Ultrasound localization system with advanced biopsy site markers
Disclosed biopsy markers are adapted to serve as localization markers during a surgical procedure. Adaptation includes incorporation of materials detectable under ultrasound during surgery, as well as features for co-registration with image guidance or other real-time imaging technologies during surgery. Such biopsy markers, when used as localization markers, improve patient comfort and reduce challenges in surgical coordination and surgery time. Additional disclosed biopsy markers are adapted to serve as monitoring and/or detection apparatuses. Localization of an implanted marker may be done with ultrasound technology. Ultrasound image data is analyzed to identify the implanted marker. A distance to the marker or a lesion may be determined and displayed. The determined distance may be a distance between the ultrasound probe and the marker or lesion, a distance between the marker or lesion and an incision instrument, and/or a distance between the ultrasound probe and the incision instrument.
1. A method for localization of an implanted marker with ultrasound technology, the method comprising:
emitting an array of ultrasonic sound waves from an ultrasonic transducer of an ultrasound probe;
detecting reflected ultrasonic sound waves by the ultrasonic transducer, wherein the reflected ultrasonic sound waves include at least a portion of the array of ultrasonic sound waves after being reflected from a marker implanted proximate a lesion within an interior of a patient;
generating image data from the reflected ultrasonic sound waves;
analyzing, by a processor having a machine learning image classifier, the generated image data to identify the marker within the interior of the patient, wherein prior to analyzing the generated image data the method includes training the machine learning image classifier with a set of ultrasound images containing the marker relative to surrounding anatomy and/or tissue in different echogenicity grades represented by different gray scale values so that the machine learning image classifier differentiates the marker from the surrounding anatomy and/or tissue for identification; and
displaying, on a display operatively connected to the processor, at least one of the marker or the lesion to facilitate navigation to the lesion during localization.
2. The method of claim 1 , wherein the set of ultrasound images contain the marker in different orientations and cross-sectional views so as to identify the marker based on a cross-section of the marker.
3. The method of claim 1 , wherein the marker is a first marker and the set of ultrasound images contain at least one second marker, the first marker being different than the second marker.
4. The method of claim 1 , further comprising providing the set of ultrasound images and the image data as input to the machine learning image classifier.
5. The method of claim 1 , further comprising:
based at least in part on the identification of the marker, determining, by the processor, a distance to at least one of the marker or the lesion; and
displaying, on the display, the determined distance to the at least one of the marker or the lesion.
6. The method of claim 5 , wherein determining the distance to at least one of the marker or the lesion is performed using an artificial intelligence system trained using a set of ultrasound images containing samples or phantoms having markings with known distances.
7. The method of claim 1 , further comprising:
based at least in part on the identification of the marker, determining, by the processor, an orientation of the marker; and
displaying, on the display, the determined orientation of the marker.
8. The method of claim 1 , further comprising, based at least in part on the identification of the marker, emphasizing, by the processor, the marker on the display.
9. The method of claim 1 , further comprising, based at least in part on the identification of the marker, generating a navigation indicator providing navigation guidance for the ultrasound probe to the at least one of the marker or the lesion.
10. The method of claim 9 , further comprising displaying, on the display, an ultrasound image including the marker based on the reflected ultrasonic sound waves concurrently with the navigation indicator.
11. A system for ultrasound localization, the system comprising:
a marker, wherein the marker is configured to be implanted in an interior of a patient;
an ultrasound probe comprising an ultrasonic transducer, the ultrasonic transducer configured to emit an array of ultrasonic sound waves and detect reflected ultrasonic sound waves, wherein the reflected ultrasonic sound waves include at least a portion of the array of ultrasonic sound waves after being reflected from the marker implanted proximate a lesion within the interior of the patient;
a display;
at least one processor operatively connected to the display and the ultrasound probe; and
memory, operatively connected to the at least one processor, storing instructions that when executed by the at least one processor perform a set of operations comprising:
generating image data from the reflected ultrasonic sound waves;
analyzing, by a machine learning image classifier, the generated image data to identify the marker within the interior of the patient, wherein prior to analyzing the generated image data the machine learning image classifier is trained with a set of ultrasound images containing the marker relative to surrounding anatomy and/or tissue in different echogenicity grades represented by different gray scale values so that the machine learning image classifier differentiates the marker from the surrounding anatomy and/or tissue for identification; and
displaying, on the display, at least one of the marker or the lesion to facilitate navigation to the lesion during localization.
12. The system of claim 11 , wherein the set of ultrasound images contain the marker in different orientations and cross-sectional views.
13. The system of claim 11 , wherein the marker is a first marker and the set of ultrasound images contain at least one second marker, the first marker being different than the second marker.
14. The system of claim 11 , wherein the set of operations the at least one processor performs further comprises:
based at least in part on the identification of the marker, determining, by the at least one processor, a distance to at least one of the marker or the lesion; and
displaying, on the display, the determined distance to the at least one of the marker or the lesion.
15. The system of claim 14 , wherein an artificial intelligence system trained using a set of ultrasound images containing samples or phantoms having markings with known distances is used to determine the distance to the at least one of the marker or the lesion.
16. The system of claim 11 , wherein the set of operations the at least one processor performs further comprises:
based at least in part on the identification of the marker, determining, by the processor, an orientation of the marker; and
displaying, on the display, the determined orientation of the marker.
17. The system of claim 11 , wherein the set of operation the at least one processor performs further comprises, based at least in part on the identification of the marker, generating a navigation indicator providing navigation guidance for the ultrasound probe to the at least one of the marker or the lesion.
18. The system of claim 17 , wherein the display displays an ultrasound image including the marker based on the reflected ultrasonic sound waves concurrently with the navigation indicator.
19. The system of claim 11 , wherein the marker includes an ultrasound activated contrasting agent.
20. The system of claim 19 , wherein the marker is a radial-spoke marker, a multi-layered marker, or a fibrous polymer marker.