Visualization Of Small Structures Research Articles

Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality. To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V). This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR). Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001). DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V. Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.

The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image noise and spatial resolution in low-dose abdominal CT.

Traditional reconstruction techniques have certain limitations in balancing image quality and reducing radiation dose. The deep learning image reconstruction (DLIR) algorithm opens the door to a new era of medical image reconstruction. The purpose of the study was to evaluate the DLIR images at 1.25 mm thickness in balancing image noise and spatial resolution in low-dose abdominal computed tomography (CT) in comparison with the conventional adaptive statistical iterative reconstruction-V at 40% strength (ASIR-V40%) at 5 and 1.25 mm. This retrospective study included 89 patients who underwent low-dose abdominal CT. Five sets of images were generated using ASIR-V40% at a 5 mm slice thickness and 1.25 mm (high-resolution) with DLIR at 1.25 mm using 3 strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). Qualitative evaluation was performed for image noise, artifacts, and visualization of small structures, while quantitative evaluation was performed for standard deviation (SD), signal-to-noise ratio (SNR), and spatial resolution (defined as the edge rising slope). At 1.25 mm, DLIR-M and DLIR-H images had significantly lower noise (SD in fat: 14.29±3.37 and 9.65±3.44 HU, respectively), higher SNR for liver (3.70±0.78 and 5.64±1.20, respectively), and higher overall image quality (4.30±0.44 and 4.67±0.40, respectively) than did the respective values in ASIR-V40% images (20.60±4.04 HU, 2.60±0.63, and 3.77±0.43; all P values <0.05). Compared with the 5 mm ASIR-V40% images, the 1.25 mm DLIR-H images had lower noise (SD: 9.65±3.44 vs. 13.63±10.03 HU), higher SNR (5.64±1.20 vs. 4.69±1.28), and higher overall image quality scores (4.67±0.40 vs. 3.94±0.46) (all P values <0.001). In addition, DLIR-L, DLIR-M, and DLIR-H images had a significantly higher spatial resolution in terms of edge rising slope (59.66±21.46, 58.52±17.48, and 59.26±13.33, respectively, vs. 33.79±9.23) and significantly higher image quality scores in the visualization of fine structures (4.43±0.50, 4.41±0.49, and 4.38±0.49, respectively vs. 2.62±0.49) than did the 5 mm ASIR-V40 images. The 1.25 mm DLIR-M and DLIR-H images had significantly reduced image noise and improved SNR and overall image quality compared to the 1.25 mm ASIR-V40% images, and they had significantly improved the spatial resolution and visualization of fine structures compared to the 5 mm ASIR-V40% images. DLIR-H images had further reduced image noise compared with the 5 mm ASIR-V40% images, and DLIR-H was the most effective technique at balancing the image noise and spatial resolution in low-dose abdominal CT.

Deep Learning Denoising of Low-Dose Computed Tomography Chest Images: A Quantitative and Qualitative Image Analysis.

To assess deep learning denoised (DLD) computed tomography (CT) chest images at various low doses by both quantitative and qualitative perceptual image analysis. Simulated noise was inserted into sinogram data from 32 chest CTs acquired at 100 mAs, generating anatomically registered images at 40, 20, 10, and 5 mAs. A DLD model was developed, with 23 scans selected for training, 5 for validation, and 4 for test.Quantitative analysis of perceptual image quality was assessed with Structural SIMilarity Index (SSIM) and Fréchet Inception Distance (FID). Four thoracic radiologists graded overall diagnostic image quality, image artifact, visibility of small structures, and lesion conspicuity. Noise-simulated and denoised image series were evaluated in comparison with one another, and in comparison with standard 100 mAs acquisition at the 4 mAs levels. Statistical tests were conducted at the 2-sided 5% significance level, with multiple comparison correction. At the same mAs levels, SSIM and FID between noise-simulated and reconstructed DLD images indicated that images were closer to a perfect match with increasing mAs (closer to 1 for SSIM, and 0 for FID).In comparing noise-simulated and DLD images to standard-dose 100-mAs images, DLD improved SSIM and FID. Deep learning denoising improved SSIM of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in SSIM from 0.91 to 0.94, 0.87 to 0.93, 0.67 to 0.87, and 0.54 to 0.84, respectively. Deep learning denoising improved FID of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in FID from 20 to 13, 46 to 21, 104 to 41, and 148 to 69, respectively.Qualitative image analysis showed no significant difference in lesion conspicuity between DLD images at any mAs in comparison with 100-mAs images. Deep learning denoising images at 10 and 5 mAs were rated lower for overall diagnostic image quality ( P < 0.001), and at 5 mAs lower for overall image artifact and visibility of small structures ( P = 0.002), in comparison with 100 mAs. Deep learning denoising resulted in quantitative improvements in image quality. Qualitative assessment demonstrated DLD images at or less than 10 mAs to be rated inferior to standard-dose images.

Joint application of hybrid iterative reconstruction and adaptive filters on neck-and-shoulder CT imaging: A clinical evaluation.

To assess whether the joint application of hybrid iterative reconstruction (HIR) and an adaptive filter (AF) could reduce streak artifacts and improve image quality of neck-and-shoulder computed tomography (CT). This study included 96 patients with suspicious neck lesions who underwent a routine nonenhanced scan on a 64-slice CT scanner. The raw data were reconstructed using four different settings: filtered back projection (FBP), HIR, FBP+AF, and HIR+AF. Regions of interest were manually drawn in erector spine, axillary fat, latissimus dorsi, and dorsal cervical fat. Mean and standard deviation (SD) of the CT number, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were obtained and compared using Wilcoxon signed-rank tests. The qualitative assessments of five factors were compared by two independent investigators. Compared to the other three settings, HIR+AF reduced noise in the area where the streak artifact of the lower neck were most serious (SD; all p≤0.001). The SNR and CNR were improved significantly (all p≤0.001). Compared to the other three settings, HIR+AF showed a significant improvement in CT image quality regarding the visibility of suspicious lesions, the extent of streaking artifacts, noise, soft-tissue contrast, and visualization of small structures (all p≤0.02). The combination of HIR and AF can significantly reduce streaking artifacts and improve image quality in neck-and-shoulder CT imaging.

Image quality improvement in low-dose chest CT with deep learning image reconstruction.

To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm. This retrospective study included 86 patients who underwent low-dose CT for lung cancer screening. Images were reconstructed with ASiR-V40% and DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures. The effective dose was 1.03±0.36mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5HU among the four reconstructions. Compared with ASiR-V40%, the DLIR-L, DLIR-M, and DLIR-H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p <0.001). ASiR-V40%, DLIR-L, and DLIR-M had equivalent score in visualizing small structures (all p >0.05), whereas DLIR-H had slightly lower score. Compared with ASiR-V40%, DLIR significantly reduces image noise in low-dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.

Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT.

To evaluate image quality and diagnostic confidence improvement using a thin slice and a deep learning image reconstruction (DLIR) in contrast-enhanced abdominal CT. Forty patients with hepatic lesions in enhanced abdominal CT were retrospectively analyzed. Images in the portal phase were reconstructed at 5mm and 1.25mm slice thickness using the 50% adaptive statistical iterative reconstruction (ASIR-V) (ASIR-V50%) and at 1.25 mm using DLIR at medium (DLIR-M) and high (DLIR-H) settings. CT number and standard deviation of the hepatic parenchyma, spleen, portal vein, and subcutaneous fat were measured, and contrast-to-noise ratio (CNR) was calculated. Edge-rise-slope (ERS) was measured on the portal vein to reflect spatial resolution and the CT number skewness on liver parenchyma was calculated to reflect image texture. Two radiologists blindly assessed the overall image quality including subjective noise, image contrast, visibility of small structures using a 5-point scale, and object sharpness and lesion contour using a 4-point scale. For the 1.25-mm images, DLIR significantly reduced image noise, improved CNR and overall subjective image quality compared to ASIR-V50%. Compared to the 5-mm ASIR-V50% images, DLIR images had significantly higher scores in the visibility and contour for small structures and lesions; as well as significantly higher ERS and lower CT number skewness. At a quarter of the signal strength, the 1.25-mm DLIR-H images had a similar subjective noise score as the 5-mm ASIR-V50% images. DLIR significantly reduces image noise and maintains a more natural image texture; image spatial resolution and diagnostic confidence can be improved using thin slice images and DLIR in abdominal CT. • DLIR further reduces image noise compared with ASIR-V while maintaining favorable image texture. • In abdominal CT, thinner slice images improve image spatial resolution and small object visualization but suffer from higher image noise. • Thinner slice images combined with DLIR in abdominal CT significantly suppress image noise for detecting low-density lesions while significantly improving image spatial resolution and overall image quality.

Photon-Counting Detector CT Virtual Monoengergetic Images for Cochlear Implant Visualization-A Head to Head Comparison to Energy-Integrating Detector CT.

Cochlear implants (CIs) are the primary treatment method in patients with profound sensorineural hearing loss. Interpretation of postoperative imaging with conventional energy-integrating detector computed tomography (EID-CT) following CI surgery remains challenging due to metal artifacts. Still, the photon-counting detector (PCD-CT) is a new emerging technology with the potential to eliminate these problems. This study evaluated the performance of virtual monoenergetic (VME) EID-CT images versus PCD-CT in CI imaging. In this cadaveric study, two temporal bone specimens with implanted CIs were scanned with EID-CT and PCD-CT. The images were assessed according to the visibility of interelectrode wire, size of electrode contact, and diameter of halo artifacts. The visibility of interelectrode wire sections was significantly higher when reviewing PCD-CT images. The difference in diameter measurements for electrode contacts between the two CT scanner modalities showed that the PCD-CT technology generally led to significantly larger diameter readings. The larger measurements were closer to the manufacturer’s specifications for the CI electrode. The size of halo artifacts surrounding the electrode contacts did not differ significantly between the two imaging modalities. PCT-CT imaging is a promising technology for CI imaging with improved spatial resolution and better visibility of small structures than conventional EID-CT.

Evaluation of image quality and radiation dose with prospective ECG-gated 80 multi-slice CT scan in 182 consecutive children examinations with congenital heart disease

Computed tomography angiography (CTA) is a reliable imaging tool to evaluate children congenital heart disease (CHD) but requires radiation exposure. This study aims to investigate image quality and radiation dose with a prospective electrocardiogram (ECG)-gated 80 multi-slice CTA, in 182 consecutive children examinations with CHD. In total, 182 consecutive examinations performed with a prospective ECG-gated 80 multi-slice CTA from March 2016 to December 2017 in Nantes university hospital were retrospectively analyzed. Radiation dose was assessed by dose length product (DLP) and effective dose (E). Objective quality image was assessed by contrast to noise ratio (CNR). Subjective quality image was assessed by two radiologists with a 10-point scale evaluating visualization of coronary arteries. Correlation between quality image, radiation dose, age, weight, heart rate was analyzed. DLP and E were 29.3 ± 15.5 mGy.cm and 0.6 ± 0.2 mSv, respectively. Agreement between radiologists in assessment of subjective image quality was 0.79. Subjective image quality score was significantly better ( P < 0.01) with higher age, weight, DLP and lower heart rate in univariate analysis. CNR was significantly lower with increasing weight and DLP ( P < 0.05) but there was no correlation between CNR and age and heart rate. Prospective ECG-gated 80 multi-slice CTA is performing for the evaluation of children CHD with good image quality and low radiation dose. Visualization of small structures is better when age and weight increase, heart rate decreases and when radiation dose is more important. CNR evolves in the opposite side to subjective image quality score. It seems less appropriate to assess image quality from clinical viewpoint of the radiologist.

Identifying transcript location in cells

In Situ Sequencing Identifying where specific RNAs occur within a cell or tissue has been limited by technology and imaging capabilities. Expansion microscopy has allowed for better visualization of small structures by expanding the tissues with a polymer- and hydrogel-based system. Alon et al. combined expansion microscopy with long-read in situ RNA sequencing, resulting in a more precise visualization of the location of specific transcripts. This method, termed “ExSeq” for expansion sequencing, was used to detect RNAs, both new transcripts and those previously demonstrated to localize to neuronal dendrites. Unlike other in situ sequencing methods, ExSeq does not target sets of genes. This technology thus unites spatial resolution, multiplexing, and an unbiased approach to reveal insights into RNA localization and its physiological roles in developing and active tissue. Science , this issue p. [eaax2656][1] [1]: /lookup/doi/10.1126/science.aax2656

Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems.

Methods for highly multiplexed RNA imaging are limited in spatial resolution and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to the mouse brain, which yielded the readout of thousands of genes, including splice variants. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in the neurons of the mouse hippocampus, revealing patterns across multiple cell types, layer-specific cell types across the mouse visual cortex, and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus, ExSeq enables highly multiplexed mapping of RNAs from nanoscale to system scale.

Improved image quality of low-dose CT combining with iterative model reconstruction algorithm for response assessment in patients after treatment of malignant tumor.

To evaluate the image quality and radiation dose of low-dose (LD) computed tomography (LD-CT) combining with iterative model reconstruction (IMR) algorithm for response assessment in patients after treatment of malignant tumor compared with routine-dose CT (RD-CT). Forty-seven patients [mean age 57.8±10.9 years, 30 males, body mass index (BMI) 22.09±2.35 kg/m2] after treatment of malignant tumor underwent contrast-enhanced chest and abdomen CT twice for response assessment with an interval of 6 months according to clinical routine. The first CT scans were performed with RD protocol at 120 kVp and images were reconstructed with filtered back projection (FBP) algorithm; while the second scans were performed with LD protocol at 100 kVp and images were reconstructed with FBP and IMR algorithm respectively. All scans were performed using an automatic tube current modulation technique with 150 mAs as reference. Objective image quality including CT attenuation, image noise, and contrast to noise ratio (CNR), and subjective image quality including artifacts, noise, visualization of small structures and confidence of targeted lesions, as well as lesion detection were assessed and compared. Effective radiation dose of LD-CT scans was reduced 54.8% compared to RD-CT scans (26.89±3.35 vs. 12.14±2.09 mSv). Higher CT attenuation was found in both LD-IMR and LD-FBP images compared to RD-FBP images. Better subjective image quality and CNR as well as lower objective noise were found in LD-IMR images (all, P<0.05). Two small lesions with the diameter less than 1 cm were missed in LD-FBP images, which were able to be observed in LD-IMR images. IMR is able to help more than half of reduction of radiation dose without compromising the quality of diagnostic information in patients after treatment of malignant tumors to chest and abdomen CT for response assessment.

Um microscópio caseiro simplificado

Neste trabalho é apresentada uma nova alternativa para a construção de microscópios caseiros, e uma nova forma de se obter lentes esféricas, utilizando-se de materiais mais baratos, o que facilita a construção e o uso do equipamento. A simplicidade da montagem e a facilidade do manuseio favorecem a aplicação do microscópio em locais com poucos recursos, além de torná-lo portátil. Além disso, são apresentadas sugestões de amostras a serem estudadas, pelos estudantes de diferentes disciplinas, o que pode fomentar a elaboração e a participação de alunos e professores em projetos interdisciplinares. O microscópio caseiro permite a visualização de pequenas estruturas, suas formas e cores, o que pode despertar o interesse do aluno pela análise de materiais e objetos, de uma forma muito mais instigante, que a mera observação a olho nu.

Systematic radiation dose optimization of abdominal dual-energy CT on a second-generation dual-source CT scanner: assessment of the accuracy of iodine uptake measurement and image quality in an in vitro and in vivo investigations

To assess the accuracy of iodine quantification in a phantom study at different radiation dose levels with dual-energy dual-source CT and to evaluate image quality and radiation doses in patients undergoing a single-energy and two dual-energy abdominal CT protocols. In a phantom study, the accuracy of iodine quantification (4.5-23.5mgI/mL) was evaluated using the manufacturer-recommended and three dose-optimized dual-energy protocols. In a patient study, 75 abdomino-pelvic CT examinations were acquired as follows: 25 CT scans with the manufacturer-recommended dual-energy protocol (protocol A); 25 CT scans with a dose-optimized dual-energy protocol (protocol B); and 25 CT scans with a single-energy CT protocol (protocol C). CTDIvol and objective noise were measured. Five readers scored each scan according to six subjective image quality parameters (noise, contrast, artifacts, visibility of small structures, sharpness, overall diagnostic confidence). In the phantom study, differences between the real and measured iodine concentrations ranged from -8.8% to 17.0% for the manufacturer-recommended protocol and from -1.6% to 20.5% for three dose-optimized protocols. In the patient study, the CTDIvol of protocol A, B, and C were 12.5±1.9, 7.5±1.2, and 6.5±1.7mGycm, respectively (p<0.001), and the average image noise values were 6.6±1.2, 7.8±1.4, and 9.6±2.2HU, respectively (p<0.001). No significant differences in the six subjective image quality parameters were observed between the dose-optimized dual-energy and the single-energy protocol. A dose reduction of 41% is feasible for the manufacturer-recommended, abdominal dual-energy CT protocol, as it maintained the accuracy of iodine measurements and subjective image quality compared to a single-energy protocol.

Size-dependent scanning parameters (kVp and mAs) for photon-counting spectral CT system in pediatric imaging: simulation study

We are developing a photon-counting spectral CT detector with a small pixel size of mm2, offering a potential advantage for better visualization of small structures in pediatric patients. The purpose of this study is to determine the patient size dependent scanning parameters (kVp and mAs) for pediatric CT in two imaging cases: adipose imaging and iodinated blood imaging.Cylindrical soft-tissue phantoms of diameters between 10–25 cm were used to mimic patients of different ages from 0 to 15 y. For adipose imaging, a 5 mm diameter adipose sphere was assumed as an imaging target, while in the case of iodinated imaging, an iodinated blood sphere of 1 mm in diameter was assumed. By applying the geometry of a commercial CT scanner (GE Lightspeed VCT), simulations were carried out to calculate the detectability index, , with tube potentials varying from 40 to 140 kVp. The optimal kVp for each phantom in each imaging case was determined such that the dose-normalized detectability index, dose, is maximized. With the assumption that the detectability index in pediatric imaging is required the same as in typical adult imaging, the value of mAs at optimal kVp for each phantom was selected to achieve a reference detectability index that was obtained by scanning an adult phantom (30 cm in diameter) in a typical adult CT procedure (120 kVp and 200 mAs) using a modeled energy-integrating system.For adipose imaging, the optimal kVps are 50, 60, 80, and 120 kVp, respectively, for phantoms of 10, 15, 20, and 25 cm in diameter. The corresponding mAs values required to achieve the reference detectability index are only 9%, 23%, 24%, and 54% of the mAs that is used for adult patients at 120 kVp, for 10, 15, 20, and 25 cm diameter phantoms, respectively. In the case of iodinated imaging, a tube potential of 60 kVp was found optimal for all phantoms investigated, and the mAs values required to achieve the reference detectability index are 2%, 9%, 37%, and 109% of the adult mAs. The results also indicate that with the use of respective optimal kVps, the photon-counting spectral system offers up to 30% higher dose than the modeled energy-integrating system for adipose imaging, and 70% for iodinated imaging.

Radiation dose reduction in CT with adaptive statistical iterative reconstruction (ASIR) for patients with bronchial carcinoma and intrapulmonary metastases

To compare the radiation dose and image quality of 64-row chest computed tomography (CT) in patients with bronchial carcinoma or intrapulmonary metastases using full-dose CT reconstructed with filtered back projection (FBP) at baseline and reduced dose with 40% adaptive statistical iterative reconstruction (ASIR) at follow-up. The chest CT images of patients who underwent FBP and ASIR studies were reviewed. Dose-length products (DLP), effective dose, and size-specific dose estimates (SSDEs) were obtained. Image quality was analysed quantitatively by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurement. In addition, image quality was assessed by two blinded radiologists evaluating images for noise, contrast, artefacts, visibility of small structures, and diagnostic acceptability using a five-point scale. The ASIR studies showed 36% reduction in effective dose compared with the FBP studies. The qualitative and quantitative image quality was good to excellent in both protocols, without significant differences. There were also no significant differences for SNR except for the SNR of lung surrounding the tumour (FBP: 35±17, ASIR: 39±22). A protocol with 40% ASIR can provide approximately 36% dose reduction in chest CT of patients with bronchial carcinoma or intrapulmonary metastases while maintaining excellent image quality.

Combined EUS and EBUS are complementary methods in lung cancer staging: Do not forget the esophagus.

Endoscopic ultrasound (endobronchial ultrasound-guided transbronchial needle aspiration [EBUS-TBNA] and transesophageal ultrasoundguided fine needle aspiration [EUS-FNA]) can be performed in an outpatient setting under local anesthesia with mild sedation. The procedures are well established for diagnosis and staging of a variety of diseases, such as lung cancer [1–3]. Accurate staging of non-small cell lung cancer (NSCLC) is crucial for allocation to surgical treatment. By using endoscopic ultrasound, surgical staging procedures can be avoided in a considerable proportion of patients with NSCLC. In short, EUS is excellent for the left and lower paraesophageal structures plus structures under the diaphragm, whereas EBUS provides access to structures close to the large airways on both sides. Thus the two procedures are complementary (● Fig.1) [4]. Currently no single endoscope offers the benefits of both endobronchial and transbronchial access. Compared to EBUS, EUS has many advantages. EUS is better tolerated (no cough), the ultrasonic window angle is larger (150–180 versus 50–60 degrees with EBUS), the ultrasonic image is better due to higher resolution, it allows better visualization of small structures, the operator can orientate independent of an endoscopic view with secretions, and the transducer is in close contact with the target, owing to endoscopic suction with deflation of the esophageal lumen. With EUS, there are also no hard cartilage rings interposed between the needle and the target and needle maneuverability is better with improved targeting due to an “elevator.”All of these benefits are outweighed by a single disadvantage: the psychological barrier to use of EUS that exists among many—but not all—thoracic physicians. The way to overcome this barrier is to let a thoracic physician perform EUS with the smallest EBUS endoscope, that is, to perform a so-called EUS-B procedure [5, 6] even if the equipment is not designed for the purpose and therefore, in many respects, inferior compared to EUS-FNA. In their current paper, Meena and Bartter performed a retrospective comparison of EUS-B and EBUS in a total of 155 procedures and found that EUS-B was faster, patients needed less sedation and oxygen, and the time to discharge was shorter, whereas the diagnostic yield was the same. The authors conclude that EUS is the procedure of choice when applicable, meaning that, for example, EUS-B should be preferable to EBUS in a patient with a suspicious lymph node station 7 or 4L. It must be remembered, however, that in the majority of cases, choosing one method over the other is not the issue because EBUS and EUS are complementary and can be performed in a single session [4]. It should be mentioned that, for obvious reasons, several of the advantages of EUS listed above are not achievable with EUS–B. One example is superior ultrasonic visualization. It must also be noted, that in the current study, regions under the diaphragm relevant for staging of NSCLC (● Fig.1) could not be reached with the relatively short EUS–B technique, meaning that EUS does not get its full credit as a comparator in the study. Further, patient preference also have been explored by asking whether an individual preferred a foreign body in the airway or in the esophagus, although the answer seems predetermined. If thoracic physicians are not to be left behind in terms of advancements in endoscopic ultrasound, it is mandatory that necessary education and training be established for EUS as well as EBUS, including theoretical courses, simulator-based education, and clinical training plus valid assessment of all three elements [7–10]. But do not forget the esophagus!

Successful Dose Reduction Using Reduced Tube Voltage With Hybrid Iterative Reconstruction in Pediatric Abdominal CT.

The purpose of this article is to assess radiation dose reduction, image quality, and diagnostic confidence using low tube voltage in combination with hybrid iterative reconstruction in contrast-enhanced pediatric abdominal CT. CT examinations of 133 patients (median age, 10 years) were performed at sequentially reduced doses. The first group (group 1) was scanned using dimension-based protocols at 120 kV for all patient sizes. The optimized group (group 5) was scanned at 80 kV for less than 18 cm in the lateral dimension and 100 kV in the 19-30 cm lateral dimension. CT examinations reconstructed with filtered back projection (FBP) and four levels of hybrid iterative reconstruction were reviewed by four blinded readers for subjective image quality and diagnostic confidence. Objective noise, volume CT dose index (CTDIvol), and size-specific dose estimate (SSDE) were recorded. Data were analyzed using t tests, one and two-way ANOVA, and the intraclass correlation coefficient. Compared with group 1, the radiation dose was reduced for group 5 by 63% measured by SSDE (4.69 vs 10.00 mGy; p < 0.001). Subjective image noise was increased for FBP images (p < 0.001) but not was statistically significantly different for all levels of hybrid iterative reconstruction; artifacts were reduced and visibility of small structures was improved (both p < 0.001). Diagnostic confidence was improved for solid organ injury and metastatic disease (both p < 0.001) and was not statistically significantly different for appendicitis (p = 0.306). Use of hybrid iterative reconstruction with low-tube-voltage protocols enables substantial radiation dose reduction for pediatric abdominal CT with equivalent to improved subjective image quality and diagnostic confidence.

Comparison of applied dose and image quality in staging CT of neuroendocrine tumor patients using standard filtered back projection and adaptive statistical iterative reconstruction

ObjectiveTo investigate whether dose reduction via adaptive statistical iterative reconstruction (ASIR) affects image quality and diagnostic accuracy in neuroendocrine tumor (NET) staging. MethodsA total of 28 NET patients were enrolled in the study. Inclusion criteria were histologically proven NET and visible tumor in abdominal computed tomography (CT). In an intraindividual study design, the patients underwent a baseline CT (filtered back projection, FBP) and follow-up CT (ASIR 40%) using matched scan parameters. Image quality was assessed subjectively using a 5-grade scoring system and objectively by determining signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNRs). Applied volume computed tomography dose index (CTDIvol) of each scan was taken from the dose report. ResultsASIR 40% significantly reduced CTDIvol (10.17±3.06mGy [FBP], 6.34±2.25mGy [ASIR] (p<0.001) by 37.6% and significantly increased CNRs (complete tumor-to-liver, 2.76±1.87 [FBP], 3.2±2.32 [ASIR]) (p<0.05) (complete tumor-to-muscle, 2.74±2.67 [FBP], 4.31±4.61 [ASIR]) (p<0.05) compared to FBP. Subjective scoring revealed no significant changes for diagnostic confidence (5.0±0 [FBP], 5.0±0 [ASIR]), visibility of suspicious lesion (4.8±0.5 [FBP], 4.8±0.5 [ASIR]) and artifacts (5.0±0 [FBP], 5.0±0 [ASIR]). ASIR 40% significantly decreased scores for noise (4.3±0.6 [FBP], 4.0±0.8 [ASIR]) (p<0.05), contrast (4.4±0.6 [FBP], 4.1±0.8 [ASIR]) (p<0.001) and visibility of small structures (4.5±0.7 [FBP], 4.3±0.8 [ASIR]) (p<0.001). ConclusionIn clinical practice ASIR can be used to reduce radiation dose without sacrificing image quality and diagnostic confidence in staging CT of NET patients. This may be beneficial for patients with frequent follow-up and significant cumulative radiation exposure.

Feasibility Study of Radiation Dose Reduction in Adult Female Pelvic CT Scan with Low Tube-Voltage and Adaptive Statistical Iterative Reconstruction.

ObjectiveTo evaluate image quality of female pelvic computed tomography (CT) scans reconstructed with the adaptive statistical iterative reconstruction (ASIR) technique combined with low tube-voltage and to explore the feasibility of its clinical application.Materials and MethodsNinety-four patients were divided into two groups. The study group used 100 kVp, and images were reconstructed with 30%, 50%, 70%, and 90% ASIR. The control group used 120 kVp, and images were reconstructed with 30% ASIR. The noise index was 15 for the study group and 11 for the control group. The CT values and noise levels of different tissues were measured. The contrast to noise ratio (CNR) was calculated. A subjective evaluation was carried out by two experienced radiologists. The CT dose index volume (CTDIvol) was recorded.ResultsA 44.7% reduction in CTDIvol was observed in the study group (8.18 ± 3.58 mGy) compared with that in the control group (14.78 ± 6.15 mGy). No significant differences were observed in the tissue noise levels and CNR values between the 70% ASIR group and the control group (p = 0.068-1.000). The subjective scores indicated that visibility of small structures, diagnostic confidence, and the overall image quality score in the 70% ASIR group was the best, and were similar to those in the control group (1.87 vs. 1.79, 1.26 vs. 1.28, and 4.53 vs. 4.57; p = 0.122-0.585). No significant difference in diagnostic accuracy was detected between the study group and the control group (42/47 vs. 43/47, p = 1.000).ConclusionLow tube-voltage combined with automatic tube current modulation and 70% ASIR allowed the low CT radiation dose to be reduced by 44.7% without losing image quality on female pelvic scan.

Submillisievert chest CT with filtered back projection and iterative reconstruction techniques.

The purpose of this study was to compare submillisievert chest CT images reconstructed with filtered back projection (FBP), SafeCT, adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR) with standard of care FBP images. Fifty patients (33 men and 17 women; mean age [± SD], 62 ± 10 years) undergoing routine chest CT gave written informed consent for acquisition of an additional submillisievert chest CT series with reduced tube current but identical scanning length as standard of care chest CT. Sinogram data of the submillisievert series were reconstructed with FBP, SafeCT, ASIR, and MBIR and compared with FBP images at standard-dose chest CT (n = 8 × 50 = 400 series). Two thoracic radiologists performed independent comparison for visualization of lesion margin, visibility of small structures, and diagnostic acceptability. Objective noise measurements and noise spectral density were obtained. Of 287 detected lesions, 162 were less than 1-cm noncalcified nodules. Lesion margins were well seen on all submillisievert reconstruction images except MBIR, on which they were poorly visualized. Likewise, only submillisievert MBIR images were suboptimal for visibility of normal structures, such as pulmonary vessels in the outer 2 cm of the lung, interlobular fissures, and subsegmental bronchial walls. MBIR had the lowest image noise compared with other techniques. FBP, SafeCT, ASIR, and MBIR can enable optimal lesion evaluation on chest CT acquired at a volume CT dose index of 2 mGy. However, all submillisievert reconstruction techniques were suboptimal for visualization of mediastinal structures. Submillisievert MBIR images were suboptimal for visibility of normal lung structures despite showing lower image noise.