spm_mb_shape.m

function varargout = spm_mb_shape(varargin)
% Shape model
%
% FORMAT psi0      = spm_mb_shape('affine',d,Mat)
% FORMAT B         = spm_mb_shape('affine_bases',code)
% FORMAT psi       = spm_mb_shape('compose',psi1,psi0)
% FORMAT id        = spm_mb_shape('identity',d)
% FORMAT dat       = spm_mb_shape('init_def',dat,sett.ms)
% FORMAT l         = spm_mb_shape('LSE0',mu,ax)
% FORMAT a1        = spm_mb_shape('pull1',a0,psi,r)
% FORMAT [f1,w1]   = spm_mb_shape('push1',f,psi,d,r)
% FORMAT sd        = spm_mb_shape('samp_dens',Mmu,Mn)
% FORMAT varargout = spm_mb_shape('shoot',v0,kernel,args)
% FORMAT mu1       = spm_mb_shape('shrink_template',mu,oMmu,sett)
% FORMAT P         = spm_mb_shape('softmax0',mu,ax)
% FORMAT E         = spm_mb_shape('template_energy',mu,sett)
% FORMAT dat       = spm_mb_shape('update_affines',dat,mu,sett)
% FORMAT [mu,dat]  = spm_mb_shape('update_mean',dat, mu, sett)
% FORMAT dat       = spm_mb_shape('update_simple_affines',dat,mu,sett)
% FORMAT dat       = spm_mb_shape('update_velocities',dat,mu,sett)
% FORMAT dat       = spm_mb_shape('update_warps',dat,sett)
% FORMAT [dat,mu]  = spm_mb_shape('zoom_volumes',dat,mu,sett,oMmu)
% FORMAT sz        = spm_mb_shape('zoom_settings', v_settings, mu, n)
% FORMAT psi       = get_def(dat,sett.ms.Mmu)
%__________________________________________________________________________
% Copyright (C) 2019-2020 Wellcome Centre for Human Neuroimaging

% $Id: spm_mb_shape.m 8226 2022-02-24 10:44:46Z john $
[varargout{1:nargout}] = spm_subfun(localfunctions,varargin{:});
%==========================================================================

%==========================================================================
function psi0 = affine(d,Mat,samp)
% Generate an affine deformation field
if nargin<3 || numel(samp)~=3
    samp = [1 1 1];
end
d     = [d 1 1];
d     = d(1:3);
id    = identity(d,samp);
psi0  = vec2vol(bsxfun(@plus,vol2vec(id)*Mat(1:3,1:3)',Mat(1:3,4)'),size(id));
if d(3) == 1, psi0(:,:,:,3) = 1; end
%==========================================================================

%==========================================================================
function B = affine_bases(code)
g     = regexpi(code,'(?<code>\w*)\((?<dim>\d*)\)','names');
g.dim = str2double(g.dim);
if numel(g.dim)~=1 || (g.dim ~=0 && g.dim~=2 && g.dim~=3)
    error('Can not use size');
end
if g.dim==0
    B        = zeros(4,4,0);
elseif g.dim==2
    switch g.code
    case 'T'
        % T(2) - translations
        B        = zeros(4,4,2);
        B(1,4,1) =  1;
        B(2,4,2) =  1;
    case 'SO'
        % SO(2) - Special Orthogonal Group - rotations
        B        = zeros(4,4,1);
        B(1,2,1) =  1;
        B(2,1,1) = -1;
    case 'SE'
        % SE(2) - Special Euclidean Group - rigid
        B        = zeros(4,4,3);
        B(1,4,1) =  1;
        B(2,4,2) =  1;
        B(1,2,3) =  1;
        B(2,1,3) = -1;
    otherwise
        error('Unknown group.');
    end
elseif g.dim==3
    switch g.code
    case 'T'
        % T(3)
        B        = zeros(4,4,3);
        B(1,4,1) =  1;
        B(2,4,2) =  1;
        B(3,4,3) =  1;
    case 'SO'
        % SO(3) - Special Orthogonal
        B        = zeros(4,4,3);
        B(1,2,1) =  1;
        B(2,1,1) = -1;
        B(1,3,2) =  1;
        B(3,1,2) = -1;
        B(2,3,3) =  1;
        B(3,2,3) = -1;
    case 'SE'
        % SE(3) - Special Euclidean
        B        = zeros(4,4,6);
        B(1,4,1) =  1;
        B(2,4,2) =  1;
        B(3,4,3) =  1;
        B(1,2,4) =  1;
        B(2,1,4) = -1;
        B(1,3,5) =  1;
        B(3,1,5) = -1;
        B(2,3,6) =  1;
        B(3,2,6) = -1;
    case 'SEZ'
        % I don't know what this one should be called
        % but it is SE(3) with an isotropic zoom.
        B        = zeros(4,4,7);
        B(1,4,1) =  1;
        B(2,4,2) =  1;
        B(3,4,3) =  1;
        B(1,2,4) =  1;
        B(2,1,4) = -1;
        B(1,3,5) =  1;
        B(3,1,5) = -1;
        B(2,3,6) =  1;
        B(3,2,6) = -1;
        B(1,1,7) =  1;
        B(2,2,7) =  1;
        B(3,3,7) =  1;        
    case 'Aff'
        % Aff(3) - Affine
        B        = zeros(4,4,12);
        B(1,1,1) =  1;
        B(2,1,2) =  1;
        B(3,1,3) =  1;
        B(1,2,4) =  1;
        B(2,2,5) =  1;
        B(3,2,6) =  1;
        B(1,3,7) =  1;
        B(2,3,8) =  1;
        B(3,3,9) =  1;
        B(1,4,10)=  1;
        B(2,4,11)=  1;
        B(3,4,12)=  1;
    otherwise
        error('Unknown group.');
    end
end
%==========================================================================

%==========================================================================
function psi = compose(psi1,psi0)
% Function composition
% \psi \gets \psi^1 \circ \psi^0
if isempty(psi1)
    psi = psi0;
else
    bc = spm_diffeo('boundary');
    spm_diffeo('boundary',0); % psi1 should have circulant boundaries
    psi = spm_diffeo('comp',psi1,psi0);
    spm_diffeo('boundary',bc);
end
if size(psi,3) == 1, psi(:,:,:,3) = 1; end % 2D
%==========================================================================

%==========================================================================
function id = identity(d,samp)
% Identity transform of dimensions d, but with subsampling (samp)
if nargin<2 || numel(samp)~=3
    samp = [1 1 1];
end
d  = [d 1 1];
d  = d(1:3);
ds = floor((d(:)'-1)./samp(:)')+1;
id = zeros([ds(:)',3],'single');
[id(:,:,:,1),id(:,:,:,2),id(:,:,:,3)] = ndgrid(single(1:samp(1):d(1)),...
                                               single(1:samp(2):d(2)),...
                                               single(1:samp(3):d(3)));
%==========================================================================

%==========================================================================
function l = LSE0(mu,ax)
% Strictly convex log-sum-exp function
% https://en.wikipedia.org/wiki/LogSumExp#A_strictly_convex_log-sum-exp_type_function

if nargin<2, ax = 4; end
mx = max(mu,[],ax);
l  = log(exp(-mx) + sum(exp(bsxfun(@minus,mu,mx)),ax)) + mx;
%==========================================================================

%==========================================================================
function a1 = pull1(a0,psi,r)
% Resample an image or set of images
% FORMAT a1 = pull1(a0,psi,r)
%
% a0  - Input image(s)
% psi - Deformation
% r   - subsampling density in each dimension (default: [1 1 1])
%
% a1  - Output image(s)
%

bc = spm_diffeo('boundary');
spm_diffeo('boundary',1); % Neumann boundary conditions for images

if nargin<3, r=[1 1 1]; end

d  = [size(a0) 1 1 1];
if isempty(a0)
    a1 = a0;
elseif isempty(psi)
    a1 = a0;
else
    if r==1
        a1 = spm_diffeo('pull',reshape(a0,[d(1:3) prod(d(4:end))]),psi);
        d1 = size(a1);
        a1 = reshape(a1,[d1(1:3) d(4:end)]);
    else
        if d(3)>1, zrange = range(r(3)); else, zrange = 0; end
        if d(2)>1, yrange = range(r(2)); else, yrange = 0; end
        if d(1)>1, xrange = range(r(1)); else, xrange = 0; end
        id = identity(size(psi));
        a1 = zeros([size(psi,1),size(psi,2),size(psi,3),size(a0,4)],'single');
        for l=1:prod(d(4:end))
            tmp = single(0);
            al  = single(a0(:,:,:,l));
            for dz=zrange
                for dy=yrange
                    for dx=xrange
                        ids  = id  + cat(4,dx,dy,dz);
                        psi1 = spm_diffeo('pull',psi-id,    ids)+ids;
                        as   = spm_diffeo('pull',al,psi1);
                       %ids  = id  - cat(4,dx,dy,dz);
                        tmp  = tmp + spm_diffeo('push',as,  ids);
                    end
                end
            end
            a1(:,:,:,l) = tmp/(numel(zrange)*numel(yrange)*numel(xrange));
            d1 = size(a1);
            a1 = reshape(a1,[d1(1:3) d(4:end)]);
        end
    end
end
spm_diffeo('boundary',bc);
%==========================================================================

%==========================================================================
function [f1,w1] = push1(f,psi,d,r)
% push an image (or set of images) accorging to a spatial transform
% FORMAT [f1,w1] = push1(f,psi,d,r)
%
% f   - Image (3D or 4D)
% psi - Spatial transform
% d   - dimensions of output (default: size of f)
% r   - subsampling density in each dimension (default: [1 1 1])
%
% f1  - "Pushed" image
%

bc = spm_diffeo('boundary');
spm_diffeo('boundary',1); % Neumann boundary conditions for images

if nargin<4, r = [1 1 1]; end
if nargin<3, d = [size(f,1) size(f,2) size(f,3)]; end
if numel(r)==1, r = repmat(r,[1 3]); end

d1 = [size(f) 1 1];
f  = reshape(single(f),[d1(1:3) prod(d1(4:end))]);

%msk    = isfinite(f);
%f(msk) = 0;
if ~isempty(psi)
    if all(r==1)
        if nargout==1
            f1      = spm_diffeo('push',f,psi,d);
        else
            [f1,w1] = spm_diffeo('push',f,psi,d);
        end
    else

        if d(3)>1, zrange = range(r(3)); else, zrange = 0; end
        if d(2)>1, yrange = range(r(2)); else, yrange = 0; end
        if d(1)>1, xrange = range(r(1)); else, xrange = 0; end

        id    = identity(size(psi));
        f1    = single(0);
        w1    = single(0);
        for dz=zrange
            for dy=yrange
                for dx=xrange
                    ids       = bsxfun(@plus, id, cat(4,dx,dy,dz));
                    psi1      = spm_diffeo('pullc',psi-id,   ids)+ids;
                    fs        = spm_diffeo('pull',f, ids);
                    if nargout==1
                        fs      = spm_diffeo('push',fs, psi1,d(1:3));
                        f1      = f1  + fs;
                    else
                        [fs,ws] = spm_diffeo('push',fs, psi1,d(1:3));
                        f1      = f1  + fs;
                        w1      = w1  + ws;
                    end
                end
            end
        end
        scale = 1/(numel(zrange)*numel(yrange)*numel(xrange));
        f1    = f1*scale;
        w1    = w1*scale;
    end
else
    msk      = isfinite(f);
    f1       = f;
    f1(~msk) = 0;
    w1       = single(all(msk,4));
end
f1 = reshape(f1,[d d1(4:end)]);
spm_diffeo('boundary',bc);
%==========================================================================

%==========================================================================
function r = range(n)
r = (-floor((n-1)/2):ceil((n-1)/2))/n;
%==========================================================================

%==========================================================================
function sd = samp_dens(Mmu,Mn)
vx_mu = sqrt(sum(Mmu(1:3,1:3).^2,1));
vx_f  = sqrt(sum( Mn(1:3,1:3).^2,1));
sd    = max(round(2.0*vx_f./vx_mu),1);
%==========================================================================

%==========================================================================
function mu = shrink_template(mu,oMmu,sett)

% Parse function settings
d     = sett.ms.d;
Mmu   = sett.ms.Mmu;
d0    = [size(mu,1) size(mu,2) size(mu,3)];
Mzoom = Mmu\oMmu;
if any(d0~=d) || norm(Mzoom-eye(4))>1e-4
    y      = affine(d0,Mzoom);
    mu     = exp(bsxfun(@minus,mu,LSE0(mu,4)));
    [mu,c] = push1(mu,y,d,1);
    e      = eps('single');
    mu     = bsxfun(@rdivide,mu,max(c,e));
    mu     = bsxfun(@minus,log(max(mu,e)),log(max(1-sum(mu,4),e)));
end
%==========================================================================

%==========================================================================
function P = softmax0_delta(mu,delta)
if nargin>=2 && ~isempty(delta)
    P = zeros(size(mu),'like',mu);
    delta = reshape(delta,[1 1 1 numel(delta)]);
    for k=1:size(mu,3) % Save memory by looping over planes
        P(:,:,k,:) = softmax0(bsxfun(@plus,mu(:,:,k,:),delta));
    end
else
    P = softmax0(mu,4);
end
%==========================================================================

%==========================================================================
function P = softmax0(mu,ax)
% safe softmax function (matches LSE0)

if nargin<2, ax = 4; end
mx  = max(mu,[],ax);
E   = exp(bsxfun(@minus,mu,mx));
den = sum(E,ax)+exp(-mx);
P   = bsxfun(@rdivide,E,den);
%==========================================================================

%==========================================================================
function E = template_energy(mu,mu_settings)
% mu(:)'*kron(eye(K)-1/(K+1),L)*mu(:), where L is the vel2mom regulariser
if ~isempty(mu_settings)
    g = reg_mu(mu, mu_settings);
    E = 0.5*mu(:)'*g(:);
else
    E = 0;
end
%==========================================================================

%==========================================================================
function dat = update_affines(dat,mu,sett)

% Update the affine parameters
B = sett.B;
if ~isempty(B)
    nw = get_num_workers(sett,dat,12,7*sett.K+20);
    if nw > 1 && numel(dat) > 1 % PARFOR
        parfor(n=1:numel(dat),nw), dat(n) = update_affines_sub(dat(n),mu,sett); end
    else % FOR
        for n=1:numel(dat), dat(n) = update_affines_sub(dat(n),mu,sett); end
    end

    groupwise = isa(sett.mu,'struct') && isfield(sett.mu,'create');
    if groupwise
        % Zero-mean the affine parameters
        mq = sum(cat(2,dat(:).q),2)/numel(dat);

        if isfield(sett.mu.create,'issym') && sett.mu.create.issym>0
            % Parameters that change x-coordinates are not mean
            % corrected when dealing with a symmetric template.
            tmp = sum(B(1,:,:).^2,2);
            tmp = squeeze(tmp~=0);
            mq(tmp) = 0;
        end

        for n=1:numel(dat)
            dat(n).q = dat(n).q - mq;
        end
    end

    % Update orientations in deformation headers when appropriate
    for n=1:numel(dat)
        Mdef       = spm_dexpm(dat(n).q,B)\sett.ms.Mmu;
        dat(n).psi = spm_mb_io('save_mat',dat(n).psi,Mdef);
    end
end
%==========================================================================

%==========================================================================
function datn = update_affines_sub(datn,mu,sett)
% This could be made more efficient.


% Parse function settings
accel = sett.accel;
B     = sett.B;
d     = sett.ms.d;
Mmu   = sett.ms.Mmu;

q     = double(datn.q);
Mn    = datn.Mat;
samp  = datn.samp;
[Mr,dM3] = spm_dexpm(q,B);
dM   = zeros(12,size(B,3));
for m=1:size(B,3)
    tmp     = Mmu\dM3(:,:,m)*Mn;
    dM(:,m) = reshape(tmp(1:3,:),12,1);
end

df   = datn.dm;
psi0 = affine(df,Mmu\Mr*Mn,samp);
ds   = [size(psi0,1),size(psi0,2),size(psi0,3)];
psi1 = get_def(datn,Mmu);
if ~isempty(psi1)
    spm_diffeo('boundary',0);
    J   = spm_diffeo('jacobian',psi1);
    J   = reshape(pull1(reshape(J,[d 3*3]),psi0),[ds 3 3]);
    psi = compose(psi1,psi0);
else
    J   = [];
    psi = psi0;
end
clear psi0  psi1

mu1 = pull1(mu,psi);
[f,datn] = spm_mb_classes(datn,mu1,sett);
M   = size(mu,4);
G   = zeros([ds M 3],'single');
for m=1:M
    % Tiny mismatch between bsplins (mirrored) boundaries and other Neumann boundaries
    [~,Gm{1},Gm{2},Gm{3}] = spm_diffeo('bsplins',mu(:,:,:,m),psi,[1 1 1  0 0 0]);
    for i1=1:3
        if ~isempty(J)
            tmp = single(0);
            for j1=1:3
                tmp = tmp + J(:,:,:,j1,i1).*Gm{j1};
            end
        else
            tmp = Gm{i1};
        end
        tmp(~isfinite(tmp)) = 0;
        G(:,:,:,m,i1) = tmp;
    end
    clear Gm
end
clear J mu

msk    = all(isfinite(f),4) & all(isfinite(mu1),4);
mu1(~isfinite(mu1)) = 0;
a      = mask(f - softmax0_delta(mu1,datn.delta),msk);
[H,g]  = affine_hessian(mu1,G,a,single(msk),datn.delta,accel,samp);
g      = double(dM'*g);
H      = dM'*H*dM;
H      = H + eye(numel(q))*(norm(H)*1e-6 + 0.001);
q      = q + (H\g);
datn.q = q;
%==========================================================================

%==========================================================================
function [H,g] = affine_hessian(mu,G,a,w,delta,accel,samp)
if nargin<6, samp = [1 1 1]; end
d = [size(mu,1),size(mu,2),size(mu,3)];
I = horder(3);
H = zeros(12,12);
g = zeros(12, 1);
[x{1:4}] = ndgrid(((1:d(1))-1)*samp(1)+1,((1:d(2))-1)*samp(2)+1,1,1);
for i=1:d(3)
    x{3} = x{3}*0+(i-1)*samp(3)+1;
    gv   = reshape(sum(bsxfun(@times,a(:,:,i,:),G(:,:,i,:,:)),4),[d(1:2) 1 3]);
    Hv   = bsxfun(@times,w(:,:,i),velocity_hessian(mu(:,:,i,:),G(:,:,i,:,:),delta,accel));
    for i1=1:12
        k1g   = rem(i1-1,3)+1;
        k1x   = floor((i1-1)/3)+1;
        g(i1) = g(i1) + sum(sum(sum(x{k1x}.*gv(:,:,:,k1g))));
        for i2=1:12
            k2g      = rem(i2-1,3)+1;
            k2x      = floor((i2-1)/3)+1;
            H(i1,i2) = H(i1,i2) + sum(sum(sum(x{k1x}.*Hv(:,:,:,I(k1g,k2g)).*x{k2x})));
        end
    end
end
%==========================================================================

%==========================================================================
function [mu,dat] = update_mean(dat, mu, sett)

[dat,mu] = adjust_delta(dat,mu);

% Parse function settings
accel       = sett.accel;
mu_settings = sett.ms.mu_settings;

g  = reg_mu(mu,mu_settings);
w  = zeros(sett.ms.d,'single');
nw = get_num_workers(sett,dat,sett.K+4,3*sett.K+6);
if nw > 1 && numel(dat) > 1 % PARFOR
    parfor(n=1:numel(dat),nw)
        [gn,wn,dat(n)] = update_mean_sub(dat(n),mu,sett);
        g              = g + gn;
        w              = w + wn;
    end
else
    for n=1:numel(dat) % FOR
        [gn,wn,dat(n)] = update_mean_sub(dat(n),mu,sett);
        g              = g + gn;
        w              = w + wn;
    end
end
clear gn wn

if isfield(sett.mu.create,'issym') && sett.mu.create.issym>0
    g  = (g  +  g(end:-1:1,:,:,:))/2;
    w  = (w  +  w(end:-1:1,:,:))/2;
    mu = (mu + mu(end:-1:1,:,:,:))/2;
end

mu = gn_mu_update(mu,g,w,mu_settings,accel);
%==========================================================================

%==========================================================================
function [dat,mu] = adjust_delta(dat,mu)
% Zero-mean the deltas over subjects, and make a corresponding
% adjustment to mu.
mean_delta = mean(cat(1,dat.delta),1);
if ~isempty(mean_delta)
    for n=1:numel(dat)
        dat(n).delta = dat(n).delta - mean_delta;
    end
    mean_delta = reshape(mean_delta,[1 1 1 size(mu,4)]);
    mu         = bsxfun(@plus,mu,mean_delta);
end
%==========================================================================

%==========================================================================
function mu = gn_mu_update(mu,g,w,mu_settings,accel,nit)
% Solve the problem
% x = (L + H)\g
% Regularisation L = kron(eye(K)-1/(K+1),L0)
% Hessian H is constructed on the fly using mu and w.
if nargin<6, nit   = [1 16 1]; end
if nargin<5, accel = 0.8;      end
s = softmax0(mu);
x = zeros(size(s),'single');
for it=1:nit(1)
    for subit=1:nit(2), x = relax_mu1(x,s,g,w,mu_settings,accel); end
    for subit=1:nit(3), x = relax_mu2(x,s,g,w,mu_settings,accel); end
end
mu = mu - x;
%==========================================================================

%==========================================================================
function x = relax_mu2(x,s,g,w,mu_settings,accel)
% Use Gauss-Seidel method for computing updates
% https://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method
% This approach is for when the regularisation is high relative
% to the Hessian of the data.

if isempty(x), x = zeros(size(g),'single'); end
spm_field('boundary',1);
d = size(g);
K = d(4);
for k=1:K

    % Diagonal elements of Hessian
    h_kk = hessel(k,k,K,accel,s).*w;

    % Dot product betwen off-diagonals of likelihood Hessian and x
    g_k = zeros(d(1:3),'single');
    for k1=1:K
        if k1~=k, g_k = g_k + x(:,:,:,k1).*hessel(k,k1,K,accel,s); end
    end
    g_k = g_k.*w;

    % Dot product between off-diagonals of regularisation Hessian and x
    g_k = g_k - spm_field('vel2mom', (sum(x,4)-x(:,:,:,k))/(K+1), mu_settings);

    % Gauss-Seidel update
    x(:,:,:,k) = spm_field(h_kk, g(:,:,:,k) - g_k, [mu_settings(1:3) mu_settings(4:end)*(1-1/(K+1)) 2 2]);
end
%==========================================================================

%==========================================================================
function x = relax_mu1(x,s,g,w,mu_settings,accel)
% https://en.wikipedia.org/wiki/Jacobi_method
% This approach is for when the regularisation is small relative
% to the Hessian of the data.

if isempty(x), x = zeros(size(g),'single'); end
L  = operator(mu_settings);
dc = L(1,1,1);
d  = size(g);
K  = d(4);
spm_field('boundary',1);

% Multiply with "off diagonal" part of matrix and compute the residual
x  = spm_field('vel2mom',x,mu_settings) - x*dc;
sx = sum(x,4)/(K+1);
for k=1:K, x(:,:,:,k) = g(:,:,:,k) - (x(:,:,:,k) - sx); end

% Divide by "diagonal" part
H = zeros([d(1:2) 1 d(4)*(d(4)+1)/2],'single');
for i=1:d(3)

    % Construct Hessian for this slice, adding the diagona term from the regularisation
    si = s(:,:,i,:);
    wi = w(:,:,i);

    % Diagonal components
    for k=1:K, H(:,:,1,k) = hessel(k, k, K, accel, si).*wi + dc*(1-1/(K+1)); end

    % Off-diagonal components
    kk = K+1;
    for k=1:K
        for k1=(k+1):K
            H(:,:,1,kk) = hessel(k, k1, K, accel, si).*wi - dc/(K+1);
            kk = kk + 1;
        end
    end

    % Update with only diagonal part of regularisation
    x(:,:,i,:) = spm_field(H,x(:,:,i,:),[mu_settings(1:3) 0 0 0  1 1]);
end
%==========================================================================

%==========================================================================
function h = hessel(k,k1,K,accel,s)
% Element of Hessian
if k==k1
    h = 0.5*(1-1/(K+1)) + 1e-5;
    if accel>0
        sk = s(:,:,:,k);
        h  = accel*(sk-sk.^2) + (1-accel)*h;
    end
else
    h = -0.5/(K+1);
    if accel>0
        h = -accel*s(:,:,:,k).*s(:,:,:,k1) + (1-accel)*h;
    end
end
%==========================================================================

%==========================================================================
function L = operator(mu_settings)
b        = spm_field('boundary');
spm_field('boundary',0);
L        = zeros([5 5 5],'single');
L(1,1,1) = 1;
L        = spm_field('vel2mom',L,mu_settings);
spm_field('boundary',b);
%==========================================================================

%==========================================================================
function g = reg_mu(mu,mu_settings)
K = size(mu,4);
spm_field('boundary',1);
g = spm_field('vel2mom', bsxfun(@minus,mu, sum(mu,4)/(K+1)), mu_settings);
%==========================================================================

%==========================================================================
function [g,w,datn] = update_mean_sub(datn,mu,sett)

% Parse function settings
B        = sett.B;
d        = sett.ms.d;
Mmu      = sett.ms.Mmu;
df       = datn.dm;
q        = double(datn.q);
Mn       = datn.Mat;
samp     = datn.samp;

psi      = compose(get_def(datn,Mmu),affine(df, Mmu\spm_dexpm(q,B)*Mn,samp));

spm_diffeo('boundary',1); % Neumann
mu       = pull1(mu,psi);
[f,datn] = spm_mb_classes(datn,mu,sett);
[g,w]    = push1(softmax0_delta(mu,datn.delta) - f,psi,d,1);
%==========================================================================

%==========================================================================
function dat = update_simple_affines(dat,mu,sett)

% Parse function settings
accel = sett.accel;
B     = sett.B;
if ~isempty(B)
    groupwise = isa(sett.mu,'struct') && isfield(sett.mu,'create') && numel(dat)>1;

    % Update the affine parameters
    spm_diffeo('boundary',1);
    G  = spm_diffeo('grad',mu);
    H0 = velocity_hessian(mu,G,[],accel); % May need to use subject-specific deltas

    nw = get_num_workers(sett,dat,3*sett.K+4,3*sett.K+5);
    if nw > 1 && numel(dat) > 1 % PARFOR
        parfor(n=1:numel(dat),nw)
            dat(n) = update_simple_affines_sub(dat(n),mu,G,H0,sett);
        end
    else
        for n=1:numel(dat)
            dat(n) = update_simple_affines_sub(dat(n),mu,G,H0,sett);
        end
    end

    if groupwise
        % Zero-mean the affine parameters
        mq = sum(cat(2,dat(:).q),2)/numel(dat);

        if isfield(sett.mu.create,'issym') && sett.mu.create.issym>0
            % Parameters that change x-coordinates are not mean
            % corrected when dealing with a symmetric template.
            tmp = sum(B(1,:,:).^2,2);
            tmp = squeeze(tmp~=0);
            mq(tmp) = 0;
        end

        for n=1:numel(dat)
            dat(n).q = dat(n).q - mq;
        end
    end

    % Update orientations in deformation headers when appropriate
    for n=1:numel(dat)
        Mdef       = spm_dexpm(dat(n).q,B)\sett.ms.Mmu;
        dat(n).psi = spm_mb_io('save_mat',dat(n).psi,Mdef);
    end
end
%==========================================================================

%==========================================================================
function datn = update_simple_affines_sub(datn,mu,G,H0,sett)
if numel(datn.E) >=1
    eprev = datn.E(1)/datn.nvox;
else
    eprev = Inf;
end

for it=1:8
    datn = update_simple_affines_sub1(datn,mu,G,H0,sett);
    if eprev - datn.E(1)/datn.nvox < sett.tol, break; end
    eprev = datn.E(1)/datn.nvox;
end
%==========================================================================

%==========================================================================
function datn = update_simple_affines_sub1(datn,mu,G,H0,sett)

% Parse function settings
B    = sett.B;
d    = sett.ms.d;
Mmu  = sett.ms.Mmu;

df   = datn.dm;
q    = double(datn.q);
Mn   = datn.Mat;
samp = datn.samp;
[Mr,dM3] = spm_dexpm(q,B);
dM   = zeros(12,size(B,3));
for m=1:size(B,3)
    tmp     = (Mr*Mmu)\dM3(:,:,m)*Mmu;
    dM(:,m) = reshape(tmp(1:3,:),12,1);
end

psi      = affine(df,Mmu\Mr*Mn,samp);
mu1      = pull1(mu,psi);
[f,datn] = spm_mb_classes(datn,mu1,sett);
[a,w]    = push1(f - softmax0_delta(mu1,datn.delta),psi,d,1);
clear mu1 psi f

[H,g]    = simple_affine_hessian(mu,G,H0,a,w);
g        = double(dM'*g);
H        = dM'*H*dM;
H        = H + eye(numel(q))*(norm(H)*1e-5 + 0.1);
q        = q + (H\g);
datn.q   = q;
%==========================================================================

%==========================================================================
function [H,g] = simple_affine_hessian(mu,G,H0,a,w)
d  = [size(mu,1),size(mu,2),size(mu,3)];
I  = horder(3);
H  = zeros(12,12);
g  = zeros(12, 1);
[x{1:4}] = ndgrid(1:d(1),1:d(2),1,1);
for i=1:d(3)
    x{3} = x{3}*0+i;
    gv   = reshape(sum(bsxfun(@times,a(:,:,i,:),G(:,:,i,:,:)),4),[d(1:2) 1 3]);
    Hv   = bsxfun(@times,w(:,:,i),H0(:,:,i,:));
    for i1=1:12
        k1g   = rem(i1-1,3)+1;
        k1x   = floor((i1-1)/3)+1;
        g(i1) = g(i1) + sum(sum(sum(x{k1x}.*gv(:,:,:,k1g))));
        for i2=1:12
            k2g      = rem(i2-1,3)+1;
            k2x      = floor((i2-1)/3)+1;
            H(i1,i2) = H(i1,i2) + sum(sum(sum(x{k1x}.*Hv(:,:,:,I(k1g,k2g)).*x{k2x})));
        end
    end
end
%==========================================================================

%==========================================================================
function dat = update_velocities(dat,mu,sett)

% Parse function settings
groupwise = isa(sett.mu,'struct') && isfield(sett.mu,'create');
if groupwise
    scal = 1-1/numel(dat);
else
    scal = 1;
end
scal = min(scal,1.0);

spm_diffeo('boundary',1); % Neumann boundary
G  = spm_diffeo('grad',mu);

nw = get_num_workers(sett,dat,4*sett.K+28,3*sett.K+7);
if nw > 1 && numel(dat) > 1 % PARFOR
    parfor(n=1:numel(dat),nw), dat(n) = update_velocities_sub(dat(n),mu,G,sett,scal); end
else % FOR
    for n=1:numel(dat), dat(n) = update_velocities_sub(dat(n),mu,G,sett,scal); end
end
%==========================================================================

%==========================================================================
function datn = update_velocities_sub(datn,mu,G,sett,scal)

% Parse function settings
B          = sett.B;
d          = sett.ms.d;
Mmu        = sett.ms.Mmu;
s_settings = [2 2];
v_settings = sett.ms.v_settings;

if nargin<6, scal=1.0; end

v        = spm_mb_io('get_data',datn.v);
q        = datn.q;
Mn       = datn.Mat;
samp     = datn.samp;
Mr       = spm_dexpm(q,B);
Mat      = Mmu\Mr*Mn;
df       = datn.dm;
psi      = compose(get_def(datn,Mmu),affine(df,Mat,samp));
mu1      = pull1(mu,psi);
[f,datn] = spm_mb_classes(datn,mu1,sett);
[a,w]    = push1(f - softmax0_delta(mu1,datn.delta),psi,d,1);
clear psi f mu1

g         = reshape(sum(bsxfun(@times,a,G),4),[d 3]);
H         = velocity_hessian(mu,G,datn.delta,sett.accel);
H         = bsxfun(@times,w,H);
if size(G,3) == 1
    % Data is 2D -> add some regularisation
    H(:,:,:,3) = H(:,:,:,3) + mean(reshape(H(:,:,:,[1 2]),[],1));
end
clear a w

spm_diffeo('boundary',0); % Circulant boundary conditions
u0        = spm_diffeo('vel2mom', v, v_settings);                           % Initial momentum
datn.E(2) = 0.5*sum(u0(:).*v(:));                                           % Prior term
v         = v - scal*spm_diffeo('fmg', H, g + u0, [v_settings s_settings]); % Gauss-Newton update

if d(3)==1, v(:,:,:,3) = 0; end % If 2D
if v_settings(1)==0             % Mean displacement should be 0
    avg = mean(mean(mean(v,1),2),3);
    v   = v - avg;
end
datn.v = spm_mb_io('set_data',datn.v,v);
%==========================================================================

%==========================================================================
function H = velocity_hessian(mu,G,delta,accel)
d  = [size(mu,1),size(mu,2),size(mu,3)];
M  = size(mu,4);
Ab = 0.5*(eye(M)-1/(M+1)); % See Bohning's paper
H  = zeros([d 6],'single');
for i=1:d(3)
    if accel>0, s = softmax0_delta(mu(:,:,i,:),delta); end
    H11 = zeros(d(1:2));
    H22 = H11;
    H33 = H11;
    H12 = H11;
    H13 = H11;
    H23 = H11;
    for m1=1:M
        Gm11 = G(:,:,i,m1,1);
        Gm12 = G(:,:,i,m1,2);
        Gm13 = G(:,:,i,m1,3);
        if accel==0
            tmp = Ab(m1,m1);
        else
            sm1 = s(:,:,1,m1);
            tmp = (max(sm1.*(1-sm1),0))*accel + (1-accel)*Ab(m1,m1);
        end
        H11 = H11 + tmp.*Gm11.*Gm11;
        H22 = H22 + tmp.*Gm12.*Gm12;
        H33 = H33 + tmp.*Gm13.*Gm13;
        H12 = H12 + tmp.*Gm11.*Gm12;
        H13 = H13 + tmp.*Gm11.*Gm13;
        H23 = H23 + tmp.*Gm12.*Gm13;
        for m2=(m1+1):M
            if accel==0
                tmp = Ab(m1,m2);
            else
                sm2 = s(:,:,1,m2);
                tmp = (-sm1.*sm2)*accel + (1-accel)*Ab(m1,m2);
            end
            Gm21 = G(:,:,i,m2,1);
            Gm22 = G(:,:,i,m2,2);
            Gm23 = G(:,:,i,m2,3);
            H11  = H11 + 2*tmp.* Gm11.*Gm21;
            H22  = H22 + 2*tmp.* Gm12.*Gm22;
            H33  = H33 + 2*tmp.* Gm13.*Gm23;
            H12  = H12 +   tmp.*(Gm11.*Gm22 + Gm21.*Gm12);
            H13  = H13 +   tmp.*(Gm11.*Gm23 + Gm21.*Gm13);
            H23  = H23 +   tmp.*(Gm12.*Gm23 + Gm22.*Gm13);
        end
    end
    H(:,:,i,:) = cat(4, H11, H22, H33, H12, H13, H23);
end
%==========================================================================

%==========================================================================
function dat = update_warps(dat,sett)

% Parse function settings
groupwise  = isa(sett.mu,'struct') && isfield(sett.mu,'create');
v_settings = sett.ms.v_settings;
d          = sett.ms.d;

if groupwise
    nw    = get_num_workers(sett,dat,9,0);
    % Total initial velocity should be zero (Khan & Beg), so mean correct
    avg_v = single(0);
    if nw > 1 && numel(dat) > 1 % PARFOR
        parfor(n=1:numel(dat),nw), avg_v = avg_v + spm_mb_io('get_data',dat(n).v); end
    else % FOR
        for n=1:numel(dat), avg_v = avg_v + spm_mb_io('get_data',dat(n).v); end
    end
    avg_v = avg_v/numel(dat);
    d     = [size(avg_v,1) size(avg_v,2) size(avg_v,3)];

    % Handle situations where there may be left-right symmetry
    if isfield(sett.mu.create,'issym') && sett.mu.create.issym>0
        avg_v(:,:,:,1)   = (avg_v(:,:,:,1)   - avg_v(end:-1:1,:,:,1)  )/2;
        avg_v(:,:,:,2:3) = (avg_v(:,:,:,2:3) + avg_v(end:-1:1,:,:,2:3))/2;
    end

else
    avg_v = [];
end

kernel = shoot(d,v_settings);
nw     = get_num_workers(sett,dat,33,0);
if nw > 1 && numel(dat) > 1 % PARFOR
    parfor(n=1:numel(dat),nw)
        dat(n) = update_warps_sub(dat(n),avg_v,kernel,sett); 
    end
else % FOR
    for n=1:numel(dat)
        dat(n) = update_warps_sub(dat(n),avg_v,kernel,sett); 
    end
end
%==========================================================================

%==========================================================================
function datn = update_warps_sub(datn,avg_v,kernel,sett)
v          = spm_mb_io('get_data',datn.v);
if ~isempty(avg_v)
    v      = v - avg_v;
    datn.v = spm_mb_io('set_data',datn.v,v);
end
spm_diffeo('boundary',0); % Circulant boundaries
u0         = spm_diffeo('vel2mom', v, kernel.v_settings); % Initial momentum
datn.E(2)  = 0.5*sum(u0(:).*v(:));                        % Prior term
psi1       = shoot(v, kernel, 8);                         % Geodesic shooting
datn       = set_def(datn,sett.ms.Mmu,psi1);
%==========================================================================

%==========================================================================
function [dat,mu] = zoom_volumes(dat,mu,sett,oMmu)

% Parse function settings
d     = sett.ms.d;
Mmu   = sett.ms.Mmu;
B     = sett.B;

d0    = [size(mu,1) size(mu,2) size(mu,3)];
z     = single(reshape(d./d0,[1 1 1 3]));
Mzoom = oMmu\Mmu;
y     = affine(d,Mzoom);
if nargout > 1 || ~isempty(mu) % only resize template if updating it
    spm_diffeo('boundary',1);  % Neumann bounday for template
    mu = spm_diffeo('pullc',mu,y);
end 

if ~isempty(dat)
    nw = get_num_workers(sett,dat,6+0.5*6,0);
    if nw > 1 && numel(dat) > 1 % PARFOR
        parfor(n=1:numel(dat),nw)
            v          = spm_mb_io('get_data',dat(n).v);
            spm_diffeo('boundary',0); % Circulant for velocities
            v          = spm_diffeo('pullc',bsxfun(@times,v,z),y);
            dat(n).v   = resize_file(dat(n).v  ,d,Mmu);
            dat(n).v   = spm_mb_io('set_data',dat(n).v,v);
            if ~isempty(B)
                Mdef   = spm_dexpm(dat(n).q,B)\Mmu;
            else
                Mdef   = Mmu;
            end
            dat(n).psi = resize_file(dat(n).psi,d,Mdef);
        end
    else % FOR
        for n=1:numel(dat)
            v          = spm_mb_io('get_data',dat(n).v);
            spm_diffeo('boundary',0); % Circulant for velocities
            v          = spm_diffeo('pullc',bsxfun(@times,v,z),y);
            dat(n).v   = resize_file(dat(n).v  ,d,Mmu);
            dat(n).v   = spm_mb_io('set_data',dat(n).v,v);
            if ~isempty(B)
                Mdef   = spm_dexpm(dat(n).q,B)\Mmu;
            else
                Mdef   = Mmu;
            end
            dat(n).psi = resize_file(dat(n).psi,d,Mdef);
        end
    end
end
%==========================================================================

%==========================================================================
%
% Utility functions
%
%==========================================================================

%==========================================================================
function I = horder(d)
I = diag(1:d);
l = d;
for i1=1:d
    for i2=(i1+1):d
        l = l + 1;
        I(i1,i2) = l;
        I(i2,i1) = l;
    end
end
%==========================================================================

%==========================================================================
function f = mask(f,msk)
f(~isfinite(f)) = 0;
f  = bsxfun(@times,f,msk);
%==========================================================================

%==========================================================================
function varargout = shoot(v0,kernel,args)
% Geodesic shooting
% FORMAT psi = shoot(v0,kernel,args)
%
% v0       - Initial velocity field n1*n2*n3*3 (single prec. float)
% kernel   - structure created previously
% args     - Integration parameters
%            - [1] Num time steps
%
% psi      - Inverse deformation field n1*n2*n3*3 (single prec. float)
%
% FORMAT kernel = shoot(d,v_settings)
% d          - dimensions of velocity fields
% v_settings - 8 settings
%              - [1][2][3] Voxel sizes
%              - [4][5][6][7][8] Regularisation settings.
%              Regularisation uses the sum of
%              - [4] - absolute displacements
%              - [5] - laplacian
%              - [6] - bending energy
%              - [7] - linear elasticity mu
%              - [8] - linear elasticity lambda
%
% kernel     - structure encoding Greens function
%
% This code generates inverse deformations from
% initial velocity fields by gedesic shooting.  See the work of Miller,
% Younes and others.
%
% LDDMM (Beg et al) uses the following evolution equation:
%     d\phi/dt = v_t(\phi_t)
% where a variational procedure is used to find the stationary solution
% for the time varying velocity field.
% In principle though, once the initial velocity is known, then the
% velocity at subsequent time points can be computed.  This requires
% initial momentum (u_0), computed (using differential operator L) by:
%     u_0 = L v_0
% Then (Ad_{\phi_t})^* m_0 is computed:
%     u_t = |d \phi_t| (d\phi_t)^T u_0(\phi_t)
% The velocity field at this time point is then obtained by using
% multigrid to solve:
%     v_t = L^{-1} u_t
%
% These equations can be found in:
% Younes (2007). "Jacobi fields in groups of diffeomorphisms and
% applications". Quarterly of Applied Mathematics, vol LXV,
% number 1, pages 113-134 (2007).

if nargin==2
    if numel(v0)>5
        d = [size(v0) 1];
        d = d(1:3);
    else
        d = v0;
    end
    v_settings = kernel;
    spm_diffeo('boundary',0); % Circulant boundaries
    F   = spm_shoot_greens('kernel',d,v_settings);
    varargout{1} = struct('d',d, 'v_settings',v_settings, 'F', F);
    return;
end

if isempty(v0)
    varargout{1} = [];
    varargout{2} = [];
    return;
end

args0 = 8;
if nargin<3
    args = args0;
else
    if numel(args)<numel(args0)
        args = [args args0((numel(args)+1):end)];
    end
end

T     = args(1);   % # Time steps
d     = size(v0);
d     = d(1:3);
id    = identity(d);

if sum(v0(:).^2)==0
    varargout{1} = id;
    varargout{2} = v0;
end

if ~isfinite(T)
    % Number of time steps from an educated guess about how far to move
    T = double(floor(sqrt(max(max(max(v0(:,:,:,1).^2+v0(:,:,:,2).^2+v0(:,:,:,3).^2)))))+1);
end

spm_diffeo('boundary',0); % Circulant for velocities and diffeos
v   = v0;
u   = spm_diffeo('vel2mom',v,kernel.v_settings); % Initial momentum (u_0 = L v_0)
psi = id - v/T;

for t=2:abs(T)
    % The update of u_t is not exactly as described in the paper, but describing this might be a bit
    % tricky. The approach here was the most stable one I could find - although it does lose some
    % energy as < v_t, u_t> decreases over time steps.
    Jdp         = spm_diffeo('jacobian',id-v/T);
    u1          = zeros(size(u),'single');
    u1(:,:,:,1) = Jdp(:,:,:,1,1).*u(:,:,:,1) + Jdp(:,:,:,2,1).*u(:,:,:,2) + Jdp(:,:,:,3,1).*u(:,:,:,3);
    u1(:,:,:,2) = Jdp(:,:,:,1,2).*u(:,:,:,1) + Jdp(:,:,:,2,2).*u(:,:,:,2) + Jdp(:,:,:,3,2).*u(:,:,:,3);
    u1(:,:,:,3) = Jdp(:,:,:,1,3).*u(:,:,:,1) + Jdp(:,:,:,2,3).*u(:,:,:,2) + Jdp(:,:,:,3,3).*u(:,:,:,3);
    clear Jdp

    u = spm_diffeo('pushc',u1,id+v/T);

    % v_t \gets L^g u_t
    v = spm_shoot_greens(u,kernel.F,kernel.v_settings); % Convolve with Greens function of L

    if size(v,3)==1, v(:,:,:,3) = 0; end

    % $\psi \gets \psi \circ (id - \tfrac{1}{T} v)$
    % I found that simply using $\psi \gets \psi - \tfrac{1}{T} (D \psi) v$ was not so stable.
    psi          = spm_diffeo('comp',psi,id-v/T);

    if size(v,3)==1, psi(:,:,:,3) = 1; end
end
varargout{1} = psi;
varargout{2} = v;
%==========================================================================

%==========================================================================
function sz = zoom_settings(v_settings, mu, n)
d   = mu.d;
Mmu = mu.Mmu;
[dz{1:n}] = deal(d);
if isfield(mu,'create')
    sz = struct('Mmu',Mmu,'d',dz, 'v_settings', [1 1 1  0 0 0  0 0],...
                'mu_settings',[1 1 1  0 0 0]);
else
    sz = struct('Mmu',Mmu,'d',dz, 'v_settings', [1 1 1  0 0 0  0 0]);
end

scale = 1/abs(det(Mmu(1:3,1:3)));
for i=1:n
    sz(i).d           = ceil(d/(2^(i-1)));
    z                 = d./sz(i).d;
    sz(i).Mmu         = Mmu*[diag(z), (1-z(:))*0.5; 0 0 0 1];
    vx                = sqrt(sum(sz(i).Mmu(1:3,1:3).^2));
    scale_i           = scale*abs(det(sz(i).Mmu(1:3,1:3)));
    % This fudge value (1.05) should really be 1, but this gives less
    % extreme warps in the early iterations, which might help the
    % clustering associate the right priors to each tissue class
    % - without warping the priors to the wrong tissue.
    scale_i           = scale_i^1.05;
    sz(i).v_settings  = [vx v_settings*scale_i];
    if isfield(mu,'create')
        mu_settings       = mu.create.mu_settings;
        sz(i).mu_settings = [vx mu_settings*scale_i];
    end
end
%==========================================================================

%==========================================================================
function X2d = vol2vec(X4d)
d   = [size(X4d) 1 1];
X2d = reshape(X4d,[prod(d(1:3)) d(4)]);
%==========================================================================

%==========================================================================
function X4d = vec2vol(X2d,dm)
dm = [dm(:)' 1 1];
if size(X2d,1)~=prod(dm(1:3))
    error('Incompatible dimensions.');
end
X4d = reshape(X2d,[dm(1:3) size(X2d,2)]);
%==========================================================================

%==========================================================================
function nw = get_num_workers(sett,dat,NumVol,NumVolNative)
% Estimate number of parfor workers from available system RAM
% Difficult to determine accurate values in practice because
% they depend on the behaviour of broadcast variables, and
% whether these are copied or not.

NumWork = sett.nworker;
if NumWork <= 1
    nw = 0;
    return
end

K = sett.K;

% Number of Template-space volumes
if nargin<3
    NumVol = (K*(K+1)/2+4*K);
end
dm = sett.ms.d;  % current template dimensions

% Maximum image dimensions
if nargin<4, NumVolNative = 0; end
maxvox = 0;
if NumVolNative>0
    for n=1:numel(dat)
        dn     = dat(n).dm(1:3);
        smp    = dat(n).samp;
        maxvox = max(maxvox,prod(ceil(dn./smp)));
    end
end

NumFloats    = NumVol*prod(dm(1:3)) + NumVolNative*maxvox;
MemReq       = (NumFloats*4)/1e6;  % MB


try
    % Users of the GitHub version of this code can get errors because
    % spm_platform('memory','available') is only available in recent
    % versions of SPM.
    MemMax = spm_platform('memory','available') / 1024 / 1024;
catch
    MemMax = 1024*4;
end

nw = max(floor(MemMax/MemReq) - 1,0); % Number of parfor workers to use (minus one..for main thread)
if NumWork >= 0
    nw = min(NumWork,nw);
end
%==========================================================================

%==========================================================================
function phi = MatDefMul(phi,M)
d   = size(phi);
phi = reshape(bsxfun(@plus,reshape(phi,[prod(d(1:3)),3])*M(1:3,1:3)',M(1:3,4)'),d);
%==========================================================================

%==========================================================================
% Functions that should be integrated better with spm_mb_io.m.
% This should include anything that relies on the nifti data
% structure.
%==========================================================================

%==========================================================================
function psi = get_def(datn,Mmu)
psi    = spm_mb_io('get_data',datn.psi);
if isa(datn.psi,'nifti')
    psi = MatDefMul(psi,inv(Mmu));
end
%==========================================================================

%==========================================================================
function datn = set_def(datn,Mmu,psi)
if isa(datn.psi,'nifti')
    psi = MatDefMul(psi,Mmu);
end
datn.psi = spm_mb_io('set_data',datn.psi,psi);
%==========================================================================

%==========================================================================
function fin = resize_file(fin,d,Mat)
if isa(fin,'char')
    fin = nifti(fin);
end
if isa(fin,'nifti')
    for m=1:numel(fin)
        fin(m).dat.dim(1:3) = d(1:3);
        fin(m).mat  = Mat;
        fin(m).mat0 = Mat;
        create(fin(m));
    end
end
%==========================================================================

%==========================================================================
function dat = init_def(dat,ms)
% Possibly break up this function and put parts of it in the io file.

% Parse function settings
d       = ms.d;
Mmu     = ms.Mmu;

v    = zeros([d,3],'single');
psi1 = identity(d);
for n=1:numel(dat)
    if ~isnumeric(dat(n).v) || ~isnumeric(dat(n).psi)
        fa       = file_array('placeholder.nii',[d(1:3) 1 3],'float32',0);
        nii      = nifti;
        nii.dat  = fa;
        nii.mat  = Mmu;
        nii.mat0 = Mmu;
    end
    if isnumeric(dat(n).v)
        dat(n).v   = v;
    else
        if ischar(dat(n).v)
            nii.dat.fname = dat(n).v;
        else
            nii.dat.fname = dat(n).v.dat.fname;
        end
        nii.descrip = 'Velocity';
        create(nii);
        nii.dat(:,:,:,:) = v;
        dat(n).v         = nii;
    end

    if isnumeric(dat(n).psi)
        dat(n).psi   = psi1;
    else
        if ischar(dat(n).psi)
            nii.dat.fname = dat(n).psi;
        else
            nii.dat.fname = dat(n).psi.dat.fname;
        end
        nii.descrip = 'Deformation';
        nii.mat     = Mmu;
        create(nii);
        dat(n).psi  = nii;
        dat(n)      = set_def(dat(n),Mmu,psi1);
    end
end
%==========================================================================