strasbourg-basel: sync data card and figures with GitHub

Syncs the strasbourg-basel card and figures on the Hub with the reviewed state on GitHub (open-h/OpenH-RF).

Adds the three figures the card shows: the BoneSRF logo, a reference B-mode, and the CT slices with the phantom segment outlined.

The card also had links that only worked in the GitHub checkout: `data/` now points at `BoneSRF/data/`, which is where the files live here; `LICENCE` was not a file in either repo, so the text links the CC BY 4.0 deed instead; and `reference_bmodes/` is generated output that is not checked in, so it is no longer a link.

Files changed: 4.

```
+ strasbourg-basel/assets/bonesrf_logo.png
+ strasbourg-basel/assets/ct_phantom2_wholebone.png
+ strasbourg-basel/assets/reference_bmode.png
~ strasbourg-basel/README.md
```

strasbourg-basel/README.md

@@ -22,80 +22,75 @@ size_categories:
   - n<1K
 ---
 
-# BoneSRF — Robot-Tracked Fractured-Femur Phantom Channel Data
+<p align="center">
+  <img src="assets/bonesrf_logo.png" alt="BoneSRF" width="200">
+</p>
 
-**BoneSRF** (Bone Surface Reflection) is an ultrasound channel-data dataset built
-around a simple question: can pre-beamformed RF data recovered from a handheld,
-point-of-care scanner support bone-surface / fracture-reflection research? It
-contributes three 3D-printed, fractured femur phantoms — scanned, beamformed-RF-
-inverted, and packaged in the OpenH-RF `zea` format — to the OpenH-RF initiative.
+# BoneSRF
 
-Nine scans in total: each of the three phantoms was swept three times (`distal`,
-`proximal`, `wholebone`), giving nine `zea` HDF5 files in [`data/`](data/). Every
-scan is **robot-tracked** — the probe was mounted on a robotic arm and its pose
-independently recorded — and every file also carries that phantom's **CT scan and
-multi-label segmentation** inside it.
+**BoneSRF** (Bone Surface Reflection) is an ultrasound channel-data dataset for
+bone-surface and fracture-reflection research. It contains three 3D-printed,
+fractured femur phantoms, scanned with a handheld point-of-care probe, inverted back
+to pre-beamformed RF and packaged in the OpenH-RF `zea` format.
+
+Each phantom was swept three times (`distal`, `proximal`, `wholebone`), giving nine
+`zea` HDF5 files in [`data/`](BoneSRF/data/). Every scan is robot-tracked: the probe was
+mounted on a robotic arm and its pose recorded separately. Each file also carries
+that phantom's CT scan and multi-label segmentation.
 
 ## Dataset Description
 
-The channel data in this dataset is **not a direct per-element sensor recording**.
-A Clarius handheld probe does not expose its raw per-element channel data, only its
-own internally beamformed RF output. Every `raw_data` array here is therefore a
-numerical estimate: the per-element channel data consistent with the probe's known
+The channel data here is not a direct per-element sensor recording. A Clarius
+handheld probe does not expose its raw per-element channel data, only its own
+internally beamformed RF output. Every `raw_data` array is therefore a numerical
+estimate: the per-element channel data consistent with the probe's known
 per-scanline focused acquisition geometry (transmit delays, apodization, walking
-sub-aperture) that, if beamformed the same way, would reproduce the real Clarius
-output. This estimate is recovered by solving a conjugate-gradient least-squares
-(CGLS) inversion of a zea `DASOperator` built from that acquisition geometry,
-against the real beamformed phantom scans as the inversion target.
+sub-aperture) that would reproduce the real Clarius output if beamformed the same
+way. It is recovered by a conjugate-gradient least-squares (CGLS) inversion of a zea
+`DASOperator` built from that geometry, using the real beamformed phantom scans as
+the inversion target.
 
-This is phantom data — not simulated, clinical, or in-vivo — intended for
+This is phantom data, not simulated, clinical or in-vivo. It is intended for
 full-matrix-capture-style beamforming and image-reconstruction research at a
-bone-tissue interface, and as a worked example of recovering pre-beamformed data
-from beamformed-only ultrasound exports.
+bone-tissue interface, and as an example of recovering pre-beamformed data from
+beamformed-only ultrasound exports.
 
 ### How the data was generated
 
-1. **Real acquisition.** A Clarius handheld-probe-class linear array (L20HD3) was
-   used to scan each phantom, producing the probe's own beamformed RF output — this
-   is the *real*, physically acquired data, not simulated.
+1. **Acquisition.** Each phantom was scanned with a Clarius L20HD3 linear array,
+   producing the probe's own beamformed RF output. This is the physically acquired
+   data, not simulated.
 2. **Inversion.** The beamformed RF is inverted back into pre-beamformed,
-   per-element channel data using
-   [`das-inverse` (`clarius` branch)](https://github.com/sankethvedula/das-inverse/tree/clarius)
-   — a CGLS solver over a `zea.inverse.DASOperator` forward model of the probe's
+   per-element channel data with
+   [`das-inverse` (`clarius` branch)](https://github.com/sankethvedula/das-inverse/tree/clarius),
+   a CGLS solver over a `zea.inverse.DASOperator` forward model of the probe's
    focused, walking-sub-aperture transmit sequence (`invert_clarius_beamformed.py`).
-   The result is what this dataset calls "simulated" channel data: not
-   sensor-captured, but numerically consistent with the real beamformed acquisition
-   it was inverted from.
+   The result is not sensor-captured, but numerically consistent with the real
+   beamformed acquisition it was inverted from.
 3. **Packaging.** The inverted channel data, transmit-sequence metadata, probe
    geometry, per-frame probe pose, and the phantom's CT + segmentation are written
    out as one `zea` HDF5 file per scan, matching the OpenH-RF format spec.
 
 ### Probe tracking
 
-Every scan is **tracked**: the probe was mounted on a robotic arm, and its pose was
-independently recorded via a trakSTAR electromagnetic tracking system with a fixed
-fCal image-to-probe calibration. The tracking stream is packaged **per-frame
-indexed** inside each file's metadata (`metadata/probe_pose`: translation, rotation,
-timestamps) — `metadata/probe_pose[i]` corresponds directly to `raw_data[i]`, index
-for index. It does not need a separate sidecar file, and the raw tracking capture is
-not shipped — only the recovered, aligned pose stream.
+The probe was mounted on a robotic arm and its pose recorded separately by a
+trakSTAR electromagnetic tracking system with a fixed fCal image-to-probe
+calibration. The pose stream is stored per frame in each file's metadata
+(`metadata/probe_pose`: translation, rotation, timestamps), so
+`metadata/probe_pose[i]` corresponds to `raw_data[i]`. The raw tracking capture is
+not shipped, only the recovered, aligned pose stream.
 
 ### The three phantoms
 
 Each phantom is a 3D-printed femur, modeled from a CC BY 4.0–licensed femur bone
-dataset, with a fracture pattern simulated differently for each of the three. Each
-printed femur is immersed in ultrasound-coupling gel and scanned by a robotic arm,
-which gives repeatable, controlled probe trajectories instead of a freehand scan.
-Each phantom is scanned three times, at three positions along the bone:
-
-- **`distal`** — a sweep over the distal region of the femur
-- **`proximal`** — a sweep over the proximal region of the femur
-- **`wholebone`** — a sweep covering the full length of the femur
+dataset, with a different simulated fracture pattern. Each printed femur is immersed
+in ultrasound-coupling gel and scanned by a robotic arm, which gives repeatable
+probe trajectories rather than a freehand scan. Each phantom is scanned three times,
+at three positions along the bone:
 
-**Fracture design:** `REQUIRES_CONTRIBUTOR` — the specific fracture pattern
-simulated in each phantom (location; type: transverse / oblique / comminuted /
-hairline; displacement) has not yet been documented. The CT segmentation carried in
-each file is ground truth for the physical phantom geometry in the meantime.
+- **`distal`**: a sweep over the distal region of the femur
+- **`proximal`**: a sweep over the proximal region of the femur
+- **`wholebone`**: a sweep covering the full length of the femur
 
 ## Folder structure
 
@@ -103,8 +98,11 @@ each file is ground truth for the physical phantom geometry in the meantime.
 BoneSRF/
 ├── README.md              ← this file (dataset overview + data card for all nine scans)
 ├── LICENCE                (CC BY 4.0)
-├── pipeline.yaml          (saved zea.Pipeline — one pipeline, shared by all nine scans)
-├── reconstruct.py         (runs pipeline.yaml on any scan → reference_bmodes/<scan>.png)
+├── pipeline.yaml          (saved zea.Pipeline, shared by all nine scans)
+├── reconstruct.py         (runs pipeline.yaml on any scan, and plots its CT)
+├── assets/
+│   ├── reference_bmode.png       (the B-mode shown below)
+│   └── ct_<scan>.png             (CT slices of the scan's phantom)
 ├── data/
 │   ├── phantom1_distal.hdf5      (zea channel data + per-frame probe pose + CT)
 │   ├── phantom1_proximal.hdf5
@@ -115,64 +113,77 @@ BoneSRF/
     └── <scan>.png                (one reference reconstruction per scan)
 ```
 
-Every file in `data/` stands alone: it holds the (CGLS-recovered) pre-beamformed
-channel data, the full transmit-sequence and probe metadata needed to beamform it,
-the per-frame tracked probe pose, and the CT + segmentation of the phantom it
-depicts.
+Every file in `data/` is self-contained: it holds the CGLS-recovered pre-beamformed
+channel data, the transmit-sequence and probe metadata needed to beamform it, the
+per-frame probe pose, and the CT and segmentation of the phantom it shows.
 
 ## Reconstructing a B-mode
 
-`reconstruct.py` loads the saved `zea.Pipeline` from `pipeline.yaml` and runs it on
-one frame of one scan:
+`reconstruct.py` loads the saved `zea.Pipeline` from `pipeline.yaml`, runs it on one
+frame of one scan, and writes `reference_bmodes/<scan>.png`:
 
 ```bash
-python reconstruct.py                          # all nine, at their reference frames
-python reconstruct.py phantom1_distal          # one scan, at its reference frame
-python reconstruct.py phantom1_distal --frame 40 --device cpu
+python reconstruct.py
 ```
 
+The constants at the top of the script select what is reconstructed:
+
+- `SCAN`: the scan to reconstruct, as a local path or an `hf://` URI.
+- `FRAME`: the frame to beamform. `None` uses that scan's reference frame, the one
+  its `reference_bmodes/<scan>.png` was rendered from.
+- `DEVICE`: where to run, e.g. `"cpu"`, `"cuda:0"` or `"auto:1"`.
+- `CT`: also plot the CT carried in the file, to `assets/ct_<scan>.png`.
+
+These values reconstruct `phantom2_wholebone` at its reference frame (45) on the CPU:
+
+```python
+SCAN = "hf://nvidia/OpenH-RF/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5"
+FRAME = None
+DEVICE = "cpu"
+```
+
+and produce the following B-mode image:
+
+<p align="center">
+  <img src="assets/reference_bmode.png" alt="BoneSRF" width="200">
+</p>
+
 The pipeline is `cast` → `apply_window` → `beamform` (delay-and-sum with a
-physically-motivated per-transmit `pfield` weighting, since this is a per-scanline
-focused walking-sub-aperture acquisition rather than full synthetic aperture) →
-`keras.ops.abs` → axial-only Gaussian blur → `normalize` → `log_compress`, with
-display parameters (dynamic range, p-field settings) also read from `pipeline.yaml`.
-Acquisition geometry comes from each file's own `scan`/`probe` groups.
+per-transmit `pfield` weighting, since this is a per-scanline focused
+walking-sub-aperture acquisition rather than full synthetic aperture) →
+`keras.ops.abs` → axial-only Gaussian blur → `normalize` → `log_compress`. Display
+parameters (dynamic range, p-field settings) also come from `pipeline.yaml`;
+acquisition geometry comes from each file's own `scan` and `probe` groups.
 
-This intentionally avoids `zea.inverse`: that module is for the CGLS inversion that
-*produced* these files, not for reconstructing from them. Runtime is ~30 s per frame
-on CPU. Each file is a full multi-frame sweep of 20.24 GB to 24.49 GB, but only the requested
-frame is read.
+The reconstruction does not use `zea.inverse`. That module is for the CGLS inversion
+that produced these files, not for reading them back. Runtime is about 30 s per
+frame on CPU. Each file is a full sweep of 20 to 25 GB, but only the requested frame
+is read.
 
 ## Dataset Contributor(s)
 
-Sidaty El Hadramy, Philippe C. Cattin, Juan Verde — IHU Strasbourg and the
+Sidaty El Hadramy, Philippe C. Cattin, Juan Verde. IHU Strasbourg and the
 Department of Biomedical Engineering, University of Basel.
 
 ## Dataset Creation Date
 
-Original Clarius acquisitions: 19/06/2026 (`phantom1_distal`) and 25/06/2026
-(`phantom1_proximal`, acquisition ID `20260625-ihu-04_BoneSRF-01_proximal_robot_r3`).
-The acquisition date of the other seven sweeps was not separately recorded — see
-[Known Issues](#known-issues). Converted to `zea` format 21/07/2026–05/08/2026 and
-re-converted 13/08/2026–14/08/2026 to align probe tracking to `raw_data`
-frame-by-frame. CT and segmentation embedded into the files 09/09/2026.
+Clarius acquisitions: 19/06/2026 (`phantom1_distal`) and 25/06/2026
+(`phantom1_proximal`). The other seven sweeps carry no recorded acquisition date,
+see [Known Issues](#known-issues). Converted to the `zea` format in 2026.
 
 ## License / Terms of Use
 
-CC BY 4.0 — see [`LICENCE`](LICENCE). Confirmed by the contributor as cleared for
-CC BY 4.0 release (phantom data; no patient consent or third-party IP encumbrance
-applies). The CT and segmentation data carried inside the files is released under
-the same terms. The femur geometry underlying the 3D-printed phantoms is itself
-sourced from a CC BY 4.0–licensed bone model dataset.
+CC BY 4.0, see [the licence deed](https://creativecommons.org/licenses/by/4.0/). The CT and segmentation data inside the files
+is released under the same terms. The femur geometry behind the 3D-printed phantoms
+comes from a CC BY 4.0–licensed bone model dataset.
 
 ## Intended Usage
 
 Full-matrix-capture-style beamforming research on recovered (not directly sensed)
-channel data at a bone-tissue interface: delay-and-sum reconstruction, adaptive or
-aberration-correction beamforming benchmarking, robot/EM-tracked probe-pose fusion
-research, and as a reference example for recovering pre-beamformed data from
-beamformed-only ultrasound exports (e.g. other handheld/point-of-care scanners with
-the same limitation).
+channel data at a bone-tissue interface: delay-and-sum reconstruction, adaptive and
+aberration-correction beamforming benchmarks, and robot/EM-tracked probe-pose fusion.
+It also serves as a reference for recovering pre-beamformed data from other
+beamformed-only scanners.
 
 ## Dataset Characterization
 
@@ -183,26 +194,35 @@ the same limitation).
   recording); probe pose independently tracked via a trakSTAR EM tracking system,
   fCal-calibrated.
 - **Labeling Method:** a CT scan of each 3D-printed phantom and a multi-label
-  segmentation of it (authored in 3D Slicer) are carried **inside each of that
-  phantom's three files**, under `custom/ct/` and `custom/ct_segmentation/` — see
+  segmentation of it (authored in 3D Slicer) are stored inside each of that
+  phantom's three files, under `custom/ct/` and `custom/ct_segmentation/`. See
   [CT reference imaging](#ct-reference-imaging). There are no annotations on the RF
   data itself.
 - **Acquisition system:** Clarius L20HD3, 192-element linear array, 0.130 mm pitch
   (24.8 mm aperture), 10 MHz center frequency, 30 MHz sampling frequency, 1540 m/s
   sound speed, ~5.1 cm imaging depth (1984–2016 axial samples depending on scan),
-  single fixed transmit focus at 25.3–25.8 mm (verified: `focus_distances` is
-  constant across all 192 transmits within each scan), 192 focused transmits per
-  frame (one per lateral scanline, no steering), walking sub-aperture per scanline —
-  Hanning-windowed, 47–97 of 192 elements active per transmit (mean ~84, i.e.
-  roughly a quarter to a half of the array, narrowest at the array edges).
+  single fixed transmit focus at 25.3–25.8 mm (`focus_distances` is constant across
+  all 192 transmits within a scan), 192 focused transmits per frame (one per lateral
+  scanline, no steering), Hanning-windowed walking sub-aperture per scanline with
+  47 to 97 of 192 elements active per transmit (mean 84, narrowest at the array
+  edges).
 
 ## CT reference imaging
 
-Each phantom's CT scan and its multi-label 3D Slicer segmentation are carried inside
-**every one** of that phantom's three `zea` files, under `custom/ct/` and
+Each phantom's CT scan and its multi-label 3D Slicer segmentation are stored inside
+all three of that phantom's `zea` files, under `custom/ct/` and
 `custom/ct_segmentation/`. They are not shipped as separate `.nrrd` sidecars, so no
 file depends on another.
 
+<p align="center">
+  <img src="assets/ct_phantom2_wholebone.png" alt="CT slices of phantom2" width="800">
+</p>
+
+Three slices of phantom2's CT, written by `reconstruct.py` with `CT = True`, with
+the `BoneSRF-2_Complete` segment outlined in red. The printed femur is hollow, so it
+reads dark against the bright coupling gel, and the coronal view shows the fracture:
+the bone is in separate, displaced pieces.
+
 | Dataset | Contents |
 |---|---|
 | `custom/ct/volume` | CT volume, `int16`, stored `(k, j, i)` (slice, row, column) |
@@ -219,9 +239,9 @@ Grid geometry differs per phantom:
 | phantom2 | `512 × 512 × 574` | `(574, 512, 512)` | `0.50390625 × 0.50390625 × 0.6` |
 | phantom3 | `512 × 512 × 594` | `(594, 512, 512)` | `0.5625 × 0.5625 × 0.6` |
 
-Each segmentation has three segments, corresponding directly to the three RF sweeps
-of that phantom. **Label values repeat across layers** — 3D Slicer keeps segments on
-separate internal labelmap layers — so read a segment's mask as
+Each segmentation has three segments, one per RF sweep of that phantom. Label values
+repeat across layers, because 3D Slicer keeps segments on separate internal labelmap
+layers, so read a segment's mask as
 `labelmap[..., segment_layers[s]] == segment_label_values[s]` rather than treating
 the array as one flat labelmap:
 
@@ -238,36 +258,34 @@ the array as one flat labelmap:
 | `BoneSRF-3_Complete` | 1 | 1 | `phantom3_wholebone` |
 
 The CT is reference imaging of the physical phantom in scanner (LPS) space. It is
-**not spatially registered** to the RF frames or to the tracked probe poses; no
-CT↔ultrasound registration is provided with this submission.
+not spatially registered to the RF frames or to the tracked probe poses; no
+CT-to-ultrasound registration is provided.
 
 ## Dataset Format
 
 Submitted in the [`zea` file format](https://zea.readthedocs.io/en/openh-rf-latest/)
-as nine HDF5 files in [`data/`](data/), blosc-compressed.
+as nine HDF5 files in [`data/`](BoneSRF/data/), blosc-compressed.
 
 The channel data was recovered from the probe's real, beamformed RF output by
 CGLS-inverting a `zea.inverse.DASOperator` built from the known acquisition
 geometry. `t0_delays`, `tx_apodizations`, `focus_distances`, `transmit_origins`,
 `polar_angles`, and `waveforms_two_way` are copied directly from the values that
-inversion's DAS operator was built with — not re-derived or guessed. The source
+inversion's DAS operator was built with, not re-derived. The source
 `.npz` had no explicit `demodulation_frequency`; it was substituted with
-`center_frequency` per the standard convention for RF (non-IQ) sources (verified:
-`demodulation_frequency` = `center_frequency` = 10 MHz in every file).
+`center_frequency` per the standard convention for RF (non-IQ) sources.
 
 Probe pose (`metadata/probe_pose`: translation, rotation, timestamps) was recovered
 from the trakSTAR tracking capture via a fixed fCal image-to-probe calibration,
 resampled onto each `raw_data` frame's own acquisition time before conversion, and
-packaged as a **per-frame indexed signal** (`metadata/probe_pose[i]` ↔
-`raw_data[i]`).
+stored per frame, so `metadata/probe_pose[i]` corresponds to `raw_data[i]`.
 
-CT and segmentation (an addition beyond the original proposal) were copied verbatim
-out of the `.nrrd` files that previously shipped alongside the RF data, so that
-every file is self-contained; the verbatim source NRRD headers are preserved with
-them. Grid geometry is converted from the NRRD's millimetres to zea's SI metres.
+CT and segmentation were copied verbatim out of the `.nrrd` files that previously
+shipped alongside the RF data, so that every file is self-contained; the source NRRD
+headers are preserved with them. Grid geometry is converted from the NRRD's
+millimetres to zea's SI metres.
 
-**Reading these files requires `h5py` built against HDF5 ≥ 2.0** (e.g. `h5py` ≥
-3.16) — see [Known Issues](#known-issues).
+Reading these files requires `h5py` built against HDF5 ≥ 2.0 (e.g. `h5py` ≥ 3.16),
+see [Known Issues](#known-issues).
 
 ### Fields
 
@@ -303,44 +321,48 @@ Every file has the same field structure; `n_frames` and `n_ax` vary per scan (se
 
 **Current OpenH-RF release:** 9 HDF5 files; 200.75 GB (200,745,025,536 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
 
-Nine acquisitions, one continuous sweep each; 1428 frames in total, 200.75 GB of stored HDF5 data. No train / val / test split (each file is a single reference acquisition).
-Every value below was read back from the files themselves.
-
-| Scan | Frames | Poses | `n_ax` | Focus | Reference frame | Size on disk |
-|---|---|---|---|---|---|---|
-| `phantom1_distal` | 144 | 144 | 2016 | 25.70 mm | 130 | 20,534,067,200 B (20.53 GB) |
-| `phantom1_proximal` | 174 | 174 | 2000 | 25.55 mm | 100 | 24,492,900,352 B (24.49 GB) |
-| `phantom1_wholebone` | 158 | 158 | 1984 | 25.30 mm | 150 | 21,930,508,288 B (21.93 GB) |
-| `phantom2_distal` | 162 | 162 | 2000 | 25.65 mm | 40 | 22,799,908,864 B (22.80 GB) |
-| `phantom2_proximal` | 142 | 142 | 2016 | 25.75 mm | 40 | 20,236,337,152 B (20.24 GB) |
-| `phantom2_wholebone` | 155 | 155 | 2016 | 25.80 mm | 45 | 21,953,183,744 B (21.95 GB) |
-| `phantom3_distal` | 166 | 166 | 1984 | 25.30 mm | 100 | 23,042,916,352 B (23.04 GB) |
-| `phantom3_proximal` | 167 | 167 | 2016 | 25.80 mm | 0 | 23,672,127,488 B (23.67 GB) |
-| `phantom3_wholebone` | 159 | 159 | 1984 | 25.35 mm | 100 | 22,083,076,096 B (22.08 GB) |
+Nine acquisitions, one continuous sweep each; 1,427 frames in total. No train / val /
+test split (each file is a single reference acquisition). Every scan has one tracked
+probe pose per frame.
+
+| Scan | Frames | `n_ax` | Focus | Reference frame | Size on disk |
+|---|---|---|---|---|---|
+| `phantom1_distal` | 144 | 2016 | 25.70 mm | 130 | 20.53 GB |
+| `phantom1_proximal` | 174 | 2000 | 25.55 mm | 100 | 24.49 GB |
+| `phantom1_wholebone` | 158 | 1984 | 25.30 mm | 150 | 21.93 GB |
+| `phantom2_distal` | 162 | 2000 | 25.65 mm | 40 | 22.80 GB |
+| `phantom2_proximal` | 142 | 2016 | 25.75 mm | 40 | 20.24 GB |
+| `phantom2_wholebone` | 155 | 2016 | 25.80 mm | 45 | 21.95 GB |
+| `phantom3_distal` | 166 | 1984 | 25.30 mm | 100 | 23.04 GB |
+| `phantom3_proximal` | 167 | 2016 | 25.80 mm | 0 | 23.67 GB |
+| `phantom3_wholebone` | 159 | 1984 | 25.35 mm | 100 | 22.08 GB |
 
 ## Subject Metadata
 
-3D-printed, bone-mimicking musculoskeletal phantoms ("BoneSRF" — bone surface
-reflection targets); no human or animal subject. Scanned with a robot-mounted
+3D-printed, bone-mimicking musculoskeletal phantoms (bone surface reflection
+targets); no human or animal subject. Scanned with a robot-mounted
 Clarius L20HD3 linear array at 10 MHz / ~5.1 cm depth / single transmit focus.
 
 ## Data Validation
 
-Every file was validated against the installed `zea` data spec — `File.validate()`
-(structural) and `File.validate_spec()` (full dtype / shape / dimension consistency)
-— and all nine report compliant, with `/data/raw_data` present and non-empty.
-`reconstruct.py` runs end-to-end on every scan and is deterministic across runs; the
-images in [`reference_bmodes/`](reference_bmodes/) are its output.
+All nine files pass the `zea` data spec, both `File.validate()` (structural) and
+`File.validate_spec()` (dtype, shape and dimension consistency). `reconstruct.py`
+runs end-to-end on every scan; the images in
+`reference_bmodes/` are its output.
 
 ## Known Issues
+- **Fracture patterns are not documented per phantom.** The location, type
+  (transverse / oblique / comminuted / hairline) and displacement of each phantom's
+  fracture are not recorded. The CT segmentation in each file is ground truth for
+  the physical phantom geometry.
 - **Acquisition dates are incomplete.** Only `phantom1_distal` (19/06/2026) and
   `phantom1_proximal` (25/06/2026) have a recorded original Clarius acquisition date;
   the other seven sweeps do not carry one in the file or in the source capture.
-- **No CT↔ultrasound registration.** The CT lives in scanner LPS space and the probe
-  poses in trakSTAR tracker space. Nothing in this submission relates the two.
+- **No CT-to-ultrasound registration.** The CT is in scanner LPS space and the probe
+  poses in trakSTAR tracker space. Nothing here relates the two.
 - **CT intensity units are unverified.** The source NRRD headers record no unit. The
-  value range (−1024 … ~500) is consistent with Hounsfield units, but this has not
-  been confirmed by the contributor.
+  value range (−1024 to about 500) is consistent with Hounsfield units, but this has
+  not been confirmed.
 
 ## Ethical Considerations