Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery
Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each ele...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sparks, Rachel [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2017 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of computer assisted radiology and surgery - Berlin : Springer, 2006, 12(2017), 8 vom: 15. Juni, Seite 1245-1255 |
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| Übergeordnetes Werk: |
volume:12 ; year:2017 ; number:8 ; day:15 ; month:06 ; pages:1245-1255 |
| Links: |
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| DOI / URN: |
10.1007/s11548-017-1628-z |
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| Katalog-ID: |
SPR02071131X |
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| 100 | 1 | |a Sparks, Rachel |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery |
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| 520 | |a Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. | ||
| 650 | 4 | |a Image-guided interventions |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Neurosurgery |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Computerized decision support |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Trajectory planning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Epilepsy |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Vakharia, Vejay |4 aut | |
| 700 | 1 | |a Rodionov, Roman |4 aut | |
| 700 | 1 | |a Vos, Sjoerd B. |4 aut | |
| 700 | 1 | |a Diehl, Beate |4 aut | |
| 700 | 1 | |a Wehner, Tim |4 aut | |
| 700 | 1 | |a Miserocchi, Anna |4 aut | |
| 700 | 1 | |a McEvoy, Andrew W. |4 aut | |
| 700 | 1 | |a Duncan, John S. |4 aut | |
| 700 | 1 | |a Ourselin, Sebastien |4 aut | |
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10.1007/s11548-017-1628-z doi (DE-627)SPR02071131X (SPR)s11548-017-1628-z-e DE-627 ger DE-627 rakwb eng Sparks, Rachel verfasserin aut Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. Image-guided interventions (dpeaa)DE-He213 Neurosurgery (dpeaa)DE-He213 Computerized decision support (dpeaa)DE-He213 Trajectory planning (dpeaa)DE-He213 Epilepsy (dpeaa)DE-He213 Vakharia, Vejay aut Rodionov, Roman aut Vos, Sjoerd B. aut Diehl, Beate aut Wehner, Tim aut Miserocchi, Anna aut McEvoy, Andrew W. aut Duncan, John S. aut Ourselin, Sebastien aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 12(2017), 8 vom: 15. Juni, Seite 1245-1255 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:12 year:2017 number:8 day:15 month:06 pages:1245-1255 https://dx.doi.org/10.1007/s11548-017-1628-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2017 8 15 06 1245-1255 |
| spelling |
10.1007/s11548-017-1628-z doi (DE-627)SPR02071131X (SPR)s11548-017-1628-z-e DE-627 ger DE-627 rakwb eng Sparks, Rachel verfasserin aut Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. Image-guided interventions (dpeaa)DE-He213 Neurosurgery (dpeaa)DE-He213 Computerized decision support (dpeaa)DE-He213 Trajectory planning (dpeaa)DE-He213 Epilepsy (dpeaa)DE-He213 Vakharia, Vejay aut Rodionov, Roman aut Vos, Sjoerd B. aut Diehl, Beate aut Wehner, Tim aut Miserocchi, Anna aut McEvoy, Andrew W. aut Duncan, John S. aut Ourselin, Sebastien aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 12(2017), 8 vom: 15. Juni, Seite 1245-1255 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:12 year:2017 number:8 day:15 month:06 pages:1245-1255 https://dx.doi.org/10.1007/s11548-017-1628-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2017 8 15 06 1245-1255 |
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10.1007/s11548-017-1628-z doi (DE-627)SPR02071131X (SPR)s11548-017-1628-z-e DE-627 ger DE-627 rakwb eng Sparks, Rachel verfasserin aut Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. Image-guided interventions (dpeaa)DE-He213 Neurosurgery (dpeaa)DE-He213 Computerized decision support (dpeaa)DE-He213 Trajectory planning (dpeaa)DE-He213 Epilepsy (dpeaa)DE-He213 Vakharia, Vejay aut Rodionov, Roman aut Vos, Sjoerd B. aut Diehl, Beate aut Wehner, Tim aut Miserocchi, Anna aut McEvoy, Andrew W. aut Duncan, John S. aut Ourselin, Sebastien aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 12(2017), 8 vom: 15. Juni, Seite 1245-1255 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:12 year:2017 number:8 day:15 month:06 pages:1245-1255 https://dx.doi.org/10.1007/s11548-017-1628-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2017 8 15 06 1245-1255 |
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10.1007/s11548-017-1628-z doi (DE-627)SPR02071131X (SPR)s11548-017-1628-z-e DE-627 ger DE-627 rakwb eng Sparks, Rachel verfasserin aut Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. Image-guided interventions (dpeaa)DE-He213 Neurosurgery (dpeaa)DE-He213 Computerized decision support (dpeaa)DE-He213 Trajectory planning (dpeaa)DE-He213 Epilepsy (dpeaa)DE-He213 Vakharia, Vejay aut Rodionov, Roman aut Vos, Sjoerd B. aut Diehl, Beate aut Wehner, Tim aut Miserocchi, Anna aut McEvoy, Andrew W. aut Duncan, John S. aut Ourselin, Sebastien aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 12(2017), 8 vom: 15. Juni, Seite 1245-1255 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:12 year:2017 number:8 day:15 month:06 pages:1245-1255 https://dx.doi.org/10.1007/s11548-017-1628-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2017 8 15 06 1245-1255 |
| allfieldsSound |
10.1007/s11548-017-1628-z doi (DE-627)SPR02071131X (SPR)s11548-017-1628-z-e DE-627 ger DE-627 rakwb eng Sparks, Rachel verfasserin aut Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. Image-guided interventions (dpeaa)DE-He213 Neurosurgery (dpeaa)DE-He213 Computerized decision support (dpeaa)DE-He213 Trajectory planning (dpeaa)DE-He213 Epilepsy (dpeaa)DE-He213 Vakharia, Vejay aut Rodionov, Roman aut Vos, Sjoerd B. aut Diehl, Beate aut Wehner, Tim aut Miserocchi, Anna aut McEvoy, Andrew W. aut Duncan, John S. aut Ourselin, Sebastien aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 12(2017), 8 vom: 15. Juni, Seite 1245-1255 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:12 year:2017 number:8 day:15 month:06 pages:1245-1255 https://dx.doi.org/10.1007/s11548-017-1628-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2017 8 15 06 1245-1255 |
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Sparks, Rachel @@aut@@ Vakharia, Vejay @@aut@@ Rodionov, Roman @@aut@@ Vos, Sjoerd B. @@aut@@ Diehl, Beate @@aut@@ Wehner, Tim @@aut@@ Miserocchi, Anna @@aut@@ McEvoy, Andrew W. @@aut@@ Duncan, John S. @@aut@@ Ourselin, Sebastien @@aut@@ |
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If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. 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Sparks, Rachel |
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Sparks, Rachel misc Image-guided interventions misc Neurosurgery misc Computerized decision support misc Trajectory planning misc Epilepsy Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery |
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Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery Image-guided interventions (dpeaa)DE-He213 Neurosurgery (dpeaa)DE-He213 Computerized decision support (dpeaa)DE-He213 Trajectory planning (dpeaa)DE-He213 Epilepsy (dpeaa)DE-He213 |
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misc Image-guided interventions misc Neurosurgery misc Computerized decision support misc Trajectory planning misc Epilepsy |
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Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery |
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Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery |
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Sparks, Rachel |
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International journal of computer assisted radiology and surgery |
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Sparks, Rachel Vakharia, Vejay Rodionov, Roman Vos, Sjoerd B. Diehl, Beate Wehner, Tim Miserocchi, Anna McEvoy, Andrew W. Duncan, John S. Ourselin, Sebastien |
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anatomy-driven multiple trajectory planning (admtp) of intracranial electrodes for epilepsy surgery |
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Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery |
| abstract |
Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. © The Author(s) 2017 |
| abstractGer |
Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. © The Author(s) 2017 |
| abstract_unstemmed |
Purpose Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions ADMTP efficiently compute safe and surgically feasible electrode trajectories. © The Author(s) 2017 |
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| container_issue |
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| title_short |
Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery |
| url |
https://dx.doi.org/10.1007/s11548-017-1628-z |
| remote_bool |
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| author2 |
Vakharia, Vejay Rodionov, Roman Vos, Sjoerd B. Diehl, Beate Wehner, Tim Miserocchi, Anna McEvoy, Andrew W. Duncan, John S. Ourselin, Sebastien |
| author2Str |
Vakharia, Vejay Rodionov, Roman Vos, Sjoerd B. Diehl, Beate Wehner, Tim Miserocchi, Anna McEvoy, Andrew W. Duncan, John S. Ourselin, Sebastien |
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| doi_str |
10.1007/s11548-017-1628-z |
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2024-07-03T17:46:23.483Z |
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| score |
7.397691 |