  Data driven models for automated diagnosis in radiology suffer from
insufficient and imbalanced datasets due to low representation of pathology in
a population and the cost of expert annotations. Datasets can be bolstered
through data augmentation. However, even when utilizing a full suite of
transformations during model training, typical data augmentations do not
address variations in human anatomy. An alternative direction is to synthesize
data using generative models, which can potentially craft datasets with
specific attributes. While this holds promise, commonly used generative models
such as Generative Adversarial Networks may inadvertently produce anatomically
inaccurate features. On the other hand, diffusion models, which offer greater
stability, tend to memorize training data, raising concerns about privacy and
generative diversity. Alternatively, inpainting has the potential to augment
data through directly inserting pathology in medical images. However, this
approach introduces a new challenge: accurately merging the generated
pathological features with the surrounding anatomical context. While inpainting
is a well established method for addressing simple lesions, its application to
pathologies that involve complex structural changes remains relatively
unexplored. We propose an efficient method for inpainting pathological features
onto healthy anatomy in MRI through voxelwise noise scheduling in a latent
diffusion model. We evaluate the method's ability to insert disc herniation and
central canal stenosis in lumbar spine sagittal T2 MRI, and it achieves
superior Frechet Inception Distance compared to state-of-the-art methods.
