# US 10,482,597 B2 · Automated method for tissue-based contrast media arrival detection for dynamic contrast enhanced MRI > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,482,597 B2 - Document ID: US10482597B2 - Status: Granted - Kind: B2 - Application number: 16/046,208 - Filed: 2018-07-26 - Published: 2018-12-06 - Granted: 2019-11-19 - Earliest claimed: 2014-09-26 - Assignees: Koninklijke Philips N.V. - Applicants: Koninklijke Philips N.V. - Inventors: Naira Muradyan - Web page: https://patentgrep.com/patent/US10482597B2 ## CPC - G06V 10/30: Noise filtering - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - G06T 7/0012: {Biomedical image inspection} - G06T 7/0016: {involving temporal comparison} - G06F 2218/02: Preprocessing - G06T 2200/04: involving 3D image data - G06T 2207/10096: Dynamic contrast-enhanced magnetic resonance imaging [DCE-MRI] - G06T 2207/30104: Vascular flow; Blood flow; Perfusion - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system and method for automated contrast arrival detection in temporally phased images or datasets of tissues effectively determines contrast arrival in regions that are substantially free of arteries. A plurality of tissue voxels in a plurality of temporally phased images are identified as a function of voxel enhancement characteristics associated with discrete tissue voxels. A processor/process computes average enhancement characteristics from the plurality of identified tissue voxels. The average enhancement characteristics are compared with predetermined average enhancement characteristics associated with contrast media arrival phases. Contrast media arrival phases in the temporally phased images are provided based on the comparison. ## Claims ### Claim 1 A method for automated contrast arrival detection in temporally phased images or datasets of tissues, the method comprising: (a) identifying a plurality of tissue voxels in a plurality of the temporally phased images as a function of predetermined enhancement characteristics associated with discrete tissue voxels that include baseline signal intensity values and late phase signal intensity values to derive a signal intensity enhancement value for each of the voxels; (b) comparing, with a processor, the signal intensity enhancement value to a signal intensity enhancement threshold for the tissue voxels identified in (a); (c) establishing a plurality of candidate arrival phases based upon the comparing in (b); and (d) identifying contrast media arrival phases in the temporally phased images from the candidate arrival phases established in (c). ### Claim 2 (depends on 1) The method of claim 1, further comprising storing at least one of the contrast media arrival phases in a data storage device. ### Claim 3 (depends on 2) The method of claim 2, further comprising outputting at least one of the contrast media arrival phases to an output device. ### Claim 4 (depends on 1) The method of claim 1, wherein the tissues in the temporally phased images or datasets of tissues are substantially free of arteries. ### Claim 5 (depends on 1) The method of claim 1, wherein the identifying in (a) identifies the plurality of tissue voxels comprising at least 20% of the voxels in the temporally phased images or datasets of tissues. ### Claim 6 A method for automated contrast arrival detection in temporally phased images or datasets of tissues, the method comprising: (a) identifying a plurality of tissue voxels in a plurality of the temporally phased images as a function of predetermined enhancement characteristics associated with discrete tissue voxels; (b) computing, with a processor, signal intensity curve madness for the tissue voxels identified in (a); (c) comparing the signal intensity curve madness computed for the tissue voxels in (b) to a smoothness threshold; and (d) identifying contrast media arrival phases in the temporally phased images based on the comparing in (c). ### Claim 7 (depends on 6) The method of claim 6, further comprising storing at least one of the contrast media arrival phases in a data storage device. ### Claim 8 (depends on 7) The method of claim 7, further comprising outputting at least one of the contrast media arrival phases to an output device. ### Claim 9 (depends on 6) The method of claim 6, wherein the tissues in temporally phased images or datasets of tissues are substantially free of arteries. ### Claim 10 (depends on 6) The method of claim 6, wherein the identifying in (a) identifies the plurality of tissue voxels comprising at least 20% of the voxels in the temporally phased images or datasets of tissues. ### Claim 11 A device for automated contrast arrival detection in temporally phased images or datasets of tissues, the device comprising: a non-transitory, computer-readable medium comprising program instructions; and a processor configured by the program instructions to: (a) identify a plurality of tissue voxels in a plurality of the temporally phased images as a function of predetermined enhancement characteristics associated with discrete tissue voxels that include baseline signal intensity values and late phase signal intensity values to derive a signal intensity enhancement value for each of the voxels; (b) compare the signal intensity enhancement value to a signal intensity enhancement threshold for the tissue voxels identified in (a); (c) establish a plurality of candidate arrival phases based upon the comparison in (b); and (d) identify contrast media arrival phases in the temporally phased images from the candidate arrival phases established in (c). ### Claim 12 (depends on 11) The device of claim 11, wherein the processor is further configured by the program instructions to store at least one of the contrast media arrival phases in a data storage device. ### Claim 13 (depends on 12) The device of claim 12, wherein the processor is further configured by the program instructions to output at least one of the contrast media arrival phases to an output device. ### Claim 14 (depends on 11) The device of claim 11, wherein the tissues in the temporally phased images or datasets of tissues are substantially free of arteries. ### Claim 15 (depends on 11) The device of claim 11, wherein the identification in (a) identifies the plurality of tissue voxels comprising at least 20% of the voxels in the temporally phased images or datasets of tissues. ### Claim 16 A device for automated contrast arrival detection in temporally phased images or datasets of tissues, the device comprising: a non-transitory, computer-readable medium comprising program instructions; and a processor configured by the program instructions to: (a) identify a plurality of tissue voxels in a plurality of the temporally phased images as a function of predetermined enhancement characteristics associated with discrete tissue voxels; (b) compute a signal intensity curve madness for the tissue voxels identified in (a); (c) compare the signal intensity curve madness computed for the tissue voxels in (b) to a smoothness threshold; and (d) identify contrast media arrival phases in the temporally phased images based on the comparison in (c). ### Claim 17 (depends on 16) The device of claim 16, wherein the processor is further configured by the program instructions to store at least one of the contrast media arrival phases in a data storage device. ### Claim 18 (depends on 17) The device of claim 17, wherein the processor is further configured by the program instructions to output at least one of the contrast media arrival phases to an output device. ### Claim 19 (depends on 16) The device of claim 16, wherein the tissues in temporally phased images or datasets of tissues are substantially free of arteries. ### Claim 20 (depends on 16) The device of claim 16, wherein the identification in (a) identifies the plurality of tissue voxels comprising at least 20% of the voxels in the temporally phased images or datasets of tissues. ### Claim 21 A non-transitory, computer-readable medium comprising program instructions that, when executed by a processor, causes the processor to: (a) identify a plurality of tissue voxels in a plurality of the temporally phased images as a function of predetermined enhancement characteristics associated with discrete tissue voxels that include baseline signal intensity values and late phase signal intensity values to derive a signal intensity enhancement value for each of the voxels; (b) compare the signal intensity enhancement value to a signal intensity enhancement threshold for the tissue voxels identified in (a); (c) establish a plurality of candidate arrival phases based upon the comparison in (b); and (d) identify contrast media arrival phases in the temporally phased images from the candidate arrival phases established in (c). ### Claim 22 A non-transitory, computer-readable medium comprising program instructions that, when executed by a processor, causes the processor to: (a) identify a plurality of tissue voxels in a plurality of the temporally phased images as a function of predetermined enhancement characteristics associated with discrete tissue voxels; (b) compute a signal intensity curve madness for the tissue voxels identified in (a); (c) compare the signal intensity curve madness computed for the tissue voxels in (b) to a smoothness threshold; and (d) identify contrast media arrival phases in the temporally phased images based on the comparison in (c).