Standard Acquisition Protocol Research Articles

Florbetaben amyloid PET acquisition time: Influence on Centiloids and interpretation.

Amyloid positron emission tomography (PET) acquisition timing impacts quantification. In florbetaben (FBB) PET scans of 245 adults with and without cognitive impairment, we investigated the impact of post-injection acquisition time on Centiloids (CLs) across five reference regions. CL equations for FBB were derived using standard methods, using FBB data collected between 90 and 110 min with paired Pittsburgh compound B data. Linear mixed models and t-tests evaluated the impact of acquisition time on CL increases. CL values increased significantly over the scan using the whole cerebellum, cerebellar gray matter, and brainstem as reference regions, particularly in amyloid-positive individuals. In contrast, CLs based on white matter-containing reference regions decreased across the scan. The quantification of CLs in FBB PET imaging is influenced by both the overall scan acquisition time and the choice of reference region. Standardized acquisition protocols or the application of acquisition time-specific CL equations should be implemented in clinical protocols. Acquisition timing affects florbetaben positron emission tomography (PET) scan quantification, especially in amyloid-positive participants. The impact of acquisition timing on quantification varies across common reference regions. Consistent acquisitions and/or appropriate post-injection adjustments are needed to ensure comparability of PET data.

Ultrasound backscatter coefficient for fat quantification is affected by the measurement depth.

It has been reported that the estimate of ultrasound attenuation coefficient (AC) is affected by depth of measurement, with linear decrease of values with depth. It is unknown whether backscatter coefficient (BSC) has similar behavior. This retrospective study was performed with Sequoia US system equipped with ultrasound derived fat fraction (UDFF) algorithm (Siemens Healthineers, Issaquah, WA, USA) that combines BSC with AC. UDFF was obtained positioning upper edge of the region of interest at 1.5,2,3,4,5cm below liver capsule. BSC data were extracted from UDFF offline. A fractional polynomial regression, which selects the best model considering the polynomial development of the variables of interest, was used. Covariates included were age, sex, skin-to-liver-capsule distance, stiffness. Distance was included as linear factor or with a power ranging from -2 to 3, and the best fitting model was chosen according to partial F test. Body mass index (BMI) was not included because of collinearity with skin-to-liver capsule distance. 104 individuals (56 females; age: 57.9 ± 13.0years; BMI: 29.0 ± 6.5kg/m2; skin-to-liver-capsule distance: 2.3 ± 0.7cm; liver stiffness: 7.5 ± 5.5 kiloPascal) were studied. Best fitting model for BSC included a combination of depth as linear factor and square root. BSC showed a decrease of -13.98dB/cm-steradian for each logarithmic increase of 1cm depth (coefficient: -13.98; 95% CI: -21.016; -5.379; p = .001). Skin-to-liver-capsule distance and stiffness also were independent predictors of BSC. The estimation of the BSC in the liver exhibits a depth dependence that significantly affects results. A standardized acquisition protocol is needed to compare results and reliably assess changes over time.

Feasibility of virtual non-iodine coronary calcium scoring on dual source photon-counting coronary CT angiography: a dynamic phantom study.

The aim of our current systematic dynamic phantom study was first, to optimize reconstruction parameters of coronary CTA (CCTA) acquired on photon counting CT (PCCT) for coronary artery calcium (CAC) scoring, and second, to assess the feasibility of calculating CAC scores from CCTA, in comparison to reference calcium scoring CT (CSCT) scans. In this phantom study, an artificial coronary artery was translated at velocities corresponding to 0, < 60, and 60-75 beats per minute (bpm) within an anthropomorphic phantom. The density of calcifications was 100 (very low), 200 (low), 400 (medium), and 800 (high) mgHA/cm3, respectively. CCTA was reconstructed with the following parameters: virtual non-iodine (VNI), with and without iterative reconstruction (QIR level 2, QIR off, respectively); kernels Qr36 and Qr44f; slice thickness/increment 3.0/1.5 mm and 0.4/0.2 mm. The agreement in risk group classification between CACCCTA and CACCSCT scoring was measured using Cohen weighted linear κ with 95% CI. For CCTA reconstructed with 0.4 mm slice thickness, calcium detectability was perfect (100%). At < 60 bpm, CACCCTA of low, and medium density calcification was underestimated by 53%, and 15%, respectively. However, CACCCTA was not significantly different from CACCSCT of very low, and high-density calcifications. The best risk agreement was achieved when CCTA was reconstructed with QIR off, Qr44f, and 0.4 mm slice thickness (κ = 0.762, 95% CI 0.671-0.853). In this dynamic phantom study, the detection of calcifications with different densities was excellent with CCTA on PCCT using thin-slice VNI reconstruction. Agatston scores were underestimated compared to CSCT but agreement in risk classification was substantial. Photon counting CT may enable the implementation of coronary artery calcium scoring from coronary CTA in daily clinical practice. Photon-counting CTA allows for excellent detectability of low-density calcifications at all heart rates. Coronary artery calcium scoring from coronary CTA acquired on photon counting CT is feasible, although improvement is needed. Adoption of the standard acquisition and reconstruction protocol for calcium scoring is needed for improved quantification of coronary artery calcium to fully employ the potential of photon counting CT.

VINNA for Neonates – Orientation Independence through Latent Augmentations

Abstract A robust, fast, and accurate segmentation of neonatal brain images is highly desired to better understand and detect changes during development and disease, specifically considering the rise in imaging studies for this cohort. Yet, the limited availability of ground truth datasets, lack of standardized acquisition protocols, and wide variations of head positioning in the scanner pose challenges for method development. A few automated image analysis pipelines exist for newborn brain MRI segmentation, but they often rely on time-consuming non-linear spatial registration procedures and require resampling to a common resolution, subject to loss of information due to interpolation and down-sampling. Without registration and image resampling, variations with respect to head positions and voxel resolutions have to be addressed differently. In deep-learning, external augmentations such as rotation, translation, and scaling are traditionally used to artificially expand the representation of spatial variability, which subsequently increases both the training dataset size and robustness. However, these transformations in the image space still require resampling, reducing accuracy specifically in the context of label interpolation. We recently introduced the concept of resolution-independence with the Voxel-size Independent Neural Network framework, VINN. Here, we extend this concept by additionally shifting all rigid-transforms into the network architecture with a four degree of freedom (4-DOF) transform module, enabling resolution-aware internal augmentations (VINNA) for deep learning. In this work we show that VINNA (i) significantly outperforms state-of-the-art external augmentation approaches, (ii) effectively addresses the head variations present specifically in newborn datasets, and (iii) retains high segmentation accuracy across a range of resolutions (0.5-1.0mm). Furthermore, the 4-DOF transform module together with internal augmentations is a powerful, general approach to implement spatial augmentation without requiring image or label interpolation. The specific network application to newborns will be made publicly available as VINNA4neonates.

High Physiological 18F-FDG Uptake in Normal Pituitary Gland on Digital PET Scanner

High Physiological 18F-FDG Uptake in Normal Pituitary Gland on Digital PET Scanner

Regional Brain Net Water Uptake in Computed Tomography after Cardiac Arrest – A Novel Biomarker for Neuroprognostication

BackgroundSelective water uptake by neurons and glial cells and subsequent brain tissue oedema are key pathophysiological processes of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA). Although brain computed tomography (CT) is widely used to assess the severity of HIE, changes of brain radiodensity over time have not been investigated. These could be used to quantify regional brain net water uptake (NWU), a potential prognostic biomarker. MethodsWe conducted an observational prognostic accuracy study including a derivation (single center cardiac arrest registry) and a validation (international multicenter TTM2 trial) cohort. Early (<6 h) and follow-up (>24 h) head CTs of CA patients were used to determine regional NWU for grey and white matter regions after co-registration with a brain atlas. Neurological outcome was dichotomized as good versus poor using the Cerebral Performance Category Scale (CPC) in the derivation cohort and Modified Rankin Scale (mRS) in the validation cohort. ResultsWe included 115 patients (81 derivation, 34 validation) with out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA). Regional brain water content remained unchanged in patients with good outcome. In patients with poor neurological outcome, we found considerable regional water uptake with the strongest effect in the basal ganglia. NWU >8% in the putamen and caudate nucleus predicted poor outcome with 100% specificity (95%-CI: 86–100%) and 43% (moderate) sensitivity (95%-CI: 31–56%). ConclusionThis pilot study indicates that NWU derived from serial head CTs is a promising novel biomarker for outcome prediction after CA. NWU >8% in basal ganglia grey matter regions predicted poor outcome while absence of NWU indicated good outcome. NWU and follow-up CTs should be investigated in larger, prospective trials with standardized CT acquisition protocols.

Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.

Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. However, this typically requires datasets with clinically confirmed labels collected in standardized environments, which is challenging, especially for a large subject pool. This study validates the efficacy of a self-supervised learning method in reducing the reliance on labels and evaluates its generalizability. We propose a new type of self-supervised loss combining Barlow Twins loss, which attempts to create similar feature representations with reduced feature redundancy for samples coming from the same subject, and a Dissimilarity loss, which promotes uncorrelated features for samples generated by different subjects. An encoder is first pre-trained using this loss on unlabeled data from an uncontrolled setting, then fine-tuned with clinically validated data. Our experiments test the model generalizability with controls and subjects with PD on 2 independent datasets. Our approach showed better generalization compared to previous methods, including a feature engineering strategy, a deep learning model pre-trained on Parkinsonian signs, and a traditional supervised model. The absence of standardized data acquisition protocols and the limited availability of annotated datasets compromise the generalizability of supervised models. In these contexts, self-supervised models offer the advantage of learning more robust patterns from the data, bypassing the need for ground truth labels. This approach has the potential to accelerate the clinical validation of touchscreen typing software for neurodegenerative diseases.

Optimisation of the bone single photon emission computed tomography in oncology patients

Introduction: The introduction of hybrid imaging systems such as Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) has completely changed the scanning procedure of conventional diagnostic nuclear medicine protocols. Modern bone scintigraphy protocols include SUV quanitification and Whole Body (WB) SPECT/CT scanning modality. The major limitation of these new technologies is relatively long scanning time. New detector systems with modern reconstruction softwares have been developed for fast scanning SPECT protocolos. These new technologies can produce images of reduced acquisition with same quality as full scanning acquisition. As a result new studies suggest that planar WB scintigraphy should be replaced with WB SPECT/CT. Methods: One hundred oncology patients performed SPECT/CT as a part as their clinical follow-up. Three different scanning and three reconstruction protocols have been evaluated. Two nuclear medicine physicians evaluated with Likert scale image sharpness, lesion visibility, and lesion background detectability. The overall image quality was determined as the sum of these three parameters. Results: In terms of scan duration reduction on image quality, Volumetrix Evolution for Bone performed during ultra-fast SPECT acquisition achieved the highest score, which is superior compared to the standard SPECT acquisition protocol. The overall image quality was the best with the Volumetrix MI Evolution for Bone protocol for ultra-fast acquisition Conclusion: The Evolution for Bone protocol for ultra-fast acquisition showed best results compared to other protocols. The adoption of new acquisition SPECT protocol may offer more comfortable exams, resulting in higher patient satisfaction. The implementation of this new protocol can lead to an improvement in SPECT sensitivity, primarily due to the reduction of SPECT motion artifacts.

Proposal for standardized image acquisition protocol templates as supplement for new manuscripts.

Proposal for standardized image acquisition protocol templates as supplement for new manuscripts.

Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox.

The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing critical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).

Newly Developed Low Dose 180-degree CBCT Protocol Reduces Radiation Dose Without Compromising Diagnostic Value.

To compare the standard 360-degree CBCT acquisition protocol to the low dose 180-degree CBCT protocol for implant planning. Two groups of patients, each consisting of 35 patients, were included in the study. The first group was imaged with the conventional 360-degree CBCT protocol, and the second group was imaged with the low dose 180-degree CBCT protocol. The primary outcome of this study was the number of scans that needed to be repeated due to poor image quality. In addition, six secondary parameters were evaluated quantitatively and qualitatively. The results showed that there was no need to repeat any of the CBCT scans that were obtained in either group, which showed that 360-degree and 180-degree protocols had comparable image quality. As for the secondary parameters, the results showed that the evaluators were able to evaluate the six chosen parameters in a comparable manner. The 180-degree low dose CBCT scan is a viable option for dental implant treatment planning in the posterior mandible as it provides comparable and adequate information regarding accuracy of measurements, identification of critical structures, evaluation of bone quality, and any pathology.

BASIL: A toolbox for perfusion quantification using arterial spin labelling

Abstract Arterial Spin Labelling (ASL) MRI is now an established non-invasive method to quantify cerebral blood flow and is increasingly being used in a variety of neuroimaging applications. With standard ASL acquisition protocols widely available, there is a growing interest in advanced options that offer added quantitative precision and information about haemodynamics beyond perfusion. In this article, we introduce the BASIL toolbox, a research tool for the analysis of ASL data included within the FMRIB Software Library (FSL), and explain its operation in a variety of typical use cases. BASIL is not offered as a clinical tool, and nor is this work intended to guide the clinical application of ASL. Built around a Bayesian model-based inference algorithm, the toolbox is designed to quantify perfusion and other haemodynamic measures, such as arterial transit times, from a variety of possible ASL input data, particularly exploiting the information available in more advanced multi-delay acquisitions. At its simplest, the BASIL toolbox offers a graphical user interface that provides the analysis options needed by most users; through command line tools, it offers more bespoke options for users needing customised analyses. As part of FSL, the toolbox exploits a range of complementary neuroimaging analysis tools so that ASL data can be easily integrated into neuroimaging studies and used alongside other modalities.

Potential Clinical Impact of LAFOV PET/CT: A Systematic Evaluation of Image Quality and Lesion Detection.

We performed a systematic evaluation of the diagnostic performance of LAFOV PET/CT with increasing acquisition time. The first 100 oncologic adult patients referred for 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose PET/CT on the Siemens Biograph Vision Quadra were included. A standard imaging protocol of 10 min was used and scans were reconstructed at 30 s, 60 s, 90 s, 180 s, 300 s, and 600 s. Paired comparisons of quantitative image noise, qualitative image quality, lesion detection, and lesion classification were performed. Image noise (n = 50, 34 women) was acceptable according to the current standard of care (coefficient-of-varianceref < 0.15) after 90 s and improved significantly with increasing acquisition time (PB < 0.001). The same was seen in observer rankings (PB < 0.001). Lesion detection (n = 100, 74 women) improved significantly from 30 s to 90 s (PB < 0.001), 90 s to 180 s (PB = 0.001), and 90 s to 300 s (PB = 0.002), while lesion classification improved from 90 s to 180 s (PB < 0.001), 180 s to 300 s (PB = 0.021), and 90 s to 300 s (PB < 0.001). We observed improved image quality, lesion detection, and lesion classification with increasing acquisition time while maintaining a total scan time of less than 5 min, which demonstrates a potential clinical benefit. Based on these results we recommend a standard imaging acquisition protocol for LAFOV PET/CT of minimum 180 s to maximum 300 s after injection of 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose.

Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods.

[18F] Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is an important tool for tumor assessment. Shortening scanning time and reducing the amount of radioactive tracer remain the most difficult challenges. Deep learning methods have provided powerful solutions, thus making it important to choose an appropriate neural network architecture. A total of 311 tumor patients who underwent 18F-FDG PET/CT were retrospectively collected. The PET collection time was 3 min/bed. The first 15 and 30 s of each bed collection time were selected to simulate low-dose collection, and the pre-90s was used as the clinical standard protocol. Low-dose PET was used as input, convolutional neural network (CNN, 3D Unet as representative) and generative adversarial network (GAN, P2P as representative) were used to predict the full-dose images. The image visual scores, noise levels and quantitative parameters of tumor tissue were compared. There was high consistency in image quality scores among all groups [Kappa =0.719, 95% confidence interval (CI): 0.697-0.741, P<0.001]. There were 264 cases (3D Unet-15s), 311 cases (3D Unet-30s), 89 cases (P2P-15s) and 247 cases (P2P-30s) with image quality score ≥3, respectively. There was significant difference in the score composition among all groups (χ2=1,325.46, P<0.001). Both deep learning models reduced the standard deviation (SD) of background, and increased the signal-to-noise ratio (SNR). When 8%PET images were used as input, P2P and 3D Unet had similar enhancement effect on SNR of tumor lesions, but 3D Unet could significantly improve the contrast-noise ratio (CNR) (P<0.05). There was no significant difference in SUVmean of tumor lesions compared with s-PET group (P>0.05). When 17%PET image was used as input, SNR, CNR and SUVmax of tumor lesion of 3D Unet group had no statistical difference with those of s-PET group (P>0.05). Both GAN and CNN can suppress image noise to varying degrees and improve image quality. However, when 3D Unet reduces the noise of tumor lesions, it can improve the CNR of tumor lesions. Moreover, quantitative parameters of tumor tissue are similar to those under the standard acquisition protocol, which can meet the needs of clinical diagnosis.

Accurate volume alignment of arbitrarily oriented tibiae based on a mutual attention network for osteoarthritis analysis.

Damage to cartilage is an important indicator of osteoarthritis progression, but manual extraction of cartilage morphology is time-consuming and prone to error. To address this, we hypothesize that automatic labeling of cartilage can be achieved through the comparison of contrasted and non-contrasted Computer Tomography (CT). However, this is non-trivial as the pre-clinical volumes are at arbitrary starting poses due to the lack of standardized acquisition protocols. Thus, we propose an annotation-free deep learning method, D-net, for accurate and automatic alignment of pre- and post-contrasted cartilage CT volumes. D-Net is based on a novel mutual attention network structure to capture large-range translation and full-range rotation without the need for a prior pose template. CT volumes of mice tibiae are used for validation, with synthetic transformation for training and tested with real pre- and post-contrasted CT volumes. Analysis of Variance (ANOVA) was used to compare the different network structures. Our proposed method, D-net, achieves a Dice coefficient of 0.87, and significantly outperforms other state-of-the-art deep learning models, in the real-world alignment of 50 pairs of pre- and post-contrasted CT volumes when cascaded as a multi-stage network.

How do sonographers improve access to cardiology care? An outpatient study

We report our experience working with cardiac sonographers (SONO) in outpatient settings between April 2019 and December 2022. Our observation focused on 7 outpatient cardiac centers in the Auvergne Rhône Alpes region, 35,743 patients had at least one consultation between April 2019 and December 2022. In total, 23,657 patients an echocardiogram (ECHO) performed (66%). Seven SONOs were involved, supervised by 15 cardiologists. All ECHOs were technically performed based on a standard acquisition protocol. Data was saved on a dedicated cloud-based Picture Archiving and Communication System (PACS) allowing the cardiologist to check the image quality, results and complete the report, automatically included in the electronic health record. Out of a total of 25,686 ECHO (i.e., an average of 1.1 ECHO/patient), ∼19,800 (77%) were performed by SONOs (range from 72% to 92% depending on the medical center). In total, ∼2000 ECHO (∼10%) required technical expertise and additional acquisition of images by the referring cardiologist (range from 5% to 25% depending on the experience level of the SONO). Based on a study sample, the average ECHO acquisition time was 12 ± 7 minutes. Thus, over the entire period of 33-month period, the total medical time saved was estimated up to 4,700 hours. Teamwork is mandatory to improve access to outpatient care in cardiology. Working with cardiac sonographers is efficient and saves cardiologists up to 700 hours/SONO/year of medical time. This organizational workflow should be paired with dedicated imaging data management systems and HER (Fig. 1).

Feasibility of photon-counting computed tomography as a novel imaging modality for challenging endodontic diagnostic tasks

Photon-counting computed tomography (PCCT) is an innovative technological advancement in relation to x-ray detectors which offers ultra-high-resolution images. The current study aimed to evaluate the visualization ability of PCCT compared to cone-beam computed tomographic (CBCT) devices for challenging endodontic diagnostic tasks. A reference image of an anthropomorphic phantom was acquired using an industrial micro-CT device. Thereafter, the phantom was scanned with three imaging devices, which included PCCT scanner (NAEOTOM Alpha) and two CBCT devices (3D Accuitomo 170 and NewTom VGi evo) having standard and high-resolution acquisition protocols. The diagnostic tasks involved visualizing fine endodontic structures (apical delta, narrow canal, and isthmus) and root cracks. Three experienced examiners assessed the images and were blinded to the PCCT and CBCT devices. Each image was rated according to a three-grade scale (appropriate, acceptable, or inappropriate) for the diagnostic tasks. In relation to fine endodontic structures grouped together, PCCT showed similar diagnostic performance compared to the reference image (p > 0.05). As for the CBCT devices, an excellent performance was only observed with the 3D Accuitomo 170 device at a high-resolution acquisition mode (p > 0.05). The visualization of root cracks was also better with 3D Accuitomo 170 compared to other devices (p < 0.05). Overall, PCCT and 3D Accuitomo 170 at a high-resolution setting showed similar performance for visualizing fine endodontic structures. In addition, the high-resolution CBCT protocol was superior for visualizing root cracks compared to both PCCT and other standard- and high-resolution CBCT protocols.

Image quality assessment in low-dose COVID-19 chest CT examinations.

Low-dose thoracic protocols were developed massively during the COVID-19 outbreak. To study the impact on image quality (IQ) and the diagnosis reliability of COVID-19 low-dose chest computed tomography (CT) protocols. COVID-19 low-dose protocols were implemented on third- and second-generation CT scanners considering two body mass index (BMI) subgroups (<25 kg/m2 and >25 kg/m2). Contrast-to-noise ratios (CNR) were compared with a Catphan phantom. Next, two radiologists retrospectively assessed IQ for 243 CT patients using a 5-point Linkert scale for general IQ and diagnostic criteria. Kappa score and Wilcoxon rank sum tests were used to compare IQ score and CTDIvol between radiologists, protocols, and scanner models. In vitro analysis of Catphan inserts showed in majority significantly decreased CNR for the low dose versus standard acquisition protocols on both CT scanners. However, in vivo, there was no impact on the diagnosis: sensitivity and specificity were ≥0.8 for all protocols and CT scanners. The third-generation scanner involved a significantly lower dose compared to the second-generation scanner (CTDIvol of 1.8 vs. 2.6 mGy for BMI <25 kg/m2 and 3.3 vs. 4.6 mGy for BMI >25 kg/m2). Still, the third-generation scanner showed a significantly higher IQ with the low-dose protocol compared to the second-generation scanner (30.9 vs. 28.1 for BMI <25 kg/m2 and 29.9 vs. 27.8 for BMI >25 kg/m2). Finally, the two radiologists had good global inter-reader agreement (kappa ≥0.6) for general IQ. Low-dose protocols provided sufficient IQ independently of BMI subgroups and CT models without any impact on diagnosis reliability.

Motion robust magnetic resonance imaging via efficient Fourier aggregation.

We present a method for suppressing motion artifacts in anatomical magnetic resonance acquisitions. Our proposed technique, termed MOTOR-MRI, can recover and salvage images which are otherwise heavily corrupted by motion induced artifacts and blur which renders them unusable. Contrary to other techniques, MOTOR-MRI operates on the reconstructed images and not on k-space data. It relies on breaking the standard acquisition protocol into several shorter ones (while maintaining the same total acquisition time) and subsequent efficient aggregation in Fourier space of locally sharp and consistent information among them, producing a sharp and motion mitigated image. We demonstrate the efficacy of the technique on T2-weighted turbo spin echo magnetic resonance brain scans with severe motion corruption from both 3T and 7T scanners and show significant qualitative and quantitative improvement in image quality. MOTOR-MRI can operate independently, or in conjunction with additional motion correction methods.

Coronary calcium scoring using virtual non-contrast reconstructions on a dual-layer spectral CT system: Feasibility in the clinical practice

PurposeTo evaluate the clinical applicability of a prototype virtual non-contrast (VNC) reconstruction algorithm based on coronary CT angiography (cCTA) to assess calcified coronary plaques by calcium scoring (CACS). MethodsEighty consecutive patients suspected of coronary artery disease were retrospectively included. All patients underwent a cardiac CT using a dual-layer spectral-detector CT system. The standardized acquisition protocol included unenhanced CACS and cCTA. Datasets were acquired using 120 keV. VNC-reconstructions were calculated from the cCTA images at 2.5 mm (VNC group 1), 2.5 of 0.9 mm (group 2), and 0.9 mm (group 3) slice thickness. We compared the Agatston score and Coronary Artery Calcium Data and Reporting System (CAC-DRS) of all VNC reconstructions with the true non-contrast (TNC)-dataset as the gold standard. ResultsIn total, 73 patients were evaluated. Fifty patients (68.5 %) had a CACS > 0 based on TNC. We found a significant difference in the Agatston score comparing all VNC-reconstructions (1: 1.35, 2: 3.7, 3: 10.4) with the TNC dataset (3.8) (p < 0.001). Correlation analysis of the datasets showed an excellent correlation of the TNC results with the different VNC-reconstructions (r = 0.904–0.974, p < 0.001) with a slope of 1.89–2.53. Mean differences and limits of agreement by Bland–Altman analysis between TNC and group 1 were 83 and −196 to 362, respectively. By using the VNC-reconstructions, in group 1 23 patients (31.5 %), in group 2 10 (13.7 %), and in group 3 23 (31.5 %) were reclassified according to CAC-DRS compared to TNC. Classification according to CAC-DRS revealed a significant difference between TNC and group 1 (p = 0.024) and no significance compared to groups 2 and 3 (p = 0.670 and 0.273). ConclusionThe investigated VNC reconstruction algorithm of routine cCTA allows the detection and evaluation of coronary calcium burden without the requirement for an additional acquisition of an unenhanced CT scan for CACS and, therefore, a reduction of radiation exposure.