Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: A computer-implemented method for medical measurement reconstruction may comprise obtaining a first reconstruction of at least one representation of at least one set of medical measurements; presenting the first reconstruction or information about the first reconstruction to a reviewer; receiving an input from the reviewer relating to the first reconstruction or the information about the first reconstruction; processing the received input; and generating a second, modified reconstruction based on the received input.

Abstract: A computer-implemented method for medical measurement reconstruction may comprise: receiving a measurement acquisition signal; based on the received measurement acquisition signal, creating a plurality of representations of the measurement acquisition signal, wherein each of the plurality of representations relates to a different aspect of the measurement acquisition signal; modifying one or more of the plurality of representations; and generating an output signal including the modified one or more of the plurality of representations.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parameterized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parameterized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for associating medical images with a patient. One method includes: receiving two or more medical images of patient anatomy in an electronic storage medium; generating an anatomical model for each of the received medical images; comparing the generated anatomical models; determining a score assessing the likelihood that the two or more medical images belong to the same patient, using the comparison of the generated anatomical models; and outputting the score to an electronic storage medium or display.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for associating medical images with a patient. One method includes: receiving two or more medical images of patient anatomy in an electronic storage medium; generating an anatomical model for each of the received medical images; comparing the generated anatomical models; determining a score assessing the likelihood that the two or more medical images belong to the same patient, using the comparison of the generated anatomical models; and outputting the score to an electronic storage medium or display.

Abstract: Systems and methods are disclosed for associating medical images with a patient. One method includes: receiving two or more medical images of patient anatomy in an electronic storage medium; generating an anatomical model for each of the received medical images; comparing the generated anatomical models; determining a score assessing the likelihood that the two or more medical images belong to the same patient, using the comparison of the generated anatomical models; and outputting the score to an electronic storage medium or display.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for associating medical images with a patient. One method includes: receiving two or more medical images of patient anatomy in an electronic storage medium; generating an anatomical model for each of the received medical images; comparing the generated anatomical models; determining a score assessing the likelihood that the two or more medical images belong to the same patient, using the comparison of the generated anatomical models; and outputting the score to an electronic storage medium or display.

Abstract: An apparatus for adjusting images is provided. The apparatus comprises an image decomposing unit for decomposing an input image I into high-pass images HP1, HP2, . . . , HPN and a low-pass image LPN, and an image adjusting values determining unit for determining high-pass image adjusting values ?1, ?2, . . . , ?N for adjusting the high-pass images HP1, HP2, . . . , HPN and a low-pass image adjusting value ?N for adjusting the low-pass image LPN. The apparatus further comprises an image adjusting unit for adjusting the high-pass images HP1, HP2, . . . , HPN using the high-pass image adjusting values ?1, ?2 . . . , ?N and the low-pass image LPN using the low-pass image adjusting value ?N, and an image recomposing unit for recomposing the adjusted high-pass images HP1*, HP2*, . . . , HPN* and the adjusted low-pass image LPN* into an adjusted image A.

Abstract: A pressure washer is provided. The pressure washer includes a fluid inlet and a pump disposed to receive fluid from the fluid inlet. A spray gun is provided that includes a first outlet and a separate second outlet. A first conduit fluidly connects the fluid inlet and the pump with the first outlet and a second conduit fluidly connects the fluid inlet with the second outlet.

Abstract: An apparatus for adjusting images is provided. The apparatus comprises an image decomposing unit for decomposing an input image I into high-pass images HP1, HP2, . . . , HPN and a low-pass image LPN, and an image adjusting values determining unit for determining high-pass image adjusting values ?1, ?2, . . . , ?N for adjusting the high-pass images HP1, HP2, . . . , HPN and a low-pass image adjusting value ?N for adjusting the low-pass image LPN. The apparatus further comprises an image adjusting unit for adjusting the high-pass images HP1, HP2, . . . , HPN using the high-pass image adjusting values ?1, ?23 . . . , ?N and the low-pass image LPN using the low-pass image adjusting value ?N, and an image recomposing unit for recomposing the adjusted high-pass images HP1*, HP2*, . . . , HPN* and the adjusted low-pass image LPN* into an adjusted image A.

Abstract: A surface cleaner system includes a base assembly having a housing movable along a surface, a pump supported by the housing and operable to pressurize a fluid, and a prime mover supported by the housing. The prime mover is coupled to the pump to drive the pump. The surface cleaner system also includes a spray assembly coupled to the housing. The spray assembly is operable to discharge fluid from the pump toward the surface. The surface cleaner system further includes a spray gun in fluid communication with the pump to receive pressurized fluid from the pump. The spray gun includes an outlet portion that is connectable to the base assembly to direct the pressurized fluid from the pump into the spray assembly. When the outlet portion is disconnected from the base assembly, the spray gun discharges the pressurized fluid from the pump toward the surface without directing the pressurized fluid into the spray assembly.

Abstract: A nozzle is provided. The nozzle includes a cylindrical housing defining a fluid flow path therethrough about a longitudinal axis and a first finger mounted to a first end portion of the housing comprising a wedge disposed on an outer surface of the first finger. A hollow shroud is coaxially disposed around the housing and longitudinally movable with respect to the housing. An outlet aperture is disposed in fluid communication with the fluid flow path, and a ramp disposed within an internal surface of the shroud and engageable with the wedge.

Abstract: A nozzle is provided. The nozzle includes a cylindrical housing defining a fluid flow path therethrough about a longitudinal axis and a first finger mounted to a first end portion of the housing comprising a wedge disposed on an outer surface of the first finger. A hollow shroud is coaxially disposed around the housing and longitudinally movable with respect to the housing. An outlet aperture is disposed in fluid communication with the fluid flow path, and a ramp disposed within an internal surface of the shroud and engageable with the wedge.

Abstract: A fluid sprayer includes a housing, a motor positioned within the housing, a pump operably coupled to the motor to draw fluid from a fluid source and pressurize the fluid, and a blower positioned within the housing and operable to discharge an airflow into the pressurized fluid discharged by the pump.