Algorithms

Every stage of the pipeline is configurable. The numerical methods are provided by QSM.rs; QSMxT orchestrates them over your BIDS data. Below are the valid values for each algorithm option.

Masking

Choose a masking recipe with --mask-preset, and override the image it operates on with --masking-input.

Value	Method
robust-threshold	Otsu thresholding of the phase-quality map, refined with dilation, hole-filling and erosion (default)
bet	Brain Extraction Tool on the magnitude image
hd-bet	HD-BET deep-learning brain extraction on the magnitude image, followed by signal-gated erosion (the QSM-CI harmonization masking). Needs a deep-learning build; the weights (~120 MB, CC-BY-NC-4.0) are downloaded on first use
bet-and-phase	BET on the first-echo magnitude intersected with an Otsu-thresholded phase-quality map, then hole-filled and eroded (the masking recommended by the ISMRM EMTP study group consensus)

Masking input (--masking-input): magnitude-first, magnitude, magnitude-last, phase-quality. For example, --mask-preset robust-threshold --masking-input magnitude thresholds the combined magnitude image instead of the phase-quality map.

For full control, compose custom mask sections with --mask (repeatable), e.g. --mask magnitude,bet:0.5,erode:2. Each section names its input followed by a generator and refinement operations.

• Generators: threshold[:otsu|fixed:<v>|percentile:<p>], bet[:<f>], and hd-bet[:<X>x<Y>x<Z>|low-memory][:tta]. HD-BET runs 192×192×96 sliding-window patches by default (about 4.5 GB peak). low-memory uses 128×128×64 patches (about 1.9 GB peak), and tta adds mirroring test-time augmentation, which is roughly 8× slower.

• Refinements: erode[:<n>], dilate[:<n>], close[:<r>], fill-holes[:<max>] (0, or omitted, is automatic: holes up to 5% of the volume), gaussian[:<sigma_mm>], and signal-erode[:<threshold>[:<depth_cap>[:<global_erosions>[:<bias_sigma>[:<min_component>]]]]]. signal-erode removes only boundary voxels whose bias-corrected magnitude is below threshold × the in-mask median (default 0.80). It works inward through sinus and skull-base signal dropout, never more than depth_cap voxels deep (default 5), after global_erosions plain erosions (default 1). Dark interior structures are never removed. bias_sigma (default 12) is the Gaussian scale in voxels of the receive-coil bias divided out first, and min_component (default 1000) keeps every connected component that size or larger rather than only the largest. In the TUI each of these is its own row under the step, so you can nudge them with ←/→ and watch the generated command update.

  global_erosions is not the same as putting an erode step in front of signal-erode: the gate and the coil-bias estimate are computed from the mask as it arrives, and the depth cap is measured from that surface, so these erosions spend the depth budget and leave the gate unchanged. An erode step beforehand shrinks the mask the gate is derived from and resets the depth budget. The default of 1 is the QSM-CI harmonization setting.

For example, --mask magnitude,hd-bet:low-memory,signal-erode is the hd-bet preset with the low-memory patch size.

Combining sections

With more than one --mask section, --mask-combine decides how they fold together: or (the default) keeps a voxel any section keeps, and and keeps only voxels every section keeps. --mask-refine (repeatable) then applies refinement operations to the combined mask — which is where hole-filling belongs, since filling a section’s holes before an intersection is not the same as filling the intersection’s.

The bet-and-phase preset is exactly this:

qsmxt run bids/ \
  --mask magnitude-first,bet:0.50 \
  --mask phase-quality,threshold:otsu \
  --mask-combine and \
  --mask-refine fill-holes:0 \
  --mask-refine erode:1

BET bounds the head, the phase-quality threshold drops voxels whose phase cannot be unwrapped reliably, and the holes their intersection leaves inside the brain are filled afterwards.

To try this on files without a pipeline run, use qsmxt mask preset bet-and-phase, or qsmxt mask and / or on masks you already have.

Oblique acquisitions

The dipole kernel is built in the voxel grid, so an acquisition whose slices are tilted relative to B0 has to be handled explicitly. QSMxT applies the first of these that fits:

1. A deep-learning method forces resampling to axial. These learned the dipole relationship from axially-acquired data and take no direction argument, so nothing else can help them.
2. A B0_dir in the JSON sidecar wins. It describes the acquisition better than the affine can.
3. --obliquity-threshold <degrees> resamples to axial when the obliquity exceeds it, as QSMxT 8.x did. Off by default (-1).
4. Otherwise the kernel is rotated — B0 is taken from the affine and used as-is. This is the default, and normally the one you want.

Rotating the kernel is preferred because it is exact and free: the dipole kernel takes the field direction as a parameter, so pointing it the right way costs nothing, keeps the acquired grid, and interpolates nothing.

The cost of the alternative is not small. A 256×288×48 UK Biobank SWI at 32.5° obliquity resamples to 272×339×77, roughly twice the voxels, and took 459 s against 246 s for the same reconstruction on the acquired grid. Resampled outputs are returned to the acquired grid afterwards unless you ask otherwise; see Output space.

Which methods need axial data

	behaviour on oblique data
all 18 classical dipole inversions, PDF, χ-sep iLSQR and MEDI	correct as acquired — B0 is an explicit parameter
SHARP, V-SHARP, RESHARP, iSMV, LBV, HARPERELLA, iHARPERELLA, BFRnet	unaffected — never uses B0
all 11 deep-learning inversions, SUSEP-Net, χ-sepnet	resampled first — assumes B0 is +z

Background removal by the spherical mean value property is genuinely direction-independent rather than merely untested, which is why PDF is the only background remover in the first row.

qsmxt validate reports what a dataset will do:

B0 direction: (0.05, 0.39, 0.92) (from the affine)
Obliquity:    32.5° (B0 22.9° off the slice normal)

Obliquity is nibabel’s definition (the norm of the per-axis angles), so 8.x thresholds carry over. It is a combined measure rather than a tilt: a single 23° oblique acquisition scores ≈32° because two voxel axes move. The tilt in brackets is the physical angle between B0 and the slice normal, which is what the kernel cares about.

Caution

Wrapped phase cannot be resampled on its own. Halfway between +3.0 and −3.0 rad a linear interpolator returns 0.0, where the answer is near ±π, so every wrap becomes a band of wrong values. The pipeline always resamples magnitude and phase together; if you use qsmxt resample by hand, pass --phase with --magnitude rather than resampling phase as a plain volume.

Runs with a matching MESE acquisition (for R2′ / χ-separation) are not resampled by the obliquity threshold — the MESE is read from BIDS on its own grid, so moving only the GRE would leave the two inconsistent. Those runs use the affine-derived B0 direction instead, and say so in the log. A deep-learning method on an oblique run with a MESE has no good answer available and is refused rather than guessed at.

Output space

A run that was resampled to axial reconstructs on the resampled grid, and by default its derivatives are returned to the grid the data arrived on. This matters more than it sounds: a FLIRT matrix is defined in a space derived from the image’s dimensions and voxel sizes, so applying one to a volume on a different grid gives a wrong registration rather than an error.

• --output-space acquired (default) writes derivatives on the acquired grid. Costs a second interpolation, so the result is slightly smoother than a reconstruction that was never resampled.
• --output-space working leaves them on the resampled grid.

Masks travel by nearest neighbour and phase travels with its magnitude through the complex domain, for the same reason phase went out that way.

Grid padding and cropping

Both of these trade reconstruction cost against the grid the FFT stages run on, and both are off by default.

--fft-padding

FFT cost is O(N log N) in the whole grid, and the transform is much faster on sizes built from small prime factors. Resampling has no reason to land on one: the UK Biobank grid above comes out 272×339×77, which is 2⁴·17, 3·113 and 7·11. Growing it to 280×343×80 took one FFT from 131 ms to 71 ms for 8% more voxels.

Padding discards nothing, but it is not a no-op. The dipole kernel is sampled at k = n / (N·Δx), so changing N changes where k-space is sampled and the deconvolution with it. Measured end to end on that acquisition, against the same run unpadded:

inversion	median difference	p99	as % of dynamic range
TKD (direct)	4e-7 ppm	9.4e-3 ppm	0.000% / 5.7%
WH-QSM (iterative)	8.3e-5 ppm	3.7e-3 ppm	0.06% / 2.8%

A direct inversion is unchanged for the typical voxel and moves only where the deconvolution is ill-conditioned. An iterative one moves slightly everywhere, since it is solving on a different grid. Neither is evidence that padding is worse — a finer k-space sampling with the boundary further from the object has reason to be better — but nothing here establishes that it is better, so it does not change anyone’s numbers unless asked for.

Note that an acquisition usually arrives on a friendly grid already: 256×288×48 is 2⁸, 2⁵·3² and 2⁴·3. Padding earns its keep on grids that resampling created.

--crop-to-mask

Reconstructing only inside a box around the brain cut background removal from 11.5 s to 7.2 s and inversion from 6.0 s to 3.0 s on the same acquisition. But the FFT stages were 5% of wall time there — HD-BET masking was 289 s of 338 s — so the end-to-end saving was inside noise. Use it for grids with large empty regions, not as a general speedup.

--crop-margin-mm defaults to 32. At 16 mm the reconstruction differed from the uncropped one by 0.8% of dynamic range at the median and 5.1% at p99; at 32 mm and 64 mm it was identical. The margin must also clear the largest SMV kernel in use, and V-SHARP defaults to 12 mm.

Coil combination

Runs whose phase is stored per receive coil (coil-<NN> entity, see Inputs) are combined with MCPC-3D-S (Eckstein et al., MRM 2018) before masking. The coil-summed Hermitian inner product of the first two echoes gives the field evolution with the coil phase offsets cancelled; it is unwrapped once (with the --unwrapping-algorithm method), each coil’s offset is estimated from its first echo and smoothed with --coil-combination-sigma (default 10 10 5 voxels; the same masked box-filter smoothing as MriResearchTools / QSMxT 8.x, so combined phase matches the 8.x converter), and the channels are summed coherently with magnitude² weights. Multi-echo phase-offset removal in the field-mapping stage then runs on the combined echoes as usual.

Phase unwrapping

Set with --unwrapping-algorithm.

Value	Method
romeo	Rapid Opensource Minimum-spanning-tree Echo Optimisation (default)
laplacian	Laplacian-based unwrapping

Background field removal

Set with --bf-algorithm.

Value	Method
vsharp	Variable-kernel SHARP (default)
pdf	Projection onto Dipole Fields
lbv	Laplacian Boundary Value
ismv	Iterative Spherical Mean Value
sharp	Sophisticated Harmonic Artifact Reduction
resharp	Regularization-enabled SHARP
harperella	HARPERELLA
iharperella	Iterative HARPERELLA

Dipole inversion

Set with --qsm-algorithm. Two families are available: classical/iterative reconstructions and deep-learning networks.

Classical & iterative

Value	Method
rts	RTS — Rapid Two-Step (default)
tv	Total Variation (TV-ADMM)
tkd	TKD — Thresholded K-space Division
tsvd	TSVD — Truncated Singular Value Decomposition
tgv	TGV — Total Generalized Variation
tikhonov	Tikhonov regularization
nltv	NLTV — Nonlinear Total Variation
medi	MEDI — Morphology-Enabled Dipole Inversion
tfi	TFI — Total Field Inversion
ilsqr	iLSQR
lsqr	Minimally regularised LSQR — HEIDI’s seed
heidi	HEIDI — Homogeneity Enabled Incremental Dipole Inversion
qsmart	QSMART two-stage reconstruction
ndi	NDI — Nonlinear Dipole Inversion
fansi	FANSI — Nonlinear TV
fansi-tgv	FANSI — Nonlinear TGV
l1qsm	L1-QSM — L1 data fidelity
whqsm	WH-QSM — Weak-Harmonic
hdqsm	HD-QSM — Hybrid data fidelity
amp-pe	AMP-PE — Approximate Message Passing with Parameter Estimation

Deep learning

Value	Method	Tileable
xqsm	xQSM	✓
qsmnet	QSMnet	✓
qsmnet-plus	QSMnet+	✓
ir2qsm	IR2QSM — unrolled	✓
lpcnn	LPCNN — learned-proximal	✓
modl-qsm	MoDL-QSM — model-based	✓
nextqsm	NeXtQSM — single-step	✓
autoqsm	AutoQSM — single-step (native patching)
qsmgan	QSMGAN — GAN-refined (native patching)
iqsm	iQSM — end-to-end from phase
iqsm-plus	iQSM+ — orientation-adaptive end-to-end

Model weights are downloaded automatically on first use (and cached) from the QSMxT weight registry on Hugging Face. Deep-learning support requires a build with the default dl feature; iqsm and iqsm-plus reconstruct susceptibility directly from phase, so they replace the background-removal + inversion stages rather than running after them.

Tip

Not sure which to pick? The defaults (threshold → romeo → vsharp → rts) are a robust, fast starting point for human brain GRE data. Use the TUI to experiment interactively.

Overlap-tiling for deep-learning inversion

Deep-learning inversions can run overlap-tiled to bound peak memory: the volume is split into cubic patches (each a core plus a context halo), inferred independently, and stitched back together. This is opt-in and applies to the tileable networks above.

Flag	Meaning
--tile-size <N>	Output core size per patch, in voxels — presence enables tiling
--tile-halo <N>	Context margin per side, in voxels (default 8)

qsmxt run bids/ output/ --qsm-algorithm xqsm --tile-size 128 --tile-halo 8

Tiling is an approximation of the whole-volume network — results are close but not bit-identical. Omit --tile-size to run the network over the whole volume. The flags are ignored by classical algorithms and by the natively-patched networks (autoqsm, qsmgan).

Per-algorithm parameters

Each algorithm exposes its own parameters (regularisation weights, kernel sizes, iteration counts, …). The easiest way to discover them — and to build a run command — is the TUI: it exposes every option with sensible defaults and shows the equivalent qsmxt run command live as you change them. You can also set them in a configuration file, or run the matching standalone tool (e.g. qsmxt invert tgv --help) to experiment directly.

Multi-orientation (COSMOS and STI)

Single-orientation dipole inversion is ill-posed because the dipole kernel vanishes on a pair of cones at the magic angle. Re-imaging the same object at a different orientation moves those cones, so combining orientations conditions the inverse problem at acquisition time rather than with a regulariser. COSMOS does that for a scalar susceptibility; STI keeps the orientations but models susceptibility as a rank-2 tensor, describing white-matter anisotropy instead of averaging it away.

Both are off unless you name an orientation group.

Naming the orientation group

BIDS has no entity meaning “the same object at a different orientation”, so nothing in the dataset can declare the intention to do COSMOS — you do, with --orientation-group. The pattern’s only job is to say which runs belong together, and it takes three forms:

Form	Example	Use it when
Entity name	acq	Every run with that entity in the session is an orientation
Glob over the run key	*acq-dir*	The session also holds unrelated acquisitions
re: + regex with one capture	re:.*-dir(\d+)_.*	One session holds two orientation sets

The glob form is usually what you want. A session like this:

sub-01_ses-01_acq-dir1_MEGRE     ┐
sub-01_ses-01_acq-dir2_MEGRE     ├─ *acq-dir* matches these three
sub-01_ses-01_acq-dir3_MEGRE     ┘
sub-01_ses-01_acq-highres_MEGRE  ← and leaves this one alone

groups all four under acq and only the intended three under *acq-dir*. The glob language is the same one --include and --exclude use, and matches against the full run key (sub-01_ses-01_acq-dir2_MEGRE), case-insensitively.

Groups never cross a subject or session, and a pattern matching only one run in a session is not a group — that run is processed on its own.

chunk- and desc- are rejected: QSMxT’s filename parser does not distinguish them, so grouping by one would silently merge two orientations into a single run. Published multi-orientation datasets use acq- (for example OpenNeuro ds007958, an in-vivo 7T COSMOS set named acq-dir1/2/3).

B0 directions, and what will be refused

Each orientation contributes its B0 direction in the common grid’s voxel frame. QSMxT takes it from a sidecar B0_dir when one is present, and otherwise derives it from the NIfTI affine.

The affine is only meaningful when the orientations were separately prescribed, so the slab followed the head and the affines genuinely differ. Two very common dataset shapes break that while leaving the affine perfectly readable:

• the head rotated inside an identically prescribed slab — every affine is the same, and the anatomy moved in voxel space instead;
• the orientations were already resampled into a reference frame before distribution, which is how ds007958 ships.

In both, the affine yields the same direction for every orientation. Handed to COSMOS, N identical directions collapse the closed form to an unregularised single-orientation inversion — which does not error and does not look obviously wrong. So QSMxT refuses rather than guesses:

• every direction came from the affines and they agree to within 3° → error naming the likely cause and the fix;
• the directions differ but span less than 3° → error;
• STI with fewer than six orientations, or with a coplanar direction set → error.

--multi-orientation-force overrides the check. Prefer fixing the metadata: declare B0_dir in each orientation’s sidecar.

Registration

QSMxT has no registration of its own, so the orientations must already sit on one common grid. Disagreeing dimensions or affines are an error, not a guess. Co-register the orientations first (and, if your tool reports the rotation, write the resulting B0 directions into each sidecar’s B0_dir).

Running it

qsmxt run bids/ out/ --orientation-group '*acq-dir*'
qsmxt run bids/ out/ --orientation-group acq --multi-orientation-algorithm sti

Check it before committing to a long run — --dry prints each group’s direction table, where each direction came from, and the verdict:

Multi-orientation (COSMOS), 1 group(s):
  sub-01_ses-01 (3 orientations) [ok]
      acq-dir1         B0 [ 0.000  0.000  1.000]  sidecar
      acq-dir2         B0 [ 0.000  0.500  0.866]  sidecar
      acq-dir3         B0 [ 0.500  0.000  0.866]  sidecar
      3 orientations, spread 41°, rank 3

In the TUI the same pattern is the Orientations field on the Input tab, below Include/Exclude, and it previews the groups as you type.

--multi-orientation-lambda sets the regularisation: 0 (the default) is plain least squares and is right whenever three or more orientations cover k-space; 1e-3 to 1e-2 helps with two orientations, or when the rotations share a single axis and streaking survives.

Outputs

Each member run still produces its own single-orientation Chimap — the first thing to check when a COSMOS map looks wrong is whether the orientations were actually aligned, and the per-orientation maps are how you see that. The combined result drops the entity that varied:

File	Contents
…_desc-cosmos_Chimap.nii	COSMOS susceptibility map (ppm)
…_desc-mms_Chimap.nii	STI mean magnetic susceptibility
…_desc-msa_Chimap.nii	STI magnetic susceptibility anisotropy
…_desc-sti11_Chitensor.nii … sti33	The six independent tensor components
…_desc-pevx_Chitensor.nii … pevz	Principal eigenvector, in voxel coordinates

A …_desc-cosmos_Chimap.json sidecar records the source runs, the direction table and where each direction came from — the half of the input that does not live in the NIfTI files, and without which the result cannot be reproduced.

Standalone

If you already have local field maps, skip BIDS entirely:

qsmxt invert cosmos -i f1.nii -i f2.nii -i f3.nii \
  --b0-direction 0 0 1 --b0-direction 0 0.5 0.866 --b0-direction 0.5 0 0.866 \
  --mask mask.nii --output cosmos.nii

qsmxt invert sti -i f1.nii … -i f6.nii --b0-dirs dirs.txt --mask mask.nii --output sti

--b0-dirs takes a text file with one x y z per line, in --input order.

Susceptibility source separation

Enable with --do-chisep to split susceptibility into paramagnetic and diamagnetic components, and choose the method with --chisep.

Value	Method
r2star-qsm	R2*-based magnitude decay modelling (default)
decompose	DECOMPOSE-QSM
chi-sep-ilsqr	χ-separation, iLSQR inversion
chi-sep-medi	χ-separation, MEDI inversion
wavesep	Wavelet-based single-step separation
hc-chisep	Hollow-cylinder χ-separation
susep-net	SUSEP-Net (deep learning)
chi-sepnet	χ-sepnet (deep learning)

Only r2star-qsm and decompose run on a GRE acquisition alone. The rest are based on R2’, so they also need a multi-echo spin-echo acquisition or a bring-your-own R2’ map. See Input data.

Outputs are written as desc-paramagnetic_Chimap, desc-diamagnetic_Chimap and desc-total_Chimap.

Referencing

A susceptibility map has no absolute zero — only differences within it mean anything — so every map is referenced to something. Choose what with --qsm-reference:

Value	What zero means
mean (default)	The mean χ inside the brain mask
none	Nothing is subtracted; raw reconstruction values
a region	The mean χ of a SynthSeg structure

mean needs no extra information, but it moves with whatever else is in the mask: two subjects with different amounts of iron-rich tissue in the field of view get different zeros. Referencing to a structure pins zero to tissue instead, which is what makes values comparable between subjects and studies.

Naming a region

qsmxt run bids/ --qsm-reference thalamus            # both sides merged
qsmxt run bids/ --qsm-reference left-thalamus       # one side
qsmxt run bids/ --qsm-reference cerebral-white-matter
qsmxt run bids/ --qsm-reference ventricles          # a named composite
qsmxt run bids/ --qsm-reference 4                   # a raw FreeSurfer id
qsmxt run bids/ --qsm-reference left-caudate,right-putamen   # your own list

A term is a structure name with both sides merged, one sided label, a raw FreeSurfer id, or the ventricles composite (ids 4, 5, 14, 15, 43, 44 — the CSF spaces a QSM paper usually means by “CSF reference”). Comma-separate terms to use their union, so thalamus and left-thalamus,right-thalamus are the same reference. Slugs are the parcellation’s own label names, lowercased with spaces as hyphens; an unrecognised name lists the valid ones rather than guessing.

csf is a label, not the composite

SynthSeg 2.0 has a label of its own named csf (id 24), which covers extraventricular CSF too. --qsm-reference csf means that label and needs --synthseg-version v2; it is not the same ROI as ventricles, and it does not exist on v1. Pick deliberately — the two give different numbers.

A region reference implies --do-segmentation, since the region has to be measured on a parcellation; --use-custom-dseg satisfies that instead. If the region turns out to have no voxels inside the brain mask for a subject, that subject’s run fails rather than falling back to mean — a map that was quietly referenced to something else looks fine and cannot be compared with the rest of the cohort.

In the TUI the same setting appears in two places, editing one value: QSM → Referencing → QSM Reference, and Supplementary → QSM Reference, beside the SynthSeg settings it depends on. Picking a region there ticks Segment (SynthSeg) and reveals its settings.

Segmentation and per-structure statistics

--do-segmentation parcellates the brain straight from the GRE magnitude with SynthSeg. It is trained on synthetic images of randomised contrast, so one set of weights segments a GRE magnitude directly — no T1w acquisition, and no registration. Outputs are a FreeSurfer-labelled _dseg.nii and the _dseg.tsv lookup table that names its integers. Use --synthseg-version v2 for the better (separately downloaded) model, or --use-custom-dseg <TOOL> to bring your own parcellation from <bids>/derivatives/<TOOL>/.

--do-analysis then summarises every quantitative map the run produced inside each structure — Chimap, the chi-separation maps, T2starmap, R2starmap, R2map and R2primemap — and writes them as a tab-separated table:

Column	Meaning
map	The map summarised, by its BIDS suffix (e.g. desc-paramagnetic_Chimap)
unit	ppm for susceptibility, s for T2*, s-1 for the R2 family
index, name	FreeSurfer label id and structure name
n_voxels	Voxels the structure occupies in the segmentation
n_valid	Of those, the voxels the map actually covers — the ones summarised
volume_mm3	SynthSeg’s partial-volume-aware volume (blank for a supplied dseg)
mean, sd	Mean and population standard deviation
median, min, max	Median and the extremes
p5, p95	5th and 95th percentiles, nearest-rank (never interpolated)

One row per map and structure, so the table keeps the same shape whatever the run produced; pivot it to one-column-per-map in a line of pandas or R if you prefer that. Voxels a map does not cover are excluded rather than averaged in as zeros, which is why n_valid can be smaller than n_voxels — R2map only exists where the spin-echo acquisition reached, for instance.

Figures

Each table comes with a PNG per map — a ranked horizontal bar chart of the mean per structure, with ±1 SD whiskers:

• sub-<X>/anat/sub-<X>_desc-<map>_stats.png — one run.
• desc-<map>_stats.png at the derivatives root — the cohort: each bar is the mean of the runs’ means, and its whisker is the spread across runs, which is a different quantity from the per-run whisker (the spread of voxels inside one structure). Each figure’s subtitle says which. Written only when more than one run contributed, since with a single run it would restate that run’s figure with every whisker at zero.

Colour encodes sign where a map has both — paramagnetic and diamagnetic χ, with a legend — and is a single hue where it does not, so colour never sits on a figure encoding nothing. Values are printed only on the extremes; the axis and the TSV carry the rest.

Two copies of the table are written:

• sub-<X>/anat/sub-<X>_desc-segmentation_stats.tsv — one run, next to the images it describes.
• desc-segmentation_stats.tsv at the top of the derivatives directory — every run in the dataset, each row prefixed with its BIDS entities (subject, session, acquisition, reconstruction, inversion, run). This is the one to open for a group analysis.

The table covers whatever the run produced, so it does not force you to compute maps you did not ask for: --no-qsm --do-r2starmap --do-segmentation --do-analysis is a valid request and gives you an R2*/T2* table with no susceptibility map in sight. --do-analysis implies --do-segmentation (unless you supply a dseg), and implies QSM only when nothing else would produce a map at all.