Multimodal brain MRI provides complementary anatomical, tissue, and pathological information to support downstream artificial intelligence (AI) applications. However, modality availability is uneven across public and pooled neuroimaging datasets: T1-weighted (T1w) MRI is widely represented, T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) MRI are less consistently available, and gadolinium-enhanced T1-weighted (T1Gd) MRI is relatively scarce. Pooled datasets therefore contain participants with different combinations of acquired MRI contrasts, reflecting variation in the study designs, acquisition protocols, and clinical or research objectives of the contributing studies. Many missing-modality benchmarks simulate incomplete MRI by withholding contrasts from complete four-modality cases, which may not represent the observed availability patterns in pooled datasets. To address this gap, we introduce C-WFDM, a ControlNet extension of our Wavelet-Fusion Diffusion Model (WFDM), for subject-specific missing-modality completion. C-WFDM is built on an earlier BrainScape snapshot comprising 157 public MRI datasets and 45,880 preprocessed scans. The pretrained WFDM backbone retains target-modality and metadata conditioning, while target-specific ControlNet adapters use MRI contrasts from the same participant to guide synthesis. C-WFDM synthesizes T2w from T1w, FLAIR from T1w and T2w, and T1Gd from T1w, T2w, and FLAIR. Acquired conditioning contrasts are used when available, and generated intermediate contrasts are used only when required. We compare C-WFDM with ALDM, cWDM, and Pix2Pix using paired structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE). In the BrainScape completion experiment, C-WFDM achieves the best T2w and FLAIR results among the evaluated models, with SSIM values of 0.9232 and 0.8904, respectively.

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