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A Domain Enriched Deep Learning Approach to Classify Atherosclerosis using Intravascular Ultrasound Imaging.

ABSTRACT: Intravascular ultrasound (IVUS) imaging is widely used for diagnostic imaging in interventional cardiology. The detection and quantification of atherosclerosis from acquired images is typically performed manually by medical experts or by virtual histology IVUS (VH-IVUS) software. VH-IVUS analyzes backscattered radio frequency (RF) signals to provide a color-coded tissue map, and is the method of choice for assessing atherosclerotic plaque in situ. However, a significant amount of tissue cannot be analyzed in reasonable time because the method can be applied just once per cardiac cycle. Furthermore, only hardware and software compatible with RF signal acquisition and processing may be used. We present an image-based tissue characterization method that can be applied to entire acquisition sequences post hoc for the assessment of diseased vessels. The pixel-based method utilizes domain knowledge of arterial pathology and physiology, and leverages technological advances of convolutional neural networks to segment diseased vessel walls into the same tissue classes as virtual histology using only grayscale IVUS images. The method was trained and tested on patches extracted from VH-IVUS images acquired from several patients, and achieved overall accuracy of 93.5% for all segmented tissue. Imposing physically-relevant spatial constraints driven by domain knowledge was key to achieving such strong performance. This enriched approach offers capabilities akin to VH-IVUS without the constraints of RF signals or limited once-per-cycle analysis, offering superior potential information acquisition speed, reduced hardware and software requirements, and more widespread applicability. Such an approach may well yield promise for future clinical and research applications.

SUBMITTER: Olender ML

PROVIDER: S-EPMC7845913 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Angiography remains a critical component for diagnostic imaging and therapeutic intervention in peripheral arterial disease (PAD). The goal of this study was to compare angiography with corresponding intravascular ultrasound (IVUS) imaging of the same vessels in patients with PAD.From 2004 to 2008, 93 patients undergoing angiography for PAD were recruited in a prospective observational analysis. At the time of angiography, diseased lower extremities were interrogated using a 10-cm IVUS pullback with registration points. IVUS data were analyzed with radiofrequency techniques for vessel and lumen diameter, plaque volume, plaque composition, and cross-sectional area. Similarly, three vascular surgeons blinded to the IVUS data graded corresponding angiographic images according to vessel diameter, degree of stenosis, degree of calcification, and extent of eccentricity. Statistical analyses of matched IVUS images and angiograms were performed.The distribution of demographic and risk variables were typical for PAD: 54% male, 96% hypertension, 78% hyperlipidemia, 44% diabetic, 87% tobacco history, 65% coronary artery disease, and 10% end-stage renal disease. Symptoms precipitating the angiographic evaluation included claudication (53%), rest pain (18%), and tissue loss (29%). Angiographic and IVUS interpretation were similar for luminal diameters, but external vessel diameter was greater by IVUS imaging (7.0 +/- 0.7 vs 5.2 +/- 0.8 mm, P < .05). The two-dimensional diameter method resulted in a significant correlation for stenosis determination (r = 0.84); however, IVUS determination of vessel area stenosis was greater by 10% (95% confidence interval, 0.3%-21%, P < .05). IVUS imaging indicated that a higher proportion of plaques were concentric. Grading of calcification was moderate to severe in 40% by angiography but in only 7% by IVUS (P < .05).In the evaluation of PAD, angiography and IVUS imaging provide similar luminal diameters and diameter-reducing stenosis measurements. Determination of overall vessel diameter and interpretation of plaque morphology by angiography are discordant from IVUS-derived data.

Project description:Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy.

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A Domain Enriched Deep Learning Approach to Classify Atherosclerosis using Intravascular Ultrasound Imaging.

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