Carotid artery dissection on non-contrast CT: does color improve the diagnostic confidence?

The Added Value of Vessel Wall MRI in the Detection of Intraluminal Thrombus in Patients Suspected of Craniocervical Artery Dissection

Patients with craniocervical artery dissection (CCAD) have a high short-term risk of ischemic stroke, which is frequently associated with thromboembolism. Previous studies have demonstrated the utility of three-dimensional vessel wall MR imaging (3D-VWMRI) in the diagnosis of dissection. Few have investigated the value of 3D-VWMRI in the detection of intraluminal thrombus. The purpose of the current study was to evaluate the added value of 3D-VWMRI for thrombus identification in patients suspected of CCAD. One hundred and four patients (mean age, 44.2 years ± 13.2) suspected of CCAD and scheduled for digital subtraction angiography (DSA) were prospectively enrolled in the study and underwent VWMRI examination. The diagnostic performance of 3D-VWMRI for CCAD was evaluated using receiver operating characteristic (ROC) analysis with the final diagnosis results as the reference. The presence/absence of intraluminal thrombus on 3D-VWMRI/DSA was independently determined. The sensitivity and specificity of 3D-VWMRI for intraluminal thrombus detection were assessed with DSA serving as the reference. The odds ratio (OR) was used to evaluate the correlation between thrombus presented on 3D-VWMRI/DSA and ischemic stroke. The 3D-VWMRI had high sensitivity (90.0%) and specificity (94.3%) in identifying arteries with CCAD. The area under the ROC curve was 0.96. With DSA as the reference, the sensitivity and accuracy of 3D-VWMRI for the detection of intraluminal thrombus were 97.4% and 79.0%, respectively. An intraluminal thrombus present on 3D-VWMRI was strongly associated with a territorial ischemic stroke (OR: 30.0; 95% confidence interval: 9.1-98.4; P < .001). In conclusion, 3D-VWMRI with a 3.0-T MR system had a high diagnostic performance for CCAD and offered added value for detecting intraluminal thrombus.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Cochlear signal alterations using pseudo-color perceptual enhancement for patients with sensorineural hearing loss

BACKGROUND

Neuroimaging detection of sensorineural hearing loss (SNHL)-related temporal bone abnormalities is limited (20-50%). We hypothesize that cochlear signal differences in gray-scale data may exceed the threshold of human eye detection. Gray-scale images can be post-processed to enhance perception of tonal difference using "pseudo-color" schemes.

OBJECTIVE

To compare patients with unilateral SNHL to age-matched normal magnetic resonance imaging (MRI) exams for "labyrinthine color differences" employing pseudo-color post-processing.

MATERIALS AND METHODS

The MRI database at an academic children's hospital was queried for "hearing loss." Only unilateral SNHL cases were analyzed. Sixty-nine imaging exams were reviewed. Thirteen age-matched normal MR exams in children without hearing loss were chosen for comparison. Pseudo-color was applied with post-processing assignment of specific hues to each gray-scale intensity value. Gray-scale and pseudo-color images were qualitatively evaluated for signal asymmetries by a board-certified neuroradiologist blinded to the side of SNHL.

RESULTS

Twenty-six SNHL (mean: 7.6±3 years) and 13 normal control exams (mean: 7.3±4 years) were included. All patients had normal gray-scale cochlear signal and all controls had symmetrical pseudo-color signal. However, pseudo-color images revealed occult asymmetries localizing to the SNHL ear with lower values in 38%. Ninety-one percent of these cases showed concordance between the side of pseudo-color positivity and the side of hearing loss.

CONCLUSION

Pseudo-color perceptual image enhancement reveals intra-labyrinthine fluid alterations on MR exams in children with unilateral SNHL. Pseudo-color image enhancement techniques improve detection of cochlear pathology and could have therapeutic implications.

Taking pigeons to heart: Birds proficiently diagnose human cardiac disease

In two experiments, we trained pigeons (Columba livia) to sort visual images (obtained by clinical myocardial perfusion imaging techniques) depicting different degrees of human cardiac disfunction (myocardial hypoperfusion of the left ventricle) into normal and abnormal categories by providing food reward only after correct choice responses. Pigeons proved to be highly proficient at categorizing pseudo-colorized images as well as highly sensitive to the degree of the perfusion deficit depicted in the abnormal images. In later testing, the pigeons completely transferred discriminative responding to novel stimuli, demonstrating that they had fully learned the normal and abnormal categories. Yet, these pigeons failed to transfer discriminative responding to grayscale images containing no color information. We therefore trained a second cohort of pigeons to categorize grayscale image sets from the outset. These birds required substantially more training to achieve similar levels of performance. Yet, they too completely transferred discriminative responding to novel stimuli by relying on both global and local disparities in brightness between the normal and abnormal images. These results confirm that pseudo-colorization can enhance pigeons' categorization of human cardiac images, a result also found with human observers. Overall, our findings further document the potential of the pigeon as a useful aide in studies of medical image perception.

Color-coded visualization of magnetic resonance imaging multiparametric maps

Multiparametric magnetic resonance imaging (mpMRI) data are emergingly used in the clinic e.g. for the diagnosis of prostate cancer. In contrast to conventional MR imaging data, multiparametric data typically include functional measurements such as diffusion and perfusion imaging sequences. Conventionally, these measurements are visualized with a one-dimensional color scale, allowing only for one-dimensional information to be encoded. Yet, human perception places visual information in a three-dimensional color space. In theory, each dimension of this space can be utilized to encode visual information. We addressed this issue and developed a new method for tri-variate color-coded visualization of mpMRI data sets. We showed the usefulness of our method in a preclinical and in a clinical setting: In imaging data of a rat model of acute kidney injury, the method yielded characteristic visual patterns. In a clinical data set of N = 13 prostate cancer mpMRI data, we assessed diagnostic performance in a blinded study with N = 5 observers. Compared to conventional radiological evaluation, color-coded visualization was comparable in terms of positive and negative predictive values. Thus, we showed that human observers can successfully make use of the novel method. This method can be broadly applied to visualize different types of multivariate MRI data.

Effect of color visualization and display hardware on the visual assessment of pseudocolor medical images

PURPOSE

Even though the use of color in the interpretation of medical images has increased significantly in recent years, the ad hoc manner in which color is handled and the lack of standard approaches have been associated with suboptimal and inconsistent diagnostic decisions with a negative impact on patient treatment and prognosis. The purpose of this study is to determine if the choice of color scale and display device hardware affects the visual assessment of patterns that have the characteristics of functional medical images.

METHODS

Perfusion magnetic resonance imaging (MRI) was the basis for designing and performing experiments. Synthetic images resembling brain dynamic-contrast enhanced MRI consisting of scaled mixtures of white, lumpy, and clustered backgrounds were used to assess the performance of a rainbow ("jet"), a heated black-body ("hot"), and a gray ("gray") color scale with display devices of different quality on the detection of small changes in color intensity. The authors used a two-alternative, forced-choice design where readers were presented with 600 pairs of images. Each pair consisted of two images of the same pattern flipped along the vertical axis with a small difference in intensity. Readers were asked to select the image with the highest intensity. Three differences in intensity were tested on four display devices: a medical-grade three-million-pixel display, a consumer-grade monitor, a tablet device, and a phone.

RESULTS

The estimates of percent correct show that jet outperformed hot and gray in the high and low range of the color scales for all devices with a maximum difference in performance of 18% (confidence intervals: 6%, 30%). Performance with hot was different for high and low intensity, comparable to jet for the high range, and worse than gray for lower intensity values. Similar performance was seen between devices using jet and hot, while gray performance was better for handheld devices. Time of performance was shorter with jet.

CONCLUSIONS

Our findings demonstrate that the choice of color scale and display hardware affects the visual comparative analysis of pseudocolor images. Follow-up studies in clinical settings are being considered to confirm the results with patient images.

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

BACKGROUND

Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.

METHODS AND RESULTS

In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.

VALIDATION

To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.

CONTEXT

Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.