantom: A comparison between experimental measurement and a commercial dose calculator

INTRODUCTION

The aim of this study was to experimentally measure organ doses for computed tomography (CT) procedures using thermoluminescence dosimeters (TLDs) on a RANDO anthropomorphic phantom and verify the measured doses using CT-Expo software.

METHODS

The phantom was irradiated using clinical CT scan protocols routinely used for specific procedures in the radiology department. Fifty TLD chips were used in this study. The scanning parameters (kVp, mA, s) used to scan the phantom were used as input parameters for CT-Expo dose estimations.

RESULTS

The TLD measured organ doses varied between 3.97 mGy for the esophagus and 56.22 mGy for the brain. High doses were recorded in the brain (37.80-56.22 mGy) and the eye lens (29.94-36.16 mGy). Comparing the organ dose measurements between TLD and CT-Expo, the maximum organ dose difference was obtained for the eye lens. A comparison between the two methods for the other organs were all less than 32 %. The effective doses from the TLD measurements for the head, chest, and abdominopelvic CT examinations were 2.78, 6.67, and 17 mSv, respectively and CT-Expo were 2.20, 10.30, and 16.70 mSv, respectively.

CONCLUSION

The experimental and computational results are comparable, and the reliability of the TLD measurements and CT-Expo dose calculator has been proven.

IMPLICATIONS FOR STUDY

A reason for the difference in dose measurements between the two methods has been attributed to the dissimilarity in the organ position in the Rando anthropomorphic phantom and the standard mathematical phantom used by CT-Expo. The experimental and computational results have been found to be comparable.

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pretrained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.

Patterns of computed tomography utilisation in injury management: latent classes approach using linked administrative data in Western Australia

PURPOSE

Whilst computed tomography (CT) imaging has been a vital component of injury management, its increasing use has raised concern regarding ionising radiation exposure. This study aims to identify latent classes (underlying patterns) of CT use over a 3-year period following the incidence of injury and factors predicting the observed patterns.

METHOD

A retrospective observational cohort study was conducted in 21,544 individuals aged 18 + years presenting to emergency departments (ED) of four tertiary public hospitals with new injury in Western Australia. Mixture modelling approach was used to identify latent classes of CT use over a 3-year period post injury.

RESULTS

Amongst injured people with at least one CT scan, three latent classes of CT use were identified including a: temporarily high CT use (46.4%); consistently high CT use (2.6%); and low CT use class (51.1%). Being 65 + years or older, having 3 + comorbidities, history with 3 + hospitalisations and history of CT use before injury were associated with consistently high use of CT. Injury to the head, neck, thorax or abdomen, being admitted to hospital after the injury and arriving to ED by ambulance were predictors for the temporarily high use class. Living in areas of higher socio-economic disadvantage was a unique factor associated with the low CT use class.

CONCLUSIONS

Instead of assuming a single pattern of CT use for all patients with injury, the advanced latent class modelling approach has provided more nuanced understanding of the underlying patterns of CT use that may be useful for developing targeted interventions.

The Dose Optimization and Evaluation of Image Quality in the Adult Brain Protocols of Multi-Slice Computed Tomography: A Phantom Study

Computed tomography examinations have caused high radiation doses for patients, especially for CT scans of the brain. This study aimed to optimize the radiation dose and image quality in adult brain CT protocols. Images were acquired using a Catphan 700 phantom. Radiation doses were recorded as CTDIvol and dose length product (DLP). CT brain protocols were optimized by varying parameters such as kVp, mAs, signal-to-noise ratio (SNR) level, and Clearview iterative reconstruction (IR). The image quality was also evaluated using AutoQA Plus v.1.8.7.0 software. CT number accuracy and linearity had a robust positive correlation with the linear attenuation coefficient (µ) and showed more inaccurate CT numbers when using 80 kVp. The modulation transfer function (MTF) showed a higher value in 100 and 120 kVp protocols (p < 0.001), while high-contrast spatial resolution showed a higher value in 80 and 100 kVp protocols (p < 0.001). Low-contrast detectability and the contrast-to-noise ratio (CNR) tended to increase when using high mAs, SNR, and the Clearview IR protocol. Noise decreased when using a high radiation dose and a high percentage of Clearview IR. CTDIvol and DLP were increased with increasing kVp, mAs, and SNR levels, while the increasing percentage of Clearview did not affect the radiation dose. Optimized protocols, including radiation dose and image quality, should be evaluated to preserve diagnostic capability. The recommended parameter settings include kVp set between 100 and 120 kVp, mAs ranging from 200 to 300 mAs, SNR level within the range of 0.7-1.0, and an iterative reconstruction value of 30% Clearview to 60% or higher.

Radiation Protection of a 3D Computer Tomography Scanning Workplace for Logs-A Case Study

Despite its undeniable advantages, the operation of a CT scanner also carries risks to human health. The CT scanner is a source of ionizing radiation, which also affects people in its surroundings. The aim of this paper is to quantify the radiation exposure of workers at a 3D CT wood scanning workplace and to determine a monitoring program based on measurements of ionizing radiation levels during the operation of a CT log scanner. The workplace is located in the Biotechnology Park of the National Forestry Centre. The ionizing radiation source is located in a protective cabin as a MICROTEC 3D CT machine with an X-ray lamp as X-ray source. The CT scanner is part of the 3D CT scanning line and its function is continuous quality scanning or detection of internal defects of the examined wood. The measurement of leakage radiation during scanning is performed with a metrologically verified meter. The measured quantity is the ambient dose equivalent rate H˙*10. The results of the measurements at the selected measurement sites have shown that, after installation of additional safety barriers, the CT scanner for the logs complies with the most strict criteria in terms of radiation protection. Workers present at the workplace during the operation of the CT scanner are not exposed to radiation higher than the background radiation level.

Establishment of diagnostic reference levels in computed tomography in two large hospitals in Oman

This study aimed to estimate diagnostic reference levels (DRLs) for the most frequent computed tomography (CT) imaging examinations to monitor and better control radiation doses delivered to patients. Seven CT imaging examinations: Head, Chest, Chest High Resolution (CHR), Abdomen Pelvis (AP), Chest Abdomen Pelvis (CAP), Kidneys Ureters Bladder (KUB) and Cardiac, were considered. CT dosimetric quantities and patient demographics were collected from data storage systems. Local typical values for DRLs were calculated for CTDIvol (mGy), dose length product (DLP) (mGy·cm) and effective doses (mSv) were estimated for each examination. The calculated DRLs were given as (median CTDIvol (mGy):median DLP (mGy·cm)): Head: 39:657; Chest: 13:451; CHR: 6:228; AP: 12:578; CAP: 20:807; KUB: 7:315, and Cardiac: 2:31. Estimated effective doses for Head, Chest, CHR, AP, CAP, KUB and Cardiac were 1.3, 12.7, 6.3, 12.5, 18.1, 5.8 and 0.8 mSv, respectively. The estimated DRLs will act as guidance doses to prevent systematic excess of patient doses.

The effect of contact radiation shielding on breast dose during CT abdomen-pelvis: a phantom study

This study aims to investigate if contact shielding reduces breast radiation dose during computed tomography (CT) abdomen-pelvis examinations using automatic tube current modulation to protect one of the four most radiosensitive organs during CT examinations. Dose measurements were taken with and without contact shielding across the anterior and lateral aspects of the breasts and with and without organ dose modulation (ODM) to quantify achievable dose reductions. Although there are no statistically significant findings, when comparing with and without shielding, the mean breast surface dose was reduced by 0.01 μSv without ODM (1.92-1.91 μSv, p = 0.49) and increased by 0.03 μSv with ODM (1.53-1.56 μSv, p = 0.44). Comparing with and without ODM, the mean breast surface dose was reduced by 0.35 μSv with shielding (1.91-1.56 μSv, p = 0.24) and by 0.39 μSv without shielding (1.92-1.53 μSv, p = 0.17). The addition of contact shielding does not provide significant breast surface radiation dose reduction during CT abdomen-pelvis.

Clinical Guidance on the Monitoring and Management of Trastuzumab Deruxtecan (T-DXd)-Related Adverse Events: Insights from an Asia-Pacific Multidisciplinary Panel

Trastuzumab deruxtecan (T-DXd)-an antibody-drug conjugate targeting the human epidermal growth factor receptor 2 (HER2)-improved outcomes of patients with HER2-positive and HER2-low metastatic breast cancer. Guidance on monitoring and managing T-DXd-related adverse events (AEs) is an emerging unmet need as translating clinical trial experience into real-world practice may be difficult due to practical and cultural considerations and differences in health care infrastructure. Thus, 13 experts including oncologists, pulmonologists and a radiologist from the Asia-Pacific region gathered to provide recommendations for T-DXd-related AE monitoring and management by using the latest evidence from the DESTINY-Breast trials, our own clinical trial experience and loco-regional health care considerations. While subgroup analysis of Asian (excluding Japanese) versus overall population in the DESTINY-Breast03 uncovered no major differences in the AE profile, we concluded that proactive monitoring and management are essential in maximising the benefits with T-DXd. As interstitial lung disease (ILD)/pneumonitis is a serious AE, patients should undergo regular computed tomography scans, but the frequency may have to account for the median time of ILD/pneumonitis onset and access. Trastuzumab deruxtecan appears to be a highly emetic regimen, and prophylaxis with serotonin receptor antagonists and dexamethasone (with or without neurokinin-1 receptor antagonist) should be considered. Health care professionals should be vigilant for treatable causes of fatigue, and patients should be encouraged to use support groups and practice low-intensity exercises. To increase treatment acceptance, patients should be made aware of alopecia risk prior to starting T-DXd. Detailed monitoring and management recommendations for T-DXd-related AEs are discussed further.

A retrospective survey to establish institutional diagnostic reference levels for CT urography examinations based on clinical indications: preliminary results

Objective. To establish institutional diagnostic reference levels (IDRLs) based on clinical indications (CIs) for three- and four-phase computed tomography urography (CTU).Methods. Volumetric computed tomography dose index (CTDIvol), dose-length product (DLP), patients' demographics, selected CIs like lithiasis, cancer, and other diseases, and protocols' parameters were retrospectively recorded for 198 CTUs conducted on a Toshiba Aquilion Prime 80 scanner. Patients were categorised based on CIs and number of phases. These groups' 75th percentiles of CTDIvoland DLP were proposed as IDRLs. The mean, median and IDRLs were compared with previously published values.Results. For the three-phase protocol, the CTDIvol(mGy) and DLP (mGy.cm) were 22.7/992 for the whole group, 23.4/992 for lithiasis, 22.8/1037 for cancer, and 21.2/981 for other diseases. The corresponding CTDIvol(mGy) and DLP (mGy.cm) values for the four-phase protocol were 28.6/1172, 30.6/1203, 27.3/1077, and 28.7/1252, respectively. A significant difference was found in CTDIvoland DLP between the two protocols, among the phases of three-phase (except cancer) and four-phase protocols (except DLP for other diseases), and in DLP between the second and third phases (except for cancer group). The results are comparable or lower than most studies published in the last decade.Conclusions. The CT technologist must be aware of the critical dose dependence on the scan length and the applied exposure parameters for each phase, according to the patient's clinical background and the corresponding imaging anatomy, which must have been properly targeted by the competent radiologist. When clinically feasible, restricting the number of phases to three instead of four could remarkably reduce the patient's radiation dose. CI-based IDRLs will serve as a baseline for comparison with CTU practice in other hospitals and could contribute to national DRL establishment. The awareness and knowledge of dose levels during CTU will prompt optimisation strategies in CT facilities.

Direct Estimation of Equivalent Bioelectric Sources Based on Huygens' Principle

An estimation of the electric sources in the heart was conducted using a novel method, based on Huygens' Principle, aiming at a direct estimation of equivalent bioelectric sources over the heart's surface in real time. The main scope of this work was to establish a new, fast approach to the solution of the inverse electrocardiography problem. The study was based on recorded electrocardiograms (ECGs). Based on Huygens' Principle, measurements obtained from the surfaceof a patient's thorax were interpolated over the surface of the employed volume conductor model and considered as secondary Huygens' sources. These sources, being non-zero only over the surface under study, were employed to determine the weighting factors of the eigenfunctions' expansion, describing the generated voltage distribution over the whole conductor volume. With the availability of the potential distribution stemming from measurements, the electromagnetics reciprocity theorem is applied once again to yield the equivalent sources over the pericardium. The methodology is self-validated, since the surface potentials calculated from these equivalent sources are in very good agreement with ECG measurements. The ultimate aim of this effort is to create a tool providing the equivalent epicardial voltage or current sources in real time, i.e., during the ECG measurements with multiple electrodes.

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images

Purpose

To present a deep learning segmentation model that can automatically and robustly segment all major anatomic structures on body CT images.

Materials and Methods

In this retrospective study, 1204 CT examinations (from 2012, 2016, and 2020) were used to segment 104 anatomic structures (27 organs, 59 bones, 10 muscles, and eight vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiation therapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, abnormalities, scanners, body parts, sequences, and sites). The authors trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients to evaluate the model's performance. The trained algorithm was applied to a second dataset of 4004 whole-body CT examinations to investigate age-dependent volume and attenuation changes.

Results

The proposed model showed a high Dice score (0.943) on the test set, which included a wide range of clinical data with major abnormalities. The model significantly outperformed another publicly available segmentation model on a separate dataset (Dice score, 0.932 vs 0.871; P < .001). The aging study demonstrated significant correlations between age and volume and mean attenuation for a variety of organ groups (eg, age and aortic volume [rs = 0.64; P < .001]; age and mean attenuation of the autochthonous dorsal musculature [rs = -0.74; P < .001]).

Conclusion

The developed model enables robust and accurate segmentation of 104 anatomic structures. The annotated dataset (https://doi.org/10.5281/zenodo.6802613) and toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.Keywords: CT, Segmentation, Neural Networks Supplemental material is available for this article. © RSNA, 2023See also commentary by Sebro and Mongan in this issue.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.