MCML_GPU

the MCML with cuda acceleration which modified by Benjamin Kao and have many purpose

Prepare
Execute the Program
Input Files
- sim_set.json
- input.txt
Output Files
output.txt
Summary.json
pathlength_SDS_#.txt or pathlength_SDS_#.bin
A_rz_SDS_#.txt
A0_z_SDS_#.txt
average_pathlength.txt
Update History
Reference

Prepare

Install Nvidia Cuda driver on the computer.
type make to make the program

Execute the Program

Use this command to run this program:
./MCML_GPU sim_set.json input.txt output.txt <option(s)>

sim_set.json A .json file for setting parameters.
input.txt The input file to set optical parameters for each layer.
output.txt The output filename for reflectance.
options
- -h Print the helping information.
- -R Replay the detected photons after first simulation, to get the pathlength in each layer or absorbance matrix.
- -A Output the absorbance array for each detector in A_rz_SDS_#.txt and A0_z_SDS_#.txt.
- -P Output the pathlength for each photon in pathlength_SDS_#.txt, otherwise output the calculated average pathlength in average_pathlength.txt.
- -AP Calaulate and output the average pathlength.
- -B Output the pathlength file in binary format for faster speed.
- -G # Select which GPU to use.
- -LS List the GPUs in the computer.

Input Files

sim_set.json

Use a .json file to set the parameter of simulations

number_simulation: How many simulations to run, usually set to 1.
number_photons: How many photons in one simulaiton
number_layers: How many layer the tissue is
detector_reflectance: The reflectance rate of the detector. If no reflectance, set to 0.
upper_n: The refractive index for upper layer outside the tissue
lower_n: The refractive index for lower layer outside the tissue
source_probe_oblique: The source probe is oblique or not. If this is 1, then the simulation will use fiber detection mode; otherwise, use ring detection mode.
detector_probe_oblique: The detector probes are oblique or not
probes: The parameters for the fibers
source: Parameters for source fiber
- NA: Numerical aperture of source fiber
- radius: The radius of source fiber, in cm
- angle: The angle of source fiber, in degree, toward +x direction
num_SDS: Number of detector fibers
detectors: Parameters for detector fibers, shold be an array of the same size as "num_SDS"
- pos: The position (distance of the center of source fiber to the center of this detector fiber), in cm
- NA: Numerical aperture of this detector fiber
- radius: The radius of this detector fiber, in cm
- angle: The angle of this detector fiber, in degree, toward -x direction
Example:

{
  "number_simulation": 1,
  "number_photons": 100000000,
  "number_layers": 5,
  "detector_reflectance": 0.0,
  "upper_n": 1.457,
  "lower_n": 1.457,
  "source_probe_oblique": 1,
  "detector_probe_oblique": 1,
  "probes": {
    "source": {
      "NA": 0.37,
      "radius": 0.075,
      "angle": 0
    },
    "num_SDS": 3,
    "detectors": [
      {
        "pos": 0.8,
        "NA": 0.13,
        "radius": 0.001,
        "angle": 0
      },
      {
        "pos": 1.5,
        "NA": 0.12,
        "radius": 0.02,
        "angle": 0
      },
      {
        "pos": 3.0,
        "NA": 0.12,
        "radius": 0.02,
        "angle": 0
      }
    ]
  }
}

input.txt

Set the optical parameters for each layer.

Rules:
- Each line for one simulation.
- Arrange in "height(cm) mu_a(1/cm) mu_s(1/cm) n g" for one layer.
- The last layer should have no height.
- 5 parameters for 1 layer, so n layer should be 5n-1 columns.
- The number of layers should be the same as "number_layers" in sim_set.json
Example: 0.27 0.3201 35.2861 1.4 0 0.65 0.2954 30.5833 1.4 0 0.2747 130.6813 1.4 0 This is a 3 layer tissue, parameters for each layer are setting as below:

layer	height	mu_a	mu_s	n
1	0.27	0.3201	35.2861	1.4
2	0.65	0.2954	30.5833	1.4
3	N/A	0.2747	130.6813	1.4

Output Files

output.txt

The reflectance collected by each SDS. One column is one SDS.

Summary.json

Store the summary of this simulaiton.

num_photon: Number of total simulated photon.
sim_time: The time cost to do the (first) simulate, in secs
replay_time: The time cost to do the second simulate and output the pathlength array, in secs
sim_speed(photons/s): The average speed of simulation.
sim_GPU: The name of GPU.
each_photon_weight: The initial weight of each photon at launch, when calculate WMC, the photon detected weight should be divided by this number.
number_layers: The number of tissue layers.
detect_mode: Use ring detector to collect photons, or using little fiber detector.
num_SDS: The number of derector fibers.
SDS_detected_number: The number of photon detected by each detector. The number of elements is the same as num_SDS in sim_set.json.

pathlength_SDS_#.txt or pathlength_SDS_#.bin

The pathlength information for each photon collected by SDS #, can be used to perform White MC.

Rules:
- Each row is one photon.
- The first column is the photon's weight when it been detected.
- The second column to the second-last columns are the photon's pathlength (cm) in each tissue layer.
- The last column is how many times did the photon scattered.
Example:

7155930	0.164281	2.99355	4.65341	570
6295.91	0.0202396	0.570237	0.312309	3036
827985	0.108664	2.41008	4.92397	1120
325157	0.225788	1.57377	4.2767	873
17125.5	0.0720376	1.84378	2.97216	2504
27530.4	0.106237	1.10873	1.53136	2460

Those are 6 detected photon with properties below:

weight	PL in L1	PL in L2	PL in L3	scatter
7155930	0.164281	2.99355	4.65341	570
6295.91	0.0202396	0.570237	0.312309	3036
827985	0.108664	2.41008	4.92397	1120
325157	0.225788	1.57377	4.2767	873
17125.5	0.0720376	1.84378	2.97216	2504
27530.4	0.106237	1.10873	1.53136	2460

A_rz_SDS_#.txt

The absorbance array (except the first scatter event) for SDS #.
In the format of a 2-D array, with rows are z-directional and columns are r-directional.

A0_z_SDS_#.txt

The absorbance array the first scatter event along z-axis for SDS #.
In the format of a 1-D array, with rows are z-directional.

average_pathlength.txt

The average pathlength (cm) for each detector in each layer.
Start from the PL in 1st layer for 1st detector, in 2nd layer for 1st detector......
If there are 4 detectors and 5 layers of tissue, than there will be 20 columns.

Update History

K1.01

updata: 2019/10/12
Add the -A option to control output absorbance array.
Change the original -A (average pathlength) option to -AP.

K2.01

updata: 2019/12/03
Add source_probe_oblique, detector_probe_oblique options in the setting file.
Add the oblique mode in simulation, which will use fiber detection mode.
Read and output GPU information.
Set the number of blocks and threads according to the GPU information.

K2.02

updata: 2020/02/22
Add binary output mode (-B) for absorbance array (-A).

K2.03

updata: 2020/02/23
Implement source NA and radius setting.

K3.01

updata: 2020/02/23
Can choose which GPU to run the simulation (-G #)
Can list the GPUs in the computer (-LS)

K3.02

updata: 2020/06/13
Little update in decide the scatter angle of the photon to improve speed.

Reference

The original CUDAMCML

code link

Erik Alerstam, Tomas Svensson, and Stefan Andersson-Engels. "Parallel Computing with Graphics Processing Units for High-Speed Monte Carlo Simulation of Photon Migration." Journal of Biomedical Optics 13(6), 060504 (2008)

TODO

Check Why I can't get the same reflectance while the musp is the same!
Setting of detector n different from outer n
Change the hight setting of buttom layer to true Inf
NA and radius setting for oblique probe
Improve the oblique probe performance on linux
set n_dr, n_dz in setup file
add multiple GPU
try to improve performance by increasing the num_step
add a preview function to draw the settings.

kaoben2731 / mcml_gpu Goto Github PK

mcml_gpu's Introduction