CT imaging prior to transcatheter aortic valve implantation in the UK

Computed tomography imaging in preprocedural planning of transcatheter valvular heart interventions

Cardiac Computed Tomography (CCT) has become a reliable imaging modality in cardiology providing robust information on the morphology and structure of the heart with high temporal and isotropic spatial resolution. For the past decade, there has been a paradigm shift in the management of valvular heart disease since previously unfavorable candidates for surgery are now provided with less-invasive interventions. Transcatheter heart valve interventions provide a real alternative to medical and surgical management and are often the only treatment option for valvular heart disease patients. Successful transcatheter valve interventions rely on comprehensive multimodality imaging assessment. CCT is the mainstay imaging technique for preprocedural planning of these interventions. CCT is critical in guiding patient selection, choice of procedural access, device selection, procedural guidance, as well as allowing postprocedural follow-up of complications. This article aims to review the current evidence of the role of CCT in the preprocedural planning of patients undergoing transcatheter valvular interventions.

Computed tomography to guide transcatheter aortic valve implantation

Since its introduction in 2022, transcatheter aortic valve implantation (TAVI) has revolutionized the treatment and prognosis of patients with aortic stenosis. Robust clinical trial data and a wealth of scientific evidence support its efficacy and safety. One of the key factors for success of the TAVI procedure is careful preprocedural planning using imaging. Computed tomography (CT) has developed into the standard imaging method for comprehensive patient assessment in this context. Suitability of the femoral and iliac arteries for transfemoral access, exact measurement of aortic annulus size and geometry as the basis for prosthesis selection, quantification of the spatial relationship of the coronary ostia to the aortic annular plane, and identification of optimal fluoroscopic projection angles for the implantation procedure are among the most important information that can be gained from preprocedural CT. Further research is aimed at improving risk stratification, for example, with respect to annular perforation, periprosthetic aortic regurgitation, and need for postprocedural implantation of a permanent pacemaker.

Time to TAVI: streamlining the pathway to treatment

INTRODUCTION

Severe aortic stenosis is a major cause of morbidity and mortality. The existing treatment pathway for transcatheter aortic valve implantation (TAVI) traditionally relies on tertiary Heart Valve Centre workup. However, this has been associated with delays to treatment, in breach of British Cardiovascular Intervention Society targets. A novel pathway with emphasis on comprehensive patient workup at a local centre, alongside close collaboration with a Heart Valve Centre, may help reduce the time to TAVI.

METHODS

The centre performing local workup implemented a novel TAVI referral pathway. Data were collected retrospectively for all outpatients referred for consideration of TAVI to a Heart Valve Centre from November 2020 to November 2021. The main outcome of time to TAVI was calculated as the time from Heart Valve Centre referral to TAVI, or alternative intervention, expressed in days. For the centre performing local workup, referral was defined as the date of multidisciplinary team discussion. For this centre, a total pathway time from echocardiographic diagnosis to TAVI was also evaluated. A secondary outcome of the proportion of referrals proceeding to TAVI at the Heart Valve Centre was analysed.

RESULTS

Mean±SD time from referral to TAVI was significantly lower at the centre performing local workup, when compared with centres with traditional referral pathways (32.4±64 to 126±257 days, p<0.00001). The total pathway time from echocardiographic diagnosis to TAVI for the centre performing local workup was 89.9±67.6 days, which was also significantly shorter than referral to TAVI time from all other centres (p<0.003). Centres without local workup had a significantly lower percentage of patients accepted for TAVI (49.5% vs 97.8%, p<0.00001).

DISCUSSION

A novel TAVI pathway with emphasis on local workup within a non-surgical centre significantly reduced both the time to TAVI and rejection rates from a Heart Valve Centre. If adopted across the other centres, this approach may help improve access to TAVI.

Radiation Doses in Cardiovascular Computed Tomography

We discussed the contemporary views on the effects of ionising radiation on living organisms and the process of estimating radiation doses in CT examinations and the definitions of the CTDI, CTDIvol, DLP, SSDE, ED. We reviewed the reports from large analyses on the radiation doses in CT examinations of the coronary arteries prior to TAVI procedures, including the CRESCENT, PROTECTION, German Cardiac CT Registry studies. These studies were carried out over the last 10 years and can help confront the daily practice of performing cardiovascular CT examinations in most centres. The reference dose levels for these examinations were also collected. The methods to optimise the radiation dose included tube voltage reduction, ECG-monitored tube current modulation, iterative and deep learning reconstruction techniques, a reduction in the scan range, prospective study protocols, automatic exposure control, heart rate control, rational use of the calcium score, multi-slices and dual-source and wide-field tomography. We also present the studies that indicated the need to raise the organ conversion factor for cardiovascular studies from the 0.014-0.017 mSv/mGy*cm used for chest studies to date to a value of 0.0264-0.03 mSv/mGy*cm.

Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction

Background

This study aims to evaluate the impact of a novel deep learning-based image reconstruction (DLIR) algorithm on the image quality in computed tomographic angiography (CTA) for pre-interventional planning of transcatheter aortic valve implantation (TAVI).

Methods

We analyzed 50 consecutive patients (median age 80 years, 25 men) who underwent TAVI planning CT on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) and DLIR. Intravascular image noise, edge sharpness, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantified for ascending aorta, descending aorta, abdominal aorta and iliac arteries. Two readers (one radiologist and one interventional cardiologist) scored task-specific subjective image quality on a five-point scale.

Results

DLIR significantly reduced median image noise by 29-57% at all anatomical locations (all P<0.001). Accordingly, median SNR improved by 44-133% (all P<0.001) and median CNR improved by 44-125% (all P<0.001). DLIR significantly improved subjective image quality for all four pre-specified TAVI-specific tasks (measuring the annulus, assessing valve morphology and calcifications, the coronary ostia, and the suitability of the aorto-iliac access route) for both the radiologist and the interventional cardiologist (P≤0.001). Measurements of the aortic annulus circumference, area and diameter did not differ between ASIR-V and DLIR reconstructions (all P>0.05).

Conclusions

DLIR significantly improves objective and subjective image quality in TAVI planning CT compared to a state-of-the-art iterative reconstruction without affecting measurements of the aortic annulus. This may provide an opportunity for further reductions in contrast medium volume in this population.

TAVI-CT score to evaluate the anatomic risk in patients undergoing transcatheter aortic valve implantation

Transcatheter aortic valve implantation (TAVI) requires thorough preprocedural planning with non-invasive imaging, including computed tomography (CT). The plethora of details obtained with thoraco-abdominal CT represents a challenge for accurate and synthetic decision-making. We devised and tested a comprehensive score suitable to summarize CT exams when planning TAVI. An original comprehensive scoring system (TAVI-CT score) was devised, including details on cardiac, aortic, iliac and femoral artery features. The score was applied to a prospectively collected series of patients undergoing TAVI at our institution, driving decision making on access and prosthesis choice. Different TAVI-CT score groups were compared in terms of procedural success, acute complications, and early clinical outcomes. We included a total of 200 undergoing TAVI between February 2020 and May 2021, with 74 (37.0%) having a low (0–2) TAVI-CT score, 50 (25.0%) having a moderate (3) TAVI-CT score, and 76 (38.0%) having a high (≥ 4) TAVI-CT score. Male gender was the only non-CT variable significantly associated with the TAVI-CT score (p = 0.001). As expected, access choice differed significantly across TAVI-CT scores (p = 0.009), as was device choice, with Portico more favored and Allegra less favored in the highest TAVI-CT score group (p = 0.036). Acute outcomes were similar in the 3 groups, including device and procedural success rates (respectively p = 0.717 and p = 1). One-month follow-up showed similar rates of death, myocardial infarction, stroke, and bleeding, as well as of a composite safety endpoint (all p > 0.05). However, vascular complications were significantly more common in the highest TAVI-CT score group (p = 0.041). The TAVI-CT score is a simple scoring system that could be routinely applied to CT imaging for TAVI planning, if the present hypothesis-generating findings are confirmed in larger prospective studies.

CT Image Feature Diagnosis on the Basis of Deep Learning Algorithm for Preoperative Patients and Complications of Transcatheter Aortic Valve Implantation

This work was aimed to explore the role of CT angiography information provided by deep learning algorithm in the diagnosis and complications of the disease focusing on congenital aortic valve disease and severe aortic valve stenosis. 120 patients who underwent ultrasound cardiography for aortic stenosis and underwent transcatheter aortic valve implantation (TAVI) in hospital were selected as the research objects. Patients received CT examination of deep learning algorithm within one week. The measurement methods were long and short diameter method, area method, and perimeter method. The deep learning algorithm was used to measure the long and short diameter, area, and perimeter of the target area before CT image processing. The results showed that the average diameter of long and short diameter measurement was 95% CI (0.84, 0.92), the average diameter of perimeter measurement was 95% CI (0.68, 0.87), and the average diameter of area measurement was 95% CI (0.72, 0.91). Among the 52 patients, 35 cases were hypertension (67%), 13 cases were diabetes (25%), 6 cases were chronic renal insufficiency (Cr > 2 mg/dL) (11%) (2 cases were treated with hemodialysis, 3.8%), 11 patients had chronic pulmonary disease (21%), 9 patients had cerebrovascular disease (17.3%) and atrial flutter and atrial fibrillation. Deep learning can achieve excellent results in CT image processing, and it was of great significance for the diagnosis of TAVI patients, improving the success rate of treatment and the prognosis of patients.