The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, high-quality real-time intra-operative imaging remains as a challenging problem. Meanwhile, photoacoustic imaging has appeared so promising to provide images of crucial structures such as blood vessels and microvasculature of tumors. To achieve high-quality photoacoustic images of vessels regarding the artifacts caused by the incomplete data, we proposed an approach based on the combination of time-reversal (TR) and deep learning methods. The proposed method applies a TR method in the first layer of the network which is followed by the convolutional neural network with weights adjusted to a set of simulated training data for the other layers to estimate artifact-free photoacoustic images. It was evaluated using a generated synthetic database of vessels. The mean of signal to noise ratio (SNR), peak SNR, structural similarity index, and edge preservation index for the test data were reached 14.6 dB, 35.3 dB, 0.97 and 0.90, respectively. As our results proved, by using the lower number of detectors and consequently the lower data acquisition time, our approach outperforms the TR algorithm in all criteria in a computational time compatible with clinical use.

In the last two decades, photoacoustic imaging (PAI) as a non-invasive hybrid imaging modality has been used in a wide range of preclinical and clinical applications, such as functional brain mapping,^1,2 molecular imaging,^3,4 cancer diagnosis and staging,^{5 – 8} tissue vasculature imaging,^{9 – 12} guiding interventional procedures,^13,14 and dental health.^15,16 PAI detects the optical absorption contrast in tissue through the conversion of light to heat and thermoelastic effect, leading to the generation of acoustic waves.^{17 – 19} When the tissue is illuminated by short light pulses, the endogenous chromophores, such as hemoglobin, generate a photoacoustic (PA) signal due to their optical absorption.^20,21 In this procedure, the light energy is transformed into acoustic waves, and the efficacy of this conversion is often affected by the presence of noise arising from the surrounding background.^22,23 Therefore, the PA signal is often mixed by background noise, including thermal-acoustic noise in the medium as well as the transducer and electronic noises.²⁴ White Gaussian noise is one of the most common models for these types of randomly distributed thermal and electronic noise.²⁵ However, there is another component of noise arising from the light attenuation phenomena, caused by scattering characteristics of the tissue.²⁶ Furthermore, other types of noise disturb the PA signals, such as the fixed-pattern noise caused by electromagnetic interference. The combination of these different types of noise in the PA signal leads to a low signal-to-noise ratio (SNR) and results in a poor quality reconstructed PA image.^{27 – 30}

During neurosurgical procedures, the neuro-navigation system’s accuracy is affected by the brain shift phenomenon. One popular strategy is to compensate for brain shift using intraoperative ultrasound (iUS) registration with pre-operative magnetic resonance (MR) scans. This requires a satisfactory multimodal image registration method, which is challenging due to the low image quality of ultrasound and the unpredictable nature of brain deformation during surgery. In this paper, we propose an automatic unsupervised end-to-end MR–iUS registration approach named the Dual Discriminator Bayesian Generative Adversarial Network (D²BGAN). The proposed network consists of two discriminators and a generator optimized by a Bayesian loss function to improve the functionality of the generator, and we add a mutual information loss function to the discriminator for similarity measurements. Extensive validation was performed on the RESECT and BITE datasets, where the mean target registration error (mTRE) of MR–iUS registration using D²BGAN was determined to be 0.75 ± 0.3 mm. The D²BGAN illustrated a clear advantage by achieving an 85% improvement in the mTRE over the initial error. Moreover, the results confirmed that the proposed Bayesian loss function, rather than the typical loss function, improved the accuracy of MR–iUS registration by 23%. The improvement in registration accuracy was further enhanced by the preservation of the intensity and anatomical information of the input images.

Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images are proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic-MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using an experimental phantom and ex vivo data obtained from a mouse brain. The results of the phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. The results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidates pre-operative MRI.

Medical image segmentation is an essential step in various diagnostic and treatment procedures. This study aimed to conduct a systematic review of state-of-the-art segmentation methods based on the target. The target complexity is a considerable challenge in medical image segmentation and the first issue that experts confront in diagnosing or treating patients. Additionally, each group of targets has similar characteristics, motivating to provide a target-based review to compare the deep-learning (DL)-based studies. This is the first time that a target-based review of medical image segmentation has provided a focus on recent DL developments. This study categorized publications into three targets: tumors, vessels, and pathological. Using a PRISMA strategy while considering the inclusion and exclusion criteria, 118 articles were identified on Google Scholar and PubMed from 2015 to 2023 in the fields of brain, liver, and lung tumors, blood vessels, and pathology image segmentation. This review could assist researchers in selecting the proper network and being aware of possible challenges. We also concluded that medical image segmentation using DL as a cross-disciplinary field is involved with both complex medical data and technical issues. Consequently, new interpretable approaches may be able to bridge the gap between medical specialists and artificial intelligence researchers.