The article introduces the updated version of the Prostate Imaging Quality (PI-QUAL) scoring system, known as PI-QUAL v2, which aims to address the limitations of its predecessor, PI-QUAL v1. PI-QUAL v2 is designed to simplify the assessment of image quality and expand its applicability to both multiparametric MRI (mpMRI) and MRI without intravenous contrast medium. The new scoring system focuses on critical technical and qualitative image parameters, emphasizing T2-weighted imaging (T2-WI) and diffusion-weighted imaging (DWI). The scoring sheet includes 10 criteria, with specific scores for T2-WI, DWI, and dynamic contrast-enhanced (DCE) sequences. The article outlines the methodology behind PI-QUAL v2, including inter-reader agreement testing and the development process. It also provides clinical recommendations for using PI-QUAL v2 to guide decision-making about repeat examinations and highlights the importance of optimal diagnostic quality for active surveillance and post-treatment assessments. The authors emphasize the need for education and training on the application of PI-QUAL v2 and suggest that automated methods based on deep learning could enhance the reproducibility and standardization of image quality assessments. Overall, PI-QUAL v2 is presented as a valuable tool to improve the quality and clinical utility of prostate MRI.The article introduces the updated version of the Prostate Imaging Quality (PI-QUAL) scoring system, known as PI-QUAL v2, which aims to address the limitations of its predecessor, PI-QUAL v1. PI-QUAL v2 is designed to simplify the assessment of image quality and expand its applicability to both multiparametric MRI (mpMRI) and MRI without intravenous contrast medium. The new scoring system focuses on critical technical and qualitative image parameters, emphasizing T2-weighted imaging (T2-WI) and diffusion-weighted imaging (DWI). The scoring sheet includes 10 criteria, with specific scores for T2-WI, DWI, and dynamic contrast-enhanced (DCE) sequences. The article outlines the methodology behind PI-QUAL v2, including inter-reader agreement testing and the development process. It also provides clinical recommendations for using PI-QUAL v2 to guide decision-making about repeat examinations and highlights the importance of optimal diagnostic quality for active surveillance and post-treatment assessments. The authors emphasize the need for education and training on the application of PI-QUAL v2 and suggest that automated methods based on deep learning could enhance the reproducibility and standardization of image quality assessments. Overall, PI-QUAL v2 is presented as a valuable tool to improve the quality and clinical utility of prostate MRI.