Spatio-temporal generalized Model Observers methods for Low Contrast Detectability assessment in digital angiography: Application to moving targets.

The purpose of this work is to investigate the feasibility of spatio-temporal generalized Model Observer methods for protocol optimization programs in the field of interventional radiography. Two Model Observers were taken under examination: a Channelized Hotelling Observer with 24 spatio-temporal Gabor channels and a Non Pre-Whitening Model Observer with two different implementations of the spatio-temporal contrast sensitivity function. The images of targets, both stationary and in motion, were acquired in fluoroscopic mode using a CDRAD phantom for signal-present images and an homogenous slab of PMMA for signal-absent ones. After the processing, these images were used to build three series of two alternative forced choice experiments, designed to simulate tasks of clinical interest, and submitted to three human observers in order to set a goal on detectability. A first set of images was used for model tuning and subsequently the verified models were validated throughout a second set of images. Results from the validation phase, for both models, show good agreement with the human observer performances (Root Mean Square Error RMSE ≤ 12%). The tuning phase emerges as a crucial step in building models for angiographic dynamic images; the final agreement underlines the good capability of these spatio-temporal models in simulating human performances, allowing to consider them as a useful and worthwhile tool in protocol optimization when dynamic images are involved.

A single phantom, a single statistical method for low-contrast detectability assessment.

PURPOSE: The assessment of low-contrast-details is a part of the quality control (QC) program in digital radiology. It generally consists of evaluating the threshold contrast (Cth) detectability details for different-sized inserts, appropriately located in dedicated QC test tools. This work aims to propose a simplified method, based on a statistical model approach for threshold contrast estimation, suitable for different modalities in digital radiology. METHODS: A home-madelow-contrast phantom, made of a central aluminium insert with a step-wedge, was assembled and tested. The reliability and robustness of the method were investigated for Mammography, Digital Radiography, Fluoroscopy and Angiography. Imageswere analysed using our dedicated software developed on Matlab®. TheCth is expressed in the same unit (mmAl) for all studied modalities. RESULTS: This method allows the collection of Cthinformation from different modalities and equipment by different vendors, and it could be used to define typical values. Results are summarized in detail. For 0.5 diameter detail, Cthresults are in the range of: 0.018-0.023 mmAl for 2D mammography and 0.26-0.34 mmAl DR images. For angiographic images, for 2.5 mm diameter detail, the Cths median values are 0.55, 0.4, 0.06, 0.12 mmAl for low dose fluoroscopy, coronary fluorography, cerebral and abdominal DSA, respectively. CONCLUSIONS: The statistical method proposed in this study gives a simple approach for Low-Contrast-Details assessment, and the typical values proposed can be implemented in a QA program for digital radiology modalities.

Model observers for Low Contrast Detectability evaluation in dynamic angiography: A feasible approach.

PURPOSE: To evaluate the feasibility of spatio-temporal generalisation of mathematical methods for protocol optimisation in interventional radiology. MATERIALS AND METHODS: Two model observers were considered:Furthermore, Low Contrast Detectability (LCD) was evaluated with a generalised statistical method by taking into account the noise integration capability of the human eye. A series of two alternative force choices (2AFC) experiments performed by four observers were used to evaluate the reliability of the proposed models. The evaluation of the mathematical methods was performed by comparing their results to the human observer performances in two steps: 1. Firstly, a series of simulated images were used to tune the models 2. In the second phase, tuned models were applied both to simulated images and actual images obtained with a commercial phantom to evaluate detectability scores. RESULTS: Evaluation with simulated images shows a good agreement with 2AFC results (RMSE < 10%). Phantom-based evaluations show a general decrease of such agreement, characterized by an RMSE lower than 16%. CONCLUSIONS: The agreement with human observer experiments supports the feasibility of the proposed generalisations. Thus, they could be introduced in quality control programmes for a deeper protocol-characterisation or for clinical protocol-optimization when dynamic images are involved.

Evaluation of a commercial Model Based Iterative reconstruction algorithm in computed tomography.

INTRODUCTION: Iterative reconstruction algorithms have been introduced in clinical practice to obtain dose reduction without compromising the diagnostic performance. PURPOSE: To investigate the commercial Model Based IMR algorithm by means of patient dose and image quality, with standard Fourier and alternative metrics. MATERIALS AND METHODS: A Catphan phantom, a commercial density phantom and a cylindrical water filled phantom were scanned both varying CTDIvol and reconstruction thickness. Images were then reconstructed with Filtered Back Projection and both statistical (iDose) and Model Based (IMR) Iterative reconstruction algorithms. Spatial resolution was evaluated with Modulation Transfer Function and Target Transfer Function. Noise reduction was investigated with Standard Deviation. Furthermore, its behaviour was analysed with 3D and 2D Noise Power Spectrum. Blur and Low Contrast Detectability were investigated. Patient dose indexes were collected and analysed. RESULTS: All results, related to image quality, have been compared to FBP standard reconstructions. Model Based IMR significantly improves Modulation Transfer Function with an increase between 12% and 64%. Target Transfer Function curves confirm this trend for high density objects, while Blur presents a sharpness reduction for low density details. Model Based IMR underlines a noise reduction between 44% and 66% and a variation in noise power spectrum behaviour. Low Contrast Detectability curves underline an averaged improvement of 35-45%; these results are compatible with an achievable reduction of 50% of CTDIvol. A dose reduction between 25% and 35% is confirmed by median values of CTDIvol. CONCLUSION: IMR produces an improvement in image quality and dose reduction.