Abstract: A treatment planning system for generating patient-specific treatment. The system including one or more processors programmed to receive a radiation treatment plan (RTP) for irradiating a target over the course of one or more treatment fractions, said RTP including a planned dose distribution to be delivered to the target, receive motion data for at least one of the treatment fractions of the RTP, receive temporal delivery metric data for at least one of the treatment fractions of the RTP, calculate a motion-compensated dose distribution for the target using the motion data and the temporal delivery metric data to adjust the planned dose distribution based on the received motion data and temporal delivery metric data, and compare the motion-compensated dose distribution to the planned dose distribution.

Abstract: A therapy system (10) includes one or more processors (98, 100). The processors (98, 100) are programmed to receive one or more of: (1) dosimetric data from dosimeters (26, 28, 202, 204, 206, 208, 210, 212) implanted within a patient and/or positioned on a vest (200); and (2) motion data from surrogates (18, 20, 22, 24) implanted within the patient. Based on the motion data, a current location and/or shape of a surrogate (18, 20, 22, 24) is determined and deviations between the current location and/or shape and a reference location and/or shape are determined. Based on the dosimetric data, a delivered dose distribution is compared with a planned dose distribution and deviations therebetween are determined. The deviations determined from the motion data and/or the dosimetric data are employed for adaptive planning, alignment, post treatment analysis, and safety.

Abstract: A treatment planning system (106) for generating patient-specific treatment margins. The system (106) includes one or more processors (142). The processors (142) are programmed to receive a radiation treatment plan (RTP) for irradiating a target (122) over the course of one or more treatment fractions. The RTP including one or more treatment margins around the target (122) and a planned dose distribution for the target (122). The processors (142) are further programmed to receive motion data for at least one of the treatment fractions of the RTP from one or more target surrogates (124), calculate a motion-compensated dose distribution for the target (122) using the motion data and the planned dose distribution, compare the motion-compensated dose distribution to the planned dose distribution, and adjust the treatment margins based on dosimetric differences between the motion-compensated dose distribution and the planned dose distribution.

Abstract: A diagnostic device includes a microscope configured to obtain image data on a plurality of cells and a computing device. The computing device is configured to receive the image data, identify at least a portion of each of the plurality of cells based on the received image data, determine at least one of a value of a morphological parameter for each identified at least a portion of the plurality of cells or a relative organization among the identified at least a portion of the plurality of cells, and calculate statistics for the plurality of cells based on the at least one of the determined values of the morphological parameter or the determined relative organization, the statistics including information suitable for distinguishing metastatic cells from non-metastatic cells. The diagnostic device further includes an output device configured to output the statistics for diagnosis.

Abstract: Real-time monitoring of tissue ablation is possible by using a vibrating ablation needle coupling lateral shear waves to the tissue. Ultrasonic imaging may characterize the velocity of these shear waves to reveal Young's modulus of the tissue and, at a discontinuity in Young's modulus, a boundary of the ablated lesion reflecting an underlying increase in stiffness of ablated tissue. This technique may be coupled with quasi-static elastography-based ablation monitoring techniques for improved measurement.

Abstract: Real-time monitoring of tissue ablation is possible by using a vibrating ablation needle coupling lateral shear waves to the tissue. Ultrasonic imaging may characterize the velocity of these shear waves to reveal Young's modulus of the tissue and, at a discontinuity in Young's modulus, a boundary of the ablated lesion reflecting an underlying increase in stiffness of ablated tissue. This technique may be coupled with quasi-static elastography-based ablation monitoring techniques for improved measurement.