Musculoskeletal (MSK) MRI has always had a high demand for maximum achievable spatial resolution to improve sensitivity to changes related to the aging process, pathological changes, injury, and postsurgical or therapeutic assessment. MSK pulse sequences have been in a continuous evolution of improving spatial resolution and contrast sensitivity across all tissue types whilst still endeavoring to reduce the required clinical scan times due to increasing demands on thruput universally. The future in clinical MRI assessment is transitioning from purely imaging-based diagnostics to the inclusion of quantitative-based imaging where we are measuring tissue changes, often prior to them being able to be visualized in routine imaging. Whilst quantitative MRI has been explored in the research environment for many years, the evolution of artificial intelligence (AI) based deep learning (DL)/neural network-based reconstruction is constantly being validated in performing quantitative MRI with improved data quality in clinically achievable scan times. This chapter looks from a clinical perspective at the overall concepts and optimization of imaging and quantitative MRI methods for MSK applications, many of which are still in the transitional stages from the research environment to the clinical mainstream. We initially cover optimization of the work horse of MSK MRI which is 2D and 3D fast spin echo (FSE), with a brief look at alternative gradient echo methods. This includes looking at the way that data is acquired has just as an important impact on image quality and resolution as the basics of voxel size and relative signal-to-noise tradeoffs. We then look at multiple concepts of quantitative MRI techniques, predominantly for cartilage mapping, and important considerations in what is being measured and parameter optimization. After which we will address Ultra Short TE (UTE) and Zero TE (zTE) sequence concepts and optimization used for MSK tissue which routinely appears black such as bone, calcified cartilage, tendons, and ligaments. AI reconstruction influence will be discussed throughout this chapter; however, we will look at basic DL reconstruction applications across both imaging and quantitative scanning overall with future consideration.