[Gate-users] Using Medcon to convert NCAT to uint16

Siti Salasiah MusNMey ctsalasiah1950 at gmail.com
Fri Jan 25 07:05:48 CET 2013


Hi & Good day,
This is my first time using Vgate. I would like to convert NCAT phantom to
uint16 using Medcon. Is my definition  right below? The image should be
64x64x64(slices). Why does the generated image is 64x64x2 (each slice).

Thank you so much.
-----------------------------------------------------------------------------------------------------------------------------

gate at vgate:~/Simulation$ medcon -f cuba1_act_av.bin -c intf -i

(process:3511): (X)MedCon-WARNING **: Enabling negative pixels & disabling
quantification
-------------------------------------------------------------------------------
INTERACTIVE  PROCEDURE
-------------------------------------------------------------------------------

Filename: cuba1_act_av.bin

Number of images [1]? 64
General header offset to binary data [0 bytes]?
Image   header offset to binary data [0 bytes]?
Image   header repeated before each image [No]?
Swap the pixel bytes [No]?
Same characteristics for all images [Yes]?

ALL IMAGES
----------
Absolute offset in bytes [0]?
Image columns [0]? 64
Image rows    [0]? 64
Pixel data type:

  1  ->  bit
  2  ->  Int8   3 -> Uint8
  4  ->  Int16  5 -> Uint16
  6  ->  Int32  7 -> Uint32
 10  ->  float 11 -> double
 12  ->  ascii
 20  ->  RGB

Your choice [4]? 5

-------------------------------------------------------------------------------
SUMMARY OF IMAGE LAYOUT
-------------------------------------------------------------------------------
#------------------#
! Image #1         ! (64x64x2)
! Image #2         ! (64x64x2)
! Image #3         ! (64x64x2)
! Image #4         ! (64x64x2)
! Image #5         ! (64x64x2)
! Image #6         ! (64x64x2)
! Image #7         ! (64x64x2)
! Image #8         ! (64x64x2)
! Image #9         ! (64x64x2)
! Image #10        ! (64x64x2)
! Image #11        ! (64x64x2)
! Image #12        ! (64x64x2)
! Image #13        ! (64x64x2)
! Image #14        ! (64x64x2)
! Image #15        ! (64x64x2)
! Image #16        ! (64x64x2)
! Image #17        ! (64x64x2)
! Image #18        ! (64x64x2)
! Image #19        ! (64x64x2)
! Image #20        ! (64x64x2)
! Image #21        ! (64x64x2)
! Image #22        ! (64x64x2)
! Image #23        ! (64x64x2)
! Image #24        ! (64x64x2)
! Image #25        ! (64x64x2)
! Image #26        ! (64x64x2)
! Image #27        ! (64x64x2)
! Image #28        ! (64x64x2)
! Image #29        ! (64x64x2)
! Image #30        ! (64x64x2)
! Image #31        ! (64x64x2)
! Image #32        ! (64x64x2)
! Image #33        ! (64x64x2)
! Image #34        ! (64x64x2)
! Image #35        ! (64x64x2)
! Image #36        ! (64x64x2)
! Image #37        ! (64x64x2)
! Image #38        ! (64x64x2)
! Image #39        ! (64x64x2)
! Image #40        ! (64x64x2)
! Image #41        ! (64x64x2)
! Image #42        ! (64x64x2)
! Image #43        ! (64x64x2)
! Image #44        ! (64x64x2)
! Image #45        ! (64x64x2)
! Image #46        ! (64x64x2)
! Image #47        ! (64x64x2)
! Image #48        ! (64x64x2)
! Image #49        ! (64x64x2)
! Image #50        ! (64x64x2)
! Image #51        ! (64x64x2)
! Image #52        ! (64x64x2)
! Image #53        ! (64x64x2)
! Image #54        ! (64x64x2)
! Image #55        ! (64x64x2)
! Image #56        ! (64x64x2)
! Image #57        ! (64x64x2)
! Image #58        ! (64x64x2)
! Image #59        ! (64x64x2)
! Image #60        ! (64x64x2)
! Image #61        ! (64x64x2)
! Image #62        ! (64x64x2)
! Image #63        ! (64x64x2)
! Image #64        ! (64x64x2)
#------------------#

Redo input [no]?
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