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Imaging of HIFU-induced lesions provides non-invasive, real-time treatment monitoring and control. This work presents results obtained with HIFU-induced lesion detection algorithms specifically designed for multiple lesion detection. Algorithms sensitive to relative tissue changes during HIFU -measuring signal energy, tissue displacement, entropy, and tissue attenuation are compared for their ability to detect the creation of multiple and adjacent HIFU lesions. In vivo (N=4) canine prostate backscattered RF data was acquired with a custom Sonablate®500 HIFU device during 7 treatments. A total of 815 sites were treated, forming the algorithm evaluation dataset. It was found that the algorithm based on signal energy performed best, detecting 82% of all HIFU lesions created, while showing false-alarm rates below 5%. All methods are completely non-invasive, and make use of tissue reference/normalization information obtained before, during, and after the HIFU treatment. Algorithm specifics, data acquisition methodologies, in vivo experimental results, and algorithm comparison results are shown.