Computed tomography--an increasing source of radiation exposure

Evaluation and Management of Primary Spontaneous Pneumothorax in Adolescents and Young Adults: A Systematic Review From the APSA Outcomes & Evidence-Based Practice Committee

INTRODUCTION

Controversy exists in the optimal management of adolescent and young adult primary spontaneous pneumothorax. The American Pediatric Surgical Association (APSA) Outcomes and Evidence-Based Practice Committee performed a systematic review of the literature to develop evidence-based recommendations.

METHODS

Ovid MEDLINE, Elsevier Embase, EBSCOhost CINAHL, Elsevier Scopus, and Wiley Cochrane Central Register of Controlled Trials databases were queried for literature related to spontaneous pneumothorax between January 1, 1990, and December 31, 2020, addressing (1) initial management, (2) advanced imaging, (3) timing of surgery, (4) operative technique, (5) management of contralateral side, and (6) management of recurrence. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines were followed.

RESULTS

Seventy-nine manuscripts were included. Initial management of adolescent and young adult primary spontaneous pneumothorax should be guided by symptoms and can include observation, aspiration, or tube thoracostomy. There is no evidence of benefit for cross-sectional imaging. Patients with ongoing air leak may benefit from early operative intervention within 24-48 h. A video-assisted thoracoscopic surgery (VATS) approach with stapled blebectomy and pleural procedure should be considered. There is no evidence to support prophylactic management of the contralateral side. Recurrence after VATS can be treated with repeat VATS with intensification of pleural treatment.

CONCLUSIONS

The management of adolescent and young adult primary spontaneous pneumothorax is varied. Best practices exist to optimize some aspects of care. Further prospective studies are needed to better determine optimal timing of operative intervention, the most effective operation, and management of recurrence after observation, tube thoracostomy, or operative intervention.

LEVEL OF EVIDENCE

Level 4.

TYPE OF STUDY

Systematic Review of Level 1-4 studies.

Ten-year follow-up of dilatation of aortic structures in Fallot-type anomalies

OBJECTIVES

Dilatation of the aortic root structures or ascending aorta is often observed in patients with Fallot-type anomalies. We aimed to determine the dilation rate of the aortic structures and investigate strategies for managing this phenomenon.

METHODS

In this retrospective study, we enrolled 66 out of 801 patients who underwent corrective surgery for Fallot-type anomalies (tetralogy of Fallot [TOF] and Fallot-type double outlet right ventricle [DORV]) between 2004 and 2020. These 66 patients had follow-up cardiac computed tomography (CT) angiography images taken at least 5 years after the initial CT study. We analyzed the diameters and aortic cross-sectional area/height ratio (AH) of the aortic annulus, sinus of Valsalva, sinotubular junction, and ascending aorta between the initial and follow-up CT scans. "Dilatation" was defined as a z-score over 2 for each aortic structure.

RESULTS

The median age at the initial and follow-up CT scans was 5.9 years (interquartile range [IQR]: 0.4 ~ 12.4) and 15.9 years (IQR 9.3 ~ 23.4), respectively. The median CT interval (from initial to latest CT) was 9.5 years (IQR 6.6 ~ 12.0). The sinus of Valsalva exhibited the most significant dilation (32.8 mm at follow-up CT) over the study period. The AH ratio increased significantly in all four aortic structures. The patient's age was significantly associated with higher AH in the follow-up CT. Aortic dilatation was present in 74.2% of patients at the initial CT and increased to 86.4% at the follow-up CT.

CONCLUSION

In Fallot-type anomalies, the AH ratio of aortic root structures significantly increased over an average period of approximately 9.5 years. The number of patients diagnosed with aortic dilatation also increased. Based on our observations in this study, these patients' group should be considered for more frequent follow-up examinations, as significant dilatation could occur in their mid-20 s.

Dual-Layer Detector Head CT to Maintain Image Quality While Reducing the Radiation Dose in Pediatric Patients

BACKGROUND AND PURPOSE

Radiation exposure in the CT diagnostic imaging process is a conspicuous concern in pediatric patients. This study aimed to evaluate whether 60-keV virtual monoenergetic images of the pediatric cranium in dual-layer CT can reduce the radiation dose while maintaining image quality compared with conventional images.

MATERIALS AND METHODS

One hundred six unenhanced pediatric head scans acquired by dual-layer CT were retrospectively assessed. The patients were assigned to 2 groups of 53 and scanned with 250 and 180 mAs, respectively. Dose-length product values were retrieved, and noise, SNR, and contrast-to-noise ratio were calculated for each case. Two radiologists blinded to the reconstruction technique used evaluated image quality on a 5-point Likert scale. Statistical assessment was performed with ANOVA and the Wilcoxon test, adjusted for multiple comparisons.

RESULTS

Mean dose-length product values were 717.47 (SD, 41.52) mGy×cm and 520.74 (SD, 42) mGy×cm for the 250- and 180-mAs groups, respectively. Irrespective of the radiation dose, noise was significantly lower, SNR and contrast-to-noise ratio were significantly higher, and subjective analysis revealed significant superiority of 60-keV virtual monoenergetic images compared with conventional images (all P < .001). SNR, contrast-to-noise ratio, and subjective evaluation in 60-keV virtual monoenergetic images were not significantly different between the 2 scan groups (P > .05). Radiation dose parameters were significantly lower in the 180-mAs group compared with the 250-mAs group (P < .001).

CONCLUSIONS

Dual-layer CT 60-keV virtual monoenergetic images allowed a radiation dose reduction of 28% without image-quality loss in pediatric cranial CT.

Have the UK Pediatric Trauma Protocols resulted in a reduction in chest computed tomography imaging for children presenting with major blunt trauma?

Objectives

To observe variation in imaging requests after publication of the Royal College of Radiologists UK Paediatric Trauma Protocols in 2014, recommending limited use of thoracic computed tomography (CT) to appropriately clinically risk stratified children.

Method

A retrospective observational study using data from the Trauma Audit & Research Network in the United Kingdom, for children (0-16 years of age) for the years 2012-2021. Percentages were calculated to facilitate comparison between year groups (under 1 year of age, 1-10 years of age, 11-15 years of age), and CT imaging categories reviewed: (1) whole-body CT (WBCT); (2) abdominopelvic CT (CTAP) with chest radiograph (CXR); (3) chest, abdomen, and pelvic CT (CTCAP) with CXR; (4) CTCAP without CXR; and (5) other imaging.

Results

Increased use of the recommended protocol (CXR with CTAP) was observed after guidance publication but was not sustained: infants under 1 year old, 0.0% in 2012, 7% in 2017, 0.0% in 2021; 1-10-year-olds, 4% in 2012, 13.9% in 2017, 5.5% in 2021; 11-15-year-olds, 7.1% in 2012, 10.2% in 2017, 6.6% in 2021. Requests for WBCT increased from 2012-2021 (all age groups, 2.4%, 2012, to 5.3%, 2021) and requests for CTCAP were consistently at a higher level than that of the recommended protocol.

Conclusion

The increased use of CXR with CTAP after publication of the guidelines, was not sustained with a decreasing trend observed from ∼2017, raising concern for the ionizing radiation burden in this population.

Clinical evaluation of radiation-induced sinusitis by MRI-based scoring system in nasopharyngeal carcinoma patients

OBJECTIVE

To explore the application of magnetic resonance imaging (MRI) in the evaluation of radiation-induced sinusitis (RIS), MRI-based scoring system was used to evaluate the development regularity, characteristics and influencing factors of RIS in nasopharyngeal carcinoma (NPC) patients.

PATIENTS AND METHODS

A retrospective analysis was performed by collecting the clinical and MRI data of 346 NPC patients to analyze the characteristics and prognosis of RIS. The predictive model was constructed according to the influencing factors of RIS.

RESULTS

(1) In the 2-year follow-up after radiotherapy (RT), there was significant change in L-M score in both groups of NPC patients (sinusitis before RT group: p = 0.000 vs. non-sinusitis before RT group: p = 0.000). After 6 months of RT, the L-M scores of the two groups tended to plateau (sinusitis before RT group: p = 0.311 vs. non-sinusitis before RT group: p = 0.469). (2) The prevalence of sinusitis in two groups of NPC patients (without or with sinusitis before RT) was 83% vs. 93%, 91% vs. 99%, 94% vs. 98% at 1, 6 and 24 months after RT, respectively. (3) In the patients without sinusitis before RT, the incidence of sinusitis in maxillary and anterior/posterior ethmoid, sphenoid and frontal sinuses was 87.1%, 90.0%/87.1%, 49.5%, 11.8% respectively, 1 month after RT. (4) A regression model was established according to the univariate and multivariate analysis of the factors related to RIS (smoking history: p = 0.000, time after RT: p = 0.008 and TNM staging: p = 0.040).

CONCLUSION

(1) RIS is a common complication in NPC patients after RT. This disorder progressed within 6 months after RT, stabilized and persisted within 6 months to 2 years. There is a high incidence of maxillary sinus and ethmoid sinus inflammation in NPC patients after RT. (2) Smoking history, time after RT and TNM staging were significant independent risk factors for RIS. (3) The intervention of the risk factors in the model may prevent or reduce the occurrence of RIS in NPC patients.

Association between pre- and postoperative rotational mismatches of the femorotibial components and bones in bi-cruciate retaining and posterior stabilized total knee arthroplasty

To clarify the association between pre- and postoperative rotational mismatches of the femorotibial components and bones for total knee arthroplasty (TKA) with bi-cruciate retaining (BCR) design and with fixed bearing posterior stabilized (PS) design. This retrospective cohort study included 40 BCR TKAs and 50 PS TKAs. Pre- and postoperative rotational mismatches of the femorotibial components and bones were measured by three-dimensional assessment based on computed tomography imaging. The mean value and percentage within ± 5° of pre- and postoperative rotational mismatches were compared between BCR TKA and PS TKA. Correlations between pre- and postoperative rotational mismatches of the femorotibial components and bones were investigated in BCR TKA and PS TKA. There was no significant difference in mean preoperative rotational mismatch of femorotibial components and bones between BCR TKA and PS TKA. Mean postoperative rotational mismatch of femorotibial components and bones was significantly greater in BCR TKA than in PS TKA. Postoperative rotational mismatch of the femorotibial components was within ± 5° in 21 knees (52.5%) for BCR TKA and in 43 knees (86.0%) for PS TKA. The rate of postoperative rotational mismatch of the femorotibial components and bones within ± 5° was significantly lower for BCR TKA than for PS TKA. In BCR TKA, there was a positive correlation between pre- and postoperative rotational mismatches of the femorotibial components and of bones. We consider these results can be attributed to the retention of both cruciate ligaments, which may affect the reduction of rotational permittance of the components and bones.

Radiation dose analysis of computed tomography coronary angiography in Children with Kawasaki disease

BACKGROUND

There is evolving role of computed tomography coronary angiography (CTCA) in non-invasive evaluation of coronary artery abnormalities in children with Kawasaki disease (KD). Despite this, there is lack of data on radiation dose in this group of children undergoing CTCA.

AIM

To audit the radiation dose of CTCA in children with KD.

METHODS

Study (December 2013-February 2018) was performed on dual source CT scanner using adaptive prospective electrocardiography-triggering. The dose length product (DLP in milligray-centimeters-mGy.cm) was recorded. Effective radiation dose (millisieverts-mSv) was calculated by applying appropriate age adjusted conversion factors as per recommendations of International Commission on Radiological Protection. Radiation dose was compared across the groups (0-1, 1-5, 5-10, and > 10 years).

RESULTS

Eighty-five children (71 boys, 14 girls) with KD underwent CTCA. The median age was 5 years (range, 2 mo-11 years). Median DLP and effective dose was 21 mGy.cm, interquartile ranges (IQR) = 15 (13, 28) and 0.83 mSv, IQR = 0.33 (0.68, 1.01) respectively. Mean DLP increased significantly across the age groups. Mean effective dose in infants (0.63 mSv) was significantly lower than the other age groups (1-5 years 0.85 mSv, 5-10 years 1.04 mSv, and > 10 years 1.38 mSv) (P < 0.05). There was no significant difference in the effective dose between the other groups of children. All the CTCA studies were of diagnostic quality. No child required a repeat examination.

CONCLUSION

CTCA is feasible with submillisievert radiation dose in most children with KD. Thus, CTCA has the potential to be an important adjunctive imaging modality in children with KD.

Unsupervised learning-based dual-domain method for low-dose CT denoising

Objective. Low-dose CT (LDCT) is an important research topic in the field of CT imaging because of its ability to reduce radiation damage in clinical diagnosis. In recent years, deep learning techniques have been widely applied in LDCT imaging and a large number of denoising methods have been proposed. However, One major challenge of supervised deep learning-based methods is the exactly geometric pairing of datasets with different doses. Therefore, the aim of this study is to develop an unsupervised learning-based LDCT imaging method to address the aforementioned challenges.Approach. In this paper, we propose an unsupervised learning-based dual-domain method for LDCT denoising, which consists of two stages: the first stage is projection domain denoising, in which the unsupervised learning method Noise2Self is applied to denoise the projection data with statistically independent and zero-mean noise. The second stage is an iterative enhancement approach, which combines the prior information obtained from the generative model with an iterative reconstruction algorithm to enhance the details of the reconstructed image.Main results. Experimental results show that our proposed method outperforms the comparison method in terms of denoising effect. Particularly, in terms of SSIM, the denoised results obtained using our method achieve the highest SSIM.Significance. In conclusion, our unsupervised learning-based method can be a promising alternative to the traditional supervised methods for LDCT imaging, especially when the availability of the labeled datasets is limited.

Accuracy of augmented reality with computed tomography-based navigation in total hip arthroplasty

BACKGROUND

Augmented reality (AR) provides the surgeon with direct visualization of radiological images by overlaying them on the patient. This study aimed to evaluate the accuracy of cup placement using a computed tomography (CT)-based AR navigation system.

METHODS

Sixty-five prospectively enrolled patients underwent primary cementless total hip arthroplasty (THA) in a supine position using this novel AR navigation system, and changes in pelvic flexion angle (PFA) were evaluated. Absolute navigation errors were defined as the absolute differences between angles in the intraoperative navigation record and those measured on postoperative CT. Factors affecting the absolute navigation error in cup alignment were determined.

RESULTS

Mean absolute change in PFA between preoperative CT and reduction was 2.1° ± 1.6°. Mean absolute navigation errors were 2.5° ± 1.7° in radiographic inclination (RI) and 2.5° ± 2.2° in radiographic anteversion (RA). While no factors significantly affecting absolute navigation error were found for RI, absolute change in PFA between preoperative CT and reduction correlated significantly with the absolute navigation error for RA.

CONCLUSION

This CT-based navigation system with AR enabled surgeons to place the cup more accurately than was possible by freehand placement during THA in a supine position.

Increasing use of computed tomography scans in the North Denmark Region raises patient safety concern

PURPOSE

Use of computed tomography (CT) scans raises safety concern as lifetime cumulative ionising radiation exposure is associated with risk of developing malignancies. This study aimed to investigate use of abdominal CT scans in the Danish health care sector.

METHODS

Data on abdominal CT scans performed annually in the North Denmark Region between 2005 and 2018 were extracted from the regional registry with emphasis on patients with a medical history of a repeated abdominal CT scan within 28 days. An audit of the medical files was subsequently conducted in 100 randomly selected patient cases to evaluate clinical information being provided, in addition to justification for a repeated abdominal CT scan, and finally if other radiology modalities could have been applied.

RESULTS

Number of annually performed abdominal CT scans in this demographically stable regional population increased by a factor 4.3 from 15 in 2005 to 65 in 2018 per 1,000 inhabitants. The audit revealed that 31% of the secondabdominal CT scans within a 28 days period were categorized as either doubtful whether justified or not justified. Moreover, 20% of theCT scans were considered replaceable by ultrasonography.

CONCLUSIONS

Annual performance of abdominal CT scans increased fourfold during the 14 years period. This tendency is probably attributable to changes in the Danish health care sector by which CT scan examination are used more frequently aiming at more accelerated patient investigation flow in conjunction with shorter length of hospitalization stay. Alertness is strongly warranted towards the associated risk of cancer due to life-time cumulative ionising radiation exposure by this strategy.

CAIR: Combining integrated attention with iterative optimization learning for sparse-view CT reconstruction

Sparse-view CT is an efficient way for low dose scanning but degrades image quality. Inspired by the successful use of non-local attention in natural image denoising and compression artifact removal, we proposed a network combining integrated attention and iterative optimization learning for sparse-view CT reconstruction (CAIR). Specifically, we first unrolled the proximal gradient descent into a deep network and added an enhanced initializer between the gradient term and the approximation term. It can enhance the information flow between different layers, fully preserve the image details, and improve the network convergence speed. Secondly, the integrated attention module was introduced into the reconstruction process as a regularization term. It adaptively fuses the local and non-local features of the image which are used to reconstruct the complex texture and repetitive details of the image, respectively. Note that we innovatively designed a one-shot iteration strategy to simplify the network structure and reduce the reconstruction time while maintaining image quality. Experiments showed that the proposed method is very robust and outperforms state-of-the-art methods in terms of both quantitative and qualitative, greatly improving the preservation of structures and the removal of artifacts.

Brain imaging with portable low-field MRI

The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparse k-space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems.

Theranostics applications of quantum dots in regenerative medicine, cancer medicine, and infectious diseases

Quantum dots (QDs) have attracted attention for their application and commercialization in all industrial fields, including communications, displays, and solar cells, due to their excellent optical properties based on the quantum size effect. In recent years, the development of QDs that do not contain cadmium which is toxic to cells and living organisms, has progressed, and they have attracted considerable attention in the bio-imaging field for targeting molecules and cells. Furthermore, recently, the need for diagnostics and treatment at the single molecule and single cell level in the medical field has been increasing, and the application of QDs in the medical field is also accelerating. Therefore, this paper outlines the frontiers of diagnostic and therapeutic applications (theranostics) of QDs, especially in advanced medical fields such as regenerative medicine, oncology, and infectious diseases.

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

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

Computed Tomography of the Head : A Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose

In 1971, the first computed tomography (CT) scan was performed on a patient's brain. Clinical CT systems were introduced in 1974 and dedicated to head imaging only. New technological developments, broader availability, and the clinical success of CT led to a steady growth in examination numbers. Most frequent indications for non-contrast CT (NCCT) of the head include the assessment of ischemia and stroke, intracranial hemorrhage and trauma, while CT angiography (CTA) has become the standard for first-line cerebrovascular evaluation; however, resulting improvements in patient management and clinical outcomes come at the cost of radiation exposure, increasing the risk for secondary morbidity. Therefore, radiation dose optimization should always be part of technical advancements in CT imaging but how can the dose be optimized? What dose reduction can be achieved without compromising diagnostic value, and what is the potential of the upcoming technologies artificial intelligence and photon counting CT? In this article, we look for answers to these questions by reviewing dose reduction techniques with respect to the major clinical indications of NCCT and CTA of the head, including a brief perspective on what to expect from current and future developments in CT technology with respect to radiation dose optimization.

Twinned Residual Auto-Encoder (TRAE)-A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images

The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a "twinned" Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering × 2 , × 4 , and × 8 super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as × 8 .

Clinical significance of plasma PD-L1+ exosomes in the management of diffuse large B cell lymphoma

PD-L1+ exosome have been reported to be a promising prognostic biomarker in various cancers. However, its clinical value in diffuse large B cell lymphoma (DLBCL) has not been defined yet. In this study, a total of 165 plasma samples from 78 patients with DLBCL undergoing standard first-line R-CHOP regimens were collected at three different time points (pretreatment, and after 3 and 6 cycles of R-CHOP) to determine the proportions of PD-L1+ exosomes by flow cytometry. We found that high pretreatment plasma PD-L1+ exosome correlated with indicators of poor clinical outcome that included high Ki-67 expression (P = 0.02), double expressor lymphoma (P = 0.005), immunohistochemical PD-L1+ tumor tissue (P = 0.006), and the baseline maximal standardized uptake values (P = 0.0003). Pretreatment plasma PD-L1+ exosome was an independent factor by multivariate analysis with logistic regression (P = 0.0301). Moreover, the pretreatment PD-L1+ exosome was a strong predictor of final treatment responses of either CR or non-CR by ROC analysis (P < 0.001). PD-L1+ exosome level declined significantly in patients who experienced CR (pretreatment vs. after 3 cycles/after 6 cycles, P < 0.05), but not in the non-CR group. Intriguingly, plasma PD-L1+ exosome after 3 cycles (AUC = 0.857; 95%CI: 0.728-0.939) might represent a more sensitive indicator than radiographic assessment after 3 cycles (AUC = 0.626; 95%CI: 0.477-0.758) for evaluating the therapeutic response of DLBCL patients (P = 0.0136). Our results suggest that plasma PD-L1+ exosomes may represent a new biomarker for the dynamic monitoring of treatment response.

Knee Bone Models From Ultrasound

The number of total knee arthroplasties performed worldwide is on the rise. Patient-specific planning and implants may improve surgical outcomes but require 3-D models of the bones involved. Ultrasound (US) may become a cheap and nonharmful imaging modality if the shortcomings of segmentation techniques in terms of automation, accuracy, and robustness are overcome; furthermore, any kind of US-based bone reconstruction must involve some kind of model completion to handle occluded areas, for example, the frontal femur. A fully automatic and robust processing pipeline is proposed, generating full bone models from 3-D freehand US scanning. A convolutional neural network (CNN) is combined with a statistical shape model (SSM) to segment and extrapolate the bone surface. We evaluate the method in vivo on ten subjects, comparing the US-based model to a magnetic resonance imaging (MRI) reference. The partial freehand 3-D record of the femur and tibia bones deviate by 0.7-0.8 mm from the MRI reference. After completion, the full bone model shows an average submillimetric error in the case of the femur and 1.24 mm in the case of the tibia. Processing of the images is performed in real time, and the final model fitting step is computed in less than a minute. It took an average of 22 min for a full record per subject.

Self-adaption and texture generation: A hybrid loss function for low-dose CT denoising

BACKGROUND

Deep learning has been successfully applied to low-dose CT (LDCT) denoising. But the training of the model is very dependent on an appropriate loss function. Existing denoising models often use per-pixel loss, including mean abs error (MAE) and mean square error (MSE). This ignores the difference in denoising difficulty between different regions of the CT images and leads to the loss of large texture information in the generated image.

PURPOSE

In this paper, we propose a new hybrid loss function that adapts to the noise in different regions of CT images to balance the denoising difficulty and preserve texture details, thus acquiring CT images with high-quality diagnostic value using LDCT images, providing strong support for condition diagnosis.

METHODS

We propose a hybrid loss function consisting of weighted patch loss (WPLoss) and high-frequency information loss (HFLoss). To enhance the model's denoising ability of the local areas which are difficult to denoise, we improve the MAE to obtain WPLoss. After the generated image and the target image are divided into several patches, the loss weight of each patch is adaptively and dynamically adjusted according to its loss ratio. In addition, considering that texture details are contained in the high-frequency information of the image, we use HFLoss to calculate the difference between CT images in the high-frequency information part.

RESULTS

Our hybrid loss function improves the denoising performance of several models in the experiment, and obtains a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Moreover, through visual inspection of the generated results of the comparison experiment, the proposed hybrid function can effectively suppress noise and retain image details.

CONCLUSIONS

We propose a hybrid loss function for LDCT image denoising, which has good interpretation properties and can improve the denoising performance of existing models. And the validation results of multiple models using different datasets show that it has good generalization ability. By using this loss function, high-quality CT images with low radiation are achieved, which can avoid the hazards caused by radiation and ensure the disease diagnosis for patients.

A novel denoising method for low-dose CT images based on transformer and CNN

Computed Tomography (CT) has become a mainstream imaging tool in medical diagnosis. However, the issue of increased cancer risk due to radiation exposure has raised public concern. Low-dose computed tomography (LDCT) technique is a CT scan with lower radiation dose than conventional scans. LDCT is used to make a diagnosis of lesions with the smallest dose of x-rays, and is currently mainly used for early lung cancer screening. However, LDCT has severe image noise, and these noises affect adversely the quality of medical images and thus the diagnosis of lesions. In this paper, we propose a novel LDCT image denoising method based on transformer combined with convolutional neural network (CNN). The encoder part of the network is based on CNN, which is mainly used to extract the image detail information. In the decoder part, we propose a dual-path transformer block (DPTB), which extracts the features of input of the skip connection and the features of input of the previous level through two paths respectively. DPTB can better restore the detail and structure information of the denoised image. In order to pay more attention to the key regions of the feature images extracted at the shallow level of the network, we also propose a multi-feature spatial attention block (MSAB) in the skip connection part. Experimental studies are conducted, and comparisons with the state-of-the-art networks are made, and the results demonstrate that the developed method can effectively remove the noise in CT images and improve the image quality in the evaluation metrics of peak signal to noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) and is superior to the state-of-the-art models. Our method achieved 28.9720 of PSNR, 0.8595 of SSIM and 14.8657 of RMSE on the Mayo Clinic LDCT Grand Challenge dataset. For different noise level σ (15, 35, and 55) on the QIN_LUNG_CT dataset, our proposed also achieved better performances.

Impact of a reduced iodine load with deep learning reconstruction on abdominal MDCT

To evaluate the impact of a reduced iodine load using deep learning reconstruction (DLR) on the hepatic parenchyma compared to conventional iterative reconstruction (hybrid IR) and its consequence on the radiation dose and image quality. This retrospective monocentric intraindividual comparison study included 66 patients explored at the portal phase using different multidetector computed tomography parameters: Group A, hybrid IR algorithm (hybrid IR) and a nonionic low-osmolality contrast agent (350 mgI/mL); Group B, DLR algorithm (DLR) and a nonionic iso-osmolality contrast agent (270 mgI/mL). We recorded the attenuation of the liver parenchyma, image quality, and radiation dose parameters. The mean hounsfield units (HU) value of the liver parenchyma was significantly lower in group B, at 105.9 ± 10.9 HU versus 118.5 ± 14.6 HU in group A. However, the 90%IC of mean liver attenuation in the group B (DLR) was between 100.8 HU and 109.3 HU. The signal-to-noise ratio of the liver parenchyma was significantly higher on DLR images, increasing by 56%. However, for both the contrast-to-noise ratio (CNR) and CNR liver/PV no statistical difference was found, even if the CNR liver/PV ratio was slightly higher for group A. The mean dose-length product and computed tomography dose index volume values were significantly lower with DLR, corresponding to a radiation dose reduction of 36% for the DLR. Using a DLR algorithm for abdominal multidetector computed tomography with a low iodine load can provide sufficient enhancement of the liver parenchyma up to 100 HU in addition to the advantages of a higher image quality, a better signal-to-noise ratio and a lower radiation dose.

Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning

Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed tomography (LDCT) images from different commercial scanners may contain different amounts and types of image noise, violating this assumption. Moreover, in the application of DL based image processing methods to LDCT, the feature distributions of LDCT images from simulation and clinical CT examination can be quite different. Therefore, the network models trained with simulated image data or LDCT images from one specific scanner may not work well for another CT scanner and image processing task. To solve such domain adaptation problem, in this study, a novel generative adversarial network (GAN) with noise encoding transfer learning (NETL), or GAN-NETL, is proposed to generate a paired dataset with a different noise style. Specifically, we proposed a method to perform noise encoding operator and incorporate it into the generator to extract a noise style. Meanwhile, with a transfer learning (TL) approach, the image noise encoding operator transformed the noise type of the source domain to that of the target domain for realistic noise generation. One public and two private datasets are used to evaluate the proposed method. Experiment results demonstrated the feasibility and effectiveness of our proposed GAN-NETL model in LDCT image synthesis. In addition, we conduct additional image denoising study using the synthesized clinical LDCT data, which verified the merit of the proposed synthesis in improving the performance of the DL based LDCT processing method.

Role of Cardiovascular Imaging in Risk Assessment: Recent Advances, Gaps in Evidence, and Future Directions

Optimal risk assessment for primary prevention remains highly challenging. Recent registries have highlighted major discrepancies between guidelines and daily practice. Although guidelines have improved over time and provide updated risk scores, they still fail to identify a significant proportion of at-risk individuals, who then miss out on effective prevention measures until their initial ischemic events. Cardiovascular imaging is progressively assuming an increasingly pivotal role, playing a crucial part in enhancing the meticulous categorization of individuals according to their risk profiles, thus enabling the customization of precise therapeutic strategies for patients with increased cardiovascular risks. For the most part, the current approach to patients with atherosclerotic cardiovascular disease (ASCVD) is homogeneous. However, data from registries (e.g., REACH, CORONOR) and randomized clinical trials (e.g., COMPASS, FOURIER, and ODYSSEY outcomes) highlight heterogeneity in the risks of recurrent ischemic events, which are especially higher in patients with poly-vascular disease and/or multivessel coronary disease. This indicates the need for a more individualized strategy and further research to improve definitions of individual residual risk, with a view of intensifying treatments in the subgroups with very high residual risk. In this narrative review, we discuss advances in cardiovascular imaging, its current place in the guidelines, the gaps in evidence, and perspectives for primary and secondary prevention to improve risk assessment and therapeutic strategies using cardiovascular imaging.

Pediatric Spinal Cord Injury: A Review

A spinal cord injury (SCI) can be a devastating condition in children, with profound implications for their overall health and quality of life. In this review, we aim to provide a concise overview of the key aspects associated with SCIs in the pediatric population. Firstly, we discuss the etiology and epidemiology of SCIs in children, highlighting the diverse range of causes. We explore the unique anatomical and physiological characteristics of the developing spinal cord that contribute to the specific challenges faced by pediatric patients. Next, we delve into the clinical presentation and diagnostic methods, emphasizing the importance of prompt and accurate diagnosis to facilitate appropriate interventions. Furthermore, we approach the multidisciplinary management of pediatric SCIs, encompassing acute medical care, surgical interventions, and ongoing supportive therapies. Finally, we explore emerging research as well as innovative therapies in the field, and we emphasize the need for continued advancements in understanding and treating SCIs in children to improve their functional independence and overall quality of life.

Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context

Computed tomography (CT) is a widely used examination technique that usually requires a compromise between image quality and radiation exposure. Reconstruction algorithms aim to reduce radiation exposure while maintaining comparable image quality. Recently, unsupervised deep learning methods have been proposed for this purpose. In this study, a promising sparse-view reconstruction method (posterior temperature optimized Bayesian inverse model; POTOBIM) is tested for its clinical applicability. For this study, 17 whole-body CTs of deceased were performed. In addition to POTOBIM, reconstruction was performed using filtered back projection (FBP). An evaluation was conducted by simulating sinograms and comparing the reconstruction with the original CT slice for each case. A quantitative analysis was performed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The quality was assessed visually using a modified Ludewig's scale. In the qualitative evaluation, POTOBIM was rated worse than the reference images in most cases. A partially equivalent image quality could only be achieved with 80 projections per rotation. Quantitatively, POTOBIM does not seem to benefit from more than 60 projections. Although deep learning methods seem suitable to produce better image quality, the investigated algorithm (POTOBIM) is not yet suitable for clinical routine.

Dual-Source Contrast-Enhanced Multiphasic CT of the Liver Using Low Voltage (70 kVp): Feasibility of a Reduced Radiation Dose and a 50% of Contrast Dose

This study investigated the feasibility of both a reduced radiation dose and a 50% of contrast dose in multiphasic CT of the liver with a 70 kVp protocol compared with a standard-tube-voltage protocol derived from dual-energy (DE) CT (blended DE protocol) with a full-dose contrast-agents in the same patient group. This study included 46 patients who underwent multiphasic contrast-enhanced dynamic CT of the liver with both a 70 kVp and a blended DE protocols. For quantitative analysis, median CT values for the liver, aorta, and portal vein, as well as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), were measured and calculated. In addition, as a qualitative analysis, the contrast effect and overall image quality of the abdominal organs were evaluated on a five-point scale. CNR and SNR of the hepatic parenchyma were not significantly different between the 70kV protocol and the Blended DE protocol in all phases. The 70 kVp protocol showed significantly better image quality compared with the blended DE protocol in the arterial phase (p = 0.035) and the equilibrium layer phase (p = 0.016). A 70 kVp CT protocol in combination with a reduced radiation dose and half-dose iodine load is feasible for multiphasic dynamic CT of the liver by maintaining the contrast enhancement effects and image quality in comparison with the blended DE CT protocol.

Comparing an imageless hand-held robotic-assisted system versus conventional technique for component positioning and early clinical outcomes in total knee arthroplasty

BACKGROUND

This study aimed to examine the component positioning and early clinical outcomes in total knee arthroplasty (TKA) using an imageless hand-held robotic-assisted system compared with those without robotics.

METHODS

Component positioning, hip-knee-ankle angle (HKA), and clinical outcome 1-year postoperatively were compared between 40 patients who underwent TKA with Navio (robot group) and 40 control patients who underwent TKA with a jig-based technique (manual group). The clinical outcomes evaluated were range of motion and the 2011 Knee Society Score.

RESULTS

Implantation errors in the coronal and sagittal alignments were significantly fewer in the robotic group than in the manual group. All knees in the robotic group achieved component positions within 3°. However, the HKA and clinical outcomes did not differ between the groups.

CONCLUSION

Although the accuracy of implantation in the robotic group was superior to that in the manual group, the clinical outcomes were similar.

Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney-Ureter-Bladder Images

Kidney-ureter-bladder (KUB) imaging is used as a frontline investigation for patients with suspected renal stones. In this study, we designed a computer-aided diagnostic system for KUB imaging to assist clinicians in accurately diagnosing urinary tract stones. The image dataset used for training and testing the model comprised 485 images provided by Kaohsiung Chang Gung Memorial Hospital. The proposed system was divided into two subsystems, 1 and 2. Subsystem 1 used Inception-ResNetV2 to train a deep learning model on preprocessed KUB images to verify the improvement in diagnostic accuracy with image preprocessing. Subsystem 2 trained an image segmentation model using the ResNet hybrid, U-net, to accurately identify the contours of renal stones. The performance was evaluated using a confusion matrix for the classification model. We conclude that the model can assist clinicians in accurately diagnosing renal stones via KUB imaging. Therefore, the proposed system can assist doctors in diagnosis, reduce patients' waiting time for CT scans, and minimize the radiation dose absorbed by the body.

Cancer mortality after low dose exposure to ionising radiation in workers in France, the United Kingdom, and the United States (INWORKS): cohort study

OBJECTIVE

To evaluate the effect of protracted low dose, low dose rate exposure to ionising radiation on the risk of cancer.

DESIGN

Multinational cohort study.

SETTING

Cohorts of workers in the nuclear industry in France, the UK, and the US included in a major update to the International Nuclear Workers Study (INWORKS).

PARTICIPANTS

309 932 workers with individual monitoring data for external exposure to ionising radiation and a total follow-up of 10.7 million person years.

MAIN OUTCOME MEASURES

Estimates of excess relative rate per gray (Gy) of radiation dose for mortality from cancer.

RESULTS

The study included 103 553 deaths, of which 28 089 were due to solid cancers. The estimated rate of mortality due to solid cancer increased with cumulative dose by 52% (90% confidence interval 27% to 77%) per Gy, lagged by 10 years. Restricting the analysis to the low cumulative dose range (0-100 mGy) approximately doubled the estimate of association (and increased the width of its confidence interval), as did restricting the analysis to workers hired in the more recent years of operations when estimates of occupational external penetrating radiation dose were recorded more accurately. Exclusion of deaths from lung cancer and pleural cancer had a modest effect on the estimated magnitude of association, providing indirect evidence that the association was not substantially confounded by smoking or occupational exposure to asbestos.

CONCLUSIONS

This major update to INWORKS provides a direct estimate of the association between protracted low dose exposure to ionising radiation and solid cancer mortality based on some of the world's most informative cohorts of radiation workers. The summary estimate of excess relative rate solid cancer mortality per Gy is larger than estimates currently informing radiation protection, and some evidence suggests a steeper slope for the dose-response association in the low dose range than over the full dose range. These results can help to strengthen radiation protection, especially for low dose exposures that are of primary interest in contemporary medical, occupational, and environmental settings.

Optimisation of exposure parameters using a phantom for thoracic spine radiographs in antero-posterior and lateral views

INTRODUCTION

To investigate the exposure parameters for thoracic spine/(TS) radiography that allows the image acquisition at the lowest dose possible, while maintaining an adequate image quality/(IQ) to identify all relevant anatomical criteria.

METHODS

An experimental phantom study was conducted, and 48 different radiographs of TS (24 AP/24 lateral) were acquired. The Automatic Exposure Control/(AEC) with the central sensor was used to select the beam intensity, while Source-to-Detector-Distance/(SDD) (AP:115/125 cm; Lateral:115/150 cm), tube potential (AP:70/81/90 kVp; Lateral: 81/90/102 kVp), use of grid/no grid and focal spot (fine/broad) were manipulated. IQ was assessed by observers with ViewDEX. Effective Dose (ED) was estimated using PCXMC2.0 software. Descriptive statistics paired with intraclass correlation coefficient (ICC) were applied to analyse data.

RESULTS

The ED increased with a greater SDD for lateral-view, presenting a significant difference (p = 0.038), however IQ was not affected. For both AP and lateral, the use of grid had a significant effect on ED (p < 0.001). Despite the images acquired without grid had lower IQ scores, the observers considered the IQ adequate for clinical use. A 20% reduction in ED (0.042mSv-0.033 mSv) was observed when increasing the beam energy from 70 to 90 kVp for AP grid in. The observers ICC ranged from moderate to good (0.5-0.75) in lateral and good to excellent (0.75-0.9) for AP views.

CONCLUSIONS

The optimised parameters in this context were 115 cm SDD, 90 kVp with grid for the best IQ and lowest ED. Further studies in clinical setting are necessary to enlarge the context and cover different body habitus and equipment.

IMPLICATIONS FOR PRACTICE

The SDD impacts on dose for TS; Higher kVp and grid are necessary to better image quality.

How Real Are Computed Tomography Low Dose Simulations? An Investigational In-Vivo Large Animal Study

OBJECTIVES

CT low-dose simulation methods have gained significant traction in protocol development, as they lack the risk of increased patient exposure. However, in-vivo validations of low-dose simulations are as uncommon as prospective low-dose image acquisition itself. Therefore, we investigated the extent to which simulated low-dose CT datasets resemble their real-dose counterparts.

MATERIALS AND METHODS

Fourteen veterinarian-sedated alive pigs underwent three CT scans on the same third generation dual-source scanner with 2 months between each scan. At each time, three additional scans ensued, with mAs reduced to 50%, 25%, and 10%. All scans were reconstructed using wFBP and ADMIRE levels 1-5. Matching low-dose datasets were generated from the 100% scans using reconstruction-based and DICOM-based simulations. Objective image quality (CT numbers stability, noise, and signal-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (-1=inferior, 0=equal, 1=superior). The points were averaged for a semiquantitative score, and inter-rater-agreement was measured using Spearman's correlation coefficient. A structural similarity index (SSIM) analyzed the voxel-wise similarity of the volumes. Adequately corrected mixed-effects analysis compared objective and subjective image quality. Multiple linear regression with three-way interactions measured the contribution of dose, reconstruction mode, simulation method, and rater to subjective image quality.

RESULTS

There were no significant differences between objective and subjective image quality of reconstruction-based and DICOM-based simulation on all dose levels (p≥0.137). However, both simulation methods produced significantly lower objective image quality than real-dose images below 25% mAs due to noise overestimation (p<0.001; SSIM≤89±3). Overall, inter-rater-agreement was strong (r≥0.68, mean 0.93±0.05, 95% CI 0.92-0.94; each p<0.001). In regression analysis, significant decreases in subjective image quality were observed for lower radiation doses (b ≤ -0.387, 95%CI -0.399 to -0.358; p<0.001) but not for reconstruction modes, simulation methods, raters, or three-way interactions (p≥0.103).

CONCLUSION

Simulated low-dose CT datasets are subjectively and objectively indistinguishable from their real-dose counterparts down to 25% mAs, making them an invaluable tool for efficient low-dose protocol development.

Imaging surveillance in multiple endocrine neoplasia type 1: Ten years of experience with somatostatin receptor positron emission tomography

Guidelines for multiple endocrine neoplasia type 1 (MEN1) recommend intensive imaging surveillance without specifying a superior regimen, including the role of somatostatin receptor imaging (SRI) with positron emission tomography (PET). The primary outcomes were to: (1) Assess change in treatment of duodenal-pancreatic neuroendocrine neoplasms (DP-NENs), bronchopulmonary NENs, and thymic tumors attributed to use of SRI PET/computed tomography (CT) and (2) estimate radiation from imaging and risk of cancer death attributed to imaging radiation. This was a retrospective single center study, including all MEN1 patients, who had had at least one SRI PET/CT. A total of 60 patients, median age 42 (range 21-54) years, median follow-up 6 (range 1-10) years were included. Of 470 cross sectional scans (MRI, CT, SRI PET/CT), 209 were SRI PET/CT. The additional information from SRI PET had implications in 1/14 surgical interventions and 2/12 medical interventions. The estimated median radiation dose per patient was 104 (range 51-468) mSv of which PET contributed with 13 (range 5-55) mSv and CT with 91 mSv (range 46-413 mSv), corresponding to an estimated increased median risk of cancer death of 0.5% during 6 years follow-up. SRI PET had a significant impact on 3/26 decisions to intervene in 60 MEN1 patients followed for a median of 6 years with SRI PET/CT as the most frequently used modality. The surveillance program showed a high radiation dose. Multi-modality imaging strategies designed to minimize radiation exposure should be considered. Based on our findings, SRI-PET combined with CT cannot be recommended for routine surveillance in MEN1 patients.

Test characteristics of ultrasound for the diagnosis of peritonsillar abscess: A systematic review and meta-analysis

BACKGROUND

Distinguishing peritonsillar abscess (PTA) from peritonsillar cellulitis using clinical assessment is challenging as many features overlap for both conditions, and physical examination is only about 75% sensitive and 50% specific for diagnosing PTA. The primary objective of this systematic review was to determine the test characteristics of ultrasound for diagnosing PTA when compared to a reference standard of computed tomography or acquisition of pus via needle aspiration or incision and drainage.

METHODS

This systematic review was performed in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy (PRISMA-DTA) guidelines. We searched seven databases from 1960 to November 2022. Two independent reviewers completed study selection, data extraction, and QUADAS-2 risk-of-bias assessment. We used a bivariate random-effects model to calculate pooled sensitivity, specificity, positive likelihood ratio (LR+), and negative likelihood ratio (LR-). We also conducted subgroup analyses on radiology ultrasound compared to point-of-care ultrasound (POCUS) and intraoral compared to transcervical scanning techniques.

RESULTS

From 339 citations, we identified 18 studies for inclusion. Because one study only reported positive cases of PTA (thereby preventing the calculation of specificity), it was excluded from the analysis, so the analysis included a total of 17 studies with 812 patients, of whom 541 had PTA. Pooled bivariate sensitivity was 86% (95% confidence interval [CI] 78%-91%), specificity 76% (95% CI 67%-82%), LR+ 3.51 (95% CI 2.59-4.89), and LR- 0.19 (95% CI 0.12-0.30). On subgroup analysis, radiology-performed ultrasound had a sensitivity and specificity of 89% and 71%, compared to POCUS, which had a sensitivity and specificity of 74% and 79%. Comparing the two different techniques, intraoral had a sensitivity and specificity of 91% and 75% while transcervical had a sensitivity and specificity of 80% and 81%.

CONCLUSIONS

Ultrasound demonstrates high sensitivity for ruling out PTA, but it only has moderate specificity for ruling in the diagnosis.

Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising

Computer tomography (CT) has played an essential role in the field of medical diagnosis. Considering the potential risk of exposing patients to X-ray radiations, low-dose CT (LDCT) images have been widely applied in the medical imaging field. Since reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures, or false lesions derived from noise. To tackle these issues, we propose a novel degradation adaption local-to-global transformer (DALG-Transformer) for restoring the LDCT image. Specifically, the DALG-Transformer is built on self-attention modules which excel at modeling long-range information between image patch sequences. Meanwhile, an unsupervised degradation representation learning scheme is first developed in medical image processing to learn abstract degradation representations of the LDCT images, which can distinguish various degradations in the representation space rather than the pixel space. Then, we introduce a degradation-aware modulated convolution and gated mechanism into the building modules (i.e., multi-head attention and feed-forward network) of each Transformer block, which can bring in the complementary strength of convolution operation to emphasize on the spatially local context. The experimental results show that the DALG-Transformer can provide superior performance in noise removal, structure preservation, and false lesions elimination compared with five existing representative deep networks. The proposed networks may be readily applied to other image processing tasks including image reconstruction, image deblurring, and image super-resolution.

Multi-Scale Feature Fusion Network for Low-Dose CT Denoising

Computed tomography (CT) is an imaging technique extensively used in medical treatment, but too much radiation dose in a CT scan will cause harm to the human body. Decreasing the dose of radiation will result in increased noise and artifacts in the reconstructed image, blurring the internal tissue and edge details. To get high-quality CT images, we present a multi-scale feature fusion network (MSFLNet) for low-dose CT (LDCT) denoising. In our MSFLNet, we combined multiple feature extraction modules, effective noise reduction modules, and fusion modules constructed using the attention mechanism to construct a horizontally connected multi-scale structure as the overall architecture of the network, which is used to construct different levels of feature maps at all scales. We innovatively define a composite loss function composed of pixel-level loss based on MS-SSIM-L1 and edge-based edge loss for LDCT denoising. In short, our approach learns a rich set of features that combine contextual information from multiple scales while maintaining the spatial details of denoised CT images. Our laboratory results indicate that compared with the existing methods, the peak signal-to-noise ratio (PSNR) value of CT images of the AAPM dataset processed by the new model is 33.6490, and the structural similarity (SSIM) value is 0.9174, which also achieves good results on the Piglet dataset with different doses. The results also show that the method removes noise and artifacts while effectively preserving CT images' architecture and grain information.

The Great Contrast Shortage of 2022-Lessons learnt in Australia

INTRODUCTION

Computed tomography (CT) imaging is one of the most commonly used diagnostic tools. Iodine-based contrast media (IBCM) are frequently administered intravenously to improve soft tissue contrast in a wide range of CT scans. Supply chain disruptions triggered by the SARS-CoV-19 pandemic led to a global shortage of IBCM in mid-2022. The purpose of this study was to explore the impact of this shortage on the delivery of healthcare in Western Australia.

METHODS

We performed a single-centre retrospective analysis of the provision of CT studies, comparing historical patterns to the shortage period. We focussed our attention on the total number of CT scans (noncontrast CT [NCCT] and contrast-enhanced CT [CECT]) and also specifically CT pulmonary angiogram (CTPA) and CT neck angiogram with or without inclusion of circle of Willis (CTNA) examinations. We also examined whether a decrease was compensated by increasing frequency of alternate examinations such as ventilation/perfusion (V/Q) scans, carotid Doppler ultrasound studies and Magnetic Resonance Angiograms (MRAs).

RESULTS

Since 2012, there has been an approximate linear increase in the frequency of CT examinations. During the period of contrast shortage, there was an abrupt drop-off by approximately 50% in the CECT, CTPA and CTNA groups compared with the preceding 6 weeks (49%, 55% and 44%, respectively, with P < 0.001 in all cases). During the contrast shortage, the frequency of V/Q scans increased fivefold (from 13 to 65; P < 0.001). However, the provision of carotid Doppler ultrasound studies and MRAs remained approximately stable in frequency across recent time intervals.

CONCLUSION

Our findings demonstrate that the IBCM shortage crisis had a very significant impact on the delivery of healthcare. While V/Q scans could (partially) substitute for CTPA studies in suspected pulmonary emboli, there appeared to be no valid alternative for CTNA studies in stroke calls. The unexpected and critical shortage of IBCM forced healthcare professionals to conserve resources, prioritise indications, triage patients based on risk, explore alternate imaging strategies and prepare for similar events recurring in the future.

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to -4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7-9.4) HU vs 12.0 (11.4-12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4-7.4) vs 4.3 (3.3-5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.

Deep Learning for Medical Image-Based Cancer Diagnosis

(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

Observer agreement in single computerized tomography use for diagnosing paediatric head and neck malignancies at Uganda Cancer Institute

BACKGROUND

In the Ugandan setting, investigation for PHNM with CT uses a protocol with both unenhanced and contrast enhanced procedures hence doubling the ionizing radiation exposure. The purpose of this study was to determine the feasibility of single CT procedures in diagnosing PHNM.

METHODS

This was a cross-sectional study using CT images from patients, aged fifteen years and below, investigated for head and neck malignancies at the Uganda Cancer Institute. Three radiologists, observers A, B and C, with 12, 5 and 2 years of experience, respectively, participated in the study. They independently reported contrast enhanced images (protocol A), unenhanced images (protocol B), then both unenhanced and contrast enhanced images (protocol C) in 2 months intervals. Inter- and intra- observer agreement was determined using Gwen's Agreement coefficient.

RESULTS

Seventy-three CT scans of 36 boys and 37 girls, with a median age of 9 (3-13) years, were used. Intra-and inter-observer agreement on primary tumour location ranged from substantial to almost perfect with the highest intra-observer agreement observed when protocols A and C were compared. Inter-observer agreement for tumour calcifications was substantial for protocol A. Observers A and C demonstrated an almost perfect intra-observer agreement when protocols A and C were compared. There was a substantial inter-observer agreement on diagnosis for all protocols.

CONCLUSIONS

In our setting and examining a limited number of CT images, we demonstrated that contrast-enhanced CT scans provide sufficient information with no evidence of additional value of unenhanced images. Using contrast-enhanced images alone reduced the radiation exposure significantly.

Diagnostic Blood Biomarkers for Acute Pulmonary Embolism: A Systematic Review

(1) Background: The current diagnostic algorithm for acute pulmonary embolism (PE) is associated with the overuse of CT pulmonary angiography (CTPA). An additional highly specific blood test could potentially lower the proportion of patients with suspected PE that require CTPA. The aim was to summarize the literature on the diagnostic performance of biomarkers of patients admitted to an emergency department with suspected acute PE. (2) Methods: Medline and Embase databases were searched from 1995 to the present. The study selection process, data extraction, and risk of bias assessment were conducted by two reviewers. Eligibility criteria accepted all blood biomarkers except D-dimer, and CTPA was used as the reference standard. Qualitative data synthesis was performed. (3) Results: Of the 8448 identified records, only 6 were included. Eight blood biomarkers were identified, of which, three were investigated in two separate studies. Red distribution width and mean platelet volume were reported to have a specificity of ≥ 90% in one study, although these findings were not confirmed by other studies. The majority of the studies contained a high risk of selection bias. (4) Conclusions: The modest findings and the uncertain validity of the included studies suggest that none of the biomarkers identified in this systematic review have the potential to improve the current diagnostic algorithm for acute PE by reducing the overuse of CTPA.

Evaluation of the relationship between γ-H2AX biomarker levels and dose received after radiation exposure in abdominal-pelvic and chest CT scans

Background

As one of the most informative diagnostic radiation instruments, computed tomography (CT) has seen considerable improvement since its implementation in the 1970s; however, the possibility of low-dose radiation risk after CT procedures is still challenging and little is known about the biological effects of CT exposure on patients. As a result, this research aimed to look at the biological and cytogenetic effects of low-dose abdominal-pelvic and chest CT scans on adults, focusing on the number of γ-H2AX foci formation.

Materials and Methods

Blood tests were taken before and 10 min after CT exams on patients aged 25-55 who were undergoing abdominal-pelvic and chest CT exams with very low-ionizing radiation exposure (TLD doses of 15.67-63.45 mGy). Blood lymphocytes that had been isolated, fixed, and stained were dyed with γ-H2AX antibodies. Finally, the percentage of phosphorylation of histone H2AX as an indicator of double-strand breaks was determined using a cytometry technique.

Results

Our findings showed that after CT examination, the mean value of γ-H2AX foci in patients increased (P < 0.0001). A statistically significant correlation between dose radiation and the number of γ-H2AX foci was also found (P = 0.047, r = 0.4731). The current study also found a pattern of elevated γ-H2AX foci in patients over 40 years of age relative to younger patients.

Conclusion

A Significant activation of γ-H2AX foci was found in lymphocytes of peripheral blood samples of patients after CT compared to before CT scan. This increase in γ-H2AX foci levels in blood cells may be a useful quantitative biomarker of low-level radiation exposure in humans.

Associated radiation exposure from medical imaging and excess lifetime risk of developing cancer in pediatric patients with pulmonary hypertension

Pediatric patients with pulmonary hypertension (PH) receive imaging studies that use ionizing radiation (radiation) such as computed tomography (CT) and cardiac catheterization to guide clinical care. Radiation exposure is associated with increased cancer risk. It is unknown how much radiation pediatric PH patients receive. The objective of this study is to quantify radiation received from imaging and compute associated lifetime cancer risks for pediatric patients with PH. Electronic health records between 2012 and 2022 were reviewed and radiation dose data were extracted. Organ doses were estimated using Monte Carlo modeling. Cancer risks for each patient were calculated from accumulated exposures using National Cancer Institute tools. Two hundred and forty-nine patients with PH comprised the study cohort; 97% of patients had pulmonary arterial hypertension, PH due to left heart disease, or PH due to chronic lung disease. Mean age at the time of the first imaging study was 2.5 years (standard deviation [SD] = 4.9 years). Patients underwent a mean of 12 studies per patient per year, SD = 32. Most (90%) exams were done in children <5 years of age. Radiation from CT and cardiac catheterization accounted for 88% of the total radiation dose received. Cumulative mean effective dose was 19 mSv per patient (SD = 30). Radiation dose exposure resulted in a mean increased estimated lifetime cancer risk of 7.6% (90% uncertainty interval 3.0%-14.2%) in females and 2.8% (1.2%-5.3%) in males. Careful consideration for the need of radiation-based imaging studies is warranted, especially in the youngest of children.

Dose length product to effective dose coefficients in children

BACKGROUND

The most accurate method for estimating effective dose (the most widely understood metric for tracking patient radiation exposure) from computed tomography (CT) requires time-intensive Monte Carlo simulation. A simpler method multiplies a scalar coefficient by the widely available scanner-reported dose length product (DLP) to estimate effective dose.

OBJECTIVE

Develop pediatric effective dose coefficients and assess their agreement with Monte Carlo simulation.

MATERIALS AND METHODS

Multicenter, population-based sample of 128,397 pediatric diagnostic CT scans prospectively assembled in 2015-2020 from the University of California San Francisco International CT Dose Registry and the University of Florida library of highly realistic hybrid computational phantoms. We generated effective dose coefficients for seven body regions, stratified by patient age, diameter, and scanner manufacturer. We applied the new coefficients to DLPs to calculate effective doses and assessed their correlations with Monte Carlo radiation transport-generated effective doses.

RESULTS

The reported effective dose coefficients, generally higher than previous studies, varied by body region and decreased in magnitude with increasing age. Coefficients were approximately 4 to 13-fold higher (across body regions) for patients  <1 year old compared with patients 15-21 years old. For example, head CT (54% of scans) dose coefficients decreased from 0.039 to 0.003 mSv/mGy-cm in patients  <1 year old vs. 15-21 years old. There were minimal differences by manufacturer. Using age-based conversion coefficients to estimate effective dose produced moderate to strong correlations with Monte Carlo results (Pearson correlations 0.52-0.80 across body regions).

CONCLUSIONS

New pediatric effective dose coefficients update existing literature and can be used to easily estimate effective dose using scanner-reported DLP.

Endoscopic Endonasal Biopsy for Diagnosis of Undifferentiated Lesions of the Cavernous Sinus

BACKGROUND

Radiologically undifferentiated lesions of the cavernous sinus can pose a diagnostic challenge. Although radiotherapy is the mainstay for treatment of cavernous sinus lesions, histologic diagnosis allows access to a wide variety of alternative treatment modalities. The region is considered a high-risk area for open transcranial surgical access, and the endoscopic endonasal approach presents an alternative technique for biopsy.

METHODS

A retrospective case series was performed of all patients undergoing endoscopic endonasal biopsy of isolated cavernous sinus lesions at 2 tertiary institutions. The primary outcomes were the percentage of patients in whom a histologic diagnosis was achieved and the proportion of patients in whom therapy differed from radiotherapy alone. Secondary outcomes included preoperative and postoperative 22-item Sino-Nasal Outcome Test symptom scores, as well as perioperative adverse outcomes.

RESULTS

Eleven patients underwent endoscopic endonasal biopsy, with a diagnosis achieved in 10 patients. The most common diagnosis was perineural spread of squamous cell carcinoma, followed by perineuroma and single cases of metastatic melanoma, metastatic adenoid cystic carcinoma, mycobacterium lepri infection, neurofibroma, and lymphoma. Six patients had treatments other than radiotherapy, including immunotherapy, antibiotics, corticosteroids, chemotherapy, and observation alone. There was no significant difference in prebiopsy and postbiopsy 22-item Sino-Nasal Outcome Test scores. There was 1 case of epistaxis requiring return to theater for cautery of the sphenopalatine artery and there were no mortalities.

CONCLUSIONS

In a limited case series, endoscopic endonasal biopsy was safe and effective in obtaining diagnosis for cavernous sinus lesions and had a significant impact on therapeutic decision making.

Postoperative computed tomography imaging of pediatric patients with craniosynostosis: radiation dose and image quality comparison between multi-slice computed tomography and O-arm cone-beam computed tomography

BACKGROUND

When postoperative multi-slice computed tomography (MSCT) imaging of patients with craniosynostosis is used, it is usually performed a few days after surgery in a radiology department. This requires additional anesthesia for the patient. Recently, intraoperative mobile cone-beam CT (CBCT) devices have gained popularity for orthopedic and neurosurgical procedures, which allows postoperative CT imaging in the operating room.

OBJECTIVE

This single-center retrospective study compared radiation dose and image quality of postoperative imaging performed using conventional MSCT scanners and O-arm CBCT.

MATERIALS AND METHODS

A total of 104 pediatric syndromic and non-syndromic patients who were operated on because of single- or multiple-suture craniosynostosis were included in this study. The mean volumetric CT dose index (CTDIvol) and dose-length product (DLP) values of optimized craniosynostosis CT examinations (58 MSCT and 46 CBCT) were compared. Two surgeons evaluated the subjective image quality.

RESULTS

CBCT resulted in significantly lower CTDIvol (up to 14%) and DLP (up to 33%) compared to MSCT. Multi-slice CT image quality was considered superior to CBCT scans. However, all scans were considered to be of sufficient quality for diagnosis.

CONCLUSION

The O-arm device allowed for an immediate postoperative CBCT examination in the operating theater using the same anesthesia induction. Radiation exposure was lower in CBCT compared to MSCT scans, thus further encouraging the use of O-arms. Cone-beam CT imaging with an O-arm is a feasible method for postoperative craniosynostosis imaging, yielding less anesthesia to patients, lower health costs and the possibility to immediately evaluate results of the surgical operation.

Glycans as Potential Diagnostic Markers of Traumatic Brain Injury in Children

Diagnosing mild traumatic brain injury (TBI) in the acute setting is challenging due to the nonspecific and often transient or delayed symptoms. Further, the criteria for acute head imaging are frequently not fulfilled, which may lead to a missed diagnosis. A rapid test to diagnose TBI using body fluids would be highly useful. Urine and saliva samples were collected from 28 pediatric patients (mean [SD] age, eight years two months [four years three months]) with acute, clinically diagnosed mild TBI and 30 healthy volunteers at Satasairaala Hospital, Pori, Finland, over 11 months. The mean (SD) time from trauma to first sampling was 3 h 56 min (1 h 14 min). Samples were analyzed to determine the number of lectin-binding glycan molecules, indicating nerve tissue damage. The relative levels of several lectin-bound glycans were measured by fluorescence. Compared with healthy controls, the TBI group showed significant increases (p < 0.05, Wilcoxon rank-sum two-sided test) in nine glycans in the saliva, one glycan in the urine, and a significant decrease in seven glycans in the urine. These findings of potentially diagnostic glycans in body fluids after TBI warrant further research and may enable the development of a rapid body fluid-based point-of-care test to identify pediatric patients with TBI after a head injury.

Usefulness of electrocardiogram mA modulation during the electrocardiogram-gated CT scan in paediatrics with high heart rate for different helical pitch: a phantom-based assessment study

We investigated the effect of electrocardiographic (ECG) mA-modulation of ECG-gated scans of computed tomography (CTA) on radiation dose and image noise at high heart rates (HR) above 100 bpm between helical pitches (HP) 0.16 and 0.24. ECG mA-modulation range during ECG-gated CTA is 50-100 mA, the phase setting is 40-60% and the scan range is 90 mm for clinical data during HR for 90, 120 and 150 bpm. Radiation dose and image noise in Housfield units are measured for CT equipment during HR for 90, 120 and 150 bpm between HP 0.16 and 0.24. ECG mA-modulation, dose reduction ratio for HR 90, 120 and 150 bpm are 19.1, 13.4 and 8.7% at HP 0.16 and 17.1, 13.3 and 7.7% at HP 0.24, respectively. No significant differences were observed in image noise between both HP. Dose reductions of 8-24% are achieved with ECG mA-modulation during ECG-gated CCTA scan, which is beneficial even in high HR more than 100 bpm.

Emerging role of computed tomography coronary angiography in evaluation of children with Kawasaki disease

Coronary artery abnormalities are the most important complications in children with Kawasaki disease (KD). Two-dimensional transthoracic echocardiography currently is the standard of care for initial evaluation and follow-up of children with KD. However, it has inherent limitations with regard to evaluation of mid and distal coronary arteries and, left circumflex artery and the poor acoustic window in older children often makes evaluation difficult in this age group. Catheter angiography (CA) is invasive, has high radiation exposure and fails to demonstrate abnormalities beyond lumen. The limitations of echocardiography and CA necessitate the use of an imaging modality that overcomes these problems. In recent years advances in computed tomography technology have enabled explicit evaluation of coronary arteries along their entire course including major branches with optimal and acceptable radiation exposure in children. Computed tomography coronary angiography (CTCA) can be performed during acute as well as convalescent phases of KD. It is likely that CTCA may soon be considered the reference standard imaging modality for evaluation of coronary arteries in children with KD.