Abstract: Techniques are presented for optimizing a treatment plan for charged particle therapy. The method includes obtaining medical image data voxels inside a subject in a reference frame of a radiation source that emits a beam of charged particles at multiple tracks with a controlled emitted energy at each track. Hydrogen density (HD) is determined based on the medical image data. Stopping power ratio (SPR) along a first beam having a first track and first emitted energy is calculated based on HD. A range to a Bragg peak is calculated along the first beam based on the SPR and the first emitted energy. The first beam track or the first emitted energy, or both, is modified based at least in part on the beam range to determine a second track and second emitted energy. Output data that indicates the second track and second emitted energy are sent.

Abstract: Techniques are presented for optimizing a treatment plan for charged particle therapy. The method includes obtaining medical image data voxels inside a subject in a reference frame of a radiation source that emits a beam of charged particles at multiple tracks with a controlled emitted energy at each track. Hydrogen density (HD) is determined based on the medical image data. Stopping power ratio (SPR) along a first beam having a first track and first emitted energy is calculated based on HD. A range to a Bragg peak is calculated along the first beam based on the SPR and the first emitted energy. The first beam track or the first emitted energy, or both, is modified based at least in part on the beam range to determine a second track and second emitted energy. Output data that indicates the second track and second emitted energy are sent.

Abstract: A user-independent, quantitative, multiparametric MRI model is developed and validated on co-registered correlative histopathology, yielding improved performance for cancer detection over single parameter estimators. A computing device may be configured to receive a first parametric map that maps imaged tissue of a patient using values of a first parameter, and a second parametric map that maps the imaged tissue using values of a second parameter, wherein the parametric maps are generated from medical imaging data for the imaged tissue. The computing device may be further configured to apply a multiparametric model to the maps to generate at least one Composite Biomarker Score for the tissue, the model being a function of the first parameter and the second parameter. The function may be determined based on co-registered histopathology data. The computing device may be further configured to generate an indication of whether the tissue includes predicted cancer, and output the indication.

Abstract: A user-independent, quantitative, multiparametric MRI model is developed and validated on co-registered correlative histopathology, yielding improved performance for cancer detection over single parameter estimators. A computing device may be configured to receive a first parametric map that maps imaged tissue of a patient using values of a first parameter, and a second parametric map that maps the imaged tissue using values of a second parameter, wherein the parametric maps are generated from medical imaging data for the imaged tissue. The computing device may be further configured to apply a multiparametric model to the maps to generate at least one Composite Biomarker Score for the tissue, the model being a function of the first parameter and the second parameter, The function may be determined based on co-registered histopathology data. The computing device may be further configured to generate an indication of whether the tissue includes predicted cancer, and output the indication.