Improvement in the reproducibility of region of interest using an auditory feedback loop: A pilot assessment using dynamic contrast‐enhanced (DCE) breast MR images

Abstract

Purpose:To augment traditional visual data perception of complex multiparametric imaging data sets by adding auditory feedback to improve the delineation of regions of interest (ROIs) in tumor assessment in dynamic contrastenhanced (DCE) MRI. Materials and Methods:In addition to conventional display methodologies, we have created an application window which interfaces with audio output using dynamically loadable sound modules, providing goodness of fit (GF) information through auditory feedback. We have assessed effectiveness of conveying sound information with three independent readers on eight DCE-MR breast image data sets. The assessment was based on either conventional visual only mode or combined visual plus auditory mode. For statistical comparison between two sensory approaches, interobserver repeatability was measured with three different criteria.Results: Adding auditory feedback improves repeatability significantly (P Ͻ 0.01), and the enhanced sensory approach had higher repeatability than visual only mode in visually complex breast tumor cases. However, in easy and moderate cases, visual only mode was more reproducible than the combined mode with very high significance (P Ͻ 0.001). Conclusion:Adding auditory information to visual based image analysis for identifying tumor ROIs provides higher interobserver repeatability for analyzing complex multidimensional/multiparametric medical image data sets with visually difficult lesions to delineate. TODAY'S ADVANCED IMAGING scanner systems generate multiparametric and multimodality (computed tomography [CT], MR, positron emission tomography [PET], ultrasound [US]) information in a short amount of time.It is important to simultaneously access and to coherently communicate with all these different types of image information for better lesion characterization and for more accurate and reliable diagnostic image analysis in many clinical applications. As the complexity and combination of different types of acquired imaging data increase, conveying this information to a human observer in a meaningful way for diagnostic purpose becomes a bottleneck.The traditional method of analyzing medical images solely relies on the visual perception system, using grayscale values and color overlays to represent composite imaging data or showing different types of image data in a side by side display regarding which the observer must be trained to mentally align corresponding positions in the image data sets. Furthermore, the visual representation of complex medical images is often limited by screen size, two-dimensional (2D) display of three-dimensional (3D) spatial information, image resolution, and the capacity of the human visual system. Therefore, it is not easy to quickly access valuable information due to the complexity of multiparametric imaging data sets and the limits of visual perception system.Dynamic contrast-enhanced MRI (DCE-MRI) is widely used as a powerful noninvasive clinical tool for the diagnosis of tumors and for therapy monitoring of a variety of treatment...

Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI

“…Additionally, novel models are rarely implemented in the commercial analysing software. As a consequence, many investigations, especially clinical studies, have been published using Approach 1 [20][21][22][23][24][25][26][27][28][29] or Approach 2 [30][31][32][33][34][35][36][37][38][39] in the recent past. Therefore, we intended to compare these two frequently used approaches with regard to: (1) the influence of differences in modelling the AIF [Approach 1 assumes a rapid-bolus injection of the contrast agent (CA), Approach 2 a short-time constant-rate infusion] and (2) the dependency of the estimated model parameters on the underlying physiological parameters.…”

Dynamic contrast-enhanced MRI in breast cancer: A comparison between distributed and compartmental tracer kinetic models

“…Many tracer kinetic models have been developed in the DCE-MRI context [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . They can be roughly subdivided into CC [8][9][10][11]15] and DP models [12,19,20] .…”

“…They can be roughly subdivided into CC [8][9][10][11]15] and DP models [12,19,20] . Although DP models might be more realistic, to date most of clinical studies have been conducted using CC models [13,14,16,[20][21][22] . In particular, in breast DCE-MRI commonly used were the Tofts [13,14,16] and Brix models [20][21][22] ; however, to the best of our knowledge, apart from a simulation study by Zwick et al [13] , there is no study comparing them on real breast DCE-MRI data.…”

“…Conventional-Compartmental (CC) or Distributed-Parameter (DP) models [17][18][19] are the two main categories of tracer kinetic models used for perfusion parameter estimation in DCE-MRI. Many investigations, especially clinical studies, have been published using CC models: in particular the Tofts [8-10, 13, 14, 16] and the Brix models [13,[20][21][22] . The former, proposed by Tofts and Kermode [11] , used a population-based AIF with two exponentials, that was drawn from the literature concerning excretion of Gd-DTPA in the normal population (Weinmann et al [25] ).…”

In vivonon-invasive optical imaging of temperature-sensitive co-polymeric nanohydrogel

“…these techniques is their volume to be bulky enough, which hinders the early diagnosis of malignant tumors and results in high mortality. Recently, new techniques for tissue metabolic dynamics including fluorodeoxyglucose positron emission tomography (FDG-PET) [5,6], single-photon emission computed tomography (SPECT) [7,8], dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) [9][10][11] and dynamic contrast-enhanced computed tomography (DCE-CT) [12][13][14], are developing in their role to distinguish the tumor from normal tissue. Although these new techniques have the ability to assess the tumors in their early stage, spatial resolution is limited, cost is high and acquisition requires significant technical expertise not available at all imaging centers.…”