Apparatus and method for determining a tissue susceptibility value
An apparatus and method for determining a tissue susceptibility value from medical data. The apparatus includes an imaging device, wherein the imaging device is configured to detect an image of a subject, wherein the subject receives tissue ablation, a memory, and at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to receive the image of the subject, generate a tissue susceptibility prediction model, wherein the tissue susceptibility prediction model is configured to receive, as input, the image of the subject, and determine a tissue susceptibility value as a function of the image, wherein the tissue susceptibility value defines a susceptibility of a tissue to the tissue ablation, display, using a downstream device, the tissue susceptibility value through a graphical user interface.
1 . An apparatus for determining a tissue susceptibility value, wherein the apparatus comprises:
an imaging device, wherein the imaging device is configured to detect an image of a subject, wherein the subject receives tissue ablation;
a memory; and
at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to:
receive the image of the subject;
generate a tissue susceptibility prediction model, wherein the tissue susceptibility prediction model is configured to:
receive, as input, the image of the subject; and
determine a tissue susceptibility value as a function of the image, wherein the tissue susceptibility value defines a susceptibility of a tissue to the tissue ablation;
generate a dose-dependent graph as a function of the tissue susceptibility value, wherein the dose-dependent graph comprises an X-axis denoting modulation and a Y-axis denoting tissue resistance; and
display, using a downstream device, the tissue susceptibility value through a graphical user interface.
2 . The apparatus of claim 1 , wherein receiving the image of the subject comprises receiving heterogeneous data comprising the image of the subject and electrocardiogram data.
3 . The apparatus of claim 2 , wherein:
the memory contains instructions further configuring the at least a processor to train the tissue susceptibility prediction model using training data comprising pre-ablation image data correlated to post-ablation resistance data, wherein the pre-ablation image data comprises pre-ablation magnetic resonance imaging data and pre-ablation computed tomography data;
receiving the image of the subject comprises receiving magnetic resonance imaging data and computed tomography data; and
the tissue susceptibility prediction model is configured to receive the magnetic resonance imaging data and the computed tomography data as input.
4 . The apparatus of claim 1 , wherein the apparatus is configured to receive a modulation data from an ablation probe.
5 . The apparatus of claim 4 , wherein the at least a processor is further configured to:
plot the modulation data on the dose-dependent graph; and
display the dose-dependent graph.
6 . The apparatus of claim 1 , wherein the tissue susceptibility model is trained using training data comprising annotated data, wherein the annotated data comprises pre-ablation image data associated with post-ablation resistance data.
7 . The apparatus of claim 1 , wherein the tissue susceptibility model comprises a convolutional neural network configured to predict a score.
8 . The apparatus of claim 7 , wherein the graphical user interface comprises a visualization tool configured to generate a visual element associated with the score.
9 . The apparatus of claim 8 , wherein generating the visual element comprises utilizing a color scale corresponding to a threshold based on the tissue susceptibility value.
10 . A method for determining a tissue susceptibility value, wherein the method comprises:
receiving, using at least a processor, an image of a subject, wherein the subject receives tissue ablation;
generating, using at least a processor, a tissue susceptibility prediction model, wherein the tissue susceptibility prediction model is configured to:
receive, as input, the image of the subject; and
determine a tissue susceptibility value as a function of the image, wherein the tissue susceptibility value defines a susceptibility of a tissue to the tissue ablation;
generate a dose-dependent graph as a function of the tissue susceptibility value, wherein the dose-dependent graph comprises an X-axis denoting modulation and a Y-axis denoting tissue resistance; and
displaying, using a downstream device, the tissue susceptibility value through a graphical user interface.
11 . The method of claim 10 , wherein receiving the image of the subject comprises receiving heterogeneous data comprising the image of the subject and electrocardiogram data.
12 . The method of claim 11 , wherein:
a memory contains instructions further configuring the at least a processor to train the tissue susceptibility prediction model using training data comprising pre-ablation image data correlated to post-ablation resistance data, wherein the pre-ablation image data comprises pre-ablation magnetic resonance imaging data and pre-ablation computed tomography data;
receiving the image of the subject comprises receiving magnetic resonance imaging data and computed tomography data; and
the tissue susceptibility prediction model is configured to receive the magnetic resonance imaging data and the computed tomography data as input.
13 . The method of claim 10 further configured to receive a modulation data from an ablation probe.
14 . The method of claim 13 further configured to:
plot the modulation data on the dose-dependent graph; and
display the dose-dependent graph.
15 . The method of claim 10 , wherein the tissue susceptibility model is trained using training data comprising annotated data, wherein the annotated data comprises pre-ablation image data associated with post-ablation resistance data.
16 . The method of claim 10 , wherein the tissue susceptibility model comprises a convolutional neural network configured to predict a score.
17 . The method of claim 16 , wherein the graphical user interface comprises a visualization tool configured to generate a visual element associated with the score.
18 . The method of claim 17 , wherein generating the visual element comprises utilizing a color scale corresponding to a threshold based on the tissue susceptibility value.