Ablation result validation system

Devices, systems, methods for validating ablation results in a patient's brain are provided. In some embodiments, the method for validating ablation result in a patient's brain includes obtaining magnetic resonance (MR) data of the patient's brain, by use of a magnetic resonance imaging (MRI) device; obtaining first imaging data of the patient's brain, by use of the MRI device; extracting, by use of computing device in communication with the MRI device, first fiber tracts passing through an anatomy in the patient's brain based on the first imaging data; obtaining, by use of the MRI device, second imaging data of the patient's brain after ablation of the anatomy in the patient's brain has started; extracting second fiber tracts passing through the anatomy in the patient's brain based on the second imaging data; and outputting a graphical representation of a comparison between the first fiber tracts and the second fiber tracts.

TECHNICAL FIELD

The present disclosure relates generally to ablation result validation, and in particular, to devices, systems, and methods for validating ablation volumes and connectivity of an ablated target by visualizing the fiber tracts passing through the ablated target.

BACKGROUND

Minimally invasive intervention has increasingly been used to treat brain tumors and medically intractable epilepsy. One of emergent minimally invasive intervention techniques is interstitial thermal therapy (LITT). In a LITT procedure, an ablation catheter delivers heat to target cells by emitting collimated light through a diffusing tip, heating the target cells to 40° C. or higher. When heated to a temperature between 40° C. and 60° C., the target cells suffer irreversible cell damage due to denaturation of their DNA. Target cells heated to above 60° C. die instantly. When the target cells are heated to above 100° C., water in the target cells vaporizes and surrounding tissues carbonize.

The most common form of medically intractable epilepsy is mesial temporal lobe epilepsy (MTLE). Of the minimally invasive LITT procedures, stereotactic laser-guided amygdalohippocampectomy (SLAH) is used to treat MTLE. During a SLAH procedure, a craniotomy is performed to create a hole in a patient's skull. A polycarbonate anchor bolt is fixed to the hole, through which an alignment rod is driven into the patient's brain to create a path to the ablation target at or near the amygdalohippocampal complex (AHC) of the patient. Once the path is created, the alignment rod is removed and a polycarbonate cooling catheter with a diode laser fiber is inserted along the path to the ablation target.

The ablation process during a LITT procedure (such as the SLAH procedure) is monitored visually using magnetic resonance (MR) thermography overlaid on a pre-surgical T1 weighted magnetic resonance imaging (T1W Mill) volume. Most clinical centers perform an additional T1W Mill at the end of a LITT procedure to estimate the ablation volume. However, post-operative data overestimates the actual ablation due to changes in tissue contrast after the ablation. A major limitation of the current clinical workflow is the inability to detect and monitor the actual effect of ablation on target regions and functional integrity/connectivity (or reduction thereof) of ablated regions.

That major limitation takes a toll on the efficacy of the LITT procedures and can result in repeat ablations. Taking the treatment of intractable epilepsy as an example, the seizure freedom rate after a SLAH procedure varies from 40% to 60%, which is worse than that of the conventional open amygdalohippocampectomy procedure. This is disappointing because LITT is otherwise superior to conventional surgery as LITT substantially reduces non-target brain tissue brain damage, risk of complications, pain, discomfort, and permanent neurologic deficits. Among the MTLE patients who received a SLAH procedure, patients with epilepsy detectable by MRI are on the higher end of the range. Furthermore, the outcome depends on the amount of ablated AHC tissue. Approximately 80% of patients with ablation of at least 70% of either the amygdala or hippocampus and ablation of at least 50% of the other structure are seizure-free after the SLAH procedures. In contrast, only 40% of patients with ablation of less than 50% of the amygdala and hippocampus are seizure-free after the SLAH procedures. Some of these patients may consider or be advised of another LITT procedure to achieve seizure freedom. A means to more accurately quantify the extent of LITT ablation is desired to allow for more complete and effective ablation, increased seizure-free outcomes and to eliminate the need to subsequent repeated visits/ablations. While the foregoing is described with respect to treatment of the MTLE, the means to accurately quantify the extent of LITT ablation is equally desired to the LITT treatment of brain tumors and brain lesions.

SUMMARY

Embodiments of the present disclosure are configured to validating ablation result in a patient's brain by comparing intra- or post-ablation fiber tracts passing through the patient's anatomy with the pre-ablation fiber tracts passing through the patient's anatomy. The pre-ablation fiber tracts passing through the patient's anatomy are extracted based on imaging data, such as diffusion tensor imaging (DTI) data, obtained before the ablation. The intra- or post-ablation fiber tracts passing through the patient's anatomy are extracted based on imaging data, such as DTI data, obtained simultaneously with or after the ablation. The systems and methods disclosed in the present disclosure can also output a graphical representation of a comparison between the post-ablation fiber tracts and the pre-ablation fiber tracts. Aspects of the present disclosure advantageously provide a method and a system to accurately evaluate LITT ablation quantitatively to improve the efficacy of LITT and eliminate the need for subsequent repeat ablations.

In one embodiment, a method for validating an ablation result in a patient's brain is provided. The method includes obtaining magnetic resonance (MR) data of the patient's brain, by use of a magnetic resonance imaging (MRI) device; obtaining first imaging data of the patient's brain, by use of the MRI device; extracting, by use of computing device in communication with the MRI device, first fiber tracts passing through an anatomy in the patient's brain based on the first imaging data; obtaining, by use of the MRI device, second imaging data of the patient's brain after ablation of the anatomy in the patient's brain has started; extracting second fiber tracts passing through the anatomy in the patient's brain based on the second imaging data; and outputting a graphical representation of a comparison between the first fiber tracts and the second fiber tracts. In some embodiments, the first and second imaging data include diffusion tensor imaging (DTI) data.

In some embodiments, the second imaging data is obtained during the ablation of the anatomy in the patient's brain. In some other embodiments, the second imaging data is obtained after the ablation of the anatomy in the patient's brain has ended. In some implementations, outputting the graphical representation includes outputting to a display the second fiber tracts along with the MR data of the patient's brain. In some embodiments, the MR data includes T1 weighted magnetic resonance (T1W MR) data of the patient's brain. In some instances, the anatomy in the patient's brain can include an amygdalohippocampal complex, an amygdala, a hippocampus, a lesion, or a tumor in the patient's brain. In some embodiments, the extracting the second fiber tracts passing through the anatomy in the patient's brain based on the second imaging data includes aligning the MR data and the second imaging data of the patient's brain, wherein the MR data includes MR data of the anatomy in the patient's brain; segmenting the MR data of the anatomy in the patient's brain into segments based on a three-dimensional (3D) brain model; and identifying the second fiber tracts passing through the segments. In some implementations, the 3D brain model is a shape-constrained deformable brain model.

In another embodiment, a system for validating an ablation result in a patient's brain is provided. The method includes a computing device in communication with a magnetic resonance imaging (MRI) device, the computing device operable to obtain magnetic resonance (MR) data of the patient's brain, by use of a magnetic resonance imaging (MRI) device; obtain first imaging data of the patient's brain, by use of the MRI device; extract first fiber tracts passing through an anatomy in the patient's brain based on the first imaging data; obtain, by use of the MRI device, second imaging data of the patient's brain after ablation of the anatomy in the patient's brain has started; extract second fiber tracts passing through the anatomy in the patient's brain based on the second imaging data; and outputting, to a display in communication with the computing device, a graphical representation of a comparison between the first fiber tracts and the second fiber tracts. In some embodiments, the system for validating ablation result in a patient's brain further includes the MRI device and the display. In some embodiments, the first and second imaging data include diffusion tensor imaging (DTI) data.

In some embodiments, the computing device of the system for validating ablation result in a patient's brain is configured to obtain the second imaging data during the ablation of the anatomy in the patient's brain. In some other embodiments, the computing device is configured to obtain the second imaging data after the ablation of the anatomy in the patient's brain has ended. In some implementations, the computing device is configured to output to the display the second fiber tracts along with the MR data of the patient's brain. In some instances, the MR data includes T1 weighted magnetic resonance (T1W MR) data of the patient's brain. In some embodiments, the anatomy of in the patient's brain can include an amygdalohippocampal complex, an amygdala, a hippocampus, a lesion, or a tumor in the patient's brain. In some embodiments, the computing device of the system is further operable to align the MR data and the second imaging data of the patient's brain, wherein the MR data includes MR data of the anatomy in the patient's brain; segment the MR data of the anatomy in the patient's brain into segments based on a three-dimensional (3D) brain model; and identify the second fiber tracts passing through the segments. In some implementations, the 3D brain model is a shape-constrained deformable brain model.

Other devices, systems, and methods specifically configured to interface with such devices and/or implement such methods are also provided.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the embodiments illustrated in the drawings, and specific language will be used to describe the same. It is nevertheless understood that no limitation to the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, and methods, and any further application of the principles of the present disclosure are fully contemplated and included within the present disclosure as would normally occur to one skilled in the art to which the disclosure relates.

Referring now toFIG. 1, shown therein is a schematic diagram of a system100for ablation result validation in a patient's brain. The system100includes a computing device120connected to a magnetic resonance imaging (MRI) device110, a user input device130, a laser ablation device140, and a display150. The computing device120includes a processing circuit, such as one or more processors in communication with memory. The memory can be tangible computer readable storage media that stores instructions that are executable by the one or more processors. The computing device120can be a workstation or a controller that serves as an interface between the MRI device110and the laser ablation device140. In some embodiments, the MRI device110can operate in different modalities, including but not limited to magnetic resonance (MR) imaging, diffusion tensor imaging (DTI), and positron emission tomography (PET) imaging and output imaging data to the computing device120. In some implementations, the MRI device110can operate in different modalities at the same time. For example, in some embodiments of the present disclosure, the MRI device can perform MR scans and PET scans simultaneously or MR scans and DTI scans simultaneously. DTI utilizes the properties of water diffusion to provide information about connectivity and function integrity of brain tissues. It is based on the principle that water molecules diffuse along the principal axes of tensors that describe the rate of diffusion. The tensors are centered at voxels in three dimensions and can be visualized as ellipsoids. As a result, voxels along common fiber pathways form “diffusion lines,” also known as fiber tracts, if viewed along the long axes of their individual tensors. DTI or DTI tractography is an image processing technique that traces such ellipsoids along their long axis by starting from a user-defined seed point/region.

In some embodiments, the computing device120can receive MR data from the MRI device110, process the same and output MR image data to the display150such that the display150can display MR images. In some embodiments, the computing device120can receive DTI data from the MRI device110, extract fiber tracts passing through an anatomy of a patient, and output image data of the fiber tracts to the display150. In some implementations where the MR data and the DTI data are obtained simultaneously by the MRI device110, no co-registration or alignment may be necessary as both data may already be aligned. In some implementations where the MR data and the DTI data are not obtained simultaneously or are obtained simultaneously, the computing device120can align or co-register the MR data and the DTI data through rigid registration, volume localization or direction cosines. In either case, the computing device120can process aligned MR data and DTI data and output MR image data overlaid with DTI data. The same applies to PET data obtained by the MRI device110. If the MR data and PET data from the MRI device110are obtained simultaneously, no co-registration or alignment may be necessary. However, if the MR data and PET data from the MRI device110are obtained sequentially or simultaneously in some instances, the imaging data can be aligned through any suitable process, such as rigid registration, volume localization, direction cosines, etc.

In an embodiment, the laser ablation device140includes an MRI-compatible ablation catheter (ablation catheter) and a catheter driver. The catheter driver can drive the ablation catheter into a patient's skull through a hole created in a craniotomy procedure. The user input device130serves as an interface between a user and the computing device120and allows the user to interact with the computing device120by entering user inputs. The user input device130can be a keyboard, a mouse, a touchpad, a track pad, a touchscreen mounted on the display150, a hand gesture control device, or a virtual reality glove. The MRI-compatible ablation catheter of the laser ablation device140allows MR image of the patient's brain to be obtained by the MRI device110in real time during a LITT procedure. The real-time MR images of the patient's brain provide visualization and guidance to the surgeon that performs the LITT procedure.

In some embodiments, the system100can be used to validate ablation result in MRI-guided LITT procedures by comparing the pre-ablation fiber tracts passing through a target brain anatomy with the intra- or post-ablation fiber tracts passing through the target brain anatomy. To obtain the pre-ablation fiber tracts, MR data and a non-MR imaging data of a patient's brain is obtained by the MRI device110before the LITT procedure. As used herein, the non-MR imaging data refer to data that are not MR data or are of a different modality from the MR data. Non-MR imaging data can be used to identify fiber tracts of a patient's brain. The non-MR imaging data can be obtained by the MRI device110or another medical imaging device. An example of non-MR imaging data is DTI data. Non-MR imaging data can include other suitable imaging data, such as PET data. It is noted that while the embodiments of the present disclosure are described in conjunction with DTI data as the non-MR imaging data, other non-MR imaging data are also envisioned. Moreover, while MRI device110, MR data, and DTI imaging data are specifically mentioned in the some embodiments, in other embodiments, any suitable imaging data can be utilized for one or more operations of the method200, described below, such as operations202,204, and/or210. For example, the imaging data can be PET data, computed tomography (CT) data, radiographic data, x-ray data, MR data, DTI data, etc. In instances where the MR data and the non-MR imaging data are not obtained simultaneously, the MR data is aligned or co-registered with the non-MR imaging data by the computing device120through rigid registration, volume localization or direction cosines.

In order to extract fiber tracts passing through the target brain anatomy, the boundary of the target brain anatomy in the MR image is segmented into segments or regions by the computing device120based on a three-dimensional (3D) brain model. In some instances, the 3D brain model is received by the computing device120from a storage media or through wired or wireless connection to a server or a remote workstation. In some other instances, the 3D brain model can be stored in a storage device in the computing device120or a storage device retrievable by the computing device120. In some implementations, the 3D brain model is a shape-constrained deformable brain model. In some instances, the 3D brain model may be the brain model described in “Evaluation of traumatic brain injury patients using a shape-constrained deformable model,” by L. Zagorchev, C. Meyer, T. Stehle, R. Kneser, S. Young and J. Weese, 2011, inMultimodal Brain Image Analysisby Liu T., Shen D., Ibanez L., Tao X. (eds). MBIA 2011.Lecture Notes in Computer Science, vol. 7012. Springer, Berlin, Heidelberg, the entirety of which is hereby incorporated by reference. In some embodiments, the 3D brain model may be the deformable brain model described in U.S. Pat. No. 9,256,951, titled “SYSTEM FOR RAPID AND ACCURATE QUANTITATIVE ASSESSMENT OF TRAUMATIC BRAIN INJURY” or the shape-constrained deformable brain model described in U.S. Pat. App. Pub. No. 20150146951, titled “METHOD AND SYSTEM FOR QUANTITATIVE EVALUATION OF IMAGE SEGMENTATION,” each of which is hereby incorporated by reference in its entirety.

With the MR data and the DTI data aligned, the segments of the target brain anatomy can be transferred to the DTI space. By utilizing the segments and regions as the seed regions or starting regions in the DTI space, the computing device120can extract from the DTI data pre-ablation fiber tracts that pass through the segments or regions. As these fiber tracts represent neurons that run through the target brain anatomy, the pre-ablation fiber tracts represent the functional integrity and connectivity of the target brain anatomy before ablation. For the ease of reference, the pre-ablation DTI data can be referred to as first DTI data and the pre-ablation fiber tracts can be referred to as first fiber tracts. The image data of these pre-ablation fiber tracts can be output to the display150for display and stored in a storage device in the computing device120or a storage device retrievable by the computing device120. In some embodiments, the pre-ablation fiber tracts can be displayed along with the MR image of the patient's brain to aid a surgeon in planning an optimal ablation trajectory through the patient's brain so as to ensure sufficient ablation of the target brain anatomy while keeping non-target brain tissue brain damage, risk of complications, pain, discomfort, and permanent neurologic deficits at a low level.

In some embodiments, the laser ablation device140can be utilized to ablate the target brain anatomy. The ablation can remove or vaporize brain tissues at or near the target brain anatomy, creating void in the patient's brain or disrupting the fiber tracts passing through the target brain anatomy. In theory, if the entire target brain anatomy is removed by ablation, the portion of fiber tracts contained within the volume of the target brain anatomy should disappear. During or after ablation of the target brain anatomy, DTI data of patient's brain is obtained again. If the DTI data of the patient's brain is obtained during the ablation of the target brain anatomy, the fiber tracts extracted from such DTI data can be referred to as intra-ablation fiber tracts. If the DTI data of the patient's brain is obtained after the ablation of the target brain anatomy, the fiber tracts extracted from such DTI data can be referred to as post-ablation fiber tracts. For the ease of reference, the intra- or post-ablation fiber tracts can be referred to as second fiber tracts. Similarly, the DTI data obtained during or after the ablation is referred to as the intra- or post-ablation DTI data or second DTI data. Because the second ablation DTI data is not simultaneously obtained with the MR data, the computing device120is to align or co-register the MR data and the second DTI data by rigid registration, volume localization, or direction cosines. The computing device120can extract from the second DTI data the second fiber tracts that pass through the segments or regions. In some embodiments, the computing device120can compare the first fiber tracts and the second fiber tracts and output a graphical representation of the comparison to the display150. This graphical representation allows the surgeons to validate the efficacy of the ablation by visualizing the reduction of functional integrity and connectivity of the target brain anatomy. In some implementations, the computing device120can quantitatively compare the number, density or volume of the pre-ablation fiber tracts and that of the post-ablation fiber facts and the graphical representation can be a percentage of reduction in number, density or volume.

Referring now toFIG. 2, shown therein is a flowchart illustrating am exemplary method200of validating ablation result in a patient's brain. The method200includes operations202,204,206,208,210,212,214, and216. It is understood that the operations of method200may be performed in a different order than shown inFIG. 2, additional operations can be provided before, during, and after the operations, and/or some of the operations described can be replaced or eliminated in other embodiments. The operations of the method200can be carried out by a computing device in an ablation trajectory planning system, such as the computing device120of the system100. The method200will be described below with reference toFIGS. 3A, 3B, 3C, 3D, 4, 5, 6A, 6B, 7, and 8.

At operation202of the method200, MR data of the patient's brain is obtained by use of the MRI device110in communication with the computing device120. The computing device120can process the MR data of a patient's brain and output MR image data to the display150to display an MR image300shown inFIG. 3A. In some embodiments, the MR data includes T1 weighted magnetic resonance (T1W MR) data. While the MR image300shown inFIG. 3Ais a top view of the patient's brain. A person of ordinary skill in the art would understand that MR images of the patient's brain from other views can be obtained or derived by the computing device120as well. The MR data obtained at operation202includes MR data of anatomies in the patient's brain, including the MR data of a target brain anatomy (sometimes referred to as the anatomy) in the patient's brain.

At operation204of the method200, first imaging data of the patient's brain is obtained by use of the MRI device110in communication with the computing device120. In some embodiments represented byFIGS. 3B, 3C and 3Dand to be described in details below, the first imaging data include first DTI data. In those embodiments, the first imaging data can be referred to as the first DTI data. However, it is noted that, in some alternative embodiments, the first imaging data can include PET data, computed tomography (CT) data, radiographic data, and x-ray data that are obtained by use of the MRI device110or another medical imaging device. In some embodiments, the operation204can take place before, after or simultaneously with the operation202. Because the first DTI data of the patient's brain is obtained before a LITT ablation using the laser ablation device140, the first DTI data can also be referred to as the pre-ablation DTI data. The computing device120can receive the first DTI data from the MRI device110and output first DTI image data to the display150to display a DTI image310inFIG. 3B. LikeFIG. 3A, which is a top view of the patient's brain,FIG. 3Bis also a top view of the patient's brain. A person of ordinary skill in the art would understand that pre-ablation DTI images of the patient's brain from other views can be obtained or derived by the computing device120based on the first DTI data.

In embodiments where operations202and204take place simultaneously by use of the MRI device110, alignment or co-registration between the MR data obtained in operation202and the first DTI data obtained in operation204is not necessary as both data are automatically aligned. However, if operations202and204are not carried out simultaneously, the MR data are to be aligned or co-registered with the first DTI data through rigid registration, volume localization or direction cosines.FIGS. 3C and 3Dshow different representation of the aligned MR data and first DTI data.FIG. 3Cdemonstrates MR image data overlaid with the DTI image data and the resulting image320is a superposition overlay of the MR image300and the DTI image310.FIG. 3Dis a compartmented view of the overlay of the MR image300and DTI image310, showing both MR image compartments like compartment321and DTI image compartments like compartment322. The alternative representation inFIG. 3Dshows how the boundaries of brain anatomies in the MR image300are aligned/co-registered with those in the DTI image310.

At operation206of the method200, the MR data of the anatomy of the patient are segmented into segments or regions based on a 3D brain model. In some embodiments, the 3D brain model is a shape-constrained deformable brain model. In some instances, the 3D brain model may be the brain model described in “Evaluation of traumatic brain injury patients using a shape-constrained deformable model,” by L. Zagorchev, C. Meyer, T. Stehle, R. Kneser, S. Young and J. Weese, 2011, inMultimodal Brain Image Analysisby Liu T., Shen D., Ibanez L., Tao X. (eds). MBIA 2011.Lecture Notes in Computer Science, vol. 7012. Springer, Berlin, Heidelberg, the entirety of which is hereby incorporated by reference, in some instances, the 3D brain model may be the deformable brain model described in U.S. Pat. No. 9,256,951 titled “SYSTEM FOR RAPID AND ACCURATE QUANTITATIVE ASSESSMENT OF TRAUMATIC BRAIN INJURY” or the shape-constrained deformable brain model described in U.S. Pat. App. Pub. No. 20150146951, titled “METHOD AND SYSTEM FOR QUANTITATIVE EVALUATION OF IMAGE SEGMENTATION,” each of which is hereby incorporated by reference in its entirety. In some implementations, the 3D brain model is stored in the computing device120or a storage device or medium retrievable by the computing device120. In some embodiments, the 3D brain model is formed of a surface mesh that includes a plurality of triangularly shaped polygons, each of which includes three vertices and edges. In some other embodiments, the 3D brain model may be formed of polygons of other shapes. The 3D brain model may be used to delineate the boundaries of anatomies in the patient's brain, including the boundaries of the target brain anatomy, for example, an AHC, an amygdala, a hippocampus, a brain tumor, or a brain lesion. One of the ways the 3D brain model delineates the boundary of an anatomy is by representing the anatomy in segments or regions. Such segmentation is demonstrated byFIG. 4, where an MR image400of the patient includes a segmented representation410of the patient's AHC. As shown inFIG. 4, the segmented representation410includes several plate-like segments/regions that track the boundary of the patient's AHC. WhileFIG. 4shows the segmented representation410of the patient's AHC, people of ordinary skill in the art would understand that such segmentation can be done to all brain anatomies, including an AHC, an amygdala, a hippocampus, a brain tumor, or a brain lesion. In some implementations, the segmentation in operation208can be automatically carried out by the computing device120based on the 3D brain model without intervention of a user (e.g. a surgeon or a nurse), saving time and reducing variability introduced by different users.

Referring now toFIG. 5, shown therein is an MR image500of the patient's brain overlaid with fiber tracts510passing through the segmented representation410of the patient's AHC. At operation208of the method200, fiber tracts510passing through the patient's AHC are extracted. In some embodiments, the plate-like segments/regions in the segmented representation410can serve as the starting point or “seed” to track the fiber tracts510passing through them, allowing the fiber tracts510to be extracted at operation208. Because the fiber tracts510are extracted before ablation of the patient's AHC, the fiber tracts510can also be referred to as pre-ablation fiber tracts510or first fiber tracts510. The process to track and extract first fiber tracts from first DTI data can be further explained by reference toFIGS. 6A and 6B.FIG. 6Ais an MR image600showing fiber tracts635that pass through the segment615and segment625. In the example shown inFIG. 6A, both segments615and625are set as the starting point to track fiber tracts. As a result, the extracted fiber tracts635pass through both the segment615and the segment625. In some embodiments, to help a surgeon to visualize the distribution of the fiber tracts in a patient's brain, an MR image645of the patient's brain is also displayed along with the fiber tracts635and seed segments615and625, as shown in the MR image610inFIG. 6B.

At operation210of the method200, second imaging data of the patient's brain is obtained. In some embodiments to be described in details below, the second imaging data include second DTI data. In those embodiments, the second imaging data can be referred to as the second DTI data. However, it is noted that, in some alternative embodiments, the second imaging data can include other suitable imaging data obtained by use of the MRI device110or another medical imaging device. In some embodiments, operation210takes place simultaneously with or during ablation of the anatomy in the patient's brain (such as the patient's AHC) using the laser ablation device140. In those embodiments, the second DTI data obtained can be referred to as intra-ablation DTI data. In some other embodiments, operation210takes place after ablation of the anatomy in the patient's brain. In those embodiments, the second DTI data may be referred to as the post-ablation DTI data. For the ease of reference, the intra- or post-ablation DTI data can be referred to as the second DTI data. Reference is now made toFIG. 7. As shown in the MR image700, during the ablation, an ablation catheter of the laser ablation device140is inserted through the patient's skull750along a planned trajectory740to ablate the patient's AHC710, which includes the patient's amygdala720and hippocampus730. The ablation catheter can heat brain tissue in contact therewith to 40° C. or higher to damage, kill or vaporize them. In some instances, the ablation can sever, damage, or disrupt neurons going through the patient's AHC. In some other instances, the ablation can leave a void in the target brain anatomy, such as the patient's AHC in this example.

At operation212of the method200, the MR data obtained in the operation202is aligned or co-registered with the second DTI data (intra- or post-ablation DTI data) obtained in the operation210. In embodiments of the present disclosure, MR data and DTI data that are not obtained simultaneously need to be aligned or co-registered in order for a segmented representation of a brain anatomy to be transferred to the DTI space to serve as the seed for extraction. In embodiments represented by the flow chart inFIG. 2, the second DTI data is obtained after the ablation and is not obtained simultaneously with the MR data. Therefore, the second DTI data is to be aligned or co-registered with the MR data through rigid registration, volume localization or direction cosines at operation212.

Reference is now made to the MR image800inFIG. 8. At operation214of the method200, second fiber tracts810passing through the patient's AHC are extracted. Because the second fiber tracts810can be extracted during or after the ablation, the second fiber tracts810may sometimes be referred to as the intra- or post-ablation fiber tracts810. As shown inFIG. 8, the ablation by use of the ablation catheter of the laser ablation device140severs and disrupts several fiber tracts that pass through the patient's AHC, leaving a void820, where diffusion of water molecules is disrupted or restricted.

At operation216of the method200, the computing device120compares the second fiber tracts810to the first fiber tracts510and outputs a graphical representation of the comparison between the second fiber tracts810and the first fiber tracts to the display150. In some embodiments, the graphical representation may be the first fiber tracts510overlaid with the second fiber tracts810. In some embodiments, the first fiber tracts510and the second fiber tracts810are displayed simultaneously but are assigned with different or contrasting colors. In some implementations, the computing device120can quantitatively compare the number of the first fiber tracts510and the number of the second fiber tracts810and output a percentage of reduction in the number of fiber tracts. In some other implementations, the computing device120can compare the volume occupied by the first fiber tracts510and the volume occupied by the second fiber tracts810and output a percentage of decrease in fiber tracts-occupied volumes. As these first and second fiber tracts510and810represent neurons that go through the patient's AHC, the graphical representation of their comparison can indicate the reduction of functional integrity and connectivity of the patient's AHC brought about by the ablation. This way, the method200disclosed in the present disclosure advantageously provides a method to assess and validate the ablation result as soon as the ablation is completed. The graphical representation can include a visual representation, a numerical representation, and/or a combination thereof.

If the ablation result is not satisfactory, the surgeon can immediately perform further ablation procedures in the same LITT operation until the ablation result is satisfactory. In some embodiments, the computing device120may compare the ablation result to normative or statistical data to determine if the ablation result is satisfactory. Taking SLAH for example, the clinical statistics reveals that 80% of patients with ablation of at least 70% of either the amygdala or hippocampus and ablation of at least 50% of the other structure are seizure-free after the SLAH procedures. If the comparison of the second fiber tracts to the first fiber tracts indicates that more than 30% of the connectivity of the patient's AHC remains. The computing device120may determine that less than 70% of the AHC is ablated and suggest further ablation.