Pitfalls in interventional X-ray organ dose assessment—combined experimental and computational phantom study: application to prostatic artery embolization

Evaluation of Sine Spin flat detector CT imaging compared with multidetector CT

BACKGROUND

Flat detector computed tomography (FDCT) is widely used for periprocedural imaging in the angiography suite. Sine Spin FDCT (SFDCT) is the latest generation of cone beam CT using a double oblique trajectory for image acquisition to reduce artefacts and improve soft tissue brain imaging. This study compared the effective dose, image quality and diagnostic performance of the latest generation of SFDCT with multidetector CT (MDCT).

METHODS

An anthropomorphic phantom equipped with MOSFET detectors was used to measure the effective dose of the new 7sDCT Sine Spin protocol on a latest generation biplane angiographic C-arm system. Diagnostic performance was evaluated on periprocedurally acquired SFDCT for depiction of anatomical details, detection of hemorrhage, and ischemia and was compared with preprocedurally acquired MDCT. Inter- and intra-rater correlation as well as sensitivity and specificity were calculated.

RESULTS

Both modalities showed equal diagnostic performance in the supratentorial ventricular system. SFDCT provided inferior image quality in grey-white matter differentiation and infratentorial structures. Intraventricular, subarachnoid and parenchymal hemorrhages were diagnosed with a sensitivity of 83.3%, 84.2% and 75% and a specificity of 97.3%, 80.0% and 100%, respectively; early ischemic lesions with a sensitivity of 73.3% and specificity 94.7%. The effective dose measured for the 7sDCT Sine Spin protocol was 2 mSv.

CONCLUSIONS

Our findings confirm the high diagnostic sensitivity and specificity of SFDCT in detecting intracranial hemorrhage and early ischemic lesions. The delineation of grey-white matter differentiation and infratentorial structures remains a limiting factor. In comparison to previous studies, the new 7sDCT Sine Spin protocol showed a lower effective dose.

Effective Dose Measurements of the Latest-Generation Angiographic System in Patients with Acute Stroke: A Comparison with the Newest Multidetector CT Generation

BACKGROUND AND PURPOSE

Patients with acute ischemic stroke are increasingly triaged with one-stop management approaches, resulting in baseline imaging with a flat detector CT scanner. This study aimed to estimate the effective dose to a patient of a novel cervical and intracranial flat detector CT angiography and a flat detector CT perfusion protocol and to compare it with the effective dose of analogous multidetector row CT protocols.

MATERIALS AND METHODS

We estimated the effective dose to the patient according to the International Commission on Radiological Protection 103 using an anthropomorphic phantom with metal oxide semiconductor field effect transistor dosimeters. Placement was according to the organ map provided by the phantom manufacturer. We used 100 measurement points within the phantom, and 18 metal oxide semiconductor field effect transistor dosimeters were placed on the surface of the phantom. All protocols followed the manufacturer's specifications, and patient positioning and collimation were performed as in routine clinical practice. Measurements were obtained on the latest-generation angiography and multidetector row CT systems with identical placement of the metal oxide semiconductor field effect transistor dosimeters.

RESULTS

The estimated effective doses of the investigated perfusion protocols were 4.52 mSv (flat detector CT perfusion without collimation), 2.88 mSv (flat detector CT perfusion with collimation), and 2.17 mSv (multidetector row CT perfusion). A novel protocol called portrait flat detector CT angiography that has a z-axis coverage area comparable with that of multidetector row CT angiography had an estimated effective dose of 0.91 mSv, while the dose from multidetector row CT was 1.35 mSv.

CONCLUSIONS

The estimated effective dose to the patient for flat detector CT perfusion and angiography on a modern biplane angiography system does not deviate substantially from that of analogous multidetector row CT protocols.

Learning-based occupational x-ray scatter estimation

Objective. During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the staff’s awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive. Yet, in the interventional environment, immediate feedback to the personnel is desirable. Approach. In this work, we propose deep neural networks to mitigate the computational effort of MC simulations. Our learning-based models consider detailed models of the (outer) patient shape and (inner) anatomy, additional objects in the room, and the x-ray tube spectrum to cover imaging settings encountered in real interventional settings. We investigate two cases of scatter prediction. First, we employ network architectures to estimate the full three-dimensional (3D) scatter distribution. Second, we investigate the prediction of two-dimensional (2D) intensity projections that facilitate the intra-procedural visualization. Main results. Depending on the dimensionality of the estimated scatter distribution and the network architecture, the mean relative error of each network is in the range of 12% and 14% compared to MC simulations. However, 3D scatter distributions can be estimated within 60 ms and 2D distributions within 15 ms. Significance. Overall, our method is suitable to support the online assessment of scattered ionizing radiation in the interventional environment and can help to lower the occupational radiation risk.

Validation of organ dose calculations with PyMCGPU-IR in realistic interventional set-ups

INTRODUCTION

Interventional radiology procedures are associated with high skin dose exposure. The 2013/59/EURATOM Directive establishes that the equipment used for interventional radiology must have a device or a feature informing the practitioner of relevant parameters for assessing patient dose at the end of the procedure. This work presents and validates PyMCGPU-IR, a patient dose monitoring tool for interventional cardiology and radiology procedures based on MC-GPU. MC-GPU is a freely available Monte Carlo (MC) code of photon transport in a voxelized geometry which uses the computational power of commodity Graphics Processing Unit cards (GPU) to accelerate calculations.

METHODOLOGIES

PyMCGPU-IR was validated against two different experimental set-ups. The first one consisted of skin dose measurements for different beam angulations on an adult Rando Alderson anthropomorphic phantom. The second consisted of organ dose measurements in three clinical procedures using the Rando Alderson phantom.

RESULTS

The results obtained for the skin dose measurements show differences below 6%. For the clinical procedures the differences are within 20% for most cases.

CONCLUSIONS

PyMCGPU-IR offers both, high performance and accuracy for dose assessment when compared with skin and organ dose measurements. It also allows the calculation of dose values at specific positions and organs, the dose distribution and the location of the maximum doses per organ. In addition, PyMCGPU-IR overcomes the time limitations of CPU-based MC codes.

Monte Carlo-based patient internal dosimetry in fluoroscopy-guided interventional procedures: A review

PURPOSE

This systematic review aims to understand the dose estimation approaches and their major challenges. Specifically, we focused on state-of-the-art Monte Carlo (MC) methods in fluoroscopy-guided interventional procedures.

METHODS

All relevant studies were identified through keyword searches in electronic databases from inception until September 2020. The searched publications were reviewed, categorised and analysed based on their respective methodology.

RESULTS

Hundred and one publications were identified which utilised existing MC-based applications/programs or customised MC simulations. Two outstanding challenges were identified that contribute to uncertainties in the virtual simulation reconstruction. The first challenge involves the use of anatomical models to represent individuals. Currently, phantom libraries best balance the needs of clinical practicality with those of specificity. However, mismatches of anatomical variations including body size and organ shape can create significant discrepancies in dose estimations. The second challenge is that the exact positioning of the patient relative to the beam is generally unknown. Most dose prediction models assume the patient is located centrally on the examination couch, which can lead to significant errors.

CONCLUSION

The continuing rise of computing power suggests a near future where MC methods become practical for routine clinical dosimetry. Dynamic, deformable phantoms help to improve patient specificity, but at present are only limited to adjustment of gross body volume. Dynamic internal organ displacement or reshaping is likely the next logical frontier. Image-based alignment is probably the most promising solution to enable this, but it must be automated to be clinically practical.

XDose: toward online cross-validation of experimental and computational X-ray dose estimation

PURPOSE

As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently.

METHODS

A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters.

RESULTS

We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under [Formula: see text] for all measurement points.

CONCLUSIONS

Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site.