plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | alcu/sms-master | mean_posit_track.m | .m | sms-master/irt/contrib/ruan/mean_posit_track.m | 4,126 | utf_8 | 9a83080ea67574b5749f40891d0e1bee | function [mean_pos,ma_pos] = mean_posit_track(t,s,tau,train_len)
%| function [mean_pos,ma_pos] = mean_posit_track(t,s,tau,train_len)
%| INPUT
%| t: time indices
%| s: position sequence (should be same length as t)
%| tau: delay parameter,
%| set to be the equivalence index to about 0.5sec for breathing.
%| train_Le... |
github | alcu/sms-master | feldkamp_wt.m | .m | sms-master/irt/contrib/fdk_paida_ajay/feldkamp_wt.m | 6,562 | utf_8 | 17c4bef8428868ce4ebf23a4812f53ba | function img = feldkamp_wt(proj, window, mask, args, varargin)
%function img = feldkamp_wt(proj, window, mask, args, [options])
% modified version by Ajay Paidi
%
% FBP reconstruction of cone-beam tomography data collected with flat detector.
% See feldkamp_example.m for example.
%
% in:
% proj [nh,nv,na] cone-beam pr... |
github | alcu/sms-master | Gtomo2_strip_sse.m | .m | sms-master/irt/contrib/sse_proj/irt/systems/Gtomo2_strip_sse.m | 8,896 | utf_8 | 6f761ea7ec88c4866faf7cd8921547d6 | function G = Gtomo2_strip(sg, ig, varargin)
%function G = Gtomo2_strip(sg, ig, options)
%
% Generate a 2D system matrix for tomographic image reconstruction,
% based on a square pixel basis and strip-integral detector model,
% i.e., a rectangular detector response.
% Works for parallel-beam, arc-fan, and flat-fan geom... |
github | alcu/sms-master | eml_osem_sse.m | .m | sms-master/irt/contrib/sse_proj/irt/emission/eml_osem_sse.m | 6,223 | utf_8 | 85c546159b335c5d7dae54f2f11ed48f | function [xs, precon] = eml_osem(x, Gb, yi, ci, ri, varargin)
%function xs = eml_osem(x, Gb, yi, ci, ri, [options])
% E-ML-OSEM algorithm for image reconstruction from Poisson emission data
% (ordered subsets expectation maximization)
% model: Y_i ~ Poisson(c_i [G x]_i + r_i)
% in
% x [np,1] initial estimate
% Gb [nd,... |
github | alcu/sms-master | xray_atten_interp.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_atten_interp.m | 1,478 | utf_8 | c5f4586b5bd90c28284e5304b9401f66 | function mac = xray_atten_interp(kev, mac, kev_in, varargin)
%function mac = xray_atten_interp(kev, mac, kev_in, [options])
%
% Interpolate mass attenuation coefficients (mac) onto desired energies.
%
% in
% kev [M,1]
% mac [M,1]
% kev_in [N,1] desired energies [in keV]
%
% option
% 'interp' {} default {'cubic', 'e... |
github | alcu/sms-master | de_ftab.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab.m | 8,606 | utf_8 | 1acbb1761bac2cd34e4f2039219c6079 | function ftab = de_ftab(xrs, mas, varargin)
%|function ftab = de_ftab(xrs, mas, [options])
%|
%| For multiple-kVp X-ray imaging, we must evaluate functions f_m(s_1, ..., s_L)
%| for m=1,..,M, where M is the number of kVp settings,
%| and L is the number of material components.
%| f_m(s1, s2) = -log( \int exp(- (m1(E)... |
github | alcu/sms-master | se_ftab_build.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/se_ftab_build.m | 5,492 | utf_8 | 9a8a95a42aea38acf48d5555696f592c | function ftab = se_ftab_build(s_arg, varargin)
%function ftab = se_ftab_build(s_arg, [options])
%
% For multiple-kVp X-ray imaging, we must evaluate functions f_m(s_1, ..., s_L)
% for m=1,..,M, where M is the number of kVp settings,
% and L is the number of material components.
% f_m(s1, s2) = -log( \int exp(- (m1(E) ... |
github | alcu/sms-master | xray_read_dens.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_read_dens.m | 1,866 | utf_8 | c7107ba59cd80e171e8ed6f44c1aad00 | function [density, mtype] = xray_read_dens(mtype, varargin)
%function [density, mtype] = xray_read_dens(mtype, [options])
%
% Read densities for given material type(s).
%
% in
% mtype 'aluminum', 'copper', ...
% (optionally can be a cell array of several)
% option
% 'units' cm | mm default: cm
% out
% density ... |
github | alcu/sms-master | de_ftab_invert.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_invert.m | 6,703 | utf_8 | 98c29d0d8bedb49b70ae189e2fd3d78f | function shat = de_ftab_invert(ftab, fhat, varargin)
%|function shat = de_ftab_invert(ftab, fhat, [options])
%|
%| given fhat (log transmission data),
%| estimate shat (component density line integrals)
%|
%| in
%| ftab strum BH "table" built by de_ftab.m
%| fhat [(n?) M] log measurements
%| option
%| smin {LL}
%| s... |
github | alcu/sms-master | de_ftab_inv2.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_inv2.m | 3,937 | utf_8 | 6ac2cb786852a48e9e1381d3fece67d6 | function inv2 = de_ftab_inv2(fit, sl, varargin)
%|function inv2 = de_ftab_inv2(fit, sl, [options])
%|
%| Build object that does polynomial inverse of BH function.
%| (To map log data into corresponding to line-integrals of material density.)
%|
%| in
%| fit strum initialized by de_ftab_fit()
%| sl cell {s1, ..., sL} ... |
github | alcu/sms-master | de_wls_dercurv.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_wls_dercurv.m | 3,321 | utf_8 | 8d29ab25b9e6b3593fa67dba26517ead | function [grad, curv, fim, diml] = de_wls_dercurv(data, sil, curvtype, iblock, nblock)
%|function [grad, curv, fim, diml] = de_wls_dercurv(data, sil, curvtype, iblock, nblock)
%|
%| evaluate gradients and curvatures for DE WLS data-fit function:
%| U(x) = \sum_i U_i(s)
%| U_i(s) = \sum_m=1^M U_im(s)
%| U_im(s_i) = 1/... |
github | alcu/sms-master | de_ftab_fit.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_fit.m | 15,536 | utf_8 | 90a471b3b02163f8ab7598c3f4bcf7c6 | function fit = de_ftab_fit(sl, fm, varargin)
%|function fit = de_ftab_fit(sl, fm, [options])
%|
%| Fit to DE table fm(), suitable for subsequent interpolation / extrapolation.
%| Uses either classic polynomial basis functions,
%| or an experimental exponential model -log(sum_k p_k exp(-m_k . s))
%|
%| in
%| sl {L} s... |
github | alcu/sms-master | de_pl_obj.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_pl_obj.m | 1,984 | utf_8 | 431e8d6c9868d7c9b2b2c475d112237b | function [obj, like, penal] = de_pl_obj(xs, G, ymi, Im, rmi, ftab, R, mask)
%function [obj, like, penal] = de_pl_obj(xs, G, ymi, Im, rmi, ftab, R, mask)
%
% compute quadratically penalized Poisson likelihood for each column of x
% for dual-energy x-ray problem
% in
% xs [np,2,niter] iterates
% [nx,ny,2,niter] if ma... |
github | alcu/sms-master | de_ftab_s_pwls.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_s_pwls.m | 5,992 | utf_8 | 96b908573af7cc0d065d3b50af9db436 | function [sh cost_eval_ls cost_eval_penal NRMS] = de_ftab_s_pwls(fit,fh,yim,varargin)
%function [sh cost_eval NRMS] = de_ftab_s_pwls(fit,fh,yim,varargin)
% estimate s from fh by PWLS
% in
% fit from de_ftab_fit()
% fh [(Nd),M] estimates of f
% yim [(Nd),M] noisy measurements
% option
% 'strue [(Nd),L] true sinog... |
github | alcu/sms-master | de_ftab_fm.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_fm.m | 1,906 | utf_8 | f96b7fde602deaedce29eb3a4ddd4166 | function fm = de_ftab_fm(sll, mac, Ide)
%|function fm = de_ftab_fm(sll, mac, Ide)
%| calculate f_m(s1, s2, ...) (nonlinear BH function) using
%| in
%| sll cell{LL} or [(Nd),LL] material density integrals
%| {s1, s2, ..., sL} (ndgrid so same size [(Nd)])
%| mac [ne,LL] mass atten coefficients
%| Ide [ne,MM] spec... |
github | alcu/sms-master | xray_make_mac.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_make_mac.m | 426 | utf_8 | 83515f1b1c71161a16fad6ec86a954f4 | %
% xray_make_mac()
% interpolate mass atten coef to source energy sampling
% in
% xrs strum X-ray spectra; see xray_read_spectra.m
% mas strum mass attenuation coefficients; see xray_read_mac.m
% out
% mac.mac [ne L]
% mac.bar [M L] effective mass attenuation coefficents near 0
%
function mac = xray_make_mac(xrs, mas)... |
github | alcu/sms-master | de_poly_eval.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_poly_eval.m | 3,224 | utf_8 | 357ced849cff17a52bc998d648d3c6e2 | function yy = de_poly_eval(xx, coef, expo, varargin)
%|function yy = de_poly_eval(xx, coef, expo, [option])
%|
%| Evaluate multi-dimensional polynomial:
%| y[n,l] = sum_k=1^K coef(k,l) x[n,1]^expo(k,1) ... x[n,M]^expo(k,M)
%|
%| in
%| xx [(nn) M] input values
%| coef [K L] coefficients
%| expo [K M] exponents aka p... |
github | alcu/sms-master | de_ftab_s_pwls2.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_s_pwls2.m | 5,555 | utf_8 | 756d27003d3adcd79fdc496e492eb5c1 | function [sh cost_eval] = de_ftab_s_pwls2(fit, fh, yim, varargin)
%function sh = de_ftab_s_pwls2(fit, fh, yim, varargin)
% estimate s from fh by PWLS with a modified curvature
% in
% fit from de_ftab_fit()
% fh [(Nd),M] estimates of f
% yim [(Nd),M] noisy measurements
% option
% 'niter # of iterations
% 'init [(Nd),... |
github | alcu/sms-master | de_ftab_inv1.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_inv1.m | 2,605 | utf_8 | 2888d2a948747fdb128005537ed3f742 | function inv1 = de_ftab_inv1(fit, s1, varargin)
%|function inv1 = de_ftab_inv1(fit, s1, [options])
%|
%| Build object that does 1D inverse of BH function for 1st material component,
%| (usually water), for conventional "water only" beam-hardening correction.
%|
%| in
%| fit strum initialized by de_ftab_fit()
%| s1 [N... |
github | alcu/sms-master | se_ftab_fit.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/se_ftab_fit.m | 9,149 | utf_8 | 6c506456a294aaf1db78ac7df358a4e0 | function fit = se_ftab_fit(sl, fm, varargin)
%function fit = se_ftab_fit(sl, fm, [options])
%
% Fit to SE table fm(), suitable for subsequent interpolation / extrapolation.
% We can get expressions for derivatives / curvatures
% Uses an experimental exponential model -log(sum_k p_k exp(-m_k . s))
% eq below eq(18)
%
%... |
github | alcu/sms-master | de_ftab_build.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_build.m | 11,253 | utf_8 | 013c39d17ff50a3b4822a3e4e67c97e0 | function ftab = de_ftab_build(s_arg, varargin)
%function ftab = de_ftab_build(s_arg, [options])
%
% For multiple-kVp X-ray imaging, we must evaluate functions f_m(s_1, ..., s_L)
% for m=1,..,M, where M is the number of kVp settings,
% and L is the number of material components.
% f_m(s1, s2) = -log( \int exp(- (m1(E) ... |
github | alcu/sms-master | xray_read_mac.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_read_mac.m | 4,511 | utf_8 | c4de6611b29ee039ac82a4a9ae6264e8 | function mas = xray_read_mac(mtype, varargin)
%function mas = xray_read_mac(mtype, [options])
%|
%| Read mass attenuation coefficients for a given material type.
%| Returns a strum that can interpolate onto desired energies.
%|
%| in
%| mtype 'aluminum', 'copper', 2, '2', '02-helium', ...
%| See xray_material_file... |
github | alcu/sms-master | de_poly_expo.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_poly_expo.m | 1,203 | utf_8 | ac1ff574b84931c6a2c0c065da1d3b97 | function expo = de_poly_expo(MM, varargin)
%|function expo = de_poly_expo(MM, [option])
%|
%| Generate exponents for multi-dimensional polynomials with terms:
%| x[n,1]^expo(k,1) ... x[n,M]^expo(k,M)
%|
%| in
%| MM dimension of polynomial
%|
%| option
%| 'emax' [1]|[M] maximum exponent of any given term. default: ... |
github | alcu/sms-master | xray_read_spectra.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_read_spectra.m | 6,873 | utf_8 | d02bdf0fc2c5b2f080e0cfb44ec45415 | function xrs = xray_read_spectra(stype, varargin)
%|function xrs = xray_read_spectra(stype, [options])
%|
%| Read X-ray spectra data and initialize a structure that describes "M"
%| piecewise constant polyenergetic X-ray spectra, where M=2 for dual-kVp case.
%|
%| in
%| stype char which spectrum model:
%| 'mono,60... |
github | alcu/sms-master | de_ftab_curv.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_curv.m | 14,816 | utf_8 | e874592210514959d859520d589746d7 | function fit = de_ftab_curv(fit, varargin)
%|function fit = de_ftab_curv(fit, [options])
%|
%| Augment a fitted DE BH function strum object
%| to also have curvature methods for LS (or WLS) data-fit term
%|
%| in
%| fit strum from de_ftab_fit (type: 'exp')
%|
%| option
%| 'ctype' char curvature type: (default: 'pre... |
github | alcu/sms-master | xray_read_atomic_mass.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_read_atomic_mass.m | 1,836 | utf_8 | f67f2bb6eed6bee8d2af7f901633fb98 | function mass = xray_read_atomic_mass(mtype, varargin)
%function mass = xray_read_atomic_mass(mtype)
%
% Read atomic mass for a given material type.
%
% in
% mtype 'aluminum', 'copper', 2, '2', '02-helium', ...
% See xray_material_file_name.m
% (Optionally mtype can be a cell array of several materials.
% If s... |
github | alcu/sms-master | de_ftab_s_iter.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_s_iter.m | 6,015 | utf_8 | 5b638c2eec19ec074a171aa11c09d667 | function sh = de_ftab_s_iter(fit, fh, varargin)
%|function sh = de_ftab_s_iter(fit, fh, [options])
%| estimate s from fh by iterative LS
%| sh = argmin{s} | fh - fm(s) |^2
%| in
%| fit from de_ftab_fit() / de_ftab_curv()
%| fh [(Nd) M] estimates of f (nonlinear BH function)
%| option
%| 'niter' # of iterations (... |
github | alcu/sms-master | de_ftab_sls.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_sls.m | 2,249 | utf_8 | 5a5a95c79fae0b00584e54b9b7770fe7 | function sls = de_ftab_sls(varargin)
%function sls = de_ftab_sls(varargin)
%|
%| Determine structure that characterizes the "s" limits for polyenergetic CT,
%| where s_l is a line integral through the lth material type, l=1...L.
%| L is the number of material components.
%|
%| in
%| option
%| 'sl' cell{LL} sample thic... |
github | alcu/sms-master | xray_read_atten.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_read_atten.m | 3,304 | utf_8 | a89a7ebe1c6b74f755d2dd5ac0303693 | function [mass_atten, kev, mtype, file] = ...
xray_read_atten(mtype, kev_in, varargin)
%function [mass_atten, kev, mtype, file] = ...
% xray_read_atten(mtype, kev_in, [options])
%
% Read mass attenuation coefficients for a given material type.
% Optionally interpolate onto desired energies.
%
% in
% mtype 'alumi... |
github | alcu/sms-master | xray_filters.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_filters.m | 1,394 | utf_8 | 2fcf27d709154c9584b2dee6457ca2d3 | function atten = xray_filters(mtype, thickness, energy, varargin)
%function atten = xray_filters(mtype, thickness, energy, [options])
%
% Compute X-ray photon survival probability as a function of energy
% for various materials.
% in
% mtype 'aluminum', 'copper', ...
% can be a cell array {L}
% thickness in cm (... |
github | alcu/sms-master | xray_material_file_name.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/xray_material_file_name.m | 3,511 | utf_8 | c5492260bd95b10574563e5ab65800e7 | function file = xray_material_file_name(name, varargin)
%function file = xray_material_file_name(name, [options])
% given name of an element, such as '01' or '01-hydrogen' or 'hydrogen',
% or of a material such as 'water', determine the full file name for the
% mass attenuation coefficient data for that material.
%
% ... |
github | alcu/sms-master | element_density.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/element_density.m | 3,739 | utf_8 | 79eed16aa31d6c83d447827a9848069d | function rho = element_density(elem)
%function rho = element_density(elem)
% density of an element (in g/cc)
if ~nargin, help(mfilename), error(mfilename), return, end
% http://environmentalchemistry.com/yogi/periodic/density.html
% awk '{print $5, $4, $3, $1, $2}' < tt | sort -n > element_density.m
all = load;
if... |
github | alcu/sms-master | de_ftab_s_pl.m | .m | sms-master/irt/contrib/noh-joonki-dect-restore/old/de_ftab_s_pl.m | 7,738 | utf_8 | c35ad47c4144519a5d086f74bccdde5d | function [sh cost_eval_li cost_eval_penal NRMS] = de_ftab_s_pl(fit,xray,mac,fh,yim,varargin)
%function sh = de_ftab_s_pl(fit, xray, fh, yim, varargin)
% estimate s from fh by PL based on possion model
% in
% fit from de_ftab_fit()
% xray from xray_read_spectra
% mac full mass atten coeff
% fh [(Nd),M] estimates o... |
github | alcu/sms-master | pcg_bls_ep.m | .m | sms-master/irt/contrib/zhao,feng/separate-mag-phase/pcg_bls_ep.m | 3,914 | utf_8 | 5c4eea343cee242e328997116f9c3838 | function x = pcg_bls_ep(A, C, yi, mi, xi, beta, del, niter, varargin)
%function x = pcg_bls_ep(A, C, yi, mi, xi, beta, del, niter, varargin)
%
% Minimize the cost function with regularizer 3 by using preconditioned conjugate
% gradient with backtracking line search method. The edge-preserving potential
% function is h... |
github | alcu/sms-master | pcg_bls_exp_ep.m | .m | sms-master/irt/contrib/zhao,feng/separate-mag-phase/pcg_bls_exp_ep.m | 4,239 | utf_8 | deb1fbb5d58a066159466e310a54b9aa | function x = pcg_bls_exp_ep(A, C, yi, mi, xi, beta, del, niter, varargin)
%function x = pcg_bls_exp_ep(A, C, yi, mi, xi, beta, del, niter, varargin)
%
% Minimize the cost function with regularizer 4 by using preconditioned conjugate
% gradient with backtracking line search method. The edge-preserving potential
% funct... |
github | alcu/sms-master | myFeldkamp.m | .m | sms-master/irt/contrib/handy-greg/myFeldkamp.m | 3,850 | utf_8 | f1933a5c5bbbc7aeafdbab95e1c93eb0 | function img = myFeldkamp(cg, ig, proj, varargin)
%|function img = feldkamp(cg, ig, proj, varargin)
%|
%| FBP reconstruction of cone-beam tomography data collected with
%| a circular source trajectory.
%| See feldkamp_example.m for example.
%|
%| in
%| cg ct_geom()
%| ig image_geom()
%| proj [ns nt na] cone-beam ... |
github | alcu/sms-master | my_cbct_back.m | .m | sms-master/irt/contrib/handy-greg/my_cbct_back.m | 9,397 | utf_8 | 6f665aafbb3df7b85b9997d9a5751532 | function back = my_cbct_back(proj, cg, ig, varargin)
% function back = cbct_back(proj, cg, ig, varargin)
%|
%| cone-beam backprojector for feldkamp.m
%| in
%| proj [ns nt na] cone-beam projection views
%| option
%| 'use_mex' 1|2|3 default: 1 mex with loop in mex
%| 2 mex with loop in matlab
%| 3 mex with "st... |
github | alcu/sms-master | helix.m | .m | sms-master/irt/contrib/handy-greg/helix.m | 6,987 | utf_8 | 5f7a0644e374abe7ac253d0954cc617a | function img = helix(cg, ig, proj, short, varargin)
%|function img = helix(cg, ig, proj, varargin)
%|
%| A single slice rebinning method for cone-beam tomography data
%| collected with a helical source trajectory
%|
%| in
%| cg ct_geom()
%| ig image_geom()
%| proj [ns nt na] cone-beam projection views (line inte... |
github | alcu/sms-master | easyhelix.m | .m | sms-master/irt/contrib/handy-greg/easyhelix.m | 4,833 | utf_8 | 8058f0aec2468623222313bd5bc7801e | function img = easyhelix(cg, ig, proj, varargin)
%|
%| FBP reconstruction of cone-beam tomography data collected with
%| a circular source trajectory.
%| See feldkamp_example.m for example.
%|
%| in
%| cg ct_geom()
%| ig image_geom()
%| proj [ns nt na] cone-beam projection views (line integrals)
%|
%|
%| out
%| i... |
github | alcu/sms-master | feldkamp_par.m | .m | sms-master/irt/contrib/handy-greg/t-fdk/feldkamp_par.m | 4,951 | utf_8 | bfd5ec9a9e5ad5b8eb118589c271a61c | function img = feldkamp_par(interpRect,rectGrid,cg, ig, proj, varargin)
%|function img = feldkamp_par(cg, ig, proj, varargin)
%|
%|
%| Modified by G. Handy; feldkamp can be rewritten to accommodate changes
%|
%| in
%| interpRect set to 1 if you want to use
%| interpolation/extrapolation
%|... |
github | alcu/sms-master | ct_geom_par.m | .m | sms-master/irt/contrib/handy-greg/t-fdk/ct_geom_par.m | 20,122 | utf_8 | 145fb4a6716c70bbeba1d9a86168d6b5 | function st = ct_geom_par(type, varargin)
%|function st = ct_geom_par(type, varargin)
%|
%| Same as ct_geom, but adds a rebinned variable
%| ct_geom should be changed to include a new type instead
%|
%| Create the "CT geometry" structure that describes the sampling
%| characteristics of a cone-beam CT system (axial o... |
github | alcu/sms-master | gridding.m | .m | sms-master/irt/contrib/handy-greg/t-fdk/gridding.m | 1,166 | utf_8 | fd8434cd8efa77a3ca6f8fd8b8d983f4 | % gridding.m
% originally by Greg Handy
% 2013-04-04 modified by Rebecca Malinas
% 2013-04-07 streamlined by J A Fessler
function [newProj, rectGrid] = gridding(cg, proj)
indexT = 1:cg.nt;
indexS = 1:cg.ns;
ws = (cg.ns+1)/2 + cg.offset_s; % RM
wt = (cg.nt+1)/2 + cg.offset_t;
coordT = (indexT - wt) * cg.dt;
coordS = ... |
github | alcu/sms-master | fdk_filter_par.m | .m | sms-master/irt/contrib/handy-greg/t-fdk/fdk_filter_par.m | 1,331 | utf_8 | 17364873cdb8bc89d5fc484ebc9b1cec | % fdk_filter()
% step 2 of FDK cone-beam CT reconstruction:
% filter the (zero padded) projections
%
function proj = fdk_filter_par(proj, window, dsd, dfs, ds, rebinned)
if nargin < 5, help(mfilename), error(mfilename), end
[ns nt na] = size(proj);
npadh = 2^ceil(log2(2*ns-1));
%printm('ns=%d npadh=%d', ns, npadh)
if... |
github | alcu/sms-master | cbct_back_mat_par.m | .m | sms-master/irt/contrib/handy-greg/t-fdk/cbct_back_mat_par.m | 3,047 | utf_8 | ad979a1f3468f230aa034fa8fd9299ad | % 2012-fall by Greg Handy, based on cbct_back.m
% 2013-04-04 refined by Rebecca Malinas
% 2013-04-07 refined by Jeff Fessler
function img = cbct_back_mat_par(interpRect,rectGrid,proj, ns, nt, na, ...
ds, dt, offset_s, offset_t, offset_source, ...
dsd, dso, dfs, orbit, orbit_start, ...
source_zs, ...
mask, nz, dx, ... |
github | alcu/sms-master | myiswt2.m | .m | sms-master/irt/contrib/ramani/al-p2/myiswt2.m | 2,199 | utf_8 | 22798141a0f91bd716545d80aa17011d | function a = myiswt2(SWC, lo_R, hi_R)
%ISWT2 Inverse discrete stationary wavelet transform 2-D.
% ISWT2 performs a multilevel 2-D stationary wavelet
% reconstruction using either a specific orthogonal wavelet
% ('wname', see WFILTERS for more information) or specific
% reconstruction filters (Lo_R and Hi_R).... |
github | alcu/sms-master | SENSERecon_ALP2.m | .m | sms-master/irt/contrib/ramani/al-p2/SENSERecon_ALP2.m | 6,760 | utf_8 | e784e876782564ed918e7768ff2de502 | % by Sathish Ramani
function [xnew, CAL, TAL, EAL, ERR] = ...
SENSERecon_ALP2(img, SP3, Data, recon_F, d, z, xnew, params)
%% Compute SER at current estimate
dispitr = params.dispitr;
dispitrnum = params.dispitrnum;
dispfig = params.dispfig;
Npix = params.Npix;
mn = params.mn;
mx = params.mx;
rs = params.rs;
cs = pa... |
github | alcu/sms-master | Jop.m | .m | sms-master/irt/contrib/khalsa/Jop.m | 5,299 | utf_8 | 440d6e3652839b0aa66b2e65f299bd9e | function ob = Jop(varargin)
%function ob = Jop(varargin)
%J = Jop({'J', 4, 'L', 4, 'kspace', kspace, 'fov', fov, ...
% 'kn.ktype', 'kaiser', 'kn.kb_alf', kb_alf, 'kn.kb_m', kb_m});
%
% Creates fatrix J for use in operations of the form y = J * x. Designed
% for use in iterative algorithms for estimating density c... |
github | alcu/sms-master | desense.m | .m | sms-master/irt/contrib/khalsa/sense/desense.m | 4,343 | utf_8 | aa6f83c950db9f4037d30049e7e94791 | function xsense = desense(xalias, smap, varargin)
%function xsense = desense(xalias, smap, varargin)
% compute 2D images using traditional cartesian SENSE recon (Pruessman 99
% paper)
% Example usage:
% xsense = desense(xalias, smap, 'phi', phi, 'sos', sosmap);
%
% This is the Oct 2010 update to mri_cartesian_sen... |
github | alcu/sms-master | sense_recon.m | .m | sms-master/irt/contrib/khalsa/sense/sense_recon.m | 4,216 | utf_8 | f106f370e7c79a5fee32b28a4f02f634 | function [xs, xc] = sense_recon(ysense, smap, varargin)
% input R?
% warn user if their R means throwing away data?
% ysense = fully samples with zeros?
% allow 2D y w/ kspace input, as well as 4D y
% Note: this is for undersampling in 1 dim only (for now)
if (nargin == 1) && isequal(ysense, 'test')
sense_reco... |
github | alcu/sms-master | Gtomo_nufft_3d_par.m | .m | sms-master/irt/contrib/nufft_proj_par_3d/Gtomo_nufft_3d_par.m | 10,854 | utf_8 | 9068f2292cd770824209bd0da6c95ac5 | function ob = Gtomo_nufft_3d_par(mask, dim_data, varargin)
%function ob = Gtomo_nufft_3d_par(mask, dim_data, options)
%
% Construct Gtomo_nufft_3d_par object. Currently only handle Fourier-based
% forward projection for 3D parallel beam case.
%
% See Gtomo_nufft_3d_par_test.m for example usage.
%
% Basically, you cr... |
github | alcu/sms-master | spsp_Af.m | .m | sms-master/irt/contrib/yip-spsp-2012-01-09/spsp_Af.m | 1,213 | utf_8 | 371c9800b1fa077bb9ee86ef227575fe | function ob = spsp_Af(z,f,kz,kf,tsp,fovf,fovz)
%function ob = spsps_Af(z,f,kz,kf,tsp,fovf,fovz)
%
% input
% z : Nz by 1 vector (cm)
% f : Nf by 1 vector (Hz)
% kz, kf : M by 1 vector
% tsp : sampling time of RF pulse in sec
% Construct spsp_Af object
%
%
%This one calls the fast projections!
%
% Copyright, Sangwoo L... |
github | alcu/sms-master | try1.m | .m | sms-master/irt/contrib/bug-todo-3b/try1.m | 2,989 | utf_8 | 8d6d133d99dd1defeca42cf80beb6bbd | function TestPointBaseProjector(ProjectorType,bBlockMatrix)
%|function TestPointBaseProjector(ProjectorType,bBlockMatrix)
%|
%| Test Gtomo3 object, for 3D ESRI image reconstruction.
%| Test 3d forward and backprojection using mex files.
%|
%| in
%| ProjectorType 'b'|'a' default: 'b'
%| 'b' is 3b improved proj... |
github | alcu/sms-master | homodyne_recon.m | .m | sms-master/irt/contrib/le-mai-sense/homodyne_recon.m | 4,331 | utf_8 | 7e1ef717528b418bc7b320c71da6dc58 | function [recon_ph_demod, recon_im, lp_im, full_kspace] = ...
homodyne_recon(partial_kspace, N, M, overlap, varargin)
%|function [recon_im, recon_ph_demod, lp_im, full_kspace] =
%| homodyne_recon(partial_kspace, N, M, overlap, varargin)
%|
%| Reconstruct image from partial kspace data via homodyne method outlined
%|... |
github | alcu/sms-master | SENSE_recon.m | .m | sms-master/irt/contrib/le-mai-sense/SENSE_recon.m | 4,650 | utf_8 | 3aba435ff85cd0950b6ac499f4c06de5 | function recon_im = SENSE_recon(smap, reduced_fft, np, varargin)
% function recon_im = SENSE_recon(smap, reduced_fft, np, varargin)
% inputs:
% smap [N M nc] complex sensitivity maps
% reduced_fft [N/np M nc] or [N M/np nc] undersampled fft
% np degree of undersampling
% optiona... |
github | alcu/sms-master | SENSE_recon.m | .m | sms-master/irt/contrib/le-mai-sense/old3/SENSE_recon.m | 3,921 | utf_8 | c276736c9de96092bb20b44db091d7a8 | function recon_im = SENSE_recon(smap, reduced_fft, reduced_dim, np, regularizer, mask, figs_on)
% function recon_im = SENSE_recon(smap, reduced_fft, reduced_dim, np,
% regularizer, figs_on)
% in:
% smap [N M nc] complex sensitivity maps
% reduced_fft [N/np M] or [N M/np] undersamp... |
github | alcu/sms-master | sense_recon.m | .m | sms-master/irt/contrib/le-mai-sense/old2/sense_recon.m | 4,351 | utf_8 | 33a907716749278a475b461d21a79108 | function recon_im = sense_recon(smap, reduced_fft, reduced_dim, np, regularizer, mask, figs_on)
% function recon_im = sense_recon(smap, reduced_fft, reduced_dim, np,
% regularizer, figs_on)
% in:
% smap [N M nc] complex sensitivity maps
% reduced_fft [N/np M] or [N M/np] undersamp... |
github | alcu/sms-master | SENSE_recon.m | .m | sms-master/irt/contrib/le-mai-sense/old/SENSE_recon.m | 3,322 | utf_8 | 19e6f41bb082b32607df6a714dc88f1e | function recon_im = SENSE_recon(smap, reduced_fft, reduced_dim, np, regularizer, figs_on)
% function recon_im = SENSE_recon(smap, reduced_fft, reduced_dim, np,
% regularizer, figs_on)
% in:
% smap [N M nc] complex sensitivity maps
% reduced_fft [N/np M] or [N M/np] undersampl... |
github | alcu/sms-master | filtmat.m | .m | sms-master/irt/mfile/filtmat.m | 3,758 | utf_8 | 4f6ac3e0ab4e6785b3698d7b1b702542 | function [mat, nexp] = filtmat(arg0, arg1, arg2, arg3, arg4)
%function mat = filtmat('1d', kernx, nx)
% 1d nonperiodic
%function mat = filtmat({'1d' 'per'}, kernx, nx)
% 1d periodic
%function mat = filtmat('2d,mul,sep', kernx, kerny, [nx ny])
% 2d multiplicatively separable nonperiodic
%function mat = filtmat('3... |
github | alcu/sms-master | poisson2.m | .m | sms-master/irt/mfile/poisson2.m | 1,144 | utf_8 | df9841b9e49e81c32f4841622d9d2358 | function data = poisson2(xm, seed)
%function data = poisson2(xm, seed)
% Generate Poisson random column vector with mean xm.
% Uses rejection method - good for large values of xm.
% See "Numerical Recipes in C", P. 222.
if isvar('seed') & ~isempty(seed)
rand('state', seed)
end
data = zeros(size(xm));
if any(xm < 0)... |
github | alcu/sms-master | psfs_tc.m | .m | sms-master/irt/blob/psfs_tc.m | 2,429 | utf_8 | 1e1fc5eb463076a15839e625c53704c1 | % psfs_tc(file_psfs, radius, dim, ny, sxz, sy)
%
% Generate PSF's for SPECT 3D reconstruction, appropriate for Tc applications
% where
% file_psfs: Output file the PSF's are to be written to ([] for no file)
% radius: Object-to-detector distance (mm)
% dim: Dimension of generated PSF's, either 1 or 2 (defaul... |
github | alcu/sms-master | zpad0.m | .m | sms-master/irt/blob/zpad0.m | 548 | utf_8 | 1a0466d71cac32dac95338e74ccb2173 | function xpad = zpad0(x, nxp, nyp, nzp)
%function xpad = zpad0(x, nxp, nyp, nzp)
% zero pad an input signal x symmetrically around "0" (image center)
% by A. Yendiki
if nargin < 1, help(mfilename), error(mfilename), end
if nargin == 1 && streq(x, 'test'), zpad0_test, return, end
if nargin == 2
xpad = pad_into_cente... |
github | alcu/sms-master | getFWHM_uhe.m | .m | sms-master/irt/blob/getFWHM_uhe.m | 752 | utf_8 | c16d1a85a246d5630b3de797ac6852e1 | % getFWHM_uhe(r)
%
% Get FWHM (mm) of the ultra-high-energy collimator PSF
% at some distances 'r' (mm) from the collimator
%
% Qiang (Victor) Lin
% Modified by A. Yendiki, 2/5/02
function FWHM = getFWHM_uhe(r)
r(r < 0) = 0;
% Radius (mm) FWHM (mm?)
% 20 5.6135
% 75 13.8277
% 130 ... |
github | alcu/sms-master | blob_li_blur.m | .m | sms-master/irt/blob/blob_li_blur.m | 4,401 | utf_8 | bf672340d232bb3b27d78bfd77947057 | % [footblur, radii] = blob_li_blur(R, dR, alpha, kb_m, blur, du, dy, Ny, chat)
% Calculate line integrals of a radially symmetric Kaiser-Bessel blob
% and blur them with each one of a set of 2D blur PSF's
%
% R: Radius of blob support [mm] (default = 2*dy)
% dR: Sample spacing [mm] in radial direction
% o... |
github | alcu/sms-master | Gblob.m | .m | sms-master/irt/blob/Gblob.m | 3,210 | utf_8 | 1a737a7965dd37ce7fc1fe117675e77b | function [out, r1, ipsf] = Gblob(varargin)
%function [out, r1, ipsf] = Gblob(options)
% generate a KB psf or corresponding Gblur object
%
% options
% mask if no mask given, then return psf, not Gblur
% ndim 2 for 2D
% J diameter
% kb_m KB order
% kb_alf KB alpha (shape)
% over over-sampling factor (default 1)
% ... |
github | alcu/sms-master | psfs_uhe.m | .m | sms-master/irt/blob/psfs_uhe.m | 2,316 | utf_8 | 1ef6374ebea76d60d8dd82be4fd39d4f | % psfs_uhe(file_psfs, radius, dim, ny, sxz, sy)
%
% Generate ultra-high-energy collimator PSF's for SPECT 3D reconstruction
% where
% file_psfs: Output file the PSF's are to be written to ([] for no file)
% radius: Object-to-detector distance (mm)
% dim: Dimension of generated PSF's, either 1 or 2 (default i... |
github | alcu/sms-master | eml_inc_em.m | .m | sms-master/irt/emission/eml_inc_em.m | 3,454 | utf_8 | 7fbb36816c18e8188b22a19c002debb5 | function xs = eml_inc_em(x, Gb, yi, ci, ri, varargin)
%|function xs = eml_inc_em(x, Gb, yi, ci, ri, varargin)
%| E-ML-INC-EM algorithm for image reconstruction from Poisson emission data
%| (incremental EM algorithm)
%| model: Y_i ~ Poisson(c_i [G x]_i + r_i)
%| in
%| x [np,1] initial estimate
%| Gb [nd,np] Gblock o... |
github | alcu/sms-master | eml_hds.m | .m | sms-master/irt/emission/eml_hds.m | 629 | utf_8 | 3f47d7f9f355f04a3ab6d5d7c9c3c839 | function gam = eml_hds(Gb, ci, ri, hds)
%function gam = eml_hds(Gb, ci, ri, hds)
% precompute hidden data space factor, based on 1995 PML SAGE paper
if nargin < 4, help(mfilename), error(mfilename), end
if iscell(ci)
gam = inf;
nblock = block_op(Gb, 'n');
for ii=1:nblock
gam_new = eml_hds_1(Gb{ii}, ci{ii}, ri{i... |
github | alcu/sms-master | epl_em_sparse_test.m | .m | sms-master/irt/emission/epl_em_sparse_test.m | 2,458 | utf_8 | 63b8ce5bec6c2ac04f18395a0709aae4 | % epl_em_sparse_test.m
% Script to test epl_em_sparse with phantom
%
% Parameters:
% beta - multiplicative factor of norm regularization term
% norm - ell-0 or ell-1 norm
% gammaj - value to make each gammaj in the gamma vector
%
% compare aspire and matlab E-ML-EM
% Copyright Jan 1998-2008 Jeff Fessler, The Universit... |
github | alcu/sms-master | eml_eterm.m | .m | sms-master/irt/emission/eml_eterm.m | 925 | utf_8 | 1c01039e4894f950619e585d245367d9 | function eterm = eml_eterm(x, Gb, yi, ci, ri, iblock)
%function eterm = eml_eterm(x, Gb, yi, ci, ri, iblock)
%
% compute E-step (for a given block)
% yb = A*x + r; e = A' * (y ./ yb), where A = D(c)*G
% in:
% x [np,1] image
% yi,ci,ri must be made 2d by "reshaper"
%
% Copyright 2005-2-17, Jeff Fessler, The University ... |
github | alcu/sms-master | eml_curvature.m | .m | sms-master/irt/emission/eml_curvature.m | 2,999 | utf_8 | 28ef226b3b96c11a2f18a7a6420bf93c | function ni = eml_curvature(yi, ci, ri, li, yb, ctype)
%|function ni = eml_curvature(yi, ci, ri, li, yb, ctype)
%|
%| compute surrogate parabola curvatures for Poisson emission model:
%| yi ~ Poisson(ci * li + ri)
%|
%| in
%| yi count data
%| ci calibration factors
%| ri mean background counts (scatter, randoms, etc.... |
github | alcu/sms-master | eml_check.m | .m | sms-master/irt/emission/eml_check.m | 1,536 | utf_8 | c88b108681d5fb82dc9e11276a03d14e | function eml_check(yi, ci, ri, varargin)
%function eml_check(yi, ci, ri [, 'os', nblock])
% check the validity of emission data from Poisson transmission model
% if 'os' option, then check that data is a 2d matrix, since only the
% second index is subsetized. so for SPECT or 3D PET this means one must
% reshape each ... |
github | alcu/sms-master | eml_osem.m | .m | sms-master/irt/emission/eml_osem.m | 4,586 | utf_8 | ae5292f0144a4f37ad5299b124764a39 | function [xs, precon] = eml_osem(x, Gb, yi, ci, ri, varargin)
%|function xs = eml_osem(x, Gb, yi, ci, ri, [options])
%| E-ML-OSEM algorithm for image reconstruction from Poisson emission data
%| (ordered subsets expectation maximization)
%| model: Y_i ~ Poisson(c_i [G x]_i + r_i)
%|
%| in
%| x [np 1] initial estimat... |
github | alcu/sms-master | mlem_deconv.m | .m | sms-master/irt/emission/arch/mlem_deconv.m | 703 | utf_8 | 3863c524bff511b2e74b7e7b78dbcdd7 | From fessler@eecs.umich.edu Thu Jun 1 16:56 EDT 2000
To: millsk@engin.umich.edu
Subject: ml-em
Cc: fessler@eecs.umich.edu
Content-Type: text
Content-Length: 539
function f = mlem(f, g, h)
if nargin < 3
ftrue = zeros(11); ftrue(3:5,4:7) = 1; ftrue(4,5) = 0;
h = ones(3,3);
g = conv2(ftrue, h, 'same');
g = g + 0.1... |
github | alcu/sms-master | makeW.m | .m | sms-master/irt/align/makeW.m | 4,750 | utf_8 | 57e7c5568dfb5657b48cfa03b3fb8eb1 | function [W Wgx Wgy Wgz] = makeW(Bcell, Acell)
%function [W Wgx Wgy Wgz] = makeW(Bcell, Acell)
%
% Create a Fatrix W operation of deformation based on rect basis
%
% in:
% 'Bcell' array of cells of fatrix operation B for warping {x}{y}{z}
% 'Acell' array of cells of warping coefficients Alpha {x}{y}{z}
%
% out:
%... |
github | alcu/sms-master | knot_geom.m | .m | sms-master/irt/align/knot_geom.m | 3,584 | utf_8 | 33e0fed3a3344fcd90c1dfd0525157d0 | function st = knot_geom(varargin)
%function st = knot_geom(varargin)
%
% Create a "knot geometry" structure that describes the B-spline
% grid of a single 2d image depending on "image geometry"
% Using this structure should facilitate "object oriented" code.
%
% options for 2d
% 'nx' # knots of x
% 'ny' # knots of y... |
github | alcu/sms-master | makeB.m | .m | sms-master/irt/align/makeB.m | 9,582 | utf_8 | f7a62f5fae237cb394e56c29f6bc580b | function [B Bgx Bgy Bgz] = makeB(ig, kg, varargin)
%|function [B Bgx Bgy Bgz] = makeB(ig, kg, [?])
%|
%| Create a Fatrix B operation of warping based on cubic B-spline
%|
%| in:
%| 'ig' image geometry
%| 'kg' knot geometry
%|
%| out:
%| B fatrix operation for warping
%| Bgx fatrix operation for warping
%| Bgy f... |
github | alcu/sms-master | read_zubal_attn.m | .m | sms-master/irt/data/read_zubal_attn.m | 1,636 | utf_8 | c3c39860ff3cee159d596158c6c0b1a4 | function x = read_zubal_attn(varargin)
%function x = read_zubal_attn(options)
% read in zubal attenuation phantom from data directory,
% and assign it attenuation coefficients in inverse mm units.
% options
% 'nx' desired size
% 'ny'
% 'ddir' data directory
if nargout == 0, help(mfilename), read_zubal_attn_test, er... |
github | alcu/sms-master | read_zubal_emis.m | .m | sms-master/irt/data/read_zubal_emis.m | 1,183 | utf_8 | 89a91f9c1ff386c38efc8b814549519e | function x = read_zubal_emis(varargin)
%function x = read_zubal_emis(options)
% read in zubal emission phantom from data directory
% options
% 'nx' desired size
% 'ny'
% 'ddir' data directory
if nargout == 0, help(mfilename), read_zubal_emis_test, error(mfilename), end
arg.ddir = '';
arg.file = 'zubal,emis.raw';
a... |
github | alcu/sms-master | read_ncat.m | .m | sms-master/irt/data/read_ncat.m | 2,354 | utf_8 | 2c889b460b59d77709798e3edab15658 | function x = read_ncat(varargin)
%|function x = read_ncat(options)
%|
%| read in a slice of the NCAT phantom
%| and assign it attenuation coefficients in inverse mm units.
%|
%| options
%| 'nx' desired size
%| 'ny'
%| 'mu' [5 1] desired image intensity values
%| 0 background, 1 lung, 2 body, 3 spine, 4 ribs
%| 'ma... |
github | alcu/sms-master | tpl_inc.m | .m | sms-master/irt/transmission/tpl_inc.m | 7,361 | utf_8 | 7497efb9b84b5448ce1211aaefe83413 | function xs = tpl_inc(x, Gb, yi, bi, ri, R, niter, pixmax, curv, ...
subout, enhance, gi, denom, relax0, chat)
%function xs = tpl_inc(x, Gb, yi, bi, ri, R, niter, pixmax, curv, ...
% subout, enhance, gi, denom, relax0, chat)
%
% TRIOT: incremental SPS algorithm for transmission Poisson problem
% (ordered subsets se... |
github | alcu/sms-master | tpl_obj.m | .m | sms-master/irt/transmission/tpl_obj.m | 2,150 | utf_8 | a751f86f1f4258b7860dfdcc1f80ae71 | function [obj, like, penal] = tpl_obj(xs, G, yi, bi, ri, R, mask, varargin)
%function [obj, like, penal] = tpl_obj(xs, G, yi, bi, ri, R, mask, [options])
%
% compute transmission penalized Poisson likelihood for each column of x
%
% model: Y_i ~ Poisson(b_i exp(-[G x]_i) + r_i)
% in
% xs [np,niter] image estimates
% G... |
github | alcu/sms-master | trl_curvature.m | .m | sms-master/irt/transmission/trl_curvature.m | 3,121 | utf_8 | 6fdd14b9a2607763fe2ac158a34c5012 | function ni = trl_curvature(yi, bi, ri, li, ctype)
%function ni = trl_curvature(yi, bi, ri, li, ctype)
%
% Compute surrogate parabola curvatures for Poisson transmission model
% ctype:
% oc erdogan's optimal curvatures
% pc precomputed curvatures, ala trpl3
% nc newton curvatures
%
% fix: align with C version of trpl2... |
github | alcu/sms-master | fbp2_sino_hilbert.m | .m | sms-master/irt/fbp/fbp2_sino_hilbert.m | 2,981 | utf_8 | ec1e7a6aae32e2dfc2b184a7408670ed | function [sino, hn, nn, Hk] = fbp2_sino_hilbert(sino, varargin)
%|function [sino, hn, nn, Hk] = fbp2_sino_hilbert(sino, [options])
%|
%| Apply band-limited Hilbert-transform filter to 2D sinogram.
%| Frequency response: H(u) = -1i * sign(u) * rect(u/2/umax)
%|
%| in
%| sino [nb (L)] sinogram(s)
%|
%| options
%| dr | ... |
github | alcu/sms-master | ct_geom.m | .m | sms-master/irt/fbp/ct_geom.m | 22,221 | utf_8 | 69bce59eb85dcda133b5f6ecb370c3bc | function st = ct_geom(type, varargin)
%|function st = ct_geom(type, varargin)
%|
%| Create the "CT geometry" structure that describes the sampling
%| characteristics of a cone-beam CT system (axial or helical).
%| (Use sino_geom() for 2D fan-beam or parallel-beam systems.)
%|
%| in
%| type 'fan' multi-slice fan-beam... |
github | alcu/sms-master | rect_im.m | .m | sms-master/irt/fbp/rect_im.m | 4,326 | utf_8 | f6bbc57287c4321a7432a37afbebacd5 | function [phantom, params] = rect_im(ig, params, varargin)
%|function [phantom, params] = rect_im(ig, params, options)
%|
%| generate rectangle phantom image from parameters:
%| [x_center y_center x_width y_width angle_degrees amplitude]
%|
%| in
%| ig image_geom() object
%| params rect parameters, if empty, use ... |
github | alcu/sms-master | cylinder_proj.m | .m | sms-master/irt/fbp/cylinder_proj.m | 6,631 | utf_8 | 45b9e0b74e235e7360e33f4764c8a0a6 | function proj = cylinder_proj(cg, params, varargin)
%|function proj = cylinder_proj(cg, params, [options])
%|
%| Compute set of 2d line-integral projection views of (elliptical) cylinder(s).
%| Works for these 3D geometries:
%| parallel beam
%| flat-detector cone-beam
%| arc-detector cone-beam (3rd generation CT)
%| ... |
github | alcu/sms-master | fbp_fan_arc.m | .m | sms-master/irt/fbp/fbp_fan_arc.m | 6,567 | utf_8 | 2358247668f590bb19c79e3a345b8e19 | function img = fbp_fan_arc(sino, G, window, varargin)
%function img = fbp_fan_arc(sino, G, window, [options])
%
% FBP reconstruction of fan-beam tomography data collected with arc detector.
% Assumes system 14 geometry and extracts parameters from Gtomo2_dsc object.
% Assumes that the arc is focused on the source, e.g... |
github | alcu/sms-master | ellipse_motion.m | .m | sms-master/irt/fbp/ellipse_motion.m | 1,286 | utf_8 | 9a86f801010ee6877a65e62ef4d79313 | function es = ellipse_motion(ells, varargin)
%function es = ellipse_motion(ells, varargin)
%
% create strum object for simple ellipse motion
% to pass to ellipse_sino()
%
% in:
% ells [ne,6] [centx centy radx rady angle_degrees amplitude]
%
% options:
% 'type' 'linear' (default)
% 'none' no motion, for testing
%... |
github | alcu/sms-master | feldkamp-jacket.m | .m | sms-master/irt/fbp/feldkamp-jacket.m | 12,207 | utf_8 | 2bb292dba8f6277fea46b8da554df3e9 | function img = feldkamp(cg, ig, proj, varargin)
%|function img = feldkamp(cg, ig, proj, varargin)
%|
%| version modified by james balter and others to try accelereys "jacket"
%| UNDER DEVELOPMENT!
%|
%| FBP reconstruction of cone-beam tomography data collected with
%| a circular source trajectory.
%| See feldkamp_exa... |
github | alcu/sms-master | fdk_filter.m | .m | sms-master/irt/fbp/fdk_filter.m | 1,605 | utf_8 | 641a4c1e8794369e4b5c403b8e5cd444 | function proj = fdk_filter(proj, window, dsd, dfs, ds)
%function proj = fdk_filter(proj, window, dsd, dfs, ds)
%|
%| step 2 of FDK cone-beam CT reconstruction:
%| filter the (zero padded) projections
%|
%| in
%| proj [ns nt na]
%| window [npad] or 'ramp' 'hann'
%| or see fbp2_window()
%|
%| out
%| proj [ns nt na]
... |
github | alcu/sms-master | ellipse_im.m | .m | sms-master/irt/fbp/ellipse_im.m | 10,054 | utf_8 | 86875eb20d91b1d13e089e456032e3c0 | function [phantom, params] = ellipse_im(ig, params, varargin)
%|function [phantom, params] = ellipse_im(ig, params, options)
%|
%| generate ellipse phantom image from parameters:
%| [x_center y_center x_radius y_radius angle_degrees amplitude]
%|
%| in
%| ig strum image_geom() object
%| params [ne 6] ellipse parame... |
github | alcu/sms-master | ellipse_sino.m | .m | sms-master/irt/fbp/ellipse_sino.m | 12,751 | utf_8 | eb1b332116538955688c14f638960970 | function [sino, pos, ang] = ellipse_sino(sg, ells, varargin)
%|function [sino, pos, ang] = ellipse_sino(sg, ells, [options])
%|
%| Create sinogram projection of one or more ellipses.
%| Works for both parallel-beam geometry and for fan-beam geometry.
%|
%| in
%| sg sinogram geometry object from sino_geom()
%| ells ... |
github | alcu/sms-master | rebin_sino.m | .m | sms-master/irt/fbp/rebin_sino.m | 1,104 | utf_8 | 02899ded58ef42a33d260b97b3f433bf | function sino2 = rebin_sino(sino1, geom1, geom2, varargin)
%|function sino2 = rebin_sino(sino1, geom1, geom2, varargin)
%|
%| Rebin a sinogram from the geomtry in "geom1" into the geometry in "geom2"
%| both of which were created using sino_geom().
%| The typical use is to convert between fan-beam and parallel-beam.
... |
github | alcu/sms-master | fbp2_sino_filter.m | .m | sms-master/irt/fbp/fbp2_sino_filter.m | 3,437 | utf_8 | f5cbff8e37ae0087b637be52862948de | function [sino, Hk, hn, nn] = fbp2_sino_filter(type, sino, varargin)
%|function [sino, Hk, hn, nn] = fbp2_sino_filter(type, sino, [options])
%|
%| Apply ramp-like filters to sinogram(s) for 2D FBP image reconstruction.
%| Both parallel-beam and fan-beam tomographic geometries are supported.
%| This approach of sampli... |
github | alcu/sms-master | fbp2.m | .m | sms-master/irt/fbp/fbp2.m | 13,304 | utf_8 | 4f57ca54e2a22eb02aced00af3d0b618 | function out = fbp2(varargin)
%function geom = fbp2(sg, ig, [setup_options])
%function image = fbp2(sino, geom, [recon_options])
%|
%| FBP 2D tomographic image reconstruction for parallel-beam or fan-beam cases,
%| with either flat or arc detector for fan-beam case.
%|
%| To use this, you first call it with the sinogr... |
github | alcu/sms-master | rebin_helix.m | .m | sms-master/irt/fbp/rebin_helix.m | 6,062 | utf_8 | 8319ffb3988aa03cf41e438563c10663 | function [sino orbits used] = rebin_helix(cg, ig, proj, varargin)
%|function [sino orbits used] = rebin_helix(cg, ig, proj, varargin)
%|
%| A single-slice rebinning (SSRB) method for cone-beam tomography data
%| collected with a helical source trajectory.
%|
%| in
%| cg ct_geom()
%| ig image_geom()
%| proj [ns nt... |
github | alcu/sms-master | rebin_par2fan.m | .m | sms-master/irt/fbp/rebin_par2fan.m | 7,464 | utf_8 | 9252672cf8184625830feb86741646d2 | function fsino = rebin_par2fan(psino, pgeom, fgeom, varargin)
%|function fsino = rebin_par2fan(psino, pgeom, fgeom, varargin)
%|
%| Rebin parallel-beam (or mojette) sinogram into fan-beam sinogram.
%| Also useful for mojette-to-parallel rebinning.
%|
%| in
%| psino [nr nphi] parallel-beam sinogram
%| pgeom sino_geom... |
github | alcu/sms-master | fbp2_sino_weight.m | .m | sms-master/irt/fbp/fbp2_sino_weight.m | 1,873 | utf_8 | c49936aa44b968b851e1fb4c730e9f67 | function sino = fbp2_sino_weight(sg, sino, varargin)
%|function sino = fbp2_sino_weight(sg, sino, varargin)
%|
%| Apply sinogram weighting for first step of 2D fan-beam FBP.
%| This matlab version is the backup alternative for users lacking mex routine.
%|
%| in
%| sg sino_geom()
%| ig image_geom()
%| sino [nb,na... |
github | alcu/sms-master | fbp2_back_fan.m | .m | sms-master/irt/fbp/fbp2_back_fan.m | 4,230 | utf_8 | 4dd62434653b7cdce46fac43b7320668 | function img = fbp2_back_fan(sg, ig, sino, varargin)
%|function img = fbp2_back_fan(sg, ig, sino, varargin)
%|
%| 2D backprojection for fan-beam FBP.
%| This matlab version is for users lacking mex backprojector.
%|
%| in
%| sg sino_geom()
%| ig image_geom()
%| sino [nb na nz] sinogram (line integrals)
%|
%| opti... |
github | alcu/sms-master | ellipsoid_proj.m | .m | sms-master/irt/fbp/ellipsoid_proj.m | 3,986 | utf_8 | 3da6b034c013b99de802ccab006c9746 | function proj = ellipsoid_proj(cg, params, varargin)
%|function proj = ellipsoid_proj(cg, params, varargin)
%|
%| Compute set of 2d line-integral projection views of ellipsoid(s).
%| Works for both parallel-beam and cone-beam geometry.
%|
%| in
%| cg ct_geom()
%| params [ne 9] ellipsoid parameters:
%| [x_center ... |
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