Applications of Parallel Data Processing for Biomedical Imaging

1. Introduction

Biomedical imaging relies closely on parallel facts processing as it shows several benefits, inclusive of speed, efficiency, handling of big datasets.

• ❖ Image Reconstruction

  • ➢

    Computed Tomography (CT): Reconstructing 3D pictures quickly from several 2D X-ray projections requires parallel processing. Reconstruction time is shortened by allowing data from many detectors to be processed simultaneously.

  • ➢

    Magnetic Resonance Imaging (MRI): MRI scans are accelerated by parallel imaging techniques including sensitivity encoding (SENSE) and parallel imaging, which use multiple receiver coils to acquire data simultaneously (Kim et al., 2010).

• ❖ Image Registration

  • ➢

    Functional MRI (FMRI) studies: By facilitation of images registration throughout time, parallel processing is utilized by researchers in align and compare patterns of brain activation across patients or sessions.

• ❖ Image Analysis

  • ➢

    Segmentation and Classification: Parallel processing expedites the lookup of medical images by introducing simultaneous processing of image segments or areas (Sharma & Sharma, 2019). Organ segmentation, disease classification, and tumor identification all are above process-dependent.

  • ➢

    Feature Extraction: Extraction of features of biomedical images is an application area of parallel processing which is an easier tactic to recognize significant patterns and structures.

• ❖ Real-Time Imaging

  • ➢

    Ultrasound Imaging: Parallel improves real-time ultrasound imaging by enabling the simultaneous processing of several beams and accelerates frame rates, according to Zhang et al. (2010). As an example it is useful in obstetrics and cardiovascular imaging.

• ❖ Drug Discovery and Development

  • ➢

    High-Throughput Screening (HTS): Large-scale imaging datasets that are during drug development through HTS benefit from parallel processing, enabling quick identification of viable treatment options and their impact on cell architecture.

• ❖ Data Fusion

  • ➢

    Multimodal Imaging: Combining of data from different modalities—like CT and MRI or PET, a standard procedure (Sharma et al., 2020) in biomedical imaging, is consequence of parallel processing is simpler to merge many datasets for a comprehensive analysis and interpretation.

• ❖ Distributed Computing for Large Datasets

  • ➢

    Genomic Imaging: Effective analysis of large-scale genomic imaging datasets, resultant of genomics and bioinformatics, can be done using parallel processing over distributed computer resources (Mann et al., 2022) which is helpful to store, retrieve, and analyze large datasets in efficient way.

• ❖ Machine Learning and Deep Learning

  • ➢

    Image-Based Diagnosis: With the help of parallel processing, training and inference phases of machine learning and deep learning models used for image-based diagnosis, such as identifying abnormalities in medical images or predicting the course of sickness can be accelerated.

• ❖ Remote Collaboration and Telemedicine

  • ➢

    Cloud-based Processing: Cloud-based parallel processing enables possibility of applications for telemedicine and distant collaboration. By real-time processing and sharing of medical images, healthcare practitioners can work together on diagnosis and treatment planning.