Purpose: To mitigate inhomogeneity at 7T for multi‐channel transmit arrays using unsupervised deep learning with convolutional neural networks (CNNs).Methods: Deep learning parallel transmit (pTx) pulse design has received attention, but such methods have relied on supervised training and did not use CNNs for multi‐channel maps. In this work, we introduce an alternative approach that facilitates the use of CNNs with multi‐channel maps while performing unsupervised training. The multi‐channel maps are concatenated along the spatial dimension to enable shift‐equivariant processing amenable to CNNs. Training is performed in an unsupervised manner using a physics‐driven loss function that minimizes the discrepancy of the Bloch simulation with the target magnetization, which eliminates the calculation of reference transmit RF weights. The training database comprises 3824 2D sagittal, multi‐channel maps of the healthy human brain from 143 subjects. data were acquired at 7T using an 8Tx/32Rx head coil. The proposed method is compared to the unregularized magnitude least‐squares (MLS) solution for the target magnetization in static pTx design.Results: The proposed method outperformed the unregularized MLS solution for RMS error and coefficient‐of‐variation and had comparable energy consumption. Additionally, the proposed method did not show local phase singularities leading to distinct holes in the resulting magnetization unlike the unregularized MLS solution.Conclusion: Proposed unsupervised deep learning with CNNs performs better than unregularized MLS in static pTx for speed and robustness.

In this work, we aimed to develop and evaluate multi-band outer volume suppression pulses for increased acceleration rates in simultaneous multi-slice accelerated cardiac MRI. MB-OVS pulses were constructed from a multi-band combination of two slab-selective saturation pulses and tested for various pulse shapes using Bloch simulation and phantom experiment. The MB-OVS pulses were interleaved between imaging pulses to ensure homogeneous suppression throughout the cardiac cycle/imaging window in vivo. Simultaneous multi-slice (SMS) CINE and first-pass myocardial perfusion scans with and without the proposed MB-OVS pulses were compared in terms of residual artifacts at high acceleration rates. Among the tested pulses, both Bloch simulation and phantom experiments showed that amplitude-optimized sinc pulses provided the best trade-off in suppression efficiency, the required $B_1^{+}$, SAR, and slab profile. CINE imaging with 5-fold SMS-OVS acceleration significantly outperformed imaging without MB-OVS, maintaining leakage-free image quality, even when adding 2-fold in-plane acceleration. SMS-OVS also enabled perfusion imaging in 9 slices with 1.7 × 1.7 mm² resolution, achieving a 16-fold spatial-only acceleration while ensuring accurate contrast dynamics without leakage artifacts. Interleaved MB-OVS modules enabled thorough leakage artifact suppression in cardiac SMS-accelerated CINE and perfusion imaging, particularly at high acceleration rates. The proposed approach may be promising for unlocking further acceleration potential of SMS in cardiac imaging.