Ultralow-dose Scans Research Articles

Influence of spectral shaping and tube voltage modulation in ultralow-dose computed tomography of the abdomen

PurposeUnenhanced abdominal CT constitutes the diagnostic standard of care in suspected urolithiasis. Aiming to identify potential for radiation dose reduction in this frequent imaging task, this experimental study compares the effect of spectral shaping and tube voltage modulation on image quality.MethodsUsing a third-generation dual-source CT, eight cadaveric specimens were scanned with varying tube voltage settings with and without tin filter application (Sn 150, Sn 100, 120, 100, and 80 kVp) at three dose levels (3 mGy: standard; 1 mGy: low; 0.5 mGy: ultralow). Image quality was assessed quantitatively by calculation of signal-to-noise ratios (SNR) for various tissues (spleen, kidney, trabecular bone, fat) and subjectively by three independent radiologists based on a seven-point rating scale (7 = excellent; 1 = very poor).ResultsIrrespective of dose level, Sn 100 kVp resulted in the highest SNR of all tube voltage settings. In direct comparison to Sn 150 kVp, superior SNR was ascertained for spleen (p ≤ 0.004) and kidney tissue (p ≤ 0.009). In ultralow-dose scans, subjective image quality of Sn 100 kVp (median score 3; interquartile range 3–3) was higher compared with conventional imaging at 120 kVp (2; 2–2), 100 kVp (1; 1–2), and 80 kVp (1; 1–1) (all p < 0.001). Indicated by an intraclass correlation coefficient of 0.945 (95% confidence interval: 0.927–0.960), interrater reliability was excellent.ConclusionsIn abdominal CT with maximised dose reduction, tin prefiltration at 100 kVp allows for superior image quality over Sn 150 kVp and conventional imaging without spectral shaping.

A Novel Artificial Intelligence Based Denoising Method for Ultra-Low Dose CT Used for Lung Cancer Screening

To assess ultra-low-dose (ULD) computed tomography as well as a novel artificial intelligence-based reconstruction denoising method for ULD (dULD) in screening for lung cancer. This prospective study included 123 patients, 84 (70.6%) men, mean age 62.6 ± 5.35 (55-75), who had a low dose and an ULD scan. A fully convolutional-network, trained using a unique perceptual loss was used for denoising. The network used for the extraction of the perceptual features was trained in an unsupervised manner on the data itself by denoising stacked auto-encoders. The perceptual features were a combination of feature maps taken from different layers of the network, instead of using a single layer for training. Two readers independently reviewed all sets of images. ULD decreased average radiation-dose by 76% (48%-85%). When comparing negative and actionable Lung-RADS categories, there was no difference between dULD and LD (p=0.22 RE, p > 0.999 RR) nor between ULD and LD scans (p=0.75 RE, p > 0.999 RR). ULD negative likelihood ratio (LR) for the readers was 0.033-0.097. dULD performed better with a negative LR of 0.021-0.051. Coronary artery calcifications (CAC) were documented on the dULD scan in 88(74%) and 81(68%) patients, and on the ULD in 74(62.2%) and 77(64.7%) patients. The dULD demonstrated high sensitivity, 93.9%-97.6%, with an accuracy of 91.7%. An almost perfect agreement between readers was noted for CAC scores: for LD (ICC=0.924), dULD (ICC=0.903), and for ULD (ICC=0.817) scans. A novel AI-based denoising method allows a substantial decrease in radiation dose, without misinterpretation of actionable pulmonary nodules or life-threatening findings such as aortic aneurysms.

Semi-automated volumetry of pulmonary nodules: Intra-individual comparison of standard dose and chest X-ray equivalent ultralow dose chest CT scans

PurposeTo assess the performance of semi-automated volumetry of solid pulmonary nodules on single-energy tin-filtered ultralow dose (ULD) chest CT scans at a radiation dose equivalent to chest X-ray relative to standard dose (SD) chest CT scans and assess the impact of kernel and iterative reconstruction selection. MethodsNinety-four consecutive patients from a prospective single-center study were included and underwent clinically indicated SD chest CT (1.9 ± 0.8 mSv) and additional ULD chest CT (0.13 ± 0.01 mSv) in the same session. All scans were reconstructed with a soft tissue (Br40) and lung (Bl64) kernel as well as with Filtered Back Projection (FBP) and Iterative Reconstruction (ADMIRE-3 and ADMIRE-5). One hundred and forty-eight solid pulmonary nodules were identified and analysed by semi-automated volumetry on all reconstructions. Nodule volumes were compared amongst all reconstructions thereby focusing on the agreement between SD and ULD scans. ResultsNodule volumes ranged from 58.5 (28.8–126) mm3 for ADMIRE-5 Br40 ULD reconstructions to 72.5 (39–134) mm3 for FBP Bl64 SD reconstructions with significant differences between reconstructions (p < 0.001). Interscan agreement of volumes between two given reconstructions ranged from ICC = 0.605 to ICC = 0.999. Between SD and ULD scans, agreement of nodule volumes was highest for FBP Br40 (ICC = 0.995), FBP Bl64 (ICC = 0.939) and ADMIRE-5 Bl64 (ICC = 0.994) reconstructions. ADMIRE-3 reconstructions exhibited reduced interscan agreement of nodule volumes (ICCs from 0.788 − 0.882). ConclusionsThe interscan agreement of node volumes between SD and ULD is high depending on the choice of kernel and reconstruction algorithm. However, caution should be exercised when comparing two image series that were not identically reconstructed.

Image enhancement of ultra-low dose CBCT images using a deep generative model

<h3>Objective</h3> The aim of this study was to improve the image quality of CBCT images, obtained with an ultra-low dose (ULD) protocol, using a deep generative model called pix2pix image-to-image translation technique. <h3>Study Design</h3> CBCT images of a dry skull and mandible were obtained using preset standard and ULD imaging protocols. We combined the scans into a dataset with 1,412 ULD scans and normal-dose DICOM image pairs (2,824 scans in total), and randomly selected 10% (140) image pairs as the test set. We trained a pix2pix deep generative model by first converting the DICOM images to JPG images and then feeding the training set image pairs to the network. We tested the performance of the trained model on the previously unseen test set ULD images and compared the synthesized images with the corresponding normal dose and ULD images. A preliminary blind study on the ULD, standard, and synthesized images by an oral and maxillofacial radiologist was done and image quality and edge definition were assessed. <h3>Results</h3> Preliminary results indicated that in the three datasets used for review, all synthesized images were rated as having higher image quality and better edge definition compared to ULD images. Analysis also indicated that synthesized images were equivalent to standard images in image quality and edge definition. <h3>Conclusion</h3> CBCT usage is expected to grow globally by 9.7%. With the expected growth, reducing radiation dose and improving image quality is of paramount importance to reduce the radiation burden on the population. The current study focused on image enhancement of CBCT images acquired with the ULD protocol. Initial analysis indicated a marked improvement in image quality of the synthesized images when compared to ULD images. With further research and successful implementation, we have an opportunity to reduce radiation risks to patients while improving image quality. <b>Statement of Ethical Review</b> Ethical Review or exemption was not warranted for this study.

MP82-16 CT FOR EVALUATION OF UROLITHIASIS: IMAGE QUALITY OF ULTRALOW-DOSE (SUB MSV) CT WITH KNOWLEDGE-BASED ITERATIVE RECONSTRUCTION AND DIAGNOSTIC PERFORMANCE OF LOW-DOSE CT WITH STATISTICAL ITERATIVE RECONSTRUCTION

You have accessJournal of UrologyStone Disease: Epidemiology & Evaluation I1 Apr 2016MP82-16 CT FOR EVALUATION OF UROLITHIASIS: IMAGE QUALITY OF ULTRALOW-DOSE (SUB MSV) CT WITH KNOWLEDGE-BASED ITERATIVE RECONSTRUCTION AND DIAGNOSTIC PERFORMANCE OF LOW-DOSE CT WITH STATISTICAL ITERATIVE RECONSTRUCTION Jin wook Kim, Young Tae Moon, Kyung Do Kim, Tae-Hyoung Kim, Soon Chul Myung, Seung Hyun Ahn, Jae Duck Choi, Jung Hoon Kim, Min Soo Kim, Shin Young Lee, Byung Hoon Chi, and In Ho Chang Jin wook KimJin wook Kim More articles by this author , Young Tae MoonYoung Tae Moon More articles by this author , Kyung Do KimKyung Do Kim More articles by this author , Tae-Hyoung KimTae-Hyoung Kim More articles by this author , Soon Chul MyungSoon Chul Myung More articles by this author , Seung Hyun AhnSeung Hyun Ahn More articles by this author , Jae Duck ChoiJae Duck Choi More articles by this author , Jung Hoon KimJung Hoon Kim More articles by this author , Min Soo KimMin Soo Kim More articles by this author , Shin Young LeeShin Young Lee More articles by this author , Byung Hoon ChiByung Hoon Chi More articles by this author , and In Ho ChangIn Ho Chang More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.2157AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES To compare radiation dose and image quality in regular, low, and ultralow-dose CT protocols, and to evaluate diagnostic performance of low-dose CT for urolithiasis. METHODS Sixty-five patients with suspected urolithiasis underwent three different scans under the regular, low, and ultralow-dose protocols. The regular dose scans were reconstructed using filtered back projection and the low-dose scans were reconstructed using a statistical iterative reconstruction. The ultralow-dose scans were reconstructed using both techniques in addition to a knowledge-based IR. Effective radiation doses were compared. Objective image noise was assessed by measuring standard deviation of HU and subjective image assessment was performed with a 3- or 5-point scale. Diagnostic performance of the low-dose image was evaluated, using the regular dose image as a standard reference and the interobserver agreement between two reviewers with different levels of experience was calculated. RESULTS The effective radiation dose was significantly different in each protocol (p < 0.001) and estimated dose reduction of the low-dose and ultralow-dose protocols was 76.4% and 89.8%, respectively. The knowledge-based iterative reconstruction algorithm showed poorer subjective image quality than the regular and lowdose protocols, but it also had the least objective image noise. Overall, the low-dose image set showed a greater than 84% concordance rate and 100% in ureter stones larger than 3 mm. Interobserver agreement was substantial (kappa value = 0.61). CONCLUSIONS The knowledge-based IR can provide a better quality image while reducing radiation exposure under the same protocol. Furthermore, the diagnostic performance of the low-dose CT protocol is comparable to the regular dose scan. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e1079 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Jin wook Kim More articles by this author Young Tae Moon More articles by this author Kyung Do Kim More articles by this author Tae-Hyoung Kim More articles by this author Soon Chul Myung More articles by this author Seung Hyun Ahn More articles by this author Jae Duck Choi More articles by this author Jung Hoon Kim More articles by this author Min Soo Kim More articles by this author Shin Young Lee More articles by this author Byung Hoon Chi More articles by this author In Ho Chang More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Knowledge-based iterative model reconstruction (IMR) algorithm in ultralow-dose CT for evaluation of urolithiasis: evaluation of radiation dose reduction, image quality, and diagnostic performance

To evaluate the efficacy of a knowledge-based iterative model reconstruction (IMR) algorithm for reducing image noise in ultralow-dose (ULD) CT for urolithiasis. A total of 103 patients diagnosed with urinary stones (n = 276) were enrolled. Regular dose (RD) scans (120 kV and 150 mAs, maximal tube current in dose modulation) were reconstructed using filtered back-projection (FBP, RD-FBP), and ULD scans (100 kV and 20 mAs, fixed tube current) were reconstructed with FBP (ULD-FBP), statistical iterative reconstruction (IR; ULD-iDose), and a knowledge-based IMR algorithm (ULD-IMR). Prospective interpretations of the two scans were performed with respect to radiation dose, objective image noise, and subjective assessment. The subjective assessment was also evaluated with regard to each patient's body mass index (BMI, < 25 or ≥ 25 kg/m(2)). Using RD CT (RD-FBP) as the reference standard, two reviewers assessed the diagnostic performance and inter-observer agreement for ULD-IMR. The average effective doses with RD CT and ULD CT were 8.31 and 0.68 mSv, respectively, and the average radiation dose reduction rate was 91.82% (p < 0.01). The lowest objective image noise was observed with ULD-IMR (p < 0.01). Subjective assessment in ULD-IMR was comparable to that of RD-FBP, although RD-FBP remained statistically superior. For BMI, there was a statistically significant difference in subjective image quality between the normal (4.7 ± 0.54) and overweight or obese groups (4.2 ± 0.5) (p < 0.05). The ULD-IMR showed a greater than 75% concordant rate in overall stones and 100% in ureter stones larger than 3 mm. However, for stones <3 mm, neither reviewer had a good detection rate (45.5% and 56.9% for the general and genitourinary radiologist, respectively). Inter-observer agreement was almost perfect (κ = 0.82). Despite a significant radiation dose reduction, ULD-IMR images were comparable in image quality and noise to RD-FBP images. Furthermore, the diagnostic performance of the ULD non-enhanced CT protocol was comparable to that of the RD scan for diagnosing urinary stones larger than 3 mm.

CT for evaluation of urolithiasis: image quality of ultralow-dose (Sub mSv) CT with knowledge-based iterative reconstruction and diagnostic performance of low-dose CT with statistical iterative reconstruction.

To compare radiation dose and image quality in regular, low, and ultralow-dose CT protocols, and to evaluate diagnostic performance of low-dose CT for urolithiasis. Sixty-five patients with suspected urolithiasis underwent three different scans under the regular, low, and ultralow-dose protocols. The regular dose scans were reconstructed using filtered back projection and the low-dose scans were reconstructed using a statistical iterative reconstruction. The ultralow-dose scans were reconstructed using both techniques in addition to a knowledge-based IR. Effective radiation doses were compared. Objective image noise was assessed by measuring standard deviation of HU and subjective image assessment was performed with a 3- or 5-point scale. Diagnostic performance of the low-dose image was evaluated, using the regular dose image as a standard reference and the interobserver agreement between two reviewers with different levels of experience was calculated. The effective radiation dose was significantly different in each protocol (p<0.001) and estimated dose reduction of the low-dose and ultralow-dose protocols was 76.4% and 89.8%, respectively. The knowledge-based iterative reconstruction algorithm showed poorer subjective image quality than the regular and low-dose protocols, but it also had the least objective image noise. Overall, the low-dose image set showed a greater than 84% concordance rate and 100% in ureter stones larger than 3mm. Interobserver agreement was substantial (kappa value=0.61). The knowledge-based IR can provide a better quality image while reducing radiation exposure under the same protocol. Furthermore, the diagnostic performance of the low-dose CT protocol is comparable to the regular dose scan.

Prospective Trial of the Detection of Urolithiasis on Ultralow Dose (Sub mSv) Noncontrast Computerized Tomography: Direct Comparison against Routine Low Dose Reference Standard

In this prospective trial we compared ultralow dose computerized tomography reconstruction algorithms and routine low dose computerized tomography for detecting urolithiasis. A total of 48 consenting adults prospectively underwent routine low dose noncontrast computerized tomography immediately followed by an ultralow dose series targeted at a 70% to 90% reduction from the routine low dose technique (sub mSv range). Ultralow dose series were reconstructed with filtered back projection, and adaptive statistical and model based iterative reconstruction techniques. Transverse (axial) and coronal images were sequentially reviewed by 3 relatively inexperienced trainees, including a radiology resident, a urology fellow and an abdominal imaging fellow. Three experienced abdominal radiologists independently reviewed the routine low dose filtered back projection images, which served as the reference standard. The mean effective dose for the ultralow dose scans was 0.91 mSv (median 0.82), representing a mean ± SD 78% ± 5% decrease compared to the routine low dose. Overall sensitivity and positive predictive value per stone for ultralow dose computerized tomography at a 4 mm threshold was 0.91 and 0.98, respectively. Sensitivity, specificity, positive and negative predictive values, and accuracy per patient were 0.87, 1.00, 1.00, 0.94 and 0.96, respectively. At a 4 mm threshold the sensitivity and positive predictive value per stone of the ultralow dose series for filtered back projection, and adaptive statistical and model based iterative reconstruction was 0.89 and 0.96, 0.91 and 0.98, and 0.93 and 1.00, respectively. Sensitivity, specificity, positive and negative predictive values, and accuracy per patient at the 4 mm threshold were 0.82, 1.00, 1.00, 0.91 and 0.94 for filtered back projection, 0.85, 1.00, 1.00, 0.93 and 0.95 for adaptive statistical iterative reconstruction, and 0.94, 1.00, 1.00, 0.97 and 0.98 for model based iterative reconstruction, respectively. Sequential review of coronal images changed the final stone reading in 13% of cases and improved diagnostic confidence in 49%. At a 4 mm renal calculus size threshold ultralow dose computerized tomography is accurate for detection when referenced against routine low dose series with dose reduction to below the level of a typical 2-view plain x-ray ofthekidneys, ureters and bladder. Slight differences were seen among the reconstruction algorithms. There was mild improvement with model based iterative reconstruction over filtered back projection and adaptive statistical iterative reconstruction. Coronal images improved detection and diagnostic confidence over axial images alone.

Supervised Enhancement Filters: Application to Fissure Detection in Chest CT Scans

In medical image processing, many filters have been developed to enhance certain structures in 3-D data. In this paper, we propose to use pattern recognition techniques to design more optimal filters. The essential difference with previous approaches is that we provide a system with examples of what it should enhance and suppress. This training data is used to construct a classifier that determines the probability that a voxel in an unseen image belongs to the target structure(s). The output of a rich set of basis filters serves as input to the classifier. In a feature selection process, this set is reduced to a compact, efficient subset. We show that the output of the system can be reused to extract new features, using the same filters, that can be processed by a new classifier. Such a multistage approach further improves performance. While the approach is generally applicable, in this work the focus is on enhancing pulmonary fissures in 3-D computed tomography (CT) chest scans. A supervised fissure enhancement filter is evaluated on two data sets, one of scans with a normal clinical dose and one of ultra-low dose scans. Results are compared with those of a recently proposed conventional fissure enhancement filter. It is demonstrated that both methods are able to enhance fissures, but the supervised approach shows better performance; the areas under the receiver operating characteristic (ROC) curve are 0.98 versus 0.90, for the normal dose data and 0.97 versus 0.87 for the ultra low dose data, respectively.