This Study is for our continued study of the Thoracolumbar Fascia (TFL) in patients with and without low back pain by our experienced multidisciplinary team: Vincent Wang PhD, VT Biomedical Engineering \& Mechanics (BEAM). Albert J Kozar DO, FAOASM, R-MSK. P. Gunnar Brolinson, DO, FAOASM, FAOCFP. David T. Redden PhD, VCOM Research Biostatistician. Matthew Chung DO, VCOM and Team Physician at Virginia Tech. Edward Magalhaes, PhD, LPC, Psychiatry and Neuro- Behavioral Sciences, VCOM. This listing is specifically for our renewed efforts via two, Department of Defense (DoD) and American Osteopathic Association (AOA), extramurally, simultaneously funded grants for similar but distinct projects. Both funding sources are aware of each other's funding and have approved their grant study moving forward simultaneously with some integration. DoD: Machine Learning Analysis of Ultrasound Images for the Investigation of Thoracolumbar Myofascial Pain and Therapeutic Efficacy of Hydrodissection. The primary objectives of the proposed project are to: 1. develop reliable, quantitative image analysis approaches to objectively distinguish images from subjects with acute or chronic TLF pain from those without pain and 2. to assess the preliminary clinical efficacy of hydrodissection of the TLF as a novel therapeutic treatment for chronic LBP. AOA: Assessment of the Therapeutic Efficacy of OMT on Chronic Low Back Pain: An Integrated Sonographic and Machine Learning Analysis of Thoracolumbar Fascia Glide Impairment. The primary objectives of the proposed project are to: 1. assess the preliminary clinical efficacy of OMT as a therapeutic treatment for CLBP of TLF origin and 2. develop reliable, quantitative image analysis approaches to objectively distinguish images from subjects with TLF pain from those without pain. These projects will share 50 no LBP subjects as controls. The DOD study will include 50 acute LBP and 50 CLBP. The AOA study will include 50 CLBP. This project uses standard surveys, physical exam, functional tests, and ultrasound imaging to obtain both static images of the TLF at multiple transition zones. It further uses ultrasound to evaluate the dynamic gliding motion, via cine loops, of this fascia in 2 different body movements in subjects with acute low back pain (ALBP), with chronic low back pain (CLBP), and without low back pain (WLBP). All images will be clinically analyzed and further assessed by textural and machine learning analysis. Patients with CLBP (only) will choose to enter one of the two studies (DoD vs AOA) at the time of consent. All images will be clinically analyzed and further assessed by textural and machine learning analysis. Patients with CLBP (only) that are found to have TLF glide impairment or positive physical exam maneuvers suggesting TLF as etiology will enter the treatment arm of the chosen study at the time of consent, either ultrasound guided hydrodissection (USGH), or Osteopathic Manipulative Therapy (OMT). After receiving 3 treatments utilizing one of these modalities, the CLBP participants will have repeat standard surveys, physical exam, functional tests, and ultrasound imaging assessments at 2,4,6,12, and 24 weeks post-treatment. At the conclusion of this project, the investigators expect to have developed, refined, and implemented robust and feasible experimental and computational approaches which can be further expanded in larger-scale studies. The development of our data-driven computer models for the objective analysis of sonographic images of the TLF has high potential impact as it seeks to transform the assessment of TLF integrity, injury and healing via establishment of reliable US imaging biomarkers. The investigators anticipate that the tools developed will have broad utility to assess a variety of clinical treatments for the TLF. The investigators also hope to validate physical exam maneuvers that may predict TLF mediated LBP and have preliminary evidence of the efficacy of hydrodissection and OMT in TLF mediated LBP. In pursuit of these objectives, the investigators will adopt an innovative approach featuring a robust integration of clinical imaging, physical exam, pain and functional outcomes, quantitative image analysis, and machine learning analyses. Specific Aim 1: Compare sonographic TLF imaging characteristics in individuals with acute versus chronic pain to those without low back pain. Specific Aim 2: Develop a machine learning (ML) classification algorithm to reliably distinguish abnormal myofascial tissue in acute versus chronic pain stages from healthy tissue. Specific Aim 3: DoD Study: Assess the preliminary therapeutic efficacy of hydrodissection as a novel treatment for TLF pain using quantitative US imaging and ML tools. AOA Study: Assess the preliminary therapeutic efficacy of OMT as a treatment for CLBP using quantitative US imaging and ML tools.