In this paper, we present a comprehensive, quantitative imaging framework for improved treatment of prostate cancer via computer-assisted targeted therapy (CATT) to facilitate radiotherapy dose escalation to regions with a high likelihood of disease presence. The framework involves identification of high likelihood prostate cancer regions using computer-aided detection (CAD) classifier on diagnostic MRI, followed by mapping of these regions from MRI onto planning computerized tomography (CT) via image registration. Treatment of prostate cancer by targeted radiotherapy requires CT to formulate a dose plan. While accurate delineation of the prostate and cancer can provide reduced exposure of benign tissue to radiation, as well as a higher dose to the cancer, CT is ineffective in localizing intraprostatic lesions and poor for highlighting the prostate boundary. MR imagery on the other hand allows for greatly improved visualization of the prostate. Further, several studies have demonstrated the utility of CAD for identifying the location of tumors on in vivo multi-functional prostate MRI. Consequently, our objective is to improve the accuracy of radiotherapy dose plans via multimodal fusion of MR and CT. To achieve this objective, the CATT framework presented in this paper comprises the following components: (1) an unsupervised pixel-wise classifier to identify suspicious regions within the prostate on diagnostic MRI, (2) elastic image registration to align corresponding diagnostic MRI, planning MRI, and CT of the prostate, (3) mapping of the suspect regions from diagnostic MRI onto CT, and (4) calculation of a modified radiotherapy plan with escalated dose for cancer. Qualitative comparison of the dose plans (with and without CAD) over a total of 79 2D slices obtained from 10 MR-CT patient studies, suggest that our CATT framework could help in improved targeted treatment of prostate cancer.