Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT

Influence of CT Radiation Dose and Field-of-View on Automatic Morphometry for Cochlear Implant Planning

INTRODUCTION

In cochlear implantation (CI), precise preoperative cochlear duct length (CDL) and angular insertion depth (AID) measurements are pivotal for individualized electrode carrier selection, since recipients benefit from sufficient cochlear coverage of the electrode carrier, enabling electric stimulation of all crucial frequency bands. Since the quality of temporal bone CT largely depends on acquisition and reconstruction settings and is limited by the technical capabilities of the CT scanner, this study aims to assess how radiation dose and reconstruction field-of-view (FOV) affect automatic cochlear morphometry and electrode contact determination in conventional multislice CT.

METHODS

Twenty fresh-frozen human petrous bone specimens were examined at three radiation dose levels (40, 20, and 10 mGy) using a multislice CT scanner. Each dataset was reconstructed with three different FOV settings (250, 125, and 50 mm). Preoperative CDL and AID measurements were performed with dedicated otological planning software. Maxed-out dose images (250 mGy) served as standard of reference for comparing the morphometric results.

RESULTS

Regardless of the selected combination of dose level and FOV, significant CDL or AID measurement differences were neither ascertained among the individual groups, nor in comparison to the reference scans (all p ≥ 0.05). Likewise, the simulation of all stimulable frequency bandwidths showed no dependency on radiation dose or FOV settings (all p ≥ 0.05).

CONCLUSION

The assessment of cochlear morphometry with conventional multislice CT imaging before CI surgery allowed a radiation dose reduction up to 75% without compromising the accuracy of software-based cochlear analysis. Notably, automatic CDL and AID measurements for surgical planning did not benefit from a smaller reconstruction FOV.

Improvement of image quality of dentomaxillofacial region in ultra-high-resolution CT: a phantom study

OBJECTIVES

The purpose of this study was to compare the image quality of ultra-high-resolution CT (U-HRCT) with that of conventional multidetector row CT (convCT) and demonstrate its usefulness in the dentomaxillofacial region.

METHODS

Phantoms were helically scanned with U-HRCT and convCT scanners using clinical protocols. In U-HRCT, phantoms were scanned in super-high-resolution (SHR) mode, and hybrid iterative reconstruction (HIR) and filtered-back projection (FBP) techniques were performed using a bone kernel (FC81). The FBP technique was performed using the same kernel as in convCT (reference). Two observers independently evaluated the 54 resulting images using a 5-point scale (5 = excellent diagnostic image quality; 4 = above average; 3 = average; 2 = subdiagnostic; and 1 = unacceptable). The system performance function (SPF) was calculated for a comprehensive evaluation of the image quality using the task transfer function and noise power spectrum. Statistical analysis using the Kruskal-Wallis test was performed to compare the image quality among the 3 protocols.

RESULTS

The observers assigned higher scores to images acquired with the SHRHIR and SHRFBP protocols than to those acquired with the reference (P < 0.0001 and P < 0.0001, respectively). The relative SPF value at 1.0 cycles/mm in SHRHIR and SHRFBP compared to the reference protocol were 151.5% and 45.6%, respectively.

CONCLUSIONS

Through phantom experiments, this study demonstrated that U-HRCT can provide superior-quality images compared to conventional CT in the dentomaxillofacial region. The development of a better image reconstruction method is required to improve image quality and optimize the radiation dose.

Ultra-high-resolution CT of the temporal bone: Technical aspects, current applications and future directions

Temporal bone imaging has historically suffered from spatial resolution issues because the spatial resolution of conventional high-resolution computed tomography (CT) is 0.5 mm, while the smallest structure of the middle ear, the stapes, has very thin components, as thin as 0.19 mm, and small structures, such as small channels containing nerves and arteries, have historically been beyond its spatial resolution. Photon-counting and ultra-high resolution CT allow for improved spatial resolution and reduced radiation dose compared to conventional high-resolution CT. This article provides a technical approach to understanding the technical aspects of these new techniques and an updated description of the middle and inner ear, as well as a practical approach to understanding the normal and pathologic anatomy of the temporal bone in the light of ultra-high resolution imaging techniques.

Photon-Counting Detector CT Radiological-Histological Correlation in Cadaveric Human Lung Nodules and Airways

OBJECTIVES

The aim of this study was to compare the performances of photon-counting detector computed tomography (PCD-CT) and energy-integrating detector computed tomography (EID-CT) for visualizing nodules and airways in human cadaveric lungs.

MATERIALS AND METHODS

Previously obtained 20 cadaveric lungs were scanned, and images were prospectively acquired by EID-CT and PCD-CT at a radiation dose with a noise level equivalent to the diagnostic reference level. PCD-CT was scanned with ultra-high-resolution mode. The EID-CT images were reconstructed with a 512 matrix, 0.6-mm thickness, and a 350-mm field of view (FOV). The PCD-CT images were reconstructed at 3 settings: PCD-512: same as EID-CT; PCD-1024-FOV350: 1024 matrix, 0.2-mm thickness, 350-mm FOV; and PCD-1024-FOV50: 1024 matrix, 0.2-mm thickness, 50-mm FOV. Two specimens per lung were examined after hematoxylin and eosin staining. The CT images were evaluated for nodules on a 5-point scale and for airways on a 4-point scale to compare the histology. The Wilcoxon signed rank test with Bonferroni correction was performed for statistical analyses.

RESULTS

Sixty-seven nodules (1321 μm; interquartile range [IQR], 758-3105 μm) and 92 airways (851 μm; IQR, 514-1337 μm) were evaluated. For nodules and airways, scores decreased in order of PCD-1024-FOV50, PCD-1024-FOV350, PCD-512, and EID-CT. Significant differences were observed between series other than PCD-1024-FOV350 versus PCD-1024-FOV50 for nodules (PCD-1024-FOV350 vs PCD-1024-FOV50, P = 0.063; others P < 0.001) and between series other than EID-CT versus PCD-512 for airways (EID-CT vs PCD-512, P = 0.549; others P < 0.005). On PCD-1024-FOV50, the median size of barely detectable nodules was 604 μm (IQR, 469-756 μm) and that of barely detectable airways was 601 μm (IQR, 489-929 μm). On EID-CT, that of barely detectable nodules was 837 μm (IQR, 678-914 μm) and that of barely detectable airways was 1210 μm (IQR, 674-1435 μm).

CONCLUSIONS

PCD-CT visualized small nodules and airways better than EID-CT and improved with high spatial resolution and potentially can detect submillimeter nodules and airways.

Recent trends in scientific research in chest radiology: What to do or not to do? That is the critical question in research

Hereby inviting young rising stars in chest radiology in Japan for contributing what they are working currently, we would like to show the potentials and directions of the near future research trends in the research field. I will provide a reflection on my own research topics. At the end, we also would like to discuss on how to choose the themes and topics of research: What to do or not to do? We strongly believe it will stimulate and help investigators in the field.

Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer

OBJECTIVES

This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients.

METHODS

In this prospective study, from October 2020 to March 2021, 375 patients with thyroid disease underwent thin-section dual-energy thyroid CT at a small field of view (FOV) and thyroid surgery. The data of 183 patients with 281 LNs were analyzed. The targeted LNs were negative or equivocal on small FOV CT images. Six deep-learning models were used to classify the LNs on conventional CT images. The performance of all models was compared with pathology reports.

RESULTS

Of the 281 LNs, 65.5% had a short diameter of less than 4 mm. Multiple quantitative dual-energy CT parameters significantly differed between benign and malignant LNs. Multivariable logistic regression analyses showed that the best combination of parameters had an area under the curve (AUC) of 0.857, with excellent consistency and discrimination, and its diagnostic accuracy and sensitivity were 74.4% and 84.2%, respectively (p < 0.001). The visual geometry group 16 (VGG16) based model achieved the best accuracy (86%) and sensitivity (88%) in differentiating between benign and malignant LNs, with an AUC of 0.89.

CONCLUSIONS

The VGG16 model based on small FOV CT images showed better diagnostic accuracy and sensitivity than the spectral parameter model. Our study presents a noninvasive and convenient imaging biomarker to predict malignant LNs without suspicious CT features in thyroid cancer patients.

CLINICAL RELEVANCE STATEMENT

Our study presents a deep-learning-based model to predict malignant lymph nodes in thyroid cancer without suspicious features on conventional CT images, which shows better diagnostic accuracy and sensitivity than the regression model based on spectral parameters.

KEY POINTS

Many cervical lymph nodes (LNs) do not express suspicious features on conventional computed tomography (CT). Dual-energy CT parameters can distinguish between benign and malignant LNs. Visual geometry group 16 model shows superior diagnostic accuracy and sensitivity for malignant LNs.

Super Resolution of Pulmonary Nodules Target Reconstruction Using a Two-Channel GAN Models

RATIONALE AND OBJECTIVES

To develop a Dual generative-adversarial-network (GAN) Cascaded Network (DGCN) for generating super-resolution computed tomography (SRCT) images from normal-resolution CT (NRCT) images and evaluate the performance of DGCN in multi-center datasets.

MATERIALS AND METHODS

This retrospective study included 278 patients with chest CT from two hospitals between January 2020 and June 2023, and each patient had all three NRCT (512×512 matrix CT images with a resolution of 0.70 mm, 0.70 mm,1.0 mm), high-resolution CT (HRCT, 1024×1024 matrix CT images with a resolution of 0.35 mm, 0.35 mm,1.0 mm), and ultra-high-resolution CT (UHRCT, 1024×1024 matrix CT images with a resolution of 0.17 mm, 0.17 mm, 0.5 mm) examinations. Initially, a deep chest CT super-resolution residual network (DCRN) was built to generate HRCT from NRCT. Subsequently, we employed the DCRN as a pre-trained model for the training of DGCN to further enhance resolution along all three axes, ultimately yielding SRCT. PSNR, SSIM, FID, subjective evaluation scores, and objective evaluation parameters related to pulmonary nodule segmentation in the testing set were recorded and analyzed.

RESULTS

DCRN obtained a PSNR of 52.16, SSIM of 0.9941, FID of 137.713, and an average diameter difference of 0.0981 mm. DGCN obtained a PSNR of 46.50, SSIM of 0.9990, FID of 166.421, and an average diameter difference of 0.0981 mm on 39 testing cases. There were no significant differences between the SRCT and UHRCT images in subjective evaluation.

CONCLUSION

Our model exhibited a significant enhancement in generating HRCT and SRCT images and outperformed established methods regarding image quality and clinical segmentation accuracy across both internal and external testing datasets.

CT angiography of the lower limbs: combining small field of view with large matrix size and iterative reconstruction further improves image quality of below-the-knee arteries

BACKGROUND

Image quality and diagnostic accuracy in computed tomography angiography (CTA) reach their limits in imaging of below-the-knee vessels.

PURPOSE

To evaluate whether image quality in CTA of lower limbs is further improvable by combining side-separate reconstruction with a larger matrix size and whether resulting noise can be compromised with iterative reconstruction (IR).

MATERIAL AND METHODS

From CTA of the lower extremities of 26 patients (5 women, 21 men; mean age = 68.5 ± 10.3 years), the lower legs were reconstructed side-separately with different reconstruction algorithms and matrix sizes including filtered back projection (FBP) with a 512 × 512 matrix, FBP with a 1024 × 1024 matrix, IR (SAFIRE) with a 512 × 512 matrix, and IR (SAFIRE) with a 1024 × 1024 matrix. A total of 208 CT series were evaluated. Subjective image quality was assessed by two readers using a 5-point Likert scale. Image noise was assessed by measuring signal-to-noise and contrast-to-noise ratios.

RESULTS

Subjective image quality was rated significantly higher when using a 1024 × 1024 matrix (P < 0.001) and could further be increased with IR. Vessel sharpness was rated significantly better with a larger matrix (P < 0.001). Visible and measured image noise was significantly higher with a 1024 × 1024 matrix but could be reduced by using IR (P < 0.001), even to a level below FBP with a 512 × 512 matrix while reconstructing with a larger matrix (P < 0.001).

CONCLUSION

Image quality, image noise, and vessel sharpness can be further improved in CTA of the lower extremities with side-separate reconstruction using a 1024 × 1024 matrix size and IR.

Optimising image quality in intravenous cerebral cone beam computed tomography

INTRODUCTION

The efficacy of intravenous cerebral Cone Beam Computed Tomography (IV CBCT) is well established; however, image quality has only ever been authenticated by subjective evaluation. The aim of this study was to quantify the factors pertinent to achieving consistent and optimal image quality when performing IV CBCT.

METHODS

Between 1 March 2021 and 30 October 2022, 79 patients received IV CBCT. These candidates were divided into three main acquisition field size categories (22/32, 42 and 48 cm) according to the clinical indication. The images were analysed using both a quantitative assessment and a subjective evaluation. Here, a comparison of Hounsfield units (HUs), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and noise index was conducted for each study and compared relative to the acquisition field size. The subjective analysis was performed in a non-blinded fashion where the diagnostic value (DV) of the exam was determined according to a graded scale. A phantom analysis for each of the acquisition field sizes was conducted and modulation transfer function (MTF) graphed.

RESULTS

Significantly higher HU, SNR, CNR and lower noise indices were achieved with the 42-cm protocol than the 22/32 and 48-cm protocols. Here a greater DV was also reported. The MTF demonstrates marginally improved spatial resolution for the 22-cm protocol, but this is near equivocal for the 32-, 42 and 48-cm protocols.

CONCLUSION

The use of larger acquisition field sizes provides improved image quality when performing IV CBCT as an alternative to intra-arterial (IA) CBCT.

Multi-instance learning based lung nodule system for assessment of CT quality after small-field-of-view reconstruction

Small-field-of-view reconstruction CT images (sFOV-CT) increase the pixel density across airway structures and reduce partial volume effects. Multi-instance learning (MIL) is proposed as a weakly supervised machine learning method, which can automatically assess the image quality. The aim of this study was to evaluate the disparities between conventional CT (c-CT) and sFOV-CT images using a lung nodule system based on MIL and assessments from radiologists. 112 patients who underwent chest CT were retrospectively enrolled in this study between July 2021 to March 2022. After undergoing c-CT examinations, sFOV-CT images with small-field-of-view were reconstructed. Two radiologists analyzed all c-CT and sFOV-CT images, including features such as location, nodule type, size, CT values, and shape signs. Then, an MIL-based lung nodule system objectively analyzed the c-CT (c-MIL) and sFOV-CT (sFOV-MIL) to explore their differences. The signal-to-noise ratio of lungs (SNR-lung) and contrast-to-noise ratio of nodules (CNR-nodule) were calculated to evaluate the quality of CT images from another perspective. The subjective evaluation by radiologists showed that feature of minimal CT value (p = 0.019) had statistical significance between c-CT and sFOV-CT. However, most features (all with p < 0.05), except for nodule type, location, volume, mean CT value, and vacuole sign (p = 0.056-1.000), had statistical differences between c-MIL and sFOV-MIL by MIL system. The SNR-lung between c-CT and sFOV-CT had no statistical significance, while the CNR-nodule showed statistical difference (p = 0.007), and the CNR of sFOV-CT was higher than that of c-CT. In detecting the difference between c-CT and sFOV-CT, features extracted by the MIL system had more statistical differences than those evaluated by radiologists. The image quality of those two CT images was different, and the CNR-nodule of sFOV-CT was higher than that of c-CT.

In vivo depiction of cortical bone vascularization with ultra-high resolution-CT and deep learning algorithm reconstruction using osteoid osteoma as a model

PURPOSE

The purpose of this study was to evaluate the ability to depict in vivo bone vascularization using ultra-high-resolution (UHR) computed tomography (CT) with deep learning reconstruction (DLR) and hybrid iterative reconstruction algorithm, compared to simulated conventional CT, using osteoid osteoma as a model.

MATERIALS AND METHODS

Patients with histopathologically proven cortical osteoid osteoma who underwent UHR-CT between October 2019 and October 2022 were retrospectively included. Images were acquired with a 1024 × 1024 matrix and reconstructed with DLR and hybrid iterative reconstruction algorithm. To simulate conventional CT, images with a 512 × 512 matrix were also reconstructed. Two radiologists (R1, R2) independently evaluated the number of blood vessels entering the nidus and crossing the bone cortex, as well as vessel identification and image quality with a 5-point scale. Standard deviation (SD) of attenuation in the adjacent muscle and that of air were used as image noise and recorded.

RESULTS

Thirteen patients with 13 osteoid osteomas were included. There were 11 men and two women with a mean age of 21.8 ± 9.1 (SD) years. For both readers, UHR-CT with DLR depicted more nidus vessels (11.5 ± 4.3 [SD] (R1) and 11.9 ± 4.6 [SD] (R2)) and cortical vessels (4 ± 3.8 [SD] and 4.3 ± 4.1 [SD], respectively) than UHR-CT with hybrid iterative reconstruction (10.5 ± 4.3 [SD] and 10.4 ± 4.6 [SD], and 4.1 ± 3.8 [SD] and 4.3 ± 3.8 [SD], respectively) and simulated conventional CT (5.3 ± 2.2 [SD] and 6.4 ± 2.5 [SD], 2 ± 1.2 [SD] and 2.4 ± 1.6 [SD], respectively) (P < 0.05). UHR-CT with DLR provided less image noise than simulated conventional CT and UHR-CT with hybrid iterative reconstruction (P < 0.05). UHR-CT with DLR received the greatest score and simulated conventional CT the lowest score for vessel identification and image quality.

CONCLUSION

UHR-CT with DLR shows less noise than UHR-CT with hybrid iterative reconstruction and significantly improves cortical bone vascularization depiction compared to simulated conventional CT.

Deep learning-driven multi-view multi-task image quality assessment method for chest CT image

BACKGROUND

Chest computed tomography (CT) image quality impacts radiologists' diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and may have human limitations (fatigue, perceptual biases, and cognitive biases). This study aims to develop and validate a deep learning (DL)-driven multi-view multi-task image quality assessment (M[Formula: see text]IQA) method for assessing the quality of chest CT images in patients, to determine if they are suitable for assessing the patient's physical condition.

METHODS

This retrospective study utilizes and analyzes chest CT images from 327 patients. Among them, 1613 images from 286 patients are used for model training and validation, while the remaining 41 patients are reserved as an additional test set for conducting ablation studies, comparative studies, and observer studies. The M[Formula: see text]IQA method is driven by DL technology and employs a multi-view fusion strategy, which incorporates three scanning planes (coronal, axial, and sagittal). It assesses image quality for multiple tasks, including inspiration evaluation, position evaluation, radiation protection evaluation, and artifact evaluation. Four algorithms (pixel threshold, neural statistics, region measurement, and distance measurement) have been proposed, each tailored for specific evaluation tasks, with the aim of optimizing the evaluation performance of the M[Formula: see text]IQA method.

RESULTS

In the additional test set, the M[Formula: see text]IQA method achieved 87% precision, 93% sensitivity, 69% specificity, and a 0.90 F1-score. Extensive ablation and comparative studies have demonstrated the effectiveness of the proposed algorithms and the generalization performance of the proposed method across various assessment tasks.

CONCLUSION

This study develops and validates a DL-driven M[Formula: see text]IQA method, complemented by four proposed algorithms. It holds great promise in automating the assessment of chest CT image quality. The performance of this method, as well as the effectiveness of the four algorithms, is demonstrated on an additional test set.

COMBINING HI-RESOLUTION SCAN MODE WITH DEEP LEARNING RECONSTRUCTION ALGORITHMS IN CARDIAC CT

To investigate the impact of combining the high-resolution (Hi-res) scan mode with deep learning image reconstruction (DLIR) algorithm in CT. Two phantoms (Catphan600® and Lungman, small, medium, large size) were CT scanned using combinations of Hi-res/standard mode and high-definition (HD)/standard kernels. Images were reconstructed with ASiR-V and three levels of DLIR. Spatial resolution, noise and contrast-to-noise ratio (CNR) were assessed. The radiation dose was recorded. The spatial resolution increased using Hi-res & HD. Image noise in the Catphan600® (69%) and the Lungman (10-70%) significantly increased when Hi-res & HD was applied. DLIR reduced the mean noise (54%). The CNR was reduced (64%) for Hi-res & HD. The radiation dose increased for both small (+70%) and medium (+43%) Lungman phantoms but decreased slightly for the large ones (-3%) when Hi-res was applied. In conclusion, the Hi-res scan mode improved the spatial resolution. The HD kernel significantly increased the image noise. DLIR improved the image noise and CNR and did not affect the spatial resolution.

Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms

OBJECTIVES

Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs.

METHODS

This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE).

RESULTS

The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%).

CONCLUSIONS

Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality.

KEY POINTS

• Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

Treatment of Recurrent Nasopharyngeal Carcinoma: A Sequential Challenge

Recurrent nasopharyngeal carcinoma (NPC), which occurs in 10-20% of patients with primary NPC after the initial treatment modality of intensity-modulated radiation therapy (IMRT), is one of the major causes of death among NPC patients. Patients with recurrent disease without distant metastases still have a chance to be saved, but re-treatment often carries more serious toxicities or higher risks. For this group of patients, both otolaryngologists and oncologists are committed to developing more appropriate treatment regimens that can prolong patient survival and improve survival therapy. Currently, there are no international guidelines for the treatment of patients with recurrent NPC. In this article, we summarize past publications on clinical research and mechanistic studies related to recurrent NPC, combined with the experience and lessons learned by our institutional multidisciplinary team in the treatment of recurrent NPC. We propose an objective protocol for the treatment of recurrent NPC.

Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study

OBJECTIVE

Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA.

MATERIALS AND METHODS

Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey's HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness.

RESULTS

For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05).

CONCLUSION

Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT.

KEY POINTS

• Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05). • Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05). • Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05).

Predictive value of computed tomography for short-term mortality in patients with acute respiratory distress syndrome: a systematic review

The best available evidence and the predictive value of computed tomography (CT) findings for prognosis in patients with acute respiratory distress syndrome (ARDS) are unknown. We systematically searched three electronic databases (MEDLINE, CENTRAL, and ClinicalTrials.gov). A total of 410 patients from six observational studies were included in this systematic review. Of these, 143 patients (34.9%) died due to ARDS in short-term. As for CT grade, the CTs used ranged from 4- to 320-row. The index test included diffuse attenuations in one study, affected lung in one study, well-aerated lung region/predicted total lung capacity in one study, CT score in one study and high-resolution CT score in two studies. Considering the CT findings, pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 62% (95% confidence interval [CI] 30–88%), 76% (95% CI 57–89%), 2.58 (95% CI 2.05–2.73), 0.50 (95% CI 0.21–0.79), and 5.16 (95% CI 2.59–3.46), respectively. This systematic review revealed that there were major differences in the definitions of CT findings, and that the integration of CT findings might not be adequate for predicting short-term mortality in ARDS. Standardisation of CT findings and accumulation of further studies by CT with unified standards are warranted.

The impact of the field of view (FOV) on image quality in MDCT angiography of the lower extremities

Objectives

To evaluate the impact of the reconstructed field-of-view (FOV) on image quality in computed-tomography angiography (CTA) of the lower extremities.

Methods

A total of 100 CTA examinations of the lower extremities were acquired on a 2 × 192-slice multidetector CT (MDCT) scanner. Three different datasets were reconstructed covering both legs (standard FOV size) as well as each leg separately (reduced FOV size). The subjective image quality was evaluated for the different vessel segments (femoral, popliteal, crural, pedal) by three readers using a semi-quantitative Likert scale. Additionally, objective image quality was assessed using an automated image quality metric on a per-slice basis.

Results

The subjective assessment of the image quality showed an almost perfect interrater agreement. The image quality of the small FOV datasets was rated significantly higher as compared to the large datasets for all patients and vessel segments (p < 0.05) with a tendency towards a higher effect in smaller vessels. The difference of the mean scores between the group with the large FOV and small FOV was 0.68 for the femoral level, 0.83 for the popliteal level, 1.12 for the crural level, and 1.08 for the pedal level. The objective image quality metric also demonstrated a significant improvement of image quality in the small FOV datasets.

Conclusions

Side-separated reconstruction of each leg in CTA of the lower extremities using a small reconstruction FOV significantly improves image quality as compared to a standard reconstruction with a large FOV covering both legs.

Key Points

• In CT angiography of the lower legs, the side-separated reconstruction of each leg using a small field-of-views improves image quality as compared to a standard reconstruction covering both legs.

• The side-separated reconstruction can be readily implemented at every commercially available CT scanner.

• There is no need for additional hardware or software and no additional burden to the patient.

A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules

OBJECTIVES

Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules.

METHODS

A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve.

RESULTS

The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843-0.940) and 0.911 (95% CI: 0.867-0.955), respectively, in the primary group and 0.826 (95% CI: 0.727-0.926) and 0.843 (95% CI: 0.749-0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis.

CONCLUSIONS

A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules.

KEY POINTS

• A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules. • The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.

X-ray Imaging for Gastrointestinal Tracking of Microscale Oral Drug Delivery Devices

Microscale devices are promising tools to overcome specific challenges within oral drug delivery. Despite the availability of advanced high-quality imaging techniques, visualization and tracking of microscale devices in the gastrointestinal (GI) tract is still a challenge. This work explores the possibilities of applying planar X-ray imaging and computed tomography (CT) scanning for visualization and tracking of microscale devices in the GI tract of rats. Microcontainers (MCs) are an example of microscale devices that have shown great potential as an oral drug delivery system. Barium sulfate (BaSO₄) loaded into the cavity of the MCs increases their overall X-ray contrast, which allows them to be easily tracked. The BaSO₄-loaded MCs are quantitatively tracked throughout the entire GI tract of rats by planar X-ray imaging and visualized in 3D by CT scanning. The majority of the BaSO₄-loaded MCs are observed to retain in the stomach for 0.5-2 h, enter the cecum after 3-4 h, and leave the cecum and colon 8-10 h post-administration. The imaging approaches can be adopted and used with other types of microscale devices when investigating GI behavior in, for example, preclinical trials and potential clinical studies.

Dual-source dual-energy thin-section CT combined with small field of view technique for small lymph node in thyroid cancer: a retrospective diagnostic study

Background

To evaluate the diagnostic performance of quantitative spectral parameters derived from dual-source dual-energy CT at small field of view (FOV) for small lymph node metastasis in thyroid cancer.

Methods

This was a retrospective diagnostic study. From 2016 to 2019, 280 patients with thyroid disease underwent thin-section dual-source dual-energy thyroid CT and thyroid surgery. The data of patients with lymph nodes having a short diameter of 2-6 mm was analyzed. The quantitative dual-energy CT parameters of targeted lymph nodes were measured, and all parameters between metastatic and non-metastatic lymph nodes were compared. These parameters were then fitted to univariable and multivariable binary logistic regression models. The diagnostic role of spectral parameters was analyzed by receiver operating characteristic (ROC) curves and compared with the McNemar test. Small FOV CT images and a mathematical model were used to judge the status of lymph nodes respectively, and then compared with the golden criterion-pathological diagnosis. The cut-off value of the model was 0.4419, with a sensitivity of 90.2% and a specificity of 92.7%.

Results

Of the 216 lymph nodes investigated in this study, 52.3% and 23.6% had a short diameter of 2-3 and 4 mm, respectively. Multiple quantitative CT parameters were significantly different between benign and malignant lymph nodes, and binary regression analysis was performed. The mathematical model was: p=ey/(1+ ey), y=-23.119+0.033× precontrast electron cloud density +0.076× arterial phase normalized iodine concentration +2.156× arterial phase normalized effective atomic number -0.540× venous phase slope of the spectral Hounsfield unit curve +1.676× venous phase iodine concentration. This parameter model had an AUC of 92%, with good discrimination and consistency, and the diagnostic accuracy was 90.3%. The diagnostic accuracy of the CT image model was 43.1%, and for lymph nodes with a short-diameter of 2-3 mm, the diagnostic accuracy was 22.1%.

Conclusions

The parameter model showed higher diagnostic accuracy than the CT image model for diagnosing small lymph node metastasis in thyroid cancer, and quantitative dual-energy CT parameters were very useful for small lymph nodes that were difficult to be diagnosed only on conventional CT images.

Trial registration

This study is retrospectively registered, and we have registered a prospective study (Registration number: ChiCTR2000035195; http://www.chictr.org.cn).