technion: sync data cards and figures with GitHub

Syncs the three technion cards and figures on the Hub with the reviewed state on GitHub (open-h/OpenH-RF).

Adds the figures each card shows: a reference B-mode for bladder, cardiac and phantom, plus cine loops for bladder and cardiac. None were on the Hub, so those images were broken here. Each card also gains the `zea process` one-liner that reproduces its figure straight from the Hub.

Files changed: 8.

```
+ technion/bladder/assets/bmode.png
+ technion/bladder/assets/cine.gif
+ technion/cardiac/assets/bmode.png
+ technion/cardiac/assets/cine.gif
+ technion/phantom/assets/bmode.png
~ technion/bladder/README.md
~ technion/cardiac/README.md
~ technion/phantom/README.md
```

technion/bladder/README.md

@@ -18,6 +18,20 @@ size_categories:
 
 # OpenH-RF — Bladder pre-beamformed RF channel data
 
+![Transverse suprapubic view of the bladder, one 10-frame cine loop](assets/cine.gif)
+
+Transverse suprapubic view: one cine loop (10 frames) from [`data/a1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/bladder/data/a1.hdf5).
+
+`zea` renders it straight from the Hub with the
+`pipeline.yaml` in this folder. Try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/technion/bladder/data/a1.hdf5 \
+  --config hf://nvidia/OpenH-RF/technion/bladder/pipeline.yaml \
+  --n-frames 10
+```
+
 ## Dataset Description
 
 Real, **in-vivo human** pre-beamformed ultrasound **channel data** for bladder
@@ -115,17 +129,18 @@ Age and sex were not recorded for these acquisitions.
 
 `reconstruct.py` reconstructs a B-mode from `raw_data` using the `zea.Pipeline`
 defined in `pipeline.yaml`: delay-and-sum beamforming on a polar scanline grid
-(one image line per transmit, receive dynamic focusing at f-number 1) → envelope
+(one image line per transmit, receive dynamic focusing) → envelope
 detection → normalization → log compression → sector scan conversion. Run it on
 any file to reproduce a reference frame:
 
 ```
-python reconstruct.py data/s2.hdf5 --frame 54 --out bmode_s2.png
+python reconstruct.py
 ```
 
-Reference output: `bmode_s2.png`. The pipeline matches the acquisition's own
-receive-beamforming geometry (`code/processing/`), so the reconstruction
-reproduces the expected sector B-mode.
+Reference output: `bmode.png` — frame 30 of `data/a1.hdf5` (in `assets/`). The
+pipeline matches the acquisition's own receive-beamforming geometry
+(`code/processing/`), so the reconstruction reproduces the expected sector
+B-mode.
 
 ## Known Issues
 

technion/cardiac/README.md

@@ -18,6 +18,20 @@ size_categories:
 
 # OpenH-RF — Cardiac pre-beamformed RF channel data (paired with DAS targets)
 
+![Apical four-chamber view, one 32-frame cardiac cine loop](assets/cine.gif)
+
+Apical four-chamber view: one cine loop (32 frames) from [`data/c1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/cardiac/data/c1.hdf5).
+
+`zea` renders it straight from the Hub with the
+`pipeline.yaml` in this folder. Try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/technion/cardiac/data/c1.hdf5 \
+  --config hf://nvidia/OpenH-RF/technion/cardiac/pipeline.yaml \
+  --n-frames 32
+```
+
 ## Dataset Description
 
 Real, **in-vivo human** pre-beamformed ultrasound **channel data** for cardiac
@@ -106,13 +120,14 @@ per acquisition line, receive dynamic focusing) → envelope detection →
 normalization → log compression → sector scan conversion. Run:
 
 ```
-python reconstruct.py data/a1.hdf5 --frame 15 --out bmode_a1.png
+python reconstruct.py
 ```
 
-Reference output: `bmode_a1.png`. Each frame is also paired with its conventional
-delay-and-sum reconstruction in `beamformed_data` (the target for the raw→image
-learning task) — note its depth scale is approximate because the acquisition axial
-rate is not stored (see Known Issues).
+Reference output: `bmode.png` — frame 8 of `data/c1.hdf5` (in `assets/`). Each
+frame is also paired with its conventional delay-and-sum reconstruction in
+`beamformed_data` (the target for the raw→image learning task) — note its depth
+scale is approximate because the acquisition axial rate is not stored (see Known
+Issues).
 
 ## Known Issues
 

technion/phantom/README.md

@@ -18,6 +18,21 @@ size_categories:
 
 # OpenH-RF — Tissue-mimicking phantom pre-beamformed RF channel data
 
+![Tissue-mimicking phantom with point targets and an anechoic cyst](assets/bmode.png)
+
+Frame 6 of [`data/ph.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/phantom/data/ph.hdf5), reconstructed by
+`reconstruct.py`.
+
+`zea` renders it straight from the Hub with the
+`pipeline.yaml` in this folder. Try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/technion/phantom/data/ph.hdf5 \
+  --config hf://nvidia/OpenH-RF/technion/phantom/pipeline.yaml \
+  --n-frames 1
+```
+
 ## Dataset Description
 
 Pre-beamformed ultrasound **channel data** from a tissue-mimicking phantom,
@@ -90,11 +105,11 @@ in `pipeline.yaml` (delay-and-sum on a polar scanline grid → envelope →
 normalization → log compression → sector scan conversion). Run:
 
 ```
-python reconstruct.py data/ph.hdf5 --frame 6 --out bmode_ph.png
+python reconstruct.py
 ```
 
-Reference output: `bmode_ph.png` — resolvable point targets and a well-defined
-anechoic cyst at ~65 mm.
+Reference output: `bmode.png` — frame 6 of `data/ph.hdf5`, shown above:
+resolvable point targets and a well-defined anechoic cyst at ~65 mm.
 
 ## Known Issues