Reducing artifacts in photoacoustic imaging by using multi-wavelength excitation and transducer displacement

Abstract

The occurrence of artifacts is a major challenge in photoacoustic imaging. The artifacts negatively affect the quality and reliability of the images. An approach using multiwavelength excitation has previously been reported for in-plane artifact identification. Yet, out-of-plane artifacts cannot be tackled with this method. Here we propose a new method using ultrasound transducer array displacement. By displacing the ultrasound transducer array axially, we can de-correlate out-of-plane artifacts with in-plane image features and thus remove them. Combining this new method with the previous one allows us to remove potentially completely both in-plane and out-of-plane artifacts in photoacoustic imaging. We experimentally demonstrate this with experiments in phantoms as well as in vivo. IntroductionRecent research has shown numerous potential clinical applications of photoacoustic imaging (PAI) [1][2][3]. This imaging technique is based on the photoacoustic (PA) effect. Samples are illuminated using short pulsed laser light. The local absorption of light generates ultrasound (US) waves which are then detected by a US transducer. PA images are reconstructed from the detected signals providing localized information about optical absorption properties of the samples. In clinical applications, the obtained information of endogenous chromophores such as hemoglobin helps diagnosing early stages of various diseases [2,[4][5][6]. A typical PAI system basically consists of a light source and US transducer array. The transducer array can be classified as one-dimensional or two-dimensional [7]. While the twodimensional transducer array provides 3D images, it requires significant users' effort and experience for acquiring and interpreting these 3D images [8]. Additionally, two-dimensional transducer arrays and the associated scanners are unaffordable for many clinical applications [8]. One-dimensional transducer arrays, in contrast, are widely used for clinical studies [9,10], so from the point of view of clinical translation the incorporation of PAI in a one-dimensional array is preferred.Several compact and low-cost PAI systems for clinical use have been developed. Integrating a laser source into a handheld US probe stands out among the approaches [9-12]. However, the occurrence of artifacts related to acoustic inhomogeneity of the tissue (clutter) is a major drawback of using a linear US transducer array. The artifacts aimed in this work include in-plane artifacts (IPAs), also called reflection artifacts, and out-of-plane artifacts (OPAs). While IPAs are caused by signals being reflected inside the imaging plane, OPAs are caused by absorbers located outside the imaging plane of the transducer array [13,14]. These artifacts appear as real image features, such as blood vessels, in the acquired image leading to misinterpretation. Therefore, correcting artifacts in PAI is of importance.Previously, we proposed a method, photoacoustic-guided focused ultrasound (PAFUSion), to reduce IPAs [13,15]. This method has several limitations:...

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“…Another challenge in PA imaging is reflection artifacts from multiple structures inside the small animal body. There are several methods proposed to solve reflection artifacts in PA images [15]. Methods for improving the imaging depth and removing artifacts will be considered in the future.…”

Feasibility of identifying reflection artifacts in photoacoustic imaging using two-wavelength excitation

“…As shown in [5], out-of-plane artifacts (with the acoustic source located outside the imaging plane) can also appear in the acquired PA images, these artifacts cannot be tackled using 4 wavelengths. This still holds for the proposed method.…”

“…Recently, we proposed a method to remove RAs using multi-wavelength excitation [4,5]. In the reported work, we used 4 [4] or 8 [5] wavelengths to image the sample and then compared the spectral response of the real image features and their RAs.…”

“…Recently, we proposed a method to remove RAs using multi-wavelength excitation [4,5]. In the reported work, we used 4 [4] or 8 [5] wavelengths to image the sample and then compared the spectral response of the real image features and their RAs. The method provides considerable advantages over existing methods such as it does not require ultrasound (US) images as in the case of [6][7][8], sufficient training and experience of the user as in the case of [7,9], or a neural network trained with simulated data as in [10].…”