Radiation dose reduction in computed tomography: techniques and future perspective

Diagnostic Potentials of Lung Ultrasound In Neonatal Care: An Updated Overview

Recent technological strides, including high-frequency probes and lung ultrasound, have become a crucial non-invasive diagnostic tool in neonatal care, revolutionizing how respiratory conditions are assessed in the neonatal intensive care unit (NICU). High-frequency probes and portable devices significantly enhance the effectiveness of lung ultrasound in identifying respiratory distress syndrome (RDS), pneumonia, and pneumothorax, and underscore its growing significance. This comprehensive review explores the historical journey of lung ultrasonography, technological advancements, contemporary applications in neonatal care, emerging trends, and collaborative initiatives, and foresees a future where personalized healthcare optimizes outcomes for neonates.

Diagnostic performance and image quality of an image-based denoising algorithm applied to radiation dose-reduced CT in diagnosing acute appendicitis

PURPOSE

To evaluate diagnostic performance and image quality of ultralow-dose CT (ULDCT) in diagnosing acute appendicitis with an image-based deep-learning denoising algorithm (IDLDA).

METHODS

This retrospective multicenter study included 180 patients (mean ± standard deviation, 29 ± 9 years; 91 female) who underwent contrast-enhanced 2-mSv CT for suspected appendicitis from February 2014 to August 2016. We simulated ULDCT from 2-mSv CT, reducing the dose by at least 50%. Then we applied an IDLDA on ULDCT to produce denoised ULDCT (D-ULDCT). Six radiologists with different experience levels (three board-certified radiologists and three residents) independently reviewed the ULDCT and D-ULDCT. They rated the likelihood of appendicitis and subjective image qualities (subjective image noise, diagnostic acceptability, and artificial sensation). One radiologist measured image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). We used the receiver operating characteristic (ROC) analyses, Wilcoxon's signed-rank tests, and paired t-tests.

RESULTS

The area under the ROC curves (AUC) for diagnosing appendicitis ranged 0.90-0.97 for ULDCT and 0.94-0.97 for D-ULDCT. The AUCs of two residents were significantly higher on D-ULDCT (AUC difference = 0.06 [95% confidence interval, 0.01-0.11; p = .022] and 0.05 [0.00-0.10; p = .046], respectively). D-ULDCT provided better subjective image noise and diagnostic acceptability to all six readers. However, the response of board-certified radiologists and residents differed in artificial sensation (all p ≤ .003). D-ULDCT showed significantly lower image noise, higher SNR, and higher CNR (all p < .001).

CONCLUSION

An IDLDA can provide better ULDCT image quality and enhance diagnostic performance for less-experienced radiologists.

Investigating the Effects of Tube Current and Tube Voltage on Patient Dose in Computed Tomography Examinations with Principial Component Analysis and Cluster Analysis: Phantom Study

ABSTRACT

The aim of this study was to investigate the effects of tube current and tube voltage choices on patient dose in adult and pediatric CT protocols by qualitative analysis using Principal Component Analysis (PCA), cluster analysis, and statistical analysis.Dose length product (DLP), Effective mAs (Eff. mAs), and volume-weighted CT dose index (CTDIvol) dose descriptors were obtained from 16 adult and pediatric head phantom CT examinations. Different tube voltage and tube current values were selected in both pediatric head and adult head CT imaging protocols, and PCA and cluster analysis were applied to the data obtained for qualitative analysis of the relationship between CTDIvol, Eff. mAs and Total DLP values. The two principial components (PC) with the highest values among those obtained as a result of the PCA method were used. PC1 was 70.97%, and PC2 was 28.03%. In the cluster analysis, it was observed that the values obtained from pediatric and adult phantom CT scans were classified into two different clusters. The correlation coefficient for adult patients was r = 0.998, and for pediatric patients, the correlation coefficient was r = 0.947. When the obtained clusters were examined, the degree of closeness or distance of the variables could be observed. In the study, as a result of the analysis of CTDIvol, Eff. mAs and Total DLP data based on manufacturer data at different kV and mA values with PCA and cluster analysis, it was shown that pediatric patients could be exposed to more radiation than the adult patients.

Radiation Dose Optimization in Radiology: A Comprehensive Review of Safeguarding Patients and Preserving Image Fidelity

Radiation dose optimization in radiology is a critical aspect of modern healthcare, aimed at balancing the necessity of diagnostic imaging with the imperative of patient safety. This comprehensive review explores the fundamental principles, techniques, and considerations in optimizing radiation dose to safeguard patients while preserving image fidelity. Beginning with acknowledging the inherent risks associated with medical radiation exposure, the review highlights strategies such as the As Low as Reasonably Achievable (ALARA) principle, technological advancements, and quality assurance measures to minimize radiation dose without compromising diagnostic accuracy. Regulatory guidelines and the importance of patient education and informed consent are also discussed. Through a synthesis of current knowledge and emerging trends, the review underscores the pivotal role of radiation dose optimization in radiology practice. Furthermore, it emphasizes the need for ongoing research and collaboration to advance dose reduction strategies, establish standards for radiation safety, and explore personalized dose optimization approaches. By prioritizing radiation dose optimization, healthcare providers can ensure the highest standards of patient care while minimizing potential risks associated with medical radiation exposure.

Contribution of CT scan to patient's radiation exposure in parathyroid SPECT/CT scintigraphy

INTRODUCTION

Dual phase technetium-99mTc-methoxy isobutyl isonitrile (MIBI) single-photon emission computed tomography with computed tomography (SPECT/CT) may be the most accurate conventional imaging approach for localization of enlarged parathyroid gland (EPG). The imaging is based on the radiopharmaceutical (RP) retention in EPG compared to washout from normal thyroid and normal parathyroid glands. This study aimed to estimate and optimize the contribution of computed tomography (CT) scan and scan range to effective dose (ED) in dual-phase MIBI SPECT/CT parathyroid scintigraphy.

METHODS

The study included seventy-four patients; thirty-seven with reduced and thirty-seven with extended CT scan range. The ED caused by the CT scan was calculated using Dose Length Product (DLP) data and estimated using the Imaging Performance Assessment of CT scanners (ImPACT) calculator.

RESULTS

For all patients, the contribution of CT to the ED in a combined SPECT/CT examination was 2.62 ± 0.29 mSv (48%). The contribution of CT to the total ED was 1.8 ± 0.18 mSv (33%) when using reduced and 3.44 ± 0.23 mSv (64%) when using extended scan range. The DLP and ED were statistically significantly different between the reduced and extended CT scan range (p < 0.001) in the first and second phases. The individual organ dose was reduced from 8% to 94%.

CONCLUSION

The hybrid SPECT/CT improves the interpretation of nuclear medicine images and also increases the radiation dose to the patient. An adequately defined CT scan range on SPECT/CT imaging, can significantly reduce a patient's ED.

IMPLICATIONS FOR PRACTICE

The research findings showed that knowledge of anatomy, pathology and technology can provide optimising diagnostic procedures and reduce patient ED after SPECT/CT scans.

Improving the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm using DenseNet

INTRODUCTION

Thinner slices are more susceptible in detecting small lesions but suffer from higher statistical fluctuation. This work aimed to reduce image noise in multiphase contrast-enhanced CT reconstructed with slice thickness thinner than the clinical setting (i.e., 5 mm) using convolutional neural network (CNN) for enabling better detection of hypo-vascular liver metastasis.

METHODS

A DenseNet model was used to generate noise map for multiphase CT reconstructed with slice thickness of 2.5 mm and 1.25 mm. Image denoising was conducted by subtracting the CNN-generated noise map from CT images with reduced photon flux due to thinner slice thickness. The performance of DenseNet was evaluated on CT scans of electron density phantoms and patients with hypovascular liver metastases less than 1.5 cm in terms of Hounsfield Unit (HU) variation, statistical fluctuation, and contrast-to-noise ratio (CNR).

RESULTS

The phantom study demonstrated that the CNN-based denoising method was able to reduce statistical fluctuation in CT images reconstructed with slice thickness of 2.5 mm and 1.25 mm without causing significant edge blurring or variation in HU values. With regards to patient study, it was found that the denoised 2.5-mm and 1.25-mm slices had higher CNR than the conventional 5-mm slices for hypo-vascular liver metastases in all 4 phases of multiphase CT.

CONCLUSION

Our results demonstrated that the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm could be improved by using the CNN-based denoising method.

IMPLICATIONS FOR PRACTICE

Reconstruction slice thickness has a strong influence on the image quality of CT imaging. A CNN-based denoising method was used in this work to reduce the image noise in multiphase contrast-enhanced CT reconstructed with slice thickness less than 5 mm.

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery

Background.Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of deep learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms require the pre-collection of a large set of image pairs (low-dose/standard-dose) and the training of networks in an end-to-end supervised manner. Meanwhile, securing such a large volume of paired, well-registered training data in clinical practice is challenging. Moreover, these algorithms often overlook the potential to utilize the abundant information in a large collection of LDCT-only images/sinograms.Methods.In this paper, we introduce a semi-supervised iterative adaptive network (SIA-Net) for LDCT imaging, utilizing both labeled and unlabeled sinograms in a cohesive network framework, integrating supervised and unsupervised learning processes. Specifically, the supervised process captures critical features (i.e. noise distribution and tissue characteristics) latent in the paired sinograms, while the unsupervised process effectively learns these features in the unlabeled low-dose sinograms, employing a conventional weighted least-squares model with a regularization term. Furthermore, the SIA-Net method is designed to adaptively transfer the learned feature distribution from the supervised to the unsupervised process, thereby obtaining a high-fidelity sinogram through iterative adaptive learning. Finally, high-quality CT images can be reconstructed from the refined sinogram using the filtered back-projection algorithm.Results.Experimental results on two clinical datasets indicate that the proposed SIA-Net method achieves competitive performance in terms of noise reduction and structure preservation in LDCT imaging, when compared to traditional supervised learning methods.

Large variation in radiation dose for routine abdomen CT: reasons for excess and easy tips for reduction

OBJECTIVE

To characterize the use and impact of radiation dose reduction techniques in actual practice for routine abdomen CT.

METHODS

We retrospectively analyzed consecutive routine abdomen CT scans in adults from a large dose registry, contributed by 95 hospitals and imaging facilities. Grouping exams into deciles by, first, patient size, and second, size-adjusted dose length product (DLP), we summarized dose and technical parameters and estimated which parameters contributed most to between-protocols dose variation. Lastly, we modeled the total population dose if all protocols with mean size-adjusted DLP above 433 or 645 mGy-cm were reduced to these thresholds.

RESULTS

A total of 748,846 CTs were performed using 1033 unique protocols. When sorted by patient size, patients with larger abdominal diameters had increased dose and effective mAs (milliampere seconds), even after adjusting for patient size. When sorted by size-adjusted dose, patients in the highest versus the lowest decile in size-adjusted DLP received 6.4 times the average dose (1680 vs 265 mGy-cm) even though diameter was no different (312 vs 309 mm). Effective mAs was 2.1-fold higher, unadjusted CTDIvol 2.9-fold, and phase 2.5-fold for patients in the highest versus lowest size-adjusted DLP decile. There was virtually no change in kV (kilovolt). Automatic exposure control was widely used to modulate mAs, whereas kV modulation was rare. Phase was the strongest driver of between-protocols variation. Broad adoption of optimized protocols could result in total population dose reductions of 18.6-40%.

CONCLUSION

There are large variations in radiation doses for routine abdomen CT unrelated to patient size. Modification of kV and single-phase scanning could result in substantial dose reduction.

CLINICAL RELEVANCE

Radiation dose-optimization techniques for routine abdomen CT are routinely under-utilized leading to higher doses than needed. Greater modification of technical parameters and number of phases could result in substantial reduction in radiation exposure to patients.

KEY POINTS

• Based on an analysis of 748,846 routine abdomen CT scans in adults, radiation doses varied tremendously across patients of the same size and optimization techniques were routinely under-utilized. • The difference in observed dose was due to variation in technical parameters and phase count. Automatic exposure control was commonly used to modify effective mAs, whereas kV was rarely adjusted for patient size. Routine abdomen CT should be performed using a single phase, yet multi-phase was common. • kV modulation by patient size and restriction to a single phase for routine abdomen indications could result in substantial reduction in radiation doses using well-established dose optimization approaches.

Channelized hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom: Part II. Repeatability study

BACKGROUND

Objective and quantitative evaluation for low-contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d'). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d' with only 1-3 repeat scans.

PURPOSE

In this work, we aim to validate the repeatability of the proposed CHO-based low-contrast evaluation on four scanner models using the ACR CT accreditation phantom.

METHODS

The repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low-contrast object sizes (4-6 mm). The CHO was also applied to images created using deep learning-based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon-counting-detector (PCD)-CT. The repeatability was evaluated by the probability that a measurement would fall within the ±15% tolerance (P<15% ).

RESULTS

With the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non-overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%-99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%-99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%-99.998% on scanner B. P<15% were 96.5%-97.2% for the two deep learning reconstructions and 97.0%-99.97% for the four scan/reconstruction modes on the PCD-CT.

CONCLUSION

The CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the ±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning-based reconstruction and various scan/reconstruction modes on a PCD-CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

Deep learning-based algorithms for low-dose CT imaging: A review

The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising concerns regarding the potential radiation damage it may cause. The reduction of X-ray exposure dose in CT scanning may result in a significant decline in imaging quality, thereby elevating the risk of missed diagnosis and misdiagnosis. The reduction of CT radiation dose and acquisition of high-quality images to meet clinical diagnostic requirements have always been a critical research focus and challenge in the field of CT. Over the years, scholars have conducted extensive research on enhancing low-dose CT (LDCT) imaging algorithms, among which deep learning-based algorithms have demonstrated superior performance. In this review, we initially introduced the conventional algorithms for CT image reconstruction along with their respective advantages and disadvantages. Subsequently, we provided a detailed description of four aspects concerning the application of deep neural networks in LDCT imaging process: preprocessing in the projection domain, post-processing in the image domain, dual-domain processing imaging, and direct deep learning-based reconstruction (DLR). Furthermore, an analysis was conducted to evaluate the merits and demerits of each method. The commercial and clinical applications of the LDCT-DLR algorithm were also presented in an overview. Finally, we summarized the existing issues pertaining to LDCT-DLR and concluded the paper while outlining prospective trends for algorithmic advancement.

Comparison of Low-Dose Non-contrast CT in Detecting Anatomical and Surgically Important Variants of Paranasal Sinuses to Standard Dose Non-contrast CT: Experience from a Tertiary Care Hospital in Sub-Himalayan Region of Northern India

Computed tomography (CT) is the gold standard for diagnosing sinusitis and anatomical variations and a guide for paranasal sinus (PNS) surgeries. High doses of radiation lead to increased risk of head and neck malignancies, radiation-induced cataracts, hypothyroidism, and hyperthyroidism. The purpose of this study was to assess the effectiveness of low-dose CT as compared to standard-dose CT in the identification of anatomical variants of paranasal sinus and rhinosinusitis. This was a prospective cross-sectional study consisting of 72 patients who were divided equally into cases (underwent low-dose CT for PNS) and controls (underwent CT for PNS using standard dose protocols). Prevalence of anatomical variants and sinusitis were compared. Image quality was assessed using volume CT dose index (CTDIvol), dose length product (DLP), scan length, and noise. Subjective assessment was done by two radiologists, and scores were given. The comparison and analysis of the quantitative and qualitative variables were done. Anatomical variants were comparable among cases and controls, with post-sellar sphenoid being most common and paradoxical middle turbinate being least common surgically important variant. The difference in mean SD of CTDIvol (mGy), DLP (mGy-cm), effective dose (mSv), globe, and air noise between low and standard doses was statistically significant. A moderate agreement (with kappa 0.50) in cases and substantial agreement (with kappa 0.69) in controls was observed between both observers. Low-dose CT PNS and standard-dose CT PNS are comparable in delineating the paranasal sinus anatomy, with a 3.53× reduction of effective radiation dose to patients.

Deep learning reconstruction improves the image quality of low-dose temporal bone CT with otitis media and mastoiditis patients

Objective

To evaluate the image quality of low-dose temporal bone computed tomography (CT) in otitis media and mastoiditis patients by using deep learning reconstruction (DLR).

Materials and methods

A total of ninety-seven temporal bones from 53 consecutive adult patients who had suspected otitis media and mastoiditis and underwent temporal bone CT were prospectively enrolled. All patients underwent high resolution CT protocol (group A) and an additional low-dose protocol (group B). In group A, high resolution data were reconstructed by filter back projection (FBP). In group B, low-dose data were reconstructed by DLR mild (B1), DLR standard (B2) and DLR strong (B3). The objective image quality was analyzed by measuring the CT value and image noise on the transverse image and calculating the signal-to-noise ratio (SNR) on incudomallear joint, retroauricular muscle, vestibule and subcutaneous fat. Subjective image quality was analyzed by using a five-point scale to evaluate nine anatomical structures of middle and inner ear. The number of temporal bone lesions which involved in five structures of middle ear were assessed in group A, B1, B2 and B3 images.

Results

There were no significant differences in the CT values of the four reconstruction methods at four structures (all p > 0.05). The DLR group B1, B2 and B3 had significantly less image noise and a significantly higher SNR than group A at four structures (all p < 0.001). The group B1 had comparable subjective image quality as group A in nine structures (all p > 0.05), however, the group B3 had lower subjective image quality than group A in modiolus, spiral osseous lamina and stapes (all p < 0.001), the group B2 had lower subjective image quality than group A in modiolus and spiral osseous lamina (both p < 0.05). The number of temporal bone lesions which involved in five structures for group A, B1 and B2 images were no significant difference (all p > 0.05), however, the number of temporal bone lesions which involved in mastoid for group B3 images were significantly more than group A (p < 0.05). The radiation dose of high resolution CT protocol and low-dose protocol were 0.55 mSv and 0.11 mSv, respectively.

Conclusion

Compared with high resolution CT protocol, in the low-dose protocol of temporal bone CT, DLR mild and standard could improve the objective image quality, maintain good subjective image quality and satisfy clinical diagnosis of otitis media and mastoiditis patients.

Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images

In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient's condition is crucial in modern medicine since it determines whether or not the patient will receive the care they need. Data from a sinus CT scan is uploaded to a computer and displayed on a high-definition monitor to give the surgeon a clear anatomical orientation before endoscopic sinus surgery. In this study, a unique method is presented for detecting and diagnosing paranasal sinus disorders using machine learning. The researchers behind the current study designed their own approach. To speed up diagnosis, one of the primary goals of our study is to create an algorithm that can accurately evaluate the paranasal sinuses in CT scans. The proposed technology makes it feasible to automatically cut down on the number of CT scan images that require investigators to manually search through them all. In addition, the approach offers an automatic segmentation that may be used to locate the paranasal sinus region and crop it accordingly. As a result, the suggested method dramatically reduces the amount of data that is necessary during the training phase. As a result, this results in an increase in the efficiency of the computer while retaining a high degree of performance accuracy. The suggested method not only successfully identifies sinus irregularities but also automatically executes the necessary segmentation without requiring any manual cropping. This eliminates the need for time-consuming and error-prone human labor. When tested with actual CT scans, the method in question was discovered to have an accuracy of 95.16 percent while retaining a sensitivity of 99.14 percent throughout.

Evaluation of image quality and radiation dose in computed tomography urography following tube voltage optimisation

INTRODUCTION

Computed tomography urography (CTU) comprehensively evaluates the urinary tract. However, the procedure is associated with a high radiation dose due to multiple scan series and therefore requires optimisation. The study performed CTU protocol optimisation based on a reduction in tube voltage (kV) using quality assurance (QA) phantom and clinical images and evaluated image quality and radiation dose.

METHODS

The study was prospectively conducted on patients referred for CTU. The patients were grouped into A and B and were scanned with the standard protocol, a protocol used for the routine CTU at the CT centre before optimisation, and optimised protocol, a protocol with reduced kV respectively. The protocols were first tried on a quality assurance (QA) phantom before being applied to patients, and image quality was assessed based on signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). In addition, the clinical images were assessed based on the visibility of the anatomical criteria for CT images by five observers with >5 years of experience. The data were analysed using both visual grading characteristic (VGC) curves and statistical package for social sciences (SPSS) version 22.0.

RESULTS

The dose was significantly lower in the optimised protocol with a 10 % reduction in both volume computed tomography dose index and (CTDIvol) and dose length product (DLP) for the phantom images, and a 26 % reduction in CTDIvol and 28 % in DLP for the clinical images. However, there was no significant difference in image quality noted between the standard and optimised protocols based on the quantitative and qualitative image quality evaluation using both the QA phantom and clinical images.

CONCLUSION

The findings revealed a significant dose reduction in the optimised protocol. Further, image quality in standard and optimised protocols did not differ significantly based on quantitative and qualitative methods.

IMPLICATION FOR PRACTICE

kV optimisation in contrast-enhanced procedures provides dose reduction and should be encouraged in the medical imaging departments.

AntiHalluciNet: A Potential Auditing Tool of the Behavior of Deep Learning Denoising Models in Low-Dose Computed Tomography

Gaining the ability to audit the behavior of deep learning (DL) denoising models is of crucial importance to prevent potential hallucinations and adversarial clinical consequences. We present a preliminary version of AntiHalluciNet, which is designed to predict spurious structural components embedded in the residual noise from DL denoising models in low-dose CT and assess its feasibility for auditing the behavior of DL denoising models. We created a paired set of structure-embedded and pure noise images and trained AntiHalluciNet to predict spurious structures in the structure-embedded noise images. The performance of AntiHalluciNet was evaluated by using a newly devised residual structure index (RSI), which represents the prediction confidence based on the presence of structural components in the residual noise image. We also evaluated whether AntiHalluciNet could assess the image fidelity of a denoised image by using only a noise component instead of measuring the SSIM, which requires both reference and test images. Then, we explored the potential of AntiHalluciNet for auditing the behavior of DL denoising models. AntiHalluciNet was applied to three DL denoising models (two pre-trained models, RED-CNN and CTformer, and a commercial software, ClariCT.AI [version 1.2.3]), and whether AntiHalluciNet could discriminate between the noise purity performances of DL denoising models was assessed. AntiHalluciNet demonstrated an excellent performance in predicting the presence of structural components. The RSI values for the structural-embedded and pure noise images measured using the 50% low-dose dataset were 0.57 ± 31 and 0.02 ± 0.02, respectively, showing a substantial difference with a p-value < 0.0001. The AntiHalluciNet-derived RSI could differentiate between the quality of the degraded denoised images, with measurement values of 0.27, 0.41, 0.48, and 0.52 for the 25%, 50%, 75%, and 100% mixing rates of the degradation component, which showed a higher differentiation potential compared with the SSIM values of 0.9603, 0.9579, 0.9490, and 0.9333. The RSI measurements from the residual images of the three DL denoising models showed a distinct distribution, being 0.28 ± 0.06, 0.21 ± 0.06, and 0.15 ± 0.03 for RED-CNN, CTformer, and ClariCT.AI, respectively. AntiHalluciNet has the potential to predict the structural components embedded in the residual noise from DL denoising models in low-dose CT. With AntiHalluciNet, it is feasible to audit the performance and behavior of DL denoising models in clinical environments where only residual noise images are available.

A comparative study on the use of ultra-low dose computed tomography scans with conventional computed tomography scans for the evaluation of proximal tibia intra-articular fractures

Purpose

A rise in the need of preoperative CT assessment of proximal tibial intra-articular fractures has given rise to an increased load of radiation exposure. This comparative study aims at assessing the efficacy of low dose CT scan by collating images of tibial plateau fractures produced by low dose CT scan with that of conventional dose CT scan. Furthermore, the study aims at computing the comprehensive reduction in the effective dosage of radiation when low dose CT scan is employed for assessing these fractures.

Methods

The study comprise of 23 cases of proximal tibial intra-articular fractures (confirmed by initial radiographs). Low-dose CT scan of the injured limb was preceded by conventional dose CT scan for generating images that were assessed and reported independently by five (single blinded) radiologists (using Schatzker's CT based classification). Quantification of fracture was done to compare them statistically.

Results

A sensitivity of 93.94 % and positive predictive value of 93.92 % was observed on comparing images from low dose and conventional dose CT scan. An effective reduction of 89.81 % in overall dosage of radiation exposure was observed.

Conclusion

Near equal quality images were generated by low dose CT scan as compared to conventional dose CT scan, thereby demonstrating its non-inferiority with an effective reduction of 89.81 % in overall dosage of radiation exposure.

Radiation Exposure in Extracorporeal Life Support

Extracorporeal membrane oxygenation (ECMO) exposes patients to multiple radiologic studies. We hypothesized ECMO patients endure radiation exposure in excess of the International Commission of Radiological Protection (ICRP) recommendations of cumulative effective dose (CED, >20 mSv and 5-year cumulative limit of CED >100 mSv). We conducted a retrospective observational study in an academic medical center between January 2016 and December 2018 involving adult admissions (N = 306) on ECMO. Ionizing radiation was calculated from reference values to determine CED. Approximately 9.4% (N = 29) patients accrued CED >50 mSv and 4.5% (N = 14) accrued CED >100 mSv during ECMO. Over the entire hospitalization, 28% (N = 85) accrued >50 mSv and 14.7% (N = 45) accrued CED >100 mSv. Median CED during ECMO was 2.3 mSv (IQR, -0.82 to 8.1 mSv), and the entire hospitalization was 17.4 mSv (IQR, -4.5 to 56.6 mSv). Thirteen percent of the median CED accrued during hospitalization could be attributed to ECMO. Longer hospitalization was associated with a higher CED (50 days [IQR, -25 to 76 days] in CED >50 vs. 19 days [IQR, -10 to 32 days] in CED <50). Computer tomography (CT) scans and interventional radiology (IR) procedures contributed to 43.8% and 44.86%, respectively, of CED accrued on ECMO and 52.2% and 37.1% of CED accumulated during the whole hospitalization. Guidelines aimed at mitigating radiation exposure are urgently needed.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Photon Counting Detector Computed Tomography: A New Frontier of Myeloma Bone Disease Evaluation

Photon counting detector (PCD) computed tomography (CT) is a paradigm-shifting innovation in CT imaging which was recently granted approval for clinical use by the US Food and Drug Administration. PCD-CT allows the generation of multi-energy images with increased contrast and scanning speed or ultra-high spatial resolution (UHR) images with lower radiation doses, compared to the currently used energy integrating detector (EID) CT. Since the recognition of bone disease related to multiple myeloma is important for the diagnosis and management of patients, the advent of PCD-CT heralds a new era in superior diagnostic evaluation of myeloma bone disease. In a first-in-human pilot study, patients with multiple myeloma were imaged with UHR-PCD-CT to validate and establish the utility of this technology in routine imaging and clinical care. We describe 2 cases from that cohort to highlight the superior imaging performance and diagnostic potential of PCD-CT for multiple myeloma compared to clinical standard EID-CT. We also discuss how the advanced imaging capabilities from PCD-CT enhances clinical diagnostics to improve care and overall outcomes for patients.

Algorithm for Reducing Overall Biological Detriment Caused by PET/CT: an Age-Based Study

Purpose

This study is to use a simple algorithm based on patient's age to reduce the overall biological detriment associated with PET/CT.

Materials and Methods

A total of 421 consecutive patients (mean age 64 ± 14 years) undergoing PET for various clinical indications were enrolled. For each scan, effective dose (ED in mSv) and additional cancer risk (ACR) were computed both in a reference condition (REF) and after applying an original algorithm (ALGO). The ALGO modified the mean dose of FDG and the PET scan time parameters; indeed, a lower dose and a longer scan time were reported in the younger, while a higher dose and a shorter scan time in the older patients. Moreover, patients were classified by age bracket (18-29, 30-60, and 61-90 years).

Results

The ED was 4.57 ± 0.92 mSv in the REF condition. The ACR were 0.020 ± 0.016 and 0.0187 ± 0.013, respectively, in REF and ALGO. The ACR for the REF and ALGO conditions were significantly reduced in males and females, although it was more evident in the latter gender (all p < 0.0001). Finally, the ACR significantly reduced from the REF condition to ALGO in all three age brackets (all p < 0.0001).

Conclusion

Implementation of ALGO protocols in PET can reduce the overall ACR, mainly in young and female patients.

Feasibility of size-specific organ-dose estimates based on water equivalent diameter for common head CT examinations: a Monte Carlo study

Computed tomography dose index (CTDI) is an unreliable dose estimate outside of the standard CTDI phantom diameters (16 and 32 cm). Size-specific dose estimate (SSDE) for head computed tomography (CT) examination was studied in the American Association of Physicists in Medicine Report 293 to provide SSDE coefficient factors based on water equivalent diameter as size metrics. However, it is limited to one protocol and for a fully irradiated organ. This study aimed to evaluate the dependency of normalized organ dose (ND) on water equivalent diameter as a size metric in three common protocols: routine head, paranasal sinus, and temporal bone. CTDI_wmeasurements were performed for outlined protocols in the Siemens Emotion 16-slice-configuration scanner. Geant4 Application for Tomographic Emission Monte Carlo simulation platform, coupled with ten GSF patient models, was used to estimate organ doses. CT scanner system was modeled. Helical CT scans were simulated using constructor scan parameters and calculated scan lengths of each patient model. Organ doses provided by simulations were normalized to CTDI_vol. The water equivalent diameters (D_w) of patient models were obtained via relationships betweenD_wand both effective diameter for a sample of patients' data.NDs received by fully, partially, and non-directly irradiated organs were then reported as a function ofD_w. For fully irradiated organs, brain (R²> 0.92), eyes (R²> 0.88), and eye lens (R²> 0.89) correlate well withD_w. For the rest of the results, a poor correlation was observed. For partially irradiated organs, the exception was scalp (R²= 0.93) in temporal bone CT. For non-directly irradiated organs, the exception was thyroid (R²> 0.90) and lungs (R²> 0.91) in routine head CT. ND correlates well in routine head CT than other protocols. For the most part, no relationship seems to exist betweenR²and scan percentage coverage. The results have revealed additional factors that may influence the ND andD_wrelationship, which explains the need for more studies in the future to investigate the effect of scan conditions and organ anatomy variation.

Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose

Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79-13.2 mGy, 0.90-4.40 mGy, and 0.20-0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66-6.60 mSv, 0.50-0.80 mGy, and 0.39-0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2-4, whereas ULD reduced the dose reduction by a factor of 8-13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.

Hematuria as a Sign of Kidney Stone Disease Evaluated Using Computed Tomography: A Review

Kidney stone is a common cause of acute pain in the abdomen in patients presenting to casualty. Being present in roughly 12% of the world's population makes it the most prevalent pathology of the urinary system. The ureters, kidneys, and bladder frequently develop calculi, resulting in hematuria. The most effective imaging technique for evaluating calculi is unenhanced helical computed tomography. The population, intervention, control, and outcomes (PICO)-formatted question was used to generate methodological medical subject heading (MeSH) phrases, which increased the search strategy's sensitivity in finding research. Some of these names ("hematuria") included "renal calculi" (MeSH) and "cone-beam computed tomography" (MeSH). Studies that satisfied these requirements were subjected to critical evaluation. The merits of the listed studies were evaluated using a unique quality assessment scale. The most accurate imaging diagnostic test for people with hematuria is multidetector computed tomography. If a patient over 40 presents with microscopic hematuria, a non-contrast computed tomography or ultrasound study should be performed, and if gross hematuria is observed, cystoscopy should be added. Pre- and post-contrast computed tomography scans and cystoscopy should be carried out on elderly patients.

Hip Imaging in Children With Cerebral Palsy: Estimation and Intrapatient Comparison of Patient-Specific Radiation Doses of Low-Dose CT and Radiography

OBJECTIVES

Hip displacement is the second most common orthopedic problem affecting children with cerebral palsy (CP). Routine radiographic hip surveillance typically involves an anteroposterior (AP) pelvis radiograph. Unfortunately, this imaging protocol is limited by its projectional technique and the positioning challenges in children with CP. Alternatively, hip low-dose computed tomography (LDCT) has been advocated as a more accurate strategy for imaging surveillance as it provides biofidelic details of the hip that is independent of patient positioning. However, the tradeoff is the (presumed) higher radiation dose to the patient. The goal of this study is to estimate patient-specific radiation doses of hip LDCTs and AP pelvis radiographs in CP patients, and perform an intrapatient dose comparison.

MATERIALS AND METHODS

A search of our imaging database was performed to identify children with CP who underwent hip LDCT and AP pelvis radiograph within 6 months of each other. The LDCTs were performed using weight-adjusted kVp and tube current modulation, whereas the radiographs were obtained with age-/size-adjusted kVp/mAs. The patient-specific organ and effective doses for LDCT were estimated by matching the patients to a nonreference pediatric phantom library from the National Cancer Institute Dosimetry System for Computed Tomography database with Monte Carlo-based dosimetry. The patient-specific organ and effective doses for radiograph were estimated using the National Cancer Institute Dosimetry System for Radiography and Fluoroscopy with Monte Carlo-based dose calculation. Dose conversion k-factors of dose area product for radiography and dose length product for LDCT were adapted, and the estimation results were compared with patient-specific dosimetry.

RESULTS

Our study cohort consisted of 70 paired imaging studies from 67 children (age, 9.1 ± 3.3 years). The patient-specific and dose length product-based effective doses for LDCT were 0.42 ± 0.21 mSv and 0.59 ± 0.28 mSv, respectively. The patient-specific and dose area product-based effective doses for radiography were 0.14 ± 0.09 mSv and 0.08 ± 0.06 mSv, respectively.

CONCLUSIONS

The radiation dose for a hip LDCT is ~4 times higher than pelvis radiograph, but it is still very low and poses minimal risk to the patient.

Accurate and efficient measurement of channelized Hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom

BACKGROUND

Current CT quality control (QC) for low-contrast detectability relies on visual inspection and measurement of contrast-to-noise ratio (CNR). However, CNR numbers become unreliable when it comes to nonlinear methods, such as iterative reconstruction (IR) and deep-learning-based techniques. Image quality metrics using channelized Hotelling observer (CHO) have been validated to be well correlated with human observer performance on phantom-based and patient-based tasks, but it has not been widely used in routine CT QC mainly because the CHO calculation typically requires a large number of repeated scans in order to provide accurate and precise estimate of index of detectability (d').

PURPOSE

The main goal of this work is to optimize channel filters and other CHO parameters and accurately estimate the low-contrast detectability with minimum number of repeated scans for the widely used American College of Radiology (ACR) CT accreditation phantom so that it can become practically feasible for routine CT QC tests.

METHODS

To provide a converged d' value, an ACR phantom was repeatedly scanned 100 times at three dose levels (24, 12, and 6 mGy). Images were reconstructed with two kernels (FBP Br44 and IR Br44-3). d' as a function of number of repeated scans was determined for different number of background regions of interest (ROIs), different number of low-contrast objects, different number of slices per each object, and different channel filter options. A reference d' was established using the optimized CHO setting, and the bias of d' was quantified using the d' calculated from all 100 repeated scans. The variation of d' at each condition was estimated using a resampling method combining random subsampling among 100 repeated scans and bootstrapping of the ensembles of signal and background ROIs.

RESULTS

Optimized parameters in CHO calculation were determined: two background ROIs per object, four objects per low-contrast object size, nine non-overlapping slices per object, and a 4-channel Gabor filter. The bias and uncertainty were estimated at different numbers of repeated scans using these parameters. When only one single scan was used in the CHO calculation, the bias of d' was below 6.2% and the uncertainty 15.6-19.6% for the 6, 5, and 4 mm objects, while with three repeated scans the bias was below 2.0% and uncertainty 8.7-10.9% for the three object sizes.

CONCLUSION

With optimized parameter settings in CHO, efficient and accurate measurement of low-contrast detectability on the commonly used ACR phantom becomes feasible, which could potentially lead to adoption of CHO-based low-contrast evaluation in routine QC tests.

Safety Considerations in MRI and CT

OBJECTIVE

MRI and CT are indispensable imaging modalities for the evaluation of patients with neurologic disease, and each is particularly well suited to address specific clinical questions. Although both of these imaging modalities have excellent safety profiles in clinical use as a result of concerted and dedicated efforts, each has potential physical and procedural risks that the practitioner should be aware of, which are described in this article.

LATEST DEVELOPMENTS

Recent advancements have been made in understanding and reducing safety risks with MR and CT. The magnetic fields in MRI create risks for dangerous projectile accidents, radiofrequency burns, and deleterious interactions with implanted devices, and serious patient injuries and deaths have occurred. Ionizing radiation in CT may be associated with shorter-term deterministic effects on biological tissues at extremely high doses and longer-term stochastic effects related to mutagenesis and carcinogenesis at low doses. The cancer risk of radiation exposure in diagnostic CT is considered extremely low, and the benefit of an appropriately indicated CT examination far outweighs the potential risk. Continuing major efforts are centered on improving image quality and the diagnostic power of CT while concurrently keeping radiation doses as low as reasonably achievable.

ESSENTIAL POINTS

An understanding of these MRI and CT safety issues that are central to contemporary radiology practice is essential for the safe and effective treatment of patients with neurologic disease.

Estimation of radiation doses and lifetime attributable risk of radiation-induced cancer in the uterus and prostate from abdomen pelvis CT examinations

Computed tomography (CT) scans are one of the most common radiation imaging modalities, and CT scans are rising steadily worldwide. CT has the potential to enhance radiography practice, but it also has the risk of drastically increasing patient doses. One CT procedure for the abdomen pelvis (AP) area can expose a patient's prostate or uterus to a substantial radiation dose, leading to concerns about radiation-induced cancer. This study aimed to estimate organ doses of the uterus and prostate and evaluate the lifetime attributable risk (LAR) of cancer incidence and mortality resulting from AP CT examinations. This retrospective study included 665 patients, of which 380 (57%) were female, and 285 (43%) were male. Data were collected from the picture archiving and communication system for AP CT procedures and exposure parameter data. Organ doses for the uterus and prostate were calculated using National Cancer Institute CT (NCICT) software. Based on the risk models proposed by the BEIR VII report, the calculated organ doses were used to estimate the LAR of prostate and uterus cancer incidence and mortality due to radiation exposure from AP CT procedures. The mean effective dose resulting from AP CT for females and males was 5.76 ± 3.22 (range: 1.13-12.71 mSv) and 4.37 ± 1.66 mSv (range: 1.36-8.07 mSv), respectively. The mean organ dose to the uterus was 10.86 ± 6.09 mGy (range: 2.13-24.06 mGy). The mean organ dose to the prostate was 7.00 ± 2.66 mGy (range: 2.18-12.94 mGy). The LAR of uterus and prostate cancer incidence was 1.75 ± 1.19 cases and 2.24 ± 1.06 cases per 100,000 persons, respectively. The LAR of cancer mortality rates from uterus and prostate cancers were 0.36 ± 0.22 and 0.48 ± 0.18 cases per 100,000 persons, respectively. The LAR of prostate and uterus cancer occurrence and mortality from radiation doses with AP CT procedures was low but not trivial. Therefore, efforts should be made to lower patient doses while retaining image quality. Although the minimization of the patient's radiation dose must guide clinical practice, the estimated slight increase in risk could aid in easing fears regarding well-justified AP CT procedures.

ESTIMATION OF PATIENT ORGAN AND WHOLE-BODY DOSES IN [18F-FDG] PET/CT SCAN

The object of this study was to estimate organ doses and whole-body effective doses from positron emission tomography/computed tomography (PET/CT) scan using [fluorine-18]-fluoro-2-deoxy-d-glucose (18F-FDG) in adult patients and to assess the CT component contribution to organ and whole-body doses. The [18F-FDG] PET/CT scan was conducted on 204 adult patients (90 females and 114 males). For all patients, females and males, the whole-body effective doses were 20.54, 23.89 and 17.89 mSv, respectively. For all patients, females and males, the CT component contribution to the whole-body effective dose was 66, 71 and 62%, respectively. Since CT is the primary contributor to the effective dose in the [18F-FDG] PET/CT scan protocol, the significance of improving CT protocols to minimize patient dose is underscored. All attempts must be made, using available mechanisms and techniques, to reduce the patient's dose of PET/CT scan, especially in obese patients.

Computed Tomography (Ct) Scan Assisted Machine Learning in the Management of Artifacts Related to Paranasal Sinuses and Anterior Cranial Fossa

Computed tomography (CT), through the use of ionizing radiation, allows us to assess the different parts of the body. It is made up of an X-ray tube that rotates rapidly around the patient generating the radiation beam. This is attenuated with the patient producing information, which is collected by the detectors that are opposite to the tube located in the gantry (part of the tomography equipment); finally, these collected data are sent to the computer that will reconstruct the information obtained and will represent it as an image on the monitor. In the practice of a study, artifices or artifacts may appear regardless of their origin, which limits the scan examination; this leads to stopping the examination and starting again, and added to this with the contrast media, they have to apply these drugs again. State-of-the-art scanners allow complete reconstructions to be performed with few projections, limiting radiation doses, by means of statistical algebraic reconstruction methods. The present work shows the simulation of artifacts in sinusitis diagnosis computed tomography images, the extraction of features from each image, and an automatic classification algorithm for the differentiation of artifacts. The results show that the algorithm is able to classify the simulated artifacts with a percentage of 90%.

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program

We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists’ subjective evaluation criteria according to the Korea institute for accreditation of medical imaging (KIAMI) guidelines, where it judges whether a scan range is beyond landmarks’ criterion. The algorithm consists of three stages: deep learning-based landmark segmentation, rule-based logical operations, and overscan determination. A total of 210 cases from a single institution (internal data) and 50 cases from 47 institutions (external data) were utilized for performance evaluation. Area under the receiver operating characteristic (AUROC), accuracy, sensitivity, specificity, and Cohen’s kappa were used as evaluation metrics. Fisher’s exact test was performed to present statistical significance for the overscan detectability, and univariate logistic regression analyses were performed for validation. Furthermore, an excessive effective dose was estimated by employing the amount of overscan and the absorbed dose to effective dose conversion factor. The algorithm presented AUROC values of 0.976 (95% confidence interval [CI]: 0.925–0.987) and 0.997 (95% CI: 0.800–0.999) for internal and external dataset, respectively. All metrics showed average performance scores greater than 90% in each evaluation dataset. The AI-assisted overscan decision and the radiologist’s manual evaluation showed a statistically significance showing a p-value less than 0.001 in Fisher’s exact test. In the logistic regression analysis, demographics (age and sex), data source, CT vendor, and slice thickness showed no statistical significance on the algorithm (each p-value > 0.05). Furthermore, the estimated excessive effective doses were 0.02 ± 0.01 mSv and 0.03 ± 0.05 mSv for each dataset, not a concern within slight deviations from an acceptable scan range. We hope that our proposed overscan decision algorithm enables the retrospective scan range monitoring in LDCT for lung cancer screening program, and follows an as low as reasonably achievable (ALARA) principle.

Using barium as an internal radioprotective shield for pregnant patients undergoing CT pulmonary angiography: A retrospective study

PURPOSE

The purpose of our retrospective study was to assess the effect of barium sulfate contrast medium on radiation dose and diagnostic quality of CT Pulmonary Angiography (CTPA) in an in-vivo study of pregnant patients.

METHODS

Our retrospective study included 33 pregnant patients who underwent CTPA to exclude pulmonary embolism. The patients received oral 40% w/v barium solution just prior to the acquisition of their planning radiograph. All CTPA were performed on 64-slice, single-source CT scanners with AEC with noise index = 28.62-31.64 and the allowed mA range of 100-450. However, only 5/33 patients had mA modulation (AEC 100-450 mA range), while 28/33 patients had mA maxed out at the set maximum mA of 450 over the entire scan range. We recorded CTDIvol (mGy), DLP (mGy.cm) and scan length. The same information was recorded in weight-and scanner-matched, non-pregnant patients. Statistical tests included descriptive data (median and interquartile range) and Mann-Whitney test.

RESULTS

There were no significant differences in CTDIvol and DLP between the barium and control group patients (p > 0.1). The median mA below the diaphragm was significantly higher in each patient with barium compared to the weight and scanner-matched patient without barium. Evaluation of lung and subsegmental lower lobe pulmonary arteries was limited in 85% barium group. Due to thin prospective section thickness (1.25 mm), most patients were scanned at maximum allowed mA for AEC.

CONCLUSION

Use of AEC with thick barium in pregnant patients undergoing CTPA as an internal radioprotective shield produces counterproductive artifacts and tube current increments.

Does Dose Matter? Ionising radiation exposure of the veterinary patient from Computed Tomography: A discussion

Advanced imaging techniques such as Computed Tomography (CT) are commonplace in human medicine and increasingly, are being utilised by the veterinary profession as scanners become more available and affordable. The benefit of CT imaging is the provision of detailed cross-sectional imaging and 3D reconstruction, but this incurs higher doses of ionising radiation to the patient. This paper reviews risks and effects associated with ionising radiation, making comparisons to human models, and a hypothetical scenario is discussed using the human risk model for age at time of exposure and increased lifetime risk of cancer with a dog to human years formula. Various issues are considered with respect to dose reduction, training, equipment and the reported 'greater need for guidance and the establishment of best practice' which may lead to future guidance from the International Commission on Radiological Protection (ICRP).

Computed Dosimeter Dose Index on a 16-Slice Computed Tomography Scanner

A computed tomography dose index can be used to quantify the radiation dose received during a CT scan and it is an indicator of the radiation dose to the polymetaylenmetaAcrylate (PMMA) standardized phantom. The objective of this study was 2-fold. The first was to measure the computed tomography (CT) radiation dose for the head and body polymetaylelenmetaAcrylate (PMMA) phantoms and to determine the accuracy of the CT radiation dose parameter displayed on the CT scanner console; these were measured in this investigation and compared with the dose displayed on the CT scanner console. The dose was calculated using the formalism described in the American Association of Physics in Medicine (AAPM) Report 96. The second was to compare the dosimetric results of the head and body polymetaylelenmetaAcrylate (PMMA) phantoms with dose reference levels published in international journals, as well as to measure the central cumulative dose (DL^′ (0)), as recommended by the American Association of Physics in Medicine (AAPM) report 111. This is a new, cutting-edge methodology for estimating the CT radiation dosage provided by the abdomen, thorax, and head of a PMMA phantom. We used a Philips Big Bore CT scanner with 16 slices. A CT dosimeter head phantom with a diameter of 16 cm, a CT dosimeter body phantom with a diameter of 32 cm, a 100 mm pencil chamber (PC), and a 20 mm short chamber (SC) were employed. These were coupled to an electrometer and a dosimetric readout device. The measured volume computed tomography dose index (CTDIvol) values were in good agreement with the CT radiation dose displayed on the corresponding CT scanner console. The percentage disagreement was less than 10%, with a maximal difference of 1.7% and 5.5% for the body and head phantom, respectively. The central cumulative dose (DL (0)) measurements (for L^′ = 100 mm) also matched nominal or the corresponding computed tomography dose index (CT) scanner console volume computed tomography dose index (CTDIvol) values. In this case, the agreement is always below 3% for abdomen scans and 1.0% for head examinations. This result implies that the radiation dose supplied by the 16-slice computed tomography (CT) system was in good agreement with the international dose reference level and we observed something different.

A Review and Case Study of 3D Imaging Modalities for Female Amniote Reproductive Anatomy

Recent advances in non-invasive imaging methods have revitalised the field of comparative anatomy, and reproductive anatomy has been no exception. The reproductive systems of female amniotes present specific challenges, namely their often internal "hidden" anatomy. Quantifying female reproductive systems is crucial to recognising reproductive pathologies, monitoring menstrual cycles, and understanding copulatory mechanics. Here we conduct a review of the application of non-invasive imaging techniques to female amniote reproductive anatomy. We introduce the commonly used imaging modalities of computed tomography (CT) and magnetic resonance imaging (MRI), highlighting their advantages and limitations when applied to female reproductive tissues, and make suggestions for future advances. We also include a case study of micro CT and MRI, along with their associated staining protocols, applied to cadavers of female adult stoats (Mustela erminea). In doing so, we will progress the discussion surrounding the imaging of female reproductive anatomy, whilst also impacting the fields of sexual selection research and comparative anatomy more broadly.

Feature Reconstruction from Incomplete Tomographic Data without Detour

In this paper, we consider the problem of feature reconstruction from incomplete X-ray CT data. Such incomplete data problems occur when the number of measured X-rays is restricted either due to limit radiation exposure or due to practical constraints, making the detection of certain rays challenging. Since image reconstruction from incomplete data is a severely ill-posed (unstable) problem, the reconstructed images may suffer from characteristic artefacts or missing features, thus significantly complicating subsequent image processing tasks (e.g., edge detection or segmentation). In this paper, we introduce a framework for the robust reconstruction of convolutional image features directly from CT data without the need of computing a reconstructed image first. Within our framework, we use non-linear variational regularization methods that can be adapted to a variety of feature reconstruction tasks and to several limited data situations. The proposed variational regularization method minimizes an energy functional being the sum of a feature dependent data-fitting term and an additional penalty accounting for specific properties of the features. In our numerical experiments, we consider instances of edge reconstructions from angular under-sampled data and show that our approach is able to reliably reconstruct feature maps in this case.

Relationship of body mass index and abdominal fat with radiation dose received during preoperative liver CT in potential living liver donors: a cross-sectional study

BACKGROUND

Although contrast-enhanced computed tomography (CT) is currently the most widely-used imaging modality for the preoperative evaluation of potential living liver donors, radiation exposure remains a major concern. The present study aimed to determine the relationship of body mass index (BMI) and abdominal fat with the effective radiation dose received during liver CT scans as part of a pre-donation work-up in potential living donors.

METHODS

This retrospective cross-sectional study included 695 potential living donors (mean age, 30.5±9.7 years; 445 men and 250 women) who had undergone preoperative liver CT scans between 2017 and 2018. The following measures were evaluated: BMI, abdominal fat as measured at the level of the third lumbar vertebra, and effective dose based on the dose length product (DLP). Correlations between the effective dose and other variables were evaluated using Pearson's correlation coefficient.

RESULTS

The mean BMI, total fat area (TFA), and effective dose were 23.6±3.3 kg/m2, 218.7±110.0 cm2, and 9.4±3.3 mSv, respectively. The effective dose during liver CT scans had a strong positive correlation with both BMI (r=0.715; P<0.001) and TFA (r=0.792; P<0.001). As BMI and TFA increased, so did the effective dose.

CONCLUSIONS

Higher BMI and TFA significantly increased the radiation dose received during liver CT scans in potential living donors.

Limited parameter denoising for low-dose X-ray computed tomography using deep reinforcement learning

BACKGROUND

The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However, the use of deep learning requires large amounts of data to train deep convolutional networks (CNNs). Moreover, due to large parameter count, such deep CNNs may cause unexpected results.

PURPOSE

In this study, we introduce a novel CT denoising framework, which has interpretable behaviour, and provides useful results with limited data.

METHODS

We employ bilateral filtering in both the projection and volume domains to remove noise. To account for non-stationary noise, we tune the σ parameters of the volume for every projection view, and for every volume pixel. The tuning is carried out by two deep CNNs. Due to impracticality of labelling, the two deep CNNs are trained via a Deep-Q reinforcement learning task. The reward for the task is generated by using a custom reward function represented by a neural network. Our experiments were carried out on abdominal scans for the Mayo Clinic TCIA dataset, and the AAPM Low Dose CT Grand Challenge.

RESULTS

Our denoising framework has excellent denoising performance increasing the PSNR from 28.53 to 28.93, and increasing the SSIM from 0.8952 to 0.9204. We outperform several state-of-the-art deep CNNs, which have several orders of magnitude higher number of parameters (p-value (PSNR) = 0.000, p-value (SSIM) = 0.000). Our method does not introduce any blurring, which is introduced by MSE loss based methods, or any deep learning artifacts, which are introduced by WGAN based models. Our ablation studies show that parameter tuning and using our reward network results in the best possible results.

CONCLUSIONS

We present a novel CT denoising framework, which focuses on interpretability to deliver good denoising performance, especially with limited data. Our method outperforms state-of-the-art deep neural networks. Future work will be focused on accelerating our method, and generalizing to different geometries and body parts. This article is protected by copyright. All rights reserved.

Image quality and diagnostic accuracy of reduced-dose computed tomography enterography with model-based iterative reconstruction in pediatric Crohn's disease patients

This study assessed the image quality and diagnostic accuracy in determining disease activity of the terminal ileum of the reduced-dose computed tomography enterography using model-based iterative reconstruction in pediatric patients with Crohn's disease (CD). Eighteen patients were prospectively enrolled and allocated to the standard-dose (SD) and reduced-dose (RD) computed tomography enterography (CTE) groups (n = 9 per group). Image quality, reader confidence in interpreting bowel findings, accuracy in determining active CD in the terminal ileum, and radiation dose were evaluated. Objective image quality did not show intergroup differences, except for image sharpness. Although reader confidence in detecting mural stratification, ulcer, and perienteric fat stranding of the RD-CTE were inferior to SD-CTE, RD-CTE correctly diagnosed active disease in all patients. The mean values of radiation dose metrics (SD-CTE vs. RD-CTE) were 4.3 versus 0.74 mGy, 6.1 versus 1.1 mGy, 211.9 versus 34.5 mGy∙cm, and 4.4 versus 0.7 mSv mGy∙cm for CTDI_vol, size-specific dose estimation, dose-length product, and effective dose, respectively. RD-CTE showed comparable diagnostic accuracy to SD-CTE in determining active disease of the terminal ileum in pediatric CD patients. However, image quality and reader confidence in detecting ulcer and perienteric fat stranding was compromised.

Estimation of radiation dose and establishment of local diagnostic reference levels for computed tomography of head in pediatric population

BACKGROUND

Pediatric population is more sensitive to the effects of radiation than adults. Establishing diagnostic reference level (DRL) is an efficient dose optimization technique implemented by many countries for reducing radiation dose during Computed Tomography (CT) examinations.

OBJECTIVES

To estimate radiation dose and establish a new local diagnostic reference level for CT head examination in the pediatric population.

MATERIALS AND METHODS

We prospectively recruited 143 pediatric patients referred for CT head examination with age ranging from 0-5 years old. All patients had undergone CT head examination using the standard pediatric head protocol. Volumetric CT dose index (CTDIvol) and dose length product (DLP) were recorded. The effective dose was first calculated. Then, 75th percentile of dose indices was calculated to establish DRLs.

RESULTS

DRLs in terms of CTDIvol and DLP are 23.84 mGy, 555.99 mGy.cm for patients <1 years old and 28.65 mGy, 794.99 mGy.cm for patients from 1-5 years old, respectively. Mean effective doses for <1 years old patients and 1-5 years old patients are 2.91 mSv and 2.78 mSv respectively.

CONCLUSION

The study concludes that DRL in terms of CTDIvol is lower but DRL in terms of DLP and the effective dose is higher compared to a few other studies which necessitate the need for dose optimization.

Pediatric craniosynostosis computed tomography: an institutional experience in reducing radiation dose while maintaining diagnostic image quality

BACKGROUND

Children with craniosynostosis may undergo multiple computed tomography (CT) examinations for diagnosis and post-treatment follow-up, resulting in cumulative radiation exposure.

OBJECTIVE

To reduce the risks associated with radiation exposure, we evaluated the compliance, radiation dose reduction and clinical image quality of a lower-dose CT protocol for pediatric craniosynostosis implemented at our institution.

MATERIALS AND METHODS

The standard of care at our institution was modified to replace pediatric head CT protocols with a lower-dose CT protocol utilizing 100 kV, 5 mAs and iterative reconstruction. Study-ordered, protocol-utilized and radiation-dose indices were collected for studies performed with routine pediatric brain protocols (n=22) and with the lower-dose CT protocol (n=135). Two pediatric neuroradiologists evaluated image quality in a subset (n=50) of the lower-dose CT studies by scoring visualization of cranial structures, confidence of diagnosis and the need for more radiation dose.

RESULTS

During the 30-month period, the lower-dose CT protocol had high compliance, with 2/137 studies performed with routine brain protocols. With the lower-dose CT protocol, volume CT dose index (CTDI_vol) was 1.1 mGy for all patients (0-9 years old) and effective dose ranged from 0.06 to 0.22 mSv, comparable to a 4-view skull radiography examination. CTDI_vol was reduced by 98% and effective dose was reduced up to 67-fold. Confidence in diagnosing craniosynostosis was high and more radiation dose was considered unnecessary in all studies (n=50) by both radiologists.

CONCLUSION

Replacing the routine pediatric brain CT protocol with a lower-dose CT craniosynostosis protocol substantially reduced radiation exposure without compromising image quality or diagnostic confidence.

Reduced-Dose Full-Body CT in Lymphoma Follow-up: A Pilot Study

BACKGROUND

How to reduce the radiation dose received from full-body CT scans during the follow-up of lymphoma patients is a concern.

OBJECTIVE

The aim of the study was to investigate the image quality and radiation dose of reduced-dose full-body computerized tomography (CT) in lymphoma patients during the follow-up.

METHODS

121 patients were included and divided into conventional CT group (group 1, 120-kVp, n = 61) or reduced-dose CT group (group 2, 100-kVp combined dual-energy CT (DECT), n = 60). 140-kVp polychromatic images and 70-keV monochromatic images were reconstructed from DECT. The abdominal virtual non-enhanced (VNE) images were reconstructed from monochromatic images. Two radiologists rated the overall image quality with a five-point scale and graded the depiction of lesions using a four-point scale. The objective image quality was evaluated using image noise, signal-to-noise ratio, and contrast-to-noise ratio. The radiation dose and image quality were compared between the groups.

RESULTS

The comparable subjective image quality was observed between 70-keV and 120-kVp images in the neck, while 120-kVp images showed better objective image quality. 70-keV images showed better objective image quality in the chest. While the subjective image quality of abdominal VNE images was inferior to that of true non-enhanced images, the improved objective image quality was observed in VNE images. In the abdominal arterial phase, similar subjective image quality was observed between the groups. Abdominal 70-keV images in the arterial phase showed improved objective image quality. Similar image quality was obtained in the abdominal venous phase between the groups. The effective radiation dose in group 2 showed a significant reduction.

CONCLUSION

The application of reduced-dose full-body CT can significantly reduce the radiation dose for lymphoma patients during the follow-up while maintaining or improving the image quality.

Diagnosis and Management of Acute Appendicitis in Adults: A Review

IMPORTANCE

Acute appendicitis is the most common abdominal surgical emergency in the world, with an annual incidence of 96.5 to 100 cases per 100 000 adults.

OBSERVATIONS

The clinical diagnosis of acute appendicitis is based on history and physical, laboratory evaluation, and imaging. Classic symptoms of appendicitis include vague periumbilical pain, anorexia/nausea/intermittent vomiting, migration of pain to the right lower quadrant, and low-grade fever. The diagnosis of acute appendicitis is made in approximately 90% of patients presenting with these symptoms. Laparoscopic appendectomy remains the most common treatment. However, increasing evidence suggests that broad-spectrum antibiotics, such as piperacillin-tazobactam monotherapy or combination therapy with either cephalosporins or fluroquinolones with metronidazole, successfully treats uncomplicated acute appendicitis in approximately 70% of patients. Specific imaging findings on computed tomography (CT), such as appendiceal dilatation (appendiceal diameter ≥7 mm), or presence of appendicoliths, defined as the conglomeration of feces in the appendiceal lumen, identify patients for whom an antibiotics-first management strategy is more likely to fail. CT findings of appendicolith, mass effect, and a dilated appendix greater than 13 mm are associated with higher risk of treatment failure (≈40%) of an antibiotics-first approach. Therefore, surgical management should be recommended in patients with CT findings of appendicolith, mass effect, or a dilated appendix who are fit for surgery, defined as having relatively low risk of adverse outcomes or postoperative mortality and morbidity. In patients without high-risk CT findings, either appendectomy or antibiotics can be considered as first-line therapy. In unfit patients without these high-risk CT findings, the antibiotics-first approach is recommended, and surgery may be considered if antibiotic treatment fails. In unfit patients with high-risk CT findings, perioperative risk assessment as well as patient preferences should be considered.

CONCLUSIONS AND RELEVANCE

Acute appendicitis affects 96.5 to 100 people per 100 000 adults per year worldwide. Appendectomy remains first-line therapy for acute appendicitis, but treatment with antibiotics rather than surgery is appropriate in selected patients with uncomplicated appendicitis.

Expanding the Utilization of Low-Dose Computed Tomography in Plastic and Reconstructive Surgery Based on Validated Practices Among Surgical Specialties

INTRODUCTION

As computed tomography (CT) usage increases, so have concerns over radiation-induced malignancy. To mitigate these risks, low-dose CT (LDCT) has emerged as a versatile alternative by other specialties, although its use in plastic surgery remains sparse. This study aimed to investigate validated uses of LDCT across surgical specialties and extrapolate these insights to expand its application for plastic surgeons.

METHODS

A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines using search terms "low dose CT" OR "low dose computed tomography" AND "surgery," where the name of each surgical specialty was substituted for word "surgery" and each specialty term was searched separately in combination with the 2 CT terms. Data on radiation dose, outcomes, and level of evidence were collected. Validated surgical applications were correlated with similar procedures and diagnostic tests performed routinely by plastic surgeons to extrapolate potential applications for plastic surgeons.

RESULTS

A total of 3505 articles were identified across surgical specialties, with 27 ultimately included. Depending on the application, use of LDCT led to a 25% to 97% reduction in radiation dose and all studies reported noninferior image quality and diagnostic capability compared with standard-dose CT. Potential identified uses included the following: evaluation of soft tissue infections, preoperative and postoperative management of facial and hand fractures, flap design, 3D modeling, and surgical planning.

DISCUSSION

Low-dose CT is a valid imaging alternative to standard-dose CT. Expanded utilization in plastic surgery should be considered to minimize the iatrogenic effects of radiation and to promote patient safety without compromising outcomes.

Low-dose CT reconstruction with Noise2Noise network and testing-time fine-tuning

PURPOSE

Deep learning-based image denoising and reconstruction methods demonstrated promising performance on low-dose CT imaging in recent years. However, most existing deep learning-based low-dose CT reconstruction methods require normal-dose images for training. Sometimes such clean images do not exist such as for dynamic CT imaging or very large patients. The purpose of this work is to develop a low-dose CT image reconstruction algorithm based on deep learning which does not need clean images for training.

METHODS

In this paper, we proposed a novel reconstruction algorithm where the image prior was expressed via the Noise2Noise network, whose weights were fine-tuned along with the image during the iterative reconstruction. The Noise2Noise network built a self-consistent loss by projection data splitting and mapping the corresponding filtered backprojection (FBP) results to each other with a deep neural network. Besides, the network weights are optimized along with the image to be reconstructed under an alternating optimization scheme. In the proposed method, no clean image is needed for network training and the testing-time fine-tuning leads to optimization for each reconstruction.

RESULTS

We used the 2016 Low-dose CT Challenge dataset to validate the feasibility of the proposed method. We compared its performance to several existing iterative reconstruction algorithms that do not need clean training data, including total variation, non-local mean, convolutional sparse coding, and Noise2Noise denoising. It was demonstrated that the proposed Noise2Noise reconstruction achieved better RMSE, SSIM and texture preservation compared to the other methods. The performance is also robust against the different noise levels, hyperparameters, and network structures used in the reconstruction. Furthermore, we also demonstrated that the proposed methods achieved competitive results without any pre-training of the network at all, that is, using randomly initialized network weights during testing. The proposed iterative reconstruction algorithm also has empirical convergence with and without network pre-training.

CONCLUSIONS

The proposed Noise2Noise reconstruction method can achieve promising image quality in low-dose CT image reconstruction. The method works both with and without pre-training, and only noisy data are required for pre-training.

Radiation dose and cancer risks from radiation exposure during abdominopelvic computed tomography (CT) scans: comparison of diagnostic and radiotherapy treatment planning CT scans

In the present study, radiation doses and cancer risks resulting from abdominopelvic radiotherapy planning computed tomography (RP-CT) and abdominopelvic diagnostic CT (DG-CT) examinations are compared. Two groups of patients who underwent abdominopelvic CT scans with RP-CT (n = 50) and DG-CT (n = 50) voluntarily participated in this study. The two groups of patients had approximately similar demographic features including mass, height, body mass index, sex, and age. Radiation dose parameters included CTDI_vol, dose-length product, scan length, effective tube current, and pitch factor, all taken from the CT scanner console. The ImPACT software was used to calculate the patient-specific radiation doses. The risks of cancer incidence and mortality were estimated based on the BEIR VII report of the US National Research Council. In the RP-CT group, the mean ± standard deviation of cancer incidence risk for all cancers, leukemia, and all solid cancers was 621.58 ± 214.76, 101.59 ± 27.15, and 516.60 ± 189.01 cancers per 100,000 individuals, respectively, for male patients. For female patients, the corresponding risks were 742.71 ± 292.35, 74.26 ± 20.26, and 667.03 ± 275.67 cancers per 100,000 individuals, respectively. In contrast, for DG-CT cancer incidence risks were 470.22 ± 170.07, 78.23 ± 18.22, and 390.25 ± 152.82 cancers per 100,000 individuals for male patients, while they were 638.65 ± 232.93, 62.14 ± 13.74, and 575.73 ± 221.21 cancers per 100,000 individuals for female patients. Cancer incidence and mortality risks were greater for RP-CT than for DG-CT scans. It is concluded that the various protocols of abdominopelvic CT scans, especially the RP-CT scans, should be optimized with respect to the radiation doses associated with these scans.

Voxel model of a rabbit: assessment of absorbed doses in organs after CT examination performed by two different protocols

The objective of this work was to assess absorbed doses in organs and tissues of a rabbit, following computed tomography (CT) examinations, using a dedicated 3D voxel model. Absorbed doses in relevant organs were calculated using the MCNP5 Monte Carlo software. Calculations were perfomed for two standard CT protocols, using tube voltages of 110 kVp and 130 kVp. Absorbed doses were calculated in 11 organs and tissues, i.e., skin, bones, brain, muscles, heart, lungs, liver, spleen, kidney, testicles, and fat tissue. The doses ranged from 15.3 to 28.3 mGy, and from 40.2 to 74.3 mGy, in the two investigated protocols. The organs that received the highest dose were bones and kidneys. In contrast, brain and spleen were organs that received the smallest doses. Doses in organs which are stretched along the body did not change significantly with distance. On the other hand, doses in organs which are localized in the body showed maximums and minimums. Using the voxel model, it is possible to calculate the dose distribution in the rabbit's body after CT scans, and study the potential biological effects of CT doses in certain organs. The voxel model presented in this work can be used to calculated doses in all radiation experiments in which rabbits are used as experimental animals.