Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction "TrueFidelity" in children with congenital heart disease

Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of manuscript: Original research

BAKGROUND

To evaluate the quantitative and qualitative image quality using deep learning image reconstruction (DLIR) of pediatric cardiac computed tomography (CT) compared with conventional image reconstruction methods.

METHODS

Between January 2020 and December 2022, 109 pediatric cardiac CT scans were included in this study. The CT scans were reconstructed using an adaptive statistical iterative reconstruction-V (ASiR-V) with a blending factor of 80% and three levels of DLIR with TrueFidelity (low-, medium-, and high-strength settings). Quantitative image quality was measured using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The edge rise distance (ERD) and angle between 25% and 75% of the line density profile were drawn to evaluate sharpness. Qualitative image quality was assessed using visual grading analysis scores.

RESULTS

A gradual improvement in the SNR and CNR was noted among the strength levels of the DLIR in sequence from low to high. Compared to ASiR-V, high-level DLIR showed significantly improved SNR and CNR (P<0.05). ERD decreased with increasing angle as the level of DLIR increased.

CONCLUSION

High-level DLIR showed improved SNR and CNR compared to ASiR-V, with better sharpness on pediatric cardiac CT scans.

Regional Arch Measurements Differ Between Imaging Modalities in Infants With Aortic Coarctation

BACKGROUND

Aortic arch measurements provide a framework for surgical decision-making in neonatal aortic coarctation, specifically in the determination of approach for arch repair by lateral thoracotomy vs median sternotomy. The purpose of this study was to evaluate our experience with transthoracic echocardiography (TTE) and computed tomography angiography (CTA) in the preoperative evaluation of infants with aortic coarctation, specifically comparing arch dimensions as a function of imaging modality.

METHODS

Imaging data were reviewed for all infants undergoing surgical repair of aortic coarctation at our institution from 2012 to 2022. Infants with both TTE and CTA evaluations were included. Aortic measurements were compared at predefined anatomic regions including ascending aorta, proximal arch, distal arch, and isthmus.

RESULTS

During the study period, 372 infants underwent surgical coarctation repair; 72 (19.4%) infants had TTE and CTA arch evaluations preoperatively. Significant discrepancies between imaging modalities were defined by poor correlation coefficients and absolute measurement differences and were most prominent in the proximal aortic arch (R2 = 0.23 [-4.4 to 3.2 mm]) and isthmus regions (R2 = 0.11 [-4.2 to 1.7 mm]). Improved correlation was demonstrated in the ascending aorta (R2 = 0.63) and distal aortic arch (R2 = 0.54).

CONCLUSIONS

Significant variability exists between TTE- and CTA-derived aortic measurements in infants with coarctation, with proximal arch measurements demonstrating the poorest correlation. This anatomic location represents a commonly used arch region for the determination of approach for repair of neonatal aortic coarctation. Thus, these findings have important implications for current preoperative surgical decision-making paradigms and future prospective study to minimize the risk of residual or recurrent arch obstruction.

Gated cardiac CT in infants: What can we expect from deep learning image reconstruction algorithm?

BACKGROUND

ECG-gated cardiac CT is now widely used in infants with congenital heart disease (CHD). Deep Learning Image Reconstruction (DLIR) could improve image quality while minimizing the radiation dose.

OBJECTIVES

To define the potential dose reduction using DLIR with an anthropomorphic phantom.

METHOD

An anthropomorphic pediatric phantom was scanned with an ECG-gated cardiac CT at four dose levels. Images were reconstructed with an iterative and a deep-learning reconstruction algorithm (ASIR-V and DLIR). Detectability of high-contrast vessels were computed using a mathematical observer. Discrimination between two vessels was assessed by measuring the CT spatial resolution. The potential dose reduction while keeping a similar level of image quality was assessed.

RESULTS

DLIR-H enhances detectability by 2.4% and discrimination performances by 20.9% in comparison with ASIR-V 50. To maintain a similar level of detection, the dose could be reduced by 64% using high-strength DLIR in comparison with ASIR-V50.

CONCLUSION

DLIR offers the potential for a substantial dose reduction while preserving image quality compared to ASIR-V.

Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness.

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments

ABSTRACT

Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.