Magnetic resonance imaging (MRI) is a slow diagnostic technique due to its time-consuming acquisition speed. To address this, parallel imaging and compressed sensing methods were developed. Parallel imaging acquires multiple anatomy views simultaneously, while compressed sensing acquires fewer samples than traditional methods. However, reconstructing images from undersampled multi-coil data remains challenging. Existing methods concatenate input slices and adjacent slices along the channel dimension to gather more information for MRI reconstruction. Implicit feature alignment within adjacent slices is crucial for optimal reconstruction performance. Hence, we propose MFormer: an accelerated MRI reconstruction transformer with cascading MFormer blocks containing multi-scale Dynamic Deformable Swin Transformer (DST) modules. Unlike other methods, our DST modules implicitly align adjacent slice features using dynamic deformable convolution and extract local non-local features before merging information. We adapt input variations by aggregating deformable convolution kernel weights and biases through a dynamic weight predictor. Extensive experiments on Stanford2D, Stanford3D, and large-scale FastMRI datasets show the merits of our contributions, achieving state-of-the-art MRI reconstruction performance. Our code and models are available at https://github.com/wafaAlghallabi/MFomer.