Jointly Optimized Deep Neural Networks to Synthesize Monoenergetic Images from Single-Energy CT Angiography for Improving Classification of Pulmonary Embolism

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep-learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two datasets: our institutional DE-CTPA dataset D1, comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism CT Dataset D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naïve approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep-learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for

Portal vein embolization using a Histoacryl/Lipiodol mixture before right liver resection

Purpose: The purpose of this retrospective study was to evaluate the efficacy and safety of percutaneous transhepatic portal vein embolization (PVE) of the right liver lobe using Histoacryl/Lipiodol mixture to induce contralateral liver hypertrophy before right-sided (or extended right-sided) hepatectomy in patients with primarily unresectable liver tumors. Methods: Twenty-one patients (9 females and 12 males) underwent PVE due to an insufficient future liver remnant; 17 showed liver metastases and 4 suffered from biliary cancer. Imaging was performed prior to and 4 weeks after PVE. Surgery was scheduled for 1 week after a CT or MRI control. The primary study end point was technical success, defined as complete angiographical occlusion of the portal vein. The secondary study end point was evaluation of liver hypertrophy by CT and MRI volumetry and transfer to operability. Results: In all the patients, PVE could be performed with a with a Histoacryl/Lipiodol mixture (n = 20) or a Histoacryl/ Lipiodol mixture with microcoils (n = 1). No procedure-related complications occurred. The volume of the left liver lobe increased significantly (p < 0.0001) by 28% from a mean of 549 ml to 709 ml. Eighteen of twenty-one patients (85.7%) could be transferred to surgery, and the intended resection could be performed as planned in 13/18 (72.3%) patients. Conclusion: Preoperative right-sided PVE using a Histoacryl/Lipiodol mixture is a safe technique and achieves a sufficient hypertrophy of the future liver remnant in the left liver lobe

In-vitro assessment of coronary artery stents in 256-multislice computed tomography angiography

BACKGROUND: The important detection of in-stent restenosis in cardiovascular computed tomography (CT) is still challenging. The first study assessing the in-vitro stent lumen visualization of the state of the art 256-multislice CT (256-MSCT), which was performed by our research group, yielded promising results. As the applied technical approach is not suitable for daily routine, we assessed the capability of the 256-MSCT and its different reconstruction kernels for the coronary stent lumen visualization employing a clinically applicable technique in a phantom study. RESULTS: The XCD kernel showed significantly lower artificial lumen narrowing (ALN) values (overall ALN < 40%) than the other reconstruction kernels (CC, CD, XCB) irrespective of the stent caliber. The ALN of coronary stents with a diameter >3 mm was significantly lower than of stents with a smaller caliber. The ALN difference between stents with a diameter of 3 mm and smaller ones was not statistically significant. Yet, the lumen visualization of the smaller stents was impaired by a halo effect. The XCD kernel showed more constant attenuation values throughout the different stent diameters than the other reconstruction kernels. CONCLUSIONS: The 256-MSCT provides a good lumen visualization of coronary stents with a diameter >3 mm. The assessment of stents with a diameter of 3 mm seems feasible but has to be validated in further studies. The clinical evaluation of smaller stents cannot be recommended so far. The XCD kernel showed the best lumen visualization and should therefore be applied in addition to the standard cardiac reconstruction kernels when assessing coronary artery stents using 256-MSCT

Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism

Detector-based spectral computed tomography is a recent dual-energy CT (DECT) technology that offers the possibility of obtaining spectral information. From this spectral data, different types of images can be derived, amongst others virtual monoenergetic (monoE) images. MonoE images potentially exhibit decreased artifacts, improve contrast, and overall contain lower noise values, making them ideal candidates for better delineation and thus improved diagnostic accuracy of vascular abnormalities. In this paper, we are training convolutional neural networks~(CNN) that can emulate the generation of monoE images from conventional single energy CT acquisitions. For this task, we investigate several commonly used image-translation methods. We demonstrate that these methods while creating visually similar outputs, lead to a poorer performance when used for automatic classification of pulmonary embolism (PE). We expand on these methods through the use of a multi-task optimization approach, under which the networks achieve improved classification as well as generation results, as reflected by PSNR and SSIM scores. Further, evaluating our proposed framework on a subset of the RSNA-PE challenge data set shows that we are able to improve the Area under the Receiver Operating Characteristic curve (AuROC) in comparison to a na\"ive classification approach from 0.8142 to 0.8420.Comment: 4 pages, ISBI 202

Primary intrathoracic malignant mesenchymal tumours: computed tomography features of a rare group of chest neoplasms

OBJECTIVES: To describe the computed tomography (CT) features in a case series of primary intrathoracic extracardiac malignant mesenchymal tumours (sarcomas). METHODS: A 5-year retrospective research was conducted, and 18 patients were selected. CT exams were reviewed by two chest radiologists, blinded to tumour pathological type, origin and grade. Lesions were described in relation to location, size, shape, margins, enhancement, presence of cavitation, calcifications, ground glass component, intratumoural enhanced vessels, pleural effusion, pleural tags, lymphangitis, chest wall/rib involvement and pathological lymph nodes. RESULTS: The readers described five pulmonary, six mediastinal and seven pleural/wall based lesions. Mean largest diameter was 103 mm. The most frequent shape was irregular (n = 12), most predominant margin was smooth (n = 12) and enhancement was mostly heterogeneous (n = 8). Intratumoural vessels and pleural effusion were seen in 11 patients. Pathological lymph nodes were present in four cases and calcifications in two cases. CONCLUSIONS: Some frequent radiological features were described independently of tumour location and subtype. A sarcoma should be included as a major differential diagnosis when the radiologist faces an intrathoracic mass of large size (>70 mm) but with well defined smooth or lobulated margins, especially if presenting intratumoural vessels, associated pleural effusion but no significant lymphadenopathy. MAIN MESSAGES: • Malignant mesenchymal tumours (sarcomas) are rare and can arise from any structure in the chest. • Intrathoracic sarcomas show some frequent radiological features, independent of location and type. • Some CT features may help the radiologist suspect for a sarcoma instead of other more common tumours

Experimental application of an automated alignment correction algorithm for geological CT imaging: phantom study and application to sediment cores from cold-water coral mounds

Background: In computed tomography (CT) quality assurance, alignment of image quality phantoms is crucial for quantitative and reproducible evaluation and may be improved by alignment correction. Our goal was to develop an alignment correction algorithm to facilitate geological sampling of sediment cores taken from a cold-water coral mount. Methods: An alignment correction algorithm was developed and tested with a CT acquisition at 120 kVp and 150 mAs of an image quality phantom. Random translation (maximum 15 mm) and rotation (maximum 2.86°) were applied and ground-truth was compared to parameters determined by alignment correction. Furthermore, mean densities were evaluated in four regions of interest (ROIs) placed in the phantom low-contrast section, comparing values before and after correction to ground truth. This process was repeated 1000 times. After validation, alignment correction was applied to CT acquisitions (140 kVp, 570 mAs) of sediment core sections up to 1 m in length, and sagittal reconstructions were calculated for sampling planning. Results: In the phantom, average absolute differences between applied and detected parameters after alignment correction were 0.01 ± 0.06 mm (mean ± standard deviation) along the x-axis, 0.11 ± 0.08 mm along the y-axis, 0.15 ± 0.07° around the x-axis, and 0.02 ± 0.02° around the y-axis, respectively. For ROI analysis, differences in densities were 63.12 ± 30.57, 31.38 ± 32.10, 18.27 ± 35.57, and 9.59 ± 26.37 HU before alignment correction and 1.22 ± 1.40, 0.76 ± 0.9, 0.45 ± 0.86, and 0.36 ± 0.48 HU after alignment correction, respectively. For sediment core segments, average absolute detected parameters were 3.93 ± 2.89 mm, 7.21 ± 2.37 mm, 0.37 ± 0.33°, and 0.21 ± 0.22°, respectively. Conclusions: The alignment correction algorithm was successfully evaluated in the phantom and allowed a correct alignment of sediment core segments, thus aiding in sampling planning. Application to other tasks, like image quality analysis, seems possible