Philips (Israel)

To evaluate whether volumetric iodine quantification of the lung allows for the automatic identification of patients with chronic thromboembolic pulmonary hypertension (CTEPH) and whether the extent of pulmonary malperfusion correlates with invasive hemodynamic parameters. Retrospective data base search identified 30 consecutive patients with CTEPH who underwent CT pulmonary angiography (CTPA) on a spectral-detector CT scanner. Thirty consecutive patients who underwent an identical CT examination for evaluation of suspected acute pulmonary embolism and had no signs of pulmonary embolism or PH, served as control cohort. Lungs were automatically segmented for all patients and normal and malperfused volumes were segmented based on iodine density thresholds. Results were compared between groups. For correlation analysis between the extent of malperfused volume and mean pulmonary artery pressure (mPAP) and pulmonary vascular resistance (PVR) 3 patients were excluded because of a time span of more than 30 days between CTPA and right heart catheterization. Patients with CTEPH had a higher percentage of malperfused lung compared to controls (43.25%±24.72% vs. 21.82%±20.72%; P=0.001) and showed reduced mean iodine density in malperfused and normal-perfused lung areas, as well as in the vessel volume. Controls showed a left-tailed distribution of iodine density in malperfused lung areas while patients with CTEPH had a more symmetrical distribution (Skew: -0.382±0.435 vs. -0.010±0.396; P=0.004). Patients with CTEPH showed a significant correlation between the percentage of malperfused lung volume and the PVR (r=0.57, P=0.001). Volumetric iodine quantification helps to identify patients with CTEPH by showing increased areas of malperfusion. The extent of malperfusion might provide a measurement for disease severity in patients with CTEPH.

In this paper, we propose a deep learning approach for the segmentation of body parts in computer tomography (CT) localizer images. Such images pose difficulties in the automatic image analysis on account of variable field-of-view, diverse patient positioning and image acquisition at low dose, but are of importance pertaining to their most prominent applications in scan planning and dose modulation. Following the success of deep learning technology in image segmentation applications, we investigate the use of a fully convolutional neural network architecture to achieve the segmentation of four anatomies: abdomen, chest, pelvis and brain. The method is further extended to generate plan boxes for individual as well as multiple combined anatomies, and compared against the existing techniques. The performance of the method is evaluated on 771 multi-site localizer images.

PurposeTo compare CT pulmonary angiographies (CTPAs) as well as phantom scans obtained at 100 kVp with a conventional CT (C-CT) to virtual monochromatic images (VMI) obtained with a spectral detector CT (SD-CT) at equivalent dose levels as well as to compare the radiation exposure of both systems. Material and MethodsIn total, 2110 patients with suspected pulmonary embolism (PE) were examined with both systems. For each system (C-CT and SD-CT), imaging data of 30 patients with the same mean CT dose index (4.85 mGy) was used for the reader study. C-CT was performed with 100 kVp and SD-CT was performed with 120 kVp; for SD-CT, virtual monochromatic images (VMI) with 40, 60 and 70 keV were calculated. All datasets were evaluated by three blinded radiologists regarding image quality, diagnostic confidence and diagnostic performance (sensitivity, specificity). Contrast-to-noise ratio (CNR) for different iodine concentrations was evaluated in a phantom study. ResultsCNR was significantly higher with VMI at 40 keV compared to all other datasets. Subjective image quality as well as sensitivity and specificity showed the highest values with VMI at 60 keV and 70 keV. Hereby, a significant difference to 100 kVp (C-CT) was found for image quality. The highest sensitivity was found using VMI at 60 keV with a sensitivity of more than 97 % for all localizations of PE. For diagnostic confidence and subjective contrast, highest values were found with VMI at 40 keV. ConclusionHigher levels of diagnostic performance and image quality were achieved for CPTAs with SD-CT compared to C-CT given similar dose levels. In the clinical setting SD-CT may be the modality of choice as additional spectral information can be obtained.

A novel beam filter consisting of multiple aperture devices (MADs) has been developed for dynamic fluence field modulation (FFM) in CT. Each MAD achieves spatial modulation of x-ray through fine-scale, highly attenuating tungsten bars of varying widths and spacings. Moiré patterns produced by relative motions between two MADs provide versatile classes of modulation profiles. The dual-MAD filter can be designed to achieve specific classes of target profiles. The designed filter was manufactured through a laser-sintering process and integrated to an experimental imaging system that enables linear actuation of the MADs. Dynamic FFM was achieved through a combination of beam shape modulation (by relative MAD motion) and amplitude modulation (by view-dependent mAs). To correct for gains associated with the MADs, we developed an algorithm to account for possible focal spot changes during/between scans and spectral effects introduced by the MADs. We performed FFM designs for phantoms following two imaging objectives: (1) to achieve minimum mean variance in filtered backprojection (FBP) reconstruction, and (2) to flatten the fluence behind the phantom. Comparisons with conventional FFM strategies involving a static bowtie and pulse width modulation were performed. The dual-MAD filter produced modulation profiles closely matched with the design target, providing varying beam widths not achievable by the static bowtie. The entire range of modulation profiles was achieved by 0.373 mm of MAD displacement. The correction algorithm effectively alleviated ring artifacts as a result of MADs while preserving phantom details such as wires and tissue boundaries. Dynamic FFM enabled by the MADs were effective in achieving the imaging objectives and demonstrated superior FFM capabilities compared to the static bowtie. In an ellipse phantom, the FFM of objective 1 achieved the lowest mean variance in all cases investigated. The FFM of objective 2 produce nearly isotropic local noise power spectrum and homogeneous noise magnitude. The dual-MAD filter provides an effective tool for fluence control in CT to overcome limitations of conventional static bowties and to further enable patient-specific FFM studies for a wide range of dose and image quality objectives.

The purpose of this study is to develop and evaluate a functionally personalized boundary condition (BC) model for estimating the fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA) using flow simulation (CT-FFR). The CCTA data of 90 subjects with subsequent invasive FFR in 123 lesions within 21 days (range: 0-83) were retrospectively collected. We developed a functionally personalized BC model accounting specifically for the coronary microvascular resistance dependency on the coronary outlets pressure suggested by several physiological studies. We used the proposed model to estimate the hemodynamic significance of coronary lesions with an open-loop physics-based flow simulation. We generated three-dimensional (3D) coronary tree geometries using automatic software and corrected manually where required. We evaluated the improvement in CT-FFR estimates achieved using a functionally personalized BC model over anatomically personalized BC model using k-fold cross-validation. The functionally personalized BC model slightly improved CT-FFR specificity in determining hemodynamic significance of lesions with intermediate diameter stenosis (30%-70%, N = 72), compared to the anatomically personalized model lesions with invasive FFR measurements as the reference (sensitivity/specificity: 0.882/0.79 vs 0.882/0.763). For the entire set of 123 coronary lesions, the functionally personalized BC model improved only the area under the curve (AUC) but not the sensitivity/specificity in determining the hemodynamic significance of lesions, compared to the anatomically personalized model (AUC: 0.884 vs 0.875, sensitivity/specificity: 0.848/0.805). The functionally personalized BC model has the potential to improve the quality of CT-FFR estimates compared to an anatomically personalized BC model.

PurposeThe region defined as ‘at risk’ penumbra by current CT perfusion (CTP) maps is largely overestimated. We aimed to quantitate the portion of true ‘at risk’ tissue within CTP penumbra and to determine the parameter and threshold that would optimally distinguish it from false ‘at risk’ tissue, that is, benign oligaemia.MethodsAmong acute stroke patients evaluated by multimodal CT (NCCT/CTA/CTP) we identified those that had not undergone endovascular/thrombolytic treatment and had follow-up NCCT. Maps of absolute and relative CBF, CBV, MTT, TTP and Tmax as well as summary maps depicting infarcted and penumbral regions were generated using the Intellispace Portal (Philips Healthcare, Best, Netherlands). Follow-up CT was automatically co-registered to the CTP scan and the final infarct region was manually outlined. Perfusion parameters were systematically analysed – the parameter that resulted in the highest true-negative-rate (ie, proportion of benign oligaemia correctly identified) at a fixed, clinically relevant false-negative-rate (ie, proportion of ‘missed’ infarct) of 15%, was chosen as optimal. It was then re-applied to the CTP data to produce corrected perfusion maps.ResultsForty seven acute stroke patients met selection criteria. Average portion of infarcted tissue within CTP penumbra was 15%±2.2%. Relative CBF at a threshold of 0.65 yielded the highest average true-negative-rate (48%), enabling reduction of the false ‘at risk’ penumbral region by ~half.ConclusionsApplying a relative CBF threshold on relative MTT-based CTP maps can significantly reduce false ‘at risk’ penumbra. This step may help to avoid unnecessary endovascular interventions.

We model the scatter radiation from the dose compensator in computed tomography (CT) using Monte Carlo (MC) simulation. Dose compensator reduces the patient radiation dose but also adds some scatter radiation which can reach detectors and degrade image quality. This scattered radiation impacts high contrast edges such as bone-soft tissue and tissue-air interfaces, similar to the well known off-focal radiation from the X-ray tube. To the best of our knowledge, this is the first study that uses MC simulation to model and investigate the compensator scatter radiation. Since detectors are far from the scatter source and have small solid angle, traditional MC is not computationally efficient. We have implemented variance reduction technique called forced detection (FD) to improve the computational efficiency, and achieved an improvement factor of 42,800. The simulated scatter to primary ratio (SPR) ranges from a few percent to 11% across the detectors. Simulations of water phantoms indicate that such scatter can lead to significant effects along edges as large as 20-30 HU, necessitating the need for correction. We demonstrate a correction in FBP reconstruction, somewhat similar to off-focal correction, and show that it significantly reduces the level of the artifact along the edge of the phantom.