Terminology and conventions#
The vocabulary used in code, docstrings and documentation. Definitions and derivations are in Explanation; this page fixes the terms. The readers are MR scientists, so an established MR term is used rather than a description of what it denotes: coil sensitivity maps, not “the coils’ weights”; autocalibration region, not “the fully sampled middle”.
Acquisition and reconstruction#
Term |
Meaning |
|---|---|
Readout, frequency-encoding direction |
The k-space direction sampled during one ADC window; the last tensor axis of Cartesian k-space. |
Phase-encoding direction |
A k-space direction stepped between readouts; |
Acceleration factor \(R\) |
The ratio of the phase encodes of a fully sampled acquisition to those acquired. |
Autocalibration (ACS) region |
The fully sampled block at the centre of k-space from which coil sensitivity maps are estimated. |
Coil sensitivity maps |
The complex receive sensitivities \(S_c(r)\) of the channels of an array. |
SENSE |
The encoding model \(A = PFS\) and its least-squares or regularized inversion. |
g-factor |
The voxel-wise noise amplification of a parallel-imaging reconstruction beyond the \(\sqrt{R}\) loss of the shorter acquisition. |
Density compensation |
Weighting of non-Cartesian samples by the inverse of the local sampling density. |
Off-resonance, \(B_0\) inhomogeneity |
A deviation of the precession frequency from the reference frequency, in hertz; its map is the field map. |
Signal model |
The dependence of the signal of a voxel on tissue parameters such as \(T_1\) and \(T_2\) and on the sequence. |
Arrays#
Term |
Meaning |
|---|---|
C-order shape |
The shape of a PyTorch tensor. The last axis varies fastest. |
BART dimension vector |
BART’s |
Axis index |
An index into a C-order shape, negative indices counting from the end. Public arguments take axis indices. |
BART bitmask |
BART’s set of dimensions as bits of an integer. No public argument takes one; axis indices are converted at the boundary. |
Batch axis |
An axis of independent items that share the operator’s trajectory or pattern, each transformed separately. |
Encoding axis |
An axis the trajectory or sampling pattern indexes, such as frames or echoes; its samples belong to one transform. |
Sets |
ESPIRiT’s multiple sensitivity maps, an axis the image carries and the samples do not. |
Sampling#
Term |
Meaning |
|---|---|
Trajectory |
The k-space coordinates |
Non-uniform FFT (NUFFT) |
The discrete Fourier transform between a Cartesian image grid and samples at arbitrary k-space positions, computed approximately to a tolerance; not “gridding”, which names one algorithm for it. |
Sampling pattern |
A binary mask on the Cartesian grid, one where a sample was acquired. |
Density weights |
Density-compensation or data weights as a diagonal in k-space applied to the samples of a non-Cartesian transform, on the forward pass and conjugated on the adjoint. |
Subspace basis |
A matrix |
Operators#
Term |
Meaning |
|---|---|
Forward operator, encoding operator |
\(A\), mapping the unknown to the data. “Encoding operator” is the MRI forward operator. |
Domain, codomain |
|
Forward, adjoint, normal |
\(Ax\), \(A^H y\) and \(A^H A x\). |
Linear operator |
|
Nonlinear operator |
|
Encoding form |
The expression every MRI encoding here reduces to: image-side factor, transform, k-space-side factor and contraction; reported by |
Lowering |
Matching a composition of operators against the encoding form and building it as one encoding. |
Regularization and solvers#
Term |
Meaning |
|---|---|
Functional |
A map from the image to a real number. |
Regularization functional, term |
A functional added to the data term; a |
Transform \(G\) |
The linear operator in \(g(Gx)\); the identity for a term whose transform is inside its proximal operator. |
Proximal operator |
\(\operatorname{prox}_{\tau g}(v) = \arg\min_u \tfrac12\lVert u - v\rVert^2 + \tau g(u)\). |
Solver |
A configured algorithm called as |
Iteration block |
One step of a solver as a |
Unrolled network |
A fixed number of iteration blocks, whose parameters may be learned ( |
Explicit unrolling |
Differentiation by recording every step of the iteration. |
Implicit differentiation |
Differentiation through the optimality or fixed-point condition, without recording the iterations. |
Interfaces and backends#
Term |
Meaning |
|---|---|
BART command, BART tool |
A program of BART’s command line, such as |
|
A BART command called in-process on tensors, or a correction or motion-estimation function with no BART command behind it. |
App |
A |
Reference command |
A BART reconstruction command answered publicly by an app, kept private in |
CLI |
The |
Substitution |
A component compiled in BART’s place: the FINUFFT and cuFINUFFT NUFFT, the point spread function, the FFT and BLAS/LAPACK routing. |
Backend |
The library that performs a computation for BART: FINUFFT, cuFINUFFT, MKL, the BLAS/LAPACK PyTorch links, SciPy’s, cuFFT, cuBLAS. |
Decline, refusal |
A configuration the substitution cannot serve, reported as an error with its reason rather than computed by another method. |
Writing conventions#
Units are stated where a quantity is physical: times in milliseconds for signal models, frequencies in hertz and readout times in seconds for field correction, trajectories in grid units. Fourier-transform centring and normalization are stated for every transform. A check or a comparison is described by what it compared and to what tolerance, not as a general guarantee.