Computed Tomography Modalities Research Articles

Analyzing computed tomography Modalities for screening pediatric patients for traumatic blunt cerebrovascular injury

BackgroundOptimal screening for BCVI in pediatric trauma patients remains debated. We hypothesized screening with CTAN would decrease the number of duplicate CT scans per patient and increase BCVI detection rate. MethodsLocal BCVI screening institutional protocol changed May 2022 to include Computed Tomography angiography neck (CTAN). We performed a retrospective review of pediatric blunt trauma patients presenting at our Level 1 trauma center between 2019 and 2023. Patients before and after implementation of universal screening were compared for demographic, clinical, radiographic, and outcome data. ResultsSix-hundred-eight patients were included with 368 before and 240 after the protocol change. Screening with CTAN decreased the number of duplicate neck scans (5.7%vs.2.1 ​%,p ​= ​0.03) and increased BCVI detection rate (0.27%v.2.5 ​%,p ​= ​0.01). Of the seven patients diagnosed with BCVI 2019–2023, no patients suffered any stroke-related morbidity. ConclusionUniversal screening for BCVI in pediatric patients with CTAN resulted in fewer scans and an increased BCVI detection rate.

Computed-Tomography-Guided Lung Biopsy: A Practice-Oriented Document on Techniques and Principles and a Review of the Literature.

Computed tomography (CT)-guided lung biopsy is one of the oldest and most widely known minimally invasive percutaneous procedures. Despite being conceptually simple, this procedure needs to be performed rapidly and can be subject to meaningful complications that need to be managed properly. Therefore, knowledge of principles and techniques is required by every general or interventional radiologist who performs the procedure. This review aims to contain all the information that the operator needs to know before performing the procedure. The paper starts with the description of indications, devices, and types of percutaneous CT-guided lung biopsies, along with their reported results in the literature. Then, pre-procedural evaluation and the practical aspects to be considered during procedure (i.e., patient positioning and breathing) are discussed. The subsequent section is dedicated to complications, with their incidence, risk factors, and the evidence-based measures necessary to both prevent or manage them; special attention is given to pneumothorax and hemorrhage. After conventional CT, this review describes other available CT modalities, including CT fluoroscopy and cone-beam CT. At the end, more advanced techniques, which are already used in clinical practice, like fusion imaging, are included.

A transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation

Background and ObjectiveAccurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information. While the existing three-dimensional (3D) segmentation method has achieved state-of-the-art (SOTA) performance, it typically relies on pure-convolution architectures, limiting its ability to capture long-range spatial dependencies due to convolution's confinement to a local receptive field. To address this limitation and further enhance esophageal GTV segmentation performance, this work proposes a transformer-guided cross-modality adaptive feature fusion network, referred to as TransAttPSNN, which is based on cross-modality PET/CT scans. MethodsSpecifically, we establish an attention progressive semantically-nested network (AttPSNN) by incorporating the convolutional attention mechanism into the progressive semantically-nested network (PSNN). Subsequently, we devise a plug-and-play transformer-guided cross-modality adaptive feature fusion model, which is inserted between the multi-scale feature counterparts of a two-stream AttPSNN backbone (one for the PET modality flow and another for the CT modality flow), resulting in the proposed TransAttPSNN architecture. ResultsThrough extensive four-fold cross-validation experiments on the clinical PET/CT cohort. The proposed approach acquires a Dice similarity coefficient (DSC) of 0.76 ± 0.13, a Hausdorff distance (HD) of 9.38 ± 8.76 mm, and a Mean surface distance (MSD) of 1.13 ± 0.94 mm, outperforming the SOTA competing methods. The qualitative results show a satisfying consistency with the lesion areas. ConclusionsThe devised transformer-guided cross-modality adaptive feature fusion module integrates the strengths of PET and CT, effectively enhancing the segmentation performance of esophageal GTV. The proposed TransAttPSNN has further advanced the research of esophageal GTV segmentation.

CACTUSS: Common Anatomical CT-US Space for US examinations

Purpose:The detection and treatment of abdominal aortic aneurysm (AAA), a vascular disorder with life-threatening consequences, is challenging due to its lack of symptoms until it reaches a critical size. Abdominal ultrasound (US) is utilized for diagnosis; however, its inherent low image quality and reliance on operator expertise make computed tomography (CT) the preferred choice for monitoring and treatment. Moreover, CT datasets have been effectively used for training deep neural networks for aorta segmentation. In this work, we demonstrate how leveraging CT labels can be used to improve segmentation in ultrasound and hence save manual annotations.Methods:We introduce CACTUSS: a common anatomical CT-US space that inherits properties from both CT and ultrasound modalities to produce an image in intermediate representation (IR) space. CACTUSS acts as a virtual third modality between CT and US to address the scarcity of annotated ultrasound training data. The generation of IR images is facilitated by re-parametrizing a physics-based US simulator. In CACTUSS we use IR images as training data for ultrasound segmentation, eliminating the need for manual labeling. In addition, an image-to-image translation network is employed for the model’s application on real B-modes.Results:The model’s performance is evaluated quantitatively for the task of aorta segmentation by comparison against a fully supervised method in terms of Dice Score and diagnostic metrics. CACTUSS outperforms the fully supervised network in segmentation and meets clinical requirements for AAA screening and diagnosis.Conclusion:CACTUSS provides a promising approach to improve US segmentation accuracy by leveraging CT labels, reducing the need for manual annotations. We generate IRs that inherit properties from both modalities while preserving the anatomical structure and are optimized for the task of aorta segmentation. Future work involves integrating CACTUSS into robotic ultrasound platforms for automated screening and conducting clinical feasibility studies.

VOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images.

Accurate and consistent contouring of organs-at-risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR-only RT, and MR-guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated. To analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the samepatients. In the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board-certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95-percentile Hausdorff distance (HD ). The mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3mm in terms of DC and HD , respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5mm, which was considered as valid for the analysis of intermodalityvariability. The contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR-only and MR-guidedRT.

An injectable fluorescent and iodinated hydrogel for preoperative localization and dual image-guided surgery of pulmonary nodules.

The widespread use of video-assisted thoracoscopic surgery (VATS) has triggered the rapid expansion in the field of computed tomography (CT)-guided preoperative localization and near-infrared (NIR) fluorescence image-guided surgery. However, its broader application has been hindered by the absence of ideal imaging contrasts that are biocompatible, minimally invasive, highly resolvable, and perfectly localized within the diseased tissue. To achieve this goal, we synthesize a dextran-based fluorescent and iodinated hydrogel, which can be injected into the tissue and imaged with both CT and NIR fluorescence modalities. By finely tuning the physical parameters such as gelation time and composition of iodinated oil (X-ray contrast agent) and indocyanine green (ICG, NIR fluorescence dye), we optimize the hydrogel for prolonged localization at the injected site without losing the dual-imaging capability. We validate the effectiveness of the developed injectable dual-imaging platform by performing image-guided resection of pulmonary nodules on tumor-bearing rabbits, which are preoperatively localized with the hydrogel. The injectable dual-imaging marker, therefore, can emerge as a powerful tool for surgical guidance.

A hybrid few-shot multiple-instance learning model predicting the aggressiveness of lymphoma in PET/CT images

Background and objectivePatients with aggressive non-Hodgkin lymphoma (NHL) undergo distinct therapy strategies compared with indolent NHL patients. However, it is challenging to estimate NHL aggressiveness based on visual inspection of positron emission tomography (PET) or computed tomography (CT) images. Since diffuse large B-cell lymphoma (DLBCL) and Follicular lymphoma (FL) are the most typical and dominant aggressive and indolent NHL, respectively, this study aims to develop an artificial-intelligence-enabled model to distinguish DLBCL from FL in PET/CT images as the first step to tackle this challenge. MethodsWe propose a hybrid few-shot multiple-instance learning model to predict the aggressiveness of the NHL. First, rotation-based self-supervision learning (SSL) has been employed to train the encoder on a large-scale, publicly available CT image dataset. Second, hybrid instance-level features are obtained for each NHL lesion by combining deep features with the radiomics features from both PET and CT modalities. Third, instance-level features are transformed into bag-level (or patient-level) representations. Finally, bag-level representations are fed into a distance-based classifier through few-shot learning to predict NHL aggressiveness. ResultsOur model achieves an accuracy of 0.751 ± 0.008, a sensitivity of 0.787 ± 0.012, a specificity of 0.715 ± 0.013, an F1-score of 0.753 ± 0.009, and an area under the curve (AUC) of 0.795 ± 0.009 at the bag level. It outperforms the typical counterparts that use the radiomic features, random forest for feature selection, and support vector machines (SVMs) as classifiers. The three counterparts yield accuracies of 0.714 ± 0.023, 0.705 ± 0.008, and 0.698 ± 0.008, respectively. Moreover, settings of the SSL training dataset (Deep lesion) and task (rotation), hybrid CT and radiomic PET features, the pool-layer strategy of maximum, and distance-based classifier generate the best model. ConclusionsA hybrid few-shot multiple-instance learning model can predict lymphoma aggressiveness in PET/CT images and could be a potential tool for determining therapy strategies. Hybrid features and the combination of SSL, few-shot learning, and weakly supervised learning are the two powerful pillars of the model, and these can be expanded to other medical applications with limited samples and incomplete annotations.

Early Prediction of Covid-19 Samples from Chest X-ray Images using Deep Learning Approach

Aims: In this study, chest X-ray (CXR) and computed tomography (CT) images are used to analyse and detect COVID-19 using an unsupervised deep learning-based feature fusion approach. Background: The reverse transcription-polymerase chain reaction (RT-PCR) test, which has a reduced viral load, sampling error, etc., is used to detect COVID-19, which has sickened millions of people worldwide. It is possible to check chest X-rays and computed tomography scans because the majority of infected persons have lung infections. The COVID-19 diagnosis can be made early using both CT and CXR imaging modalities, which is an alternative to the RT-PCR test. Objective: The manual diagnosis of CXR pictures and CT scans is labor and time-intensive. Many AI-based solutions are being investigated to tackle this problem, including deep learning-based detection models, which can be utilized to assist the radiologist in making a more accurate diagnosis. However, because of the demand for specialized knowledge and high annotation costs, the amount of annotated data available for COVID-19 identification is constrained. Additionally, the majority of current cutting-edge deep learning-based detection models use supervised learning techniques. Because a tagged dataset is not required, we have investigated various unsupervised learning models for COVID-19 identification in this work. Methods: In this study, we suggest a COVID-19 detection method based on unsupervised deep learning that makes use of the feature fusion technique to improve performance. Based on this an automated CNN model is built for the detection of COVID-19 samples from healthy and pneumonic cases using chest X-ray images. Results: This model has scored an accuracy of about 99% for the classification between covid positive and covid negative. Based on this result further classification will be done for pneumonic and non-pneumonic which has scored an accuracy of 94%. Conclusion: On both datasets, the COVID-19 detection method based on feature fusion and deep unsupervised learning showed promising results. Additionally, it outperforms four well-known unsupervised methods already in use.

A case of acute submandibular sialadenitis caused by a fish bone that strayed into the submandibular gland duct

AbstractBackgroundFish bones commonly stray into the tongue base, palatine tonsils, and pharyngeal mucosa but rarely into the submandibular gland duct.Case presentationA 74‐year‐old man developed acute sialadenitis after a fish bone strayed into the submandibular gland duct. Laboratory data revealed elevated S‐amylase level; computed tomography (CT) revealed linear opacification along the submandibular gland duct. Electron microscopic analysis confirmed the object to be a fish bone; the bone discharged spontaneously prior to a scheduled excision.ConclusionIdentification of foreign bodies is difficult with CT or other imaging modalities alone. Additional detailed examination, such as electron microscopy analysis, is required.

Reproducibility and Repeatability of Pelvic Radiomics Features with Daily Imaging on a Novel Biology-Guided Radiotherapy Machine Compared to Daily Imaging on Other Radiotherapy Delivery Systems

Radiomics features extracted from computed tomography (CT) images have the potential of being used as imaging biomarkers in diagnostic and prognostic models for prostate cancer. This study aims to evaluate reproducibility and repeatability of radiomics features using daily CT images from a novel biology-guided radiotherapy (BgRT)-capable machine compared to other daily CT imaging modalities from other radiotherapy delivery systems. We retrospectively selected thirty male patients treated to the pelvic region at our institution. Among the thirty patients, ten patients were treated on a BgRT-capable machine with daily fan-beam kilovoltage CT (kVCT) scans, ten patients were treated on a conventional Linac with daily cone-beam CT (CBCT) scans, and ten patients were treated on helical tomotherapy with daily megavoltage CT (MVCT) scans. The prostate gland and femoral heads were delineated on planning CT images and on CT images at the first treatment fraction. After all the CT images were resampled to have an identical voxel size, 107 radiomics features were calculated for the prostate and femoral heads on both the planning CT images and daily CT images. Concordance correlation coefficient (CCC) and Pearson's correlation coefficient (r) were calculated to evaluate radiomics feature reproducibility between the planning CT images and pre-treatment CT scans at the first treatment fraction. To evaluate radiomics feature repeatability with kVCT images on the BgRT-capable machine, a total of ten treatment sessions with repeat kVCT scans on the BgRT-capable machine were retrieved and radiomics features were compared between the first and the second kVCT scans. For the prostate gland volume, high concordance (CCC > 0.8) was found for 8, 6, and 0 radiomics features with the first-fraction kVCT, CBCT, and MVCT images, respectively, in relation to the planning CT images, while there was strong correlation (r > 0.8) in 38, 22, and 5 radiomics features in the kVCT, CBCT, and MVCT images, respectively, in relation to the planning CT images. For the left and right femoral heads, high concordance (CCC > 0.8) was found in no more than two radiomics features with the kVCT, CBCT, or MVCT images in relation to the planning CT images. Based on repeat kVCT scan data, 97 (90.7%) radiomics features showed high concordance (CCC > 0.8) in repeat kVCT scans with 98 (91.6%) features showing high correlation (Pearson's correlation coefficient > 0.8) for the prostate gland volume. A small number of radiomics features were found to show strong reproducibility between daily CT images on the first fraction and the planning CT images with the three IGRT CT modalities in this study. On the other hand, our results indicated that strong reproducibility was found with more radiomics features with daily kVCT images from the BgRT-capable machine compared to daily CBCT and MVCT images for the prostate gland. Strong repeatability was also found with most radiomics features in daily kVCT images.

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation

Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T1-weight (T1) image for MRIgRT synthetic CT (sCT) image generation. The network is mainly based on a generative adversarial network with sequential subtasks of intermediately generating synthetic MRIs and jointly generating the sCT image from the single T1 MRI. It contains a multitask generator and a multibranch discriminator, where the generator consists of a shared encoder and a splitted multibranch decoder. Specific attention modules are designed within the generator for feasible high-dimensional feature representation and fusion. Fifty patients with nasopharyngeal carcinoma who had undergone radiotherapy and had CT and sufficient MRI modalities scanned (5550 image slices for each modality) were used in the experiment. Results showed that our proposed network outperforms state-of-the-art sCT generation methods well with the least MAE, NRMSE, and comparable PSNR and SSIM index measure. Our proposed network exhibits comparable or even superior performance than the multimodality MRI-based generation method although it only takes a single T1 MRI image as input, thereby providing a more effective and economic solution for the laborious and high-cost generation of sCT images in clinical applications.

Cross-modality image translation: CT image synthesis of MR brain images using multi generative network with perceptual supervision

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstream imaging technologies for clinical practice. CT imaging can reveal high-quality anatomical and physiopathological structures, especially bone tissue, for clinical diagnosis. MRI provides high resolution in soft tissue and is sensitive to lesions. CT combined with MRI diagnosis has become a regular image-guided radiation treatment plan.Methods: In this paper, to reduce the dose of radiation exposure in CT examinations and ameliorate the limitations of traditional virtual imaging technologies, we propose a Generative MRI-to-CT transformation method with structural perceptual supervision. Even though structural reconstruction is structurally misaligned in the MRI-CT dataset registration, our proposed method can better align structural information of synthetic CT (sCT) images to input MRI images while simulating the modality of CT in the MRI-to-CT cross-modality transformation.Results: We retrieved a total of 3416 brain MRI-CT paired images as the train/test dataset, including 1366 train images of 10 patients and 2050 test images of 15 patients. Several methods (the baseline methods and the proposed method) were evaluated by the HU difference map, HU distribution, and various similarity metrics, including the mean absolute error (MAE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). In our quantitative experimental results, the proposed method achieves the lowest MAE mean of 0.147, highest PSNR mean of 19.27, and NCC mean of 0.431 in the overall CT test dataset.Conclusions: In conclusion, both qualitative and quantitative results of synthetic CT validate that the proposed method can preserve higher similarity of structural information of the bone tissue of target CT than the baseline methods. Furthermore, the proposed method provides better HU intensity reconstruction for simulating the distribution of the CT modality. The experimental estimation indicates that the proposed method is worth further investigation.

Total-body low-dose CT image denoising using a prior knowledge transfer technique with a contrastive regularization mechanism.

Reducing the radiation exposure experienced by patients in total-body computed tomography (CT) imaging has attracted extensive attention in the medical imaging community. A low radiation dose may result in increased noise and artifacts that greatly affect the subsequent clinical diagnosis. To obtain high-quality total-body low-dose CT (LDCT) images, previous deep learning-based research works developed various network architectures. However, most of these methods only employ normal-dose CT (NDCT) images as ground truths to guide the training process of the constructed denoising network. As a result of this simple restriction, the reconstructed images tend to lose favorable image details and easily generate oversmoothed textures. This study explores how to better utilize the information contained in the feature spaces of NDCT images to guide the LDCT image reconstruction process and achieve high-qualityresults. We propose a novel intratask knowledge transfer (KT) method that leverages the knowledge distilled from NDCT images as an auxiliary component of the LDCT image reconstruction process. Our proposed architecture is named the teacher-student consistency network (TSC-Net), which consists of teacher and student networks with identical architectures. By employing the designed KT loss, the student network is encouraged to emulate the teacher network in the representation space and gain robust prior content. In addition, to further exploit the information contained in CT scans, a contrastive regularization mechanism (CRM) built upon contrastive learning is introduced. The CRM aims to minimize and maximize the L2 distances from the predicted CT images to the NDCT samples and to the LDCT samples in the latent space, respectively. Moreover, based on attention and the deformable convolution approach, we design a dynamic enhancement module (DEM) to improve the network capability to transform input informationflows. By conducting ablation studies, we prove the effectiveness of the proposed KT loss, CRM, and DEM. Extensive experimental results demonstrate that the TSC-Net outperforms the state-of-the-art methods in both quantitative and qualitative evaluations. Additionally, the excellent results obtained for clinical readings also prove that our proposed method can reconstruct high-quality CT images for clinicalapplications. Based on the experimental results and clinical readings, the TSC-Net has better performance than other approaches. In our future work, we may explore the reconstruction of LDCT images by fusing the positron emission tomography (PET) and CT modalities to further improve the visual quality of the reconstructed CTimages.

Comparative role of scintigraphy in sporting injuries in the new millennium

Exercise for good health and as a prerequisite for most sporting endeavours is both an aspirational and necessary requirement at a time when obesity plagues much of the well-developed world. It has led to great advances in the science of exercise and a medical specialty devoted to sporting injuries. Epidemiology of sporting injury is crucial in this process in order to prevent injury and to focus attention on dangerous practices in some sports. The bone scan was historically considered a mainstay for the diagnosis of sporting injuries involving stress fractures, acute fractures and some soft tissue injuries. However, the role of scintigraphy has been supplanted by magnetic resonance imaging (MRI) in many of these settings, largely due to its high contrast resolution for soft tissues, spatial resolution of the relevant anatomy and the absence of radiation exposure. Nevertheless, there remains a valuable contribution from scintigraphy with the development of single photon emission computed tomography (SPECT) co-located with x-ray computed tomography (CT) in the same instrument. The place of scintigraphy in the evaluation of sporting injuries needs to be critically evaluated against the competing modalities of CT, MRI and high-resolution ultrasound. It is no longer appropriate or critically acceptable to examine scintigraphy in isolation from the other available imaging modalities.

SYSTEMATIC REVIEW OF COMPUTED TOMOGRAPHY PARAMETERS USED FOR THE ASSESSMENT OF SUBCHONDRAL BONE IN OSTEOARTHRITIS

Radiography and MRI are currently regarded as the imaging modalities of choice for OA assessment. However, computed tomography (CT) has several distinct advantages over these modalities, principally the capability of delivering higher resolution 3-D image reconstructions over a wider range of biological scales, in particular with respect to mineralized tissues. This enables greater standardization in analysis of bone structures compared to other imaging modalities. Considering the growing understanding of the importance of bone in OA pathology, we deem it an important juncture to recognize the full scope that CT holds in the imaging assessment of OA. To systematically review the published parameters for the assessment of subchondral bone in human OA using CT and gain an overview of current practices and standards. Medline, Embase and Cochrane Library databases were search with respective search strategies (search from 2010 to January 2023). Based on pre-determined inclusion and exclusion criteria, search results were screened independently by two reviewers. Eligible studies were conducted in vivo / ex vivo in human adults (>18 years) using CT to assess subchondral bone in any joint with OA. Data was extracted from eligible studies and compiled in a qualitative summary and formal narrative synthesis. The literature search resulted in 202 included studies. Subchondral bone across nine anatomical locations were assessed using four broad groupings of CT modality: conventional clinical-type CT; quantitative CT for human use; micro-/nano-CT; and cone-beam CT. Six categories were identified for bone parameters measuring features of OA: (i) microstructure, (ii) bone adaptation, (iii) gross morphology (iv) mineralization, (v) joint space, and (vi) mechanical properties. From long-standing roots, CT techniques have been continuing to gain popularity in OA research. We highlight clinically meaningful CT measurements for the assessment of subchondral bone in OA as well as parameters with the potential to perform in this role. We discuss the importance of quantification of parameters to improve their sensitivity and reproducibility. Finally, we emphasize a need for consistency and standardization of measurement protocols to optimize the value of these parameters in future OA research and clinical practice. None None None CORRESPONDENCE ADDRESS: [email protected]

CT FINDINGS OF THREE GIANT MEDIASTINAL TUMORS IN YOUNG AGE

Background : Mediastinal teratoma and other mediastinal tumors are very challenging to be distinguished from other mediastinal tumors. Mediastinal tumors can be optimally evaluated with computed tomography (CT). In this paper we present three young male patients with similar symptom of chronic chest pain and chronic cough. Materials and Methods: Conducting a literature review and medical records. Results : All patients in this case are young age male, below two decades. All chest CT shows giant mass, but they have different enhancing pattern, different amount of solid, cystic, calcification component. To make correct diagnosis of the mediastinal mass is important because of treatment planning difference, surgery or chemoteraphy. The first case shows benign teratoma with characteristic of heterogenous mass with large amount of fat, with presence of solid and calcification component. The second case shows malignant germ cell tumor with feature of heterogenous mass with predominantly solid component and small amount of fat. It also shows metastatic process in lung and bone. The third case shows Hodgkin lymphoma with slight enhanced mass that narrowed pulmonary artery and caused pericardial effusion. Conclusion: Various features of mediatinal tumors can be distinguished with CT modality.

PET-CT image Co-segmentation of lung tumor using joint level set model

Accurate lung tumor segmentation plays an important role in radiotherapy and targeted therapy. Positron emission tomography (PET) and computed tomography (CT) scanner imaging provide complementary evidence for lung tumor segmentation. In specific, PET can recognize the tumor tissues, while the tissue boundaries are blurred in such a modality. By contrast, CT has a better resolution but a lower contrast between tumor and normal tissues. It is well known that jointly exploiting the evidence from PET and CT images significantly benefits lung tumor delineation. A novel joint level set model is proposed in this paper to integrate PET and CT evidence in a unified energy form, providing co-segmentation results. The convergence result of our co-segmentation model can find the optimal tradeoff between PET and CT modalities. Different characteristics of these two modalities are comprehensively considered in the adaptive convergence process which starts mostly with the PET evidence to locate tumor tissues and stops mostly with the CT evidence to delineate tissue boundaries. The novelty of our joint level set model lies in its adaptability which stepwise moderates joint weights during the model convergence process. The performance of our proposed model is validated on 20 PET-CT images of the nonsmall cell lung tumor. The excellent performance of our proposed model for PET-CT image co-segmentation of the lung tumor is demonstrated by comparing it to the state-of-art models.

Association of Noncontrast Computed Tomography and Perfusion Modalities With Outcomes in Patients Undergoing Late-Window Stroke Thrombectomy

There is substantial controversy with regards to the adequacy and use of noncontrast head computed tomography (NCCT) for late-window acute ischemic stroke in selecting candidates for mechanical thrombectomy. To assess clinical outcomes of patients with acute ischemic stroke presenting in the late window who underwent mechanical thrombectomy stratified by NCCT admission in comparison with selection by CT perfusion (CTP) and diffusion-weighted imaging (DWI). In this multicenter retrospective cohort study, prospectively maintained Stroke Thrombectomy and Aneurysm (STAR) database was used by selecting patients within the late window of acute ischemic stroke and emergent large vessel occlusion from 2013 to 2021. Patients were selected by NCCT, CTP, and DWI. Admission Alberta Stroke Program Early CT Score (ASPECTS) as well as confounding variables were adjusted. Follow-up duration was 90 days. Data were analyzed from November 2021 to March 2022. Selection by NCCT, CTP, or DWI. Primary outcome was functional independence (modified Rankin scale 0-2) at 90 days. Among 3356 patients, 733 underwent late-window mechanical thrombectomy. The median (IQR) age was 69 (58-80) years, 392 (53.5%) were female, and 449 (65.1%) were White. A total of 419 were selected with NCCT, 280 with CTP, and 34 with DWI. Mean (IQR) admission ASPECTS were comparable among groups (NCCT, 8 [7-9]; CTP, 8 [7-9]; DWI 8, [7-9]; P = .37). There was no difference in the 90-day rate of functional independence (aOR, 1.00; 95% CI, 0.59-1.71; P = .99) after adjusting for confounders. Symptomatic intracerebral hemorrhage (NCCT, 34 [8.6%]; CTP, 37 [13.5%]; DWI, 3 [9.1%]; P = .12) and mortality (NCCT, 78 [27.4%]; CTP, 38 [21.1%]; DWI, 7 [29.2%]; P = .29) were similar among groups. In this cohort study, comparable outcomes were observed in patients in the late window irrespective of neuroimaging selection criteria. Admission NCCT scan may triage emergent large vessel occlusion in the late window.

Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss.

A variety of medical computer vision applications analyze 2D slices of computed tomography (CT) scans, whereas axial slices from the body trunk region are usually identified based on their relative position to the spine. A limitation of such systems is that either the correct slices must be extracted manually or labels of the vertebrae are required for each CT scan to develop an automated extraction system. In this paper, we propose an unsupervised domain adaptation (UDA) approach for vertebrae detection and identification based on a novel Domain Sanity Loss (DSL) function. With UDA the model’s knowledge learned on a publicly available (source) data set can be transferred to the target domain without using target labels, where the target domain is defined by the specific setup (CT modality, study protocols, applied pre- and processing) at the point of use (e.g., a specific clinic with its specific CT study protocols). With our approach, a model is trained on the source and target data set in parallel. The model optimizes a supervised loss for labeled samples from the source domain and the DSL loss function based on domain-specific “sanity checks” for samples from the unlabeled target domain. Without using labels from the target domain, we are able to identify vertebra centroids with an accuracy of %. By adding only ten target labels during training the accuracy increases to %, which is on par with the current state-of-the-art for full supervised learning, while using about 20 times less labels. Thus, our model can be used to extract 2D slices from 3D CT scans on arbitrary data sets fully automatically without requiring an extensive labeling effort, contributing to the clinical adoption of medical imaging by hospitals.

Lesser omental hernia without intestinal gangrene - Case report.

IntroductionThere are several types of internal hernia. Herniation through the defect in the lesser omentum is extremely rare. Symptoms of this type of hernias may vary a lot and diagnosis is difficult. In this case report a young adult with nonspecific symptoms is diagnosed with an intestinal herniation through the defect in the lesser omentum.Case presentationA 35-year-old man with the history of laparoscopic colectomy presented with abdominal pain but no symptoms of peritonitis or acute bowel obstruction. Abdominal computed tomography (CT) revealed displacement of mesenteric vessels, small intestine and stomach. Intestinal herniation through the lesser omentum was suspected. Laparoscopic reposition of small intestine was performed. The greater curvature of the stomach was sutured to the transverse mesocolon to prevent recurrence of hernia.DiscussionPrevious surgery, low body mass index (BMI), absence of adhesions may predispose the lesser omental hernia. Herniating of intestines through the large openings may occur without presence of acute obstruction or gangrene. CT is helpful in making a correct diagnosis.ConclusionWhen evaluating the patient with abdominal pain, internal hernia should be considered. CT modalities may aid in the detection of these rare hernias and ensure timely treatment. Perioperative inspection and repair of the hepatogastric ligament may help to prevent lesser omental hernias.