This paper introduces a novel longitudinal image processing framework designed to address the challenges of variability, bias, and over-regularization in longitudinal image analysis. The framework, based on unbiased, robust within-subject template creation, aims to improve the precision and discrimination power of brain MRI analysis across multiple time points. Key contributions include: 1. **Unbiased Within-Subject Template Creation**: The method treats all input images equally, reducing processing bias and variability. It uses a median image as a robust template, which is then used to initialize subsequent segmentations and surface reconstructions. 2. **Robust Registration and Normalization**: A robust and inverse-consistent registration method is employed to align all input images to a common space, ensuring consistent processing across time points. 3. **Avoidance of Over-regularization**: The processing is allowed to evolve freely, avoiding the introduction of temporal smoothness constraints that can limit the detection of large anatomical changes. 4. **Improved Reliability and Precision**: The proposed method significantly reduces variability and increases precision in both healthy controls and neurodegenerative disease studies, enabling more powerful evaluations of subtle disease effects and reducing sample sizes. 5. **Clinical Applications**: The method has been successfully applied in various studies, including those on Alzheimer's disease, Huntington's disease, and memory training, demonstrating its potential in clinical applications such as biomarker discovery and drug effect quantification. The framework is implemented as part of the FreeSurfer software package, which is widely used for cortical and subcortical measures. The paper also discusses related work, methodological details, and results from test-retest and simulated atrophy studies, highlighting the robustness, precision, and accuracy of the proposed approach.This paper introduces a novel longitudinal image processing framework designed to address the challenges of variability, bias, and over-regularization in longitudinal image analysis. The framework, based on unbiased, robust within-subject template creation, aims to improve the precision and discrimination power of brain MRI analysis across multiple time points. Key contributions include: 1. **Unbiased Within-Subject Template Creation**: The method treats all input images equally, reducing processing bias and variability. It uses a median image as a robust template, which is then used to initialize subsequent segmentations and surface reconstructions. 2. **Robust Registration and Normalization**: A robust and inverse-consistent registration method is employed to align all input images to a common space, ensuring consistent processing across time points. 3. **Avoidance of Over-regularization**: The processing is allowed to evolve freely, avoiding the introduction of temporal smoothness constraints that can limit the detection of large anatomical changes. 4. **Improved Reliability and Precision**: The proposed method significantly reduces variability and increases precision in both healthy controls and neurodegenerative disease studies, enabling more powerful evaluations of subtle disease effects and reducing sample sizes. 5. **Clinical Applications**: The method has been successfully applied in various studies, including those on Alzheimer's disease, Huntington's disease, and memory training, demonstrating its potential in clinical applications such as biomarker discovery and drug effect quantification. The framework is implemented as part of the FreeSurfer software package, which is widely used for cortical and subcortical measures. The paper also discusses related work, methodological details, and results from test-retest and simulated atrophy studies, highlighting the robustness, precision, and accuracy of the proposed approach.