GRAPPA is a novel partially parallel acquisition (PPA) method that accelerates image acquisition using an RF coil array for spatial encoding. It extends both the PILS and VD-AUTO-SMASH reconstruction techniques. Unlike previous methods, GRAPPA does not require a detailed RF field map prior to reconstruction. Instead, it uses additional k-space lines to obtain the necessary information. The GRAPPA reconstruction algorithm provides unaliased images from each component coil before image combination, resulting in higher SNR and better image quality. The study focuses on the practical implementation of GRAPPA, including the reconstruction algorithm and SNR analysis. In vivo GRAPPA images are shown to demonstrate the technique's utility. GRAPPA is a generalized implementation of the VD-AUTO-SMASH technique. It generates uncombined coil images from each coil in the array using multiple blockwise reconstructions. This process results in higher SNR and better image quality compared to previous VD-AUTO-SMASH implementations. Computer simulations and in vivo imaging studies show that GRAPPA provides improved SNR and image quality, especially at lower acceleration factors. GRAPPA is particularly useful in areas where coil sensitivity maps are difficult to obtain, such as cardiac and abdominal imaging, and in single-shot EPI applications. GRAPPA uses a sum of squares reconstruction to combine uncombined coil images, which provides higher SNR efficiency compared to previous methods. The technique is robust to coil sensitivity variations and can handle patient motion. The study also discusses the benefits of increasing the number of array elements, as systems with more channels can optimally image in a variety of planes. GRAPPA has shown promising results in cardiac, lung, and abdominal imaging, with improved resolution and reduced blurring compared to conventional methods. The autocalibrating nature of GRAPPA makes it particularly suitable for applications where accurate coil sensitivity maps are challenging to obtain.GRAPPA is a novel partially parallel acquisition (PPA) method that accelerates image acquisition using an RF coil array for spatial encoding. It extends both the PILS and VD-AUTO-SMASH reconstruction techniques. Unlike previous methods, GRAPPA does not require a detailed RF field map prior to reconstruction. Instead, it uses additional k-space lines to obtain the necessary information. The GRAPPA reconstruction algorithm provides unaliased images from each component coil before image combination, resulting in higher SNR and better image quality. The study focuses on the practical implementation of GRAPPA, including the reconstruction algorithm and SNR analysis. In vivo GRAPPA images are shown to demonstrate the technique's utility. GRAPPA is a generalized implementation of the VD-AUTO-SMASH technique. It generates uncombined coil images from each coil in the array using multiple blockwise reconstructions. This process results in higher SNR and better image quality compared to previous VD-AUTO-SMASH implementations. Computer simulations and in vivo imaging studies show that GRAPPA provides improved SNR and image quality, especially at lower acceleration factors. GRAPPA is particularly useful in areas where coil sensitivity maps are difficult to obtain, such as cardiac and abdominal imaging, and in single-shot EPI applications. GRAPPA uses a sum of squares reconstruction to combine uncombined coil images, which provides higher SNR efficiency compared to previous methods. The technique is robust to coil sensitivity variations and can handle patient motion. The study also discusses the benefits of increasing the number of array elements, as systems with more channels can optimally image in a variety of planes. GRAPPA has shown promising results in cardiac, lung, and abdominal imaging, with improved resolution and reduced blurring compared to conventional methods. The autocalibrating nature of GRAPPA makes it particularly suitable for applications where accurate coil sensitivity maps are challenging to obtain.