1. A MAP detector system including:
A. a plurality of Viterbi operators;
B. a controller for operating the system in a parallel mode in which the results produced by the plurality of Viterbi operators are used as soft decision data, and in a serial error recovery mode in which the results produced by a selected set of the Viterbi operators are used as the soft decision data;
C. a buffer for retaining input sampled data;
D. buffer addressing means for controlling the supplying of input sampled data to the plurality of Viterbi operators, the addressing means providing C length sequences of data in the order in which the data are supplied to the buffer to the plurality of Viterbi operators in the parallel mode and to the selected set of Viterbi operators in the serial error recovery mode.
2. The MAP detector system of claim 1 wherein the controller in the serial error recovery mode refrains from resetting the selected forward Viterbi operator.
3. The MAP detector system of claim 1 wherein the controller in the parallel mode periodically resets the plurality of Viterbi operators.
4. The MAP detector system of claim 1 wherein the buffer addressing means includes an incrementerdecrementer that increments and decrements an address count during the serial error recovery mode.
5. The MAP detector system of claim 4 wherein the incrementerdecrementer increments the address count during the parallel mode.
6. A method of operating a MAP detector system including the steps of:
a. operating a plurality of Viterbi operators in parallel to produce on-the-fly soft decision data for a block of input sampled data;
b. if the soft decision data does not converge, operating the plurality of Viterbi operators in a serial error recovery mode in which a selected set of one forward and two reverse Viterbi operators produce the soft decision data for the entire block.
7. The method of operating the MAP detector system of claim 6 further including, in the step of operating the plurality of Viterbi operators in the serial error recovery mode, providing the block of input sampled data to the selected set of Viterbi operators as an in order sequence of C length data sequences without resetting the selected forward Viterbi operator.
8. The method of operating the MAP detector system of claim 7 further including, in the step of providing the block of input sampled data, addressing locations of a buffer containing the input sampled data in the order in which the data are supplied to the buffer for a first predetermined number of clock cycles that starts with a clock cycle in which data are supplied to the selected set of Viterbi operators and in a reverse order for a second predetermined number of clock cycles that ends one clock cycle before data are again supplied to the selected set of Viterbi operators.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method of assessing stenosis severity for a patient, comprising:
obtaining patient information relevant to assessing stenosis severity, including anatomical imaging data of the patient;
identifying the existence of any lesions of concern in a vessel of the Patient that have an irregular shape from the anatomical imaging data;
inputting the anatomical imaging data of the patient into a computer system;
utilizing a processor associated with the computer system and task specific software to generate a three dimensional model from the patient anatomical imaging data of at least one lesion of concern within the vessel, wherein the generated three dimensional model includes a vessel inlet, the at least one lesion of concern, and at least one vessel outlet;
utilizing the processor and task specific software to create a series of comparative two dimensional lesion specific models having conditions that correspond to the three dimensional model, wherein each of the series of comparative two dimensional models represents a vessel having one or more regular shaped lesions within the vessel and wherein each of the two dimensional lesion specific models includes differently configured lesions each representing a different known level of stenosis severity;
mapping the three dimensional model to one two dimensional model of the series of comparative two dimensional models;
deriving a quantitative measure of stenosis severity of the patient’s coronary artery disease based on the mapped two dimensional model.
2. The method of claim 1, wherein the task specific software employs segmentation of the patient anatomical imaging data to create the three dimensional model from the patient anatomical imagine data.
3. The method of claim 1, wherein the three dimensional model consists of the at least one lesion of concern located within the vessel and wherein the vessel includes a section of vessel between the vessel inlet and the lesion and a section of vessel between the lesion and the vessel outlet.
4. The method of claim 1, wherein the step of mapping further comprises performing computational fluid dynamics on the three dimensional model and each of the series of comparative two dimensional models.
5. The method of claim 4, wherein the step of performing computational fluid dynamics involves selecting one or more of the following parameters: blood pressure at the inlet, a plurality of vessel diameters along the three dimensional model, a plurality of vessel diameters along the two dimensional models, blood viscosity, blood velocity, blood flow rate, vessel elasticity, vessel calcification, and differences between two dimensional models derived from a plurality of phases of the heartbeat.
6. The method of claim 1, further comprising:
utilizing task specific software to preprocess the patient anatomical imaging data prior to the step of generating the three dimensional model.
7. The method of claim 6, wherein the step of preprocessing the anatomical imaging data includes determining whether the anatomical image data for the at least one lesion of interest, includes multiple outlets; and
identifying a single inlet and a single virtual outlet from which to create the three dimensional model.
8. The method of claim 7, further comprising:
reducing the multiple outlets on the anatomical imagine data for the vessel including the at least one lesion of interest to a single virtual outlet for further processing.
9. The method of claim 8, further comprising:
manipulating the anatomical imaging data to reduce the multiple outlets to the single virtual outlet by virtually closing off any flow paths to the other multiple outlets that are not the identified single virtual outlet such that the anatomical imaging data for the at least one vessel of interest has the vessel inlet and the vessel outlet.
10. The method of claim 6, further comprising:
smoothing the anatomical image data such that a centerline of a vessel containing the at least one lesion of interest is linear and such that the vessel inlet and the vessel outlet are perpendicular to the centerline.
11. The method of 6, wherein the vessel inlet and the vessel outlet may be assigned a circular shape with each having a diameter that corresponds to an area of the opening in the anatomical image data.
12. The method of claim 1 wherein the step of creating a plurality of comparative two dimensional models includes utilizing a plurality of control parameters to assist in having the two dimensional comparative models properly reflect the three dimensional model.
13. The method of claim 12, wherein the plurality of control parameters include one or more of the following: lesion location, lesion length, bump height, inlet radius and outlet radius.
14. The method of claim 12, wherein the step of mapping the three dimensional model to one of the two dimensional models includes predicting a pressure drop across the lesion of interest from computational fluid dynamics.
15. The method of claim 14, wherein the predicted pressure drop across the three dimensional model and the plurality of two dimensional models is a statistical result from a plurality of calculations using a range of possible control parameters including: inlet blood flow, blood pressure, blood velocity, and blood density.
16. The method of claim 1, wherein the quantitative measure of stenosis severity is reported as at least one of the following: effective percent reduction in vessel diameter or effective percent reduction in vessel area.
17. The method of claim 1, wherein the patient information consists of one or more of Cardiac Computed Tomography Angiography (CCTA) data sets.
18. The method of claim 1, wherein the patient information consists of one or more data sets derived from one or more of the following: magnetic resonance angiography (MRA), ultrasound (US), digital subtraction angiography (DSA), or rotational coronary angiography (RoCA).