Add US4US data card + pipeline.yaml + reconstruct.py + LICENSE (Tristan fix branch)

us4us/LICENSE

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us4us/README.md

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+---
+pretty_name: "OpenH-RF — us4us Ring-Array USCT (forearm, breast phantom, water)"
+license: cc-by-4.0
+task_categories:
+  - other
+tags:
+  - ultrasound
+  - rf
+  - openh-rf
+  - zea
+  - usct
+  - tomography
+  - ring-array
+  - attenuation
+  - forearm
+language:
+  - en
+size_categories:
+  - n<1K
+---
+
+## Dataset Description
+Raw RF data acquired with the us4us Ltd. us4R system and a Draminski ring probe.
+
+Intended for ultrasound tomographic reconstruction.
+
+Acquisitions were performed on:
+- the left and right human forearms of 8 healthy volunteers,
+- a Yezitronix B-RG-1.2 breast phantom,
+- a single reference slice from water only.
+
+The forearm and phantom acquisitions comprise consecutive slices spaced 9 mm apart in depth (half of the probe elevation).
+
+Each slice was recorded using 1024 subsequent single-element transmissions, and received by a 512-element aperture located opposite the transmitting element.
+
+
+## Dataset Contributors
+PI: Ziemowit Klimonda
+
+Team: Piotr Jarosik, Jakub Rozbicki, Piotr Karwat, Marcin Lewandowski (all @ us4us Ltd. (https://us4us.eu/))
+
+
+## Dataset Creation Date
+09/07/2026
+
+
+## License
+CC BY 4.0
+
+
+## Intended Usage
+Ultrasound computed tomography image reconstruction.
+
+
+## Dataset Characterization
+  * Data Collection Method: phantom, healthy adult human volunteers.
+  * Labeling Method: N/A.
+  * Acquisition system: 
+      - ring probe:
+          - probe radius: 130mm,
+          - number of elements: 1024, 
+          - center frequency: 2MHz, 
+          - sampling rate: 8125000.0,
+      - us4R research system + host PC,
+      - custom positioning system for subject spatial control.
+  * Tx/Rx scheme:
+      - the scheme consisted of 1024 transmit-receive events,
+      - each event consisted of a single probe element transmitting a short pulse (1 period at 2 MHz excitation), while 512 probe elements were used for reception,
+      - the 1024 transmit events were performed sequentially, with each probe element transmitting once,
+      - for each transmission, the center of the receiving aperture was positioned on the opposite side of the probe relative to the transmitting element,
+      - for example, for the 0th transmission, the 0th probe element transmitted, and the receiving aperture consisted of probe elements 256–767.
+
+## Dataset Format
+All sub-datasets are provided in the ZEA file format.
+No preprocessing was performed on the raw channel data.
+
+
+## Dataset Quantification
+  * Each HDF5 file contains 10 slices of the subject, except `reference_water.hdf5`, which contains a single slice.
+  * Each HDF5 file has a size of 4,973,053,792 bytes, except `reference_water.hdf5`, which is 516,269,920 bytes.
+
+
+## Subject Metadata
+The dataset contains data from:
+  * breast phantom Yezitronix B-RG-1.2,
+  * both forearms of 8 healthy human volunteers (2 females and 6 males, age range [20, 55]),
+  * water only (reference data).
+
+
+## Data Validation
+  * The data files contain attenuation sinograms and images reconstructed from raw data using the filtered backprojection algorithm.
+
+
+## Known Issues
+  * The following probe elements should be considered damaged: [367, 409, 457, 764, 775, 776, 368, 390, 470, 739, 792].
+
+
+## Ethical Considerations
+  * All human subjects were healthy adults who voluntarily participated in the measurements.
+

us4us/pipeline.yaml

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+pipeline:
+    operations:
+    -   name: keras.ops.cast
+        params:
+            dtype: float32
+    -   name: transmission_attenuation
+        params:
+            reference: 1.0
+    -   name: aperture_rearrange
+        params:
+            aperture_offset: 256
+    - resort_sinogram
+    -   name: linearize_detectors
+        params:
+            probe_radius: 0.13
+    - filtered_backprojection
+    -   name: gaussian_blur
+        params:
+            sigma: 1.0
+            axes:
+            - -2
+            - -1
+    with_batch_dim: false

us4us/reconstruct.py

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+# SPDX-License-Identifier: CC-BY-4.0
+"""Reconstruct: transmission-mode attenuation FBP for the US4US ring-array USCT data.
+
+This is a full ring of point-source transmit elements with a receive sub-aperture
+on the far side of the ring (not surrounding the transmitter), so it's a
+transmission (amplitude-attenuation) acquisition, not reflection B-mode.
+`zea.ops.Beamform`/`USCTReflectivityDAS` don't apply, so this defines five custom
+operations (https://zea.readthedocs.io/en/latest/pipeline.html#custom-operations):
+
+    TransmissionAttenuation -> ApertureRearrange -> ResortSinogram
+        -> LinearizeDetectors -> FilteredBackprojection -> (built-in) GaussianBlur
+
+`LinearizeDetectors` mirrors the `linearize_detectors()` step us4us added to
+`reconstruction_example.py` on 2026-09-07: it resamples each projection from the
+ring's arc-spaced detectors onto the uniform chord axis that parallel-beam FBP
+assumes. Without it the reconstruction is radially distorted.
+
+`ApertureRearrange`'s `aperture_offset` (receive channel `k` of transmit `t` is
+ring element `(t + offset + k) mod 1024`, centered on the antipode) isn't
+documented anywhere in the file; confirmed via `validate_geometry` below, which
+predicts the direct-arrival sample from real element positions and compares it
+to the measured envelope peak.
+
+Usage:
+    python reconstruct.py --input yezitronix-b-rg-1.2.hdf5
+    python reconstruct.py --input s8_r.hdf5 --frame 0
+"""
+
+import argparse
+import re
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from keras import ops
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from zea.internal.registry import ops_registry
+from zea.ops import Cast, GaussianBlur, Operation
+
+import zea
+from zea import Config, File, Pipeline
+
+HERE = Path(__file__).parent
+CONFIG = HERE / "pipeline.yaml"
+
+N_EL = 1024  # ring elements
+N_RX = 512  # receive sub-aperture width
+APERTURE_OFFSET = (N_EL - N_RX) // 2  # 256
+PROBE_RADIUS = 13e-2  # m; matches us4us's reconstruction_example.py and the measured geometry
+
+
+@ops_registry("transmission_attenuation")
+class TransmissionAttenuation(Operation):
+    """Windowed peak RF amplitude per (tx, rx) pair, converted to attenuation via
+    ``-20 * log10(amp / reference)``. ``reference=1.0`` is arbitrary units (no
+    water-reference calibration)."""
+
+    # Default is None (resolved below) rather than 1.0 so that an explicitly
+    # passed value always differs from the signature default and is therefore
+    # written into pipeline.yaml - zea omits params left at their default.
+    def __init__(self, reference: float | None = None, **kwargs):
+        super().__init__(**kwargs)
+        self.reference = 1.0 if reference is None else reference
+
+    def call(self, **kwargs):
+        raw = kwargs[self.key]  # (n_tx, n_ax, n_rx, n_ch)
+        raw = ops.squeeze(raw, axis=-1)  # (n_tx, n_ax, n_rx)
+        n_ax = raw.shape[1]
+        # `keras.ops.bartlett` requires its length argument to be a concrete
+        # (non-traced) Python int even under jit, which a shape-derived value
+        # here isn't - so the triangular window is built directly instead.
+        i = ops.arange(n_ax, dtype="float32")
+        half = (n_ax - 1) / 2.0
+        window = (1.0 - ops.abs((i - half) / half))[None, :, None]
+        amp = ops.max(ops.abs(raw * window), axis=1)  # (n_tx, n_rx)
+        att = -20 * ops.log10(amp / self.reference)
+        return {self.output_key: att}
+
+
+@ops_registry("aperture_rearrange")
+class ApertureRearrange(Operation):
+    """Cyclically re-indexes each transmit's receive columns to a consistent
+    relative frame, so `ResortSinogram` can treat rows as consecutive projection
+    angles. `aperture_offset` is not documented in the file — see module
+    docstring."""
+
+    def __init__(self, aperture_offset: int | None = None, **kwargs):
+        super().__init__(**kwargs)
+        self.aperture_offset = APERTURE_OFFSET if aperture_offset is None else aperture_offset
+
+    def call(self, **kwargs):
+        att = kwargs[self.key]  # (n_tx, n_rx)
+        n_tx, n_rx = att.shape
+        rel_start = n_tx // 2 - self.aperture_offset
+        rel_end = n_tx // 2 + self.aperture_offset
+        tx = ops.arange(n_tx)
+        rx_end = ops.mod(tx + rel_end, n_tx)
+        rx_start = ops.mod(tx + rel_start, n_tx)
+        shift = ops.where(rx_end < rx_start, rx_end + 1, 0)  # (n_tx,)
+        col = ops.mod(ops.arange(n_rx)[None, :] + shift[:, None], n_rx)
+        rearranged = ops.take_along_axis(att, col, axis=1)
+        return {self.output_key: rearranged}
+
+
+@ops_registry("resort_sinogram")
+class ResortSinogram(Operation):
+    """Doubles angular sampling (n_tx -> 2*n_tx) and halves detector count
+    (n_rx -> n_rx // 2) by interleaving receive channels across transmits, into a
+    proper parallel-projection sinogram."""
+
+    def call(self, **kwargs):
+        att = kwargs[self.key]  # (n_tx, n_rx)
+        n_tx, n_rx = att.shape
+        rx = ops.arange(n_rx // 2)
+        tx = ops.arange(n_tx)
+        RX, TX = ops.meshgrid(rx, tx, indexing="xy")
+        src_tx = ops.mod(TX + n_rx // 4 - RX, n_tx)  # (n_tx, n_rx // 2)
+
+        # output[2t, r] = att[src_tx[t, r], 2r]; output[2t+1, r] = att[src_tx[t, r], 2r+1].
+        # Gathering the even/odd columns separately along axis 0 with the same
+        # src_tx index, then interleaving via stack+reshape, is equivalent to that
+        # scatter but keras.ops.take_along_axis-only (no fancy 2D indexing needed).
+        even = ops.take_along_axis(att[:, 0::2], src_tx, axis=0)
+        odd = ops.take_along_axis(att[:, 1::2], src_tx, axis=0)
+        sinogram = ops.reshape(ops.stack([even, odd], axis=1), (n_tx * 2, n_rx // 2))
+        return {self.output_key: sinogram}
+
+
+@ops_registry("linearize_detectors")
+class LinearizeDetectors(Operation):
+    """Resample each projection from the ring's arc positions onto a uniform
+    linear (chord) axis, which is what parallel-beam FBP assumes.
+
+    Detector `i` sits at angle `alpha_i` on the ring, so its perpendicular offset
+    from the ring centre is `R * sin(alpha_i)` — compressed towards the edges of
+    the aperture, not uniformly spaced. Backprojecting without correcting this
+    distorts the image radially. Matches `linearize_detectors()` in us4us's
+    `reconstruction_example.py` (added in their 2026-09-07 revision).
+
+    `keras.ops` has no `interp`, but both the source (`xp`) and target (`x`) axes
+    depend only on `n_detectors` and the radius, so the gather indices and
+    interpolation weights are constant and precomputed here in numpy. Endpoint
+    handling matches `np.interp` (clamp, no extrapolation).
+    """
+
+    # See the note on TransmissionAttenuation.__init__ for why this defaults to None.
+    def __init__(self, probe_radius: float | None = None, **kwargs):
+        super().__init__(**kwargs)
+        self.probe_radius = PROBE_RADIUS if probe_radius is None else probe_radius
+
+    def call(self, **kwargs):
+        sinogram = kwargs[self.key]  # (n_angles, n_detectors)
+        n_col = sinogram.shape[1]
+
+        i = np.arange(-(n_col // 2), n_col // 2)
+        xp = self.probe_radius * np.sin(i * np.pi / 2 / n_col)  # arc -> chord, non-uniform
+        x = i * np.sqrt(2) * self.probe_radius / n_col  # uniform target axis
+
+        lo = np.clip(np.searchsorted(xp, x) - 1, 0, n_col - 2)
+        w = np.clip((x - xp[lo]) / (xp[lo + 1] - xp[lo]), 0.0, 1.0).astype("float32")
+
+        left = ops.take(sinogram, ops.convert_to_tensor(lo), axis=1)
+        right = ops.take(sinogram, ops.convert_to_tensor(lo + 1), axis=1)
+        return {self.output_key: left * (1.0 - w) + right * w}
+
+
+@ops_registry("filtered_backprojection")
+class FilteredBackprojection(Operation):
+    """Ram-Lak (ramp) filter + backprojection onto a square grid sized to the
+    detector count, vectorized over all angles in one gather-and-sum."""
+
+    @staticmethod
+    def _ramp_filter_half(n_detectors: int):
+        """Ram-Lak filter, rfft-domain half. `sinogram` is real, so `rfft`/`irfft`
+        (real in, real out) are used below instead of a full complex `fft`/`ifft`
+        - there's no plain complex `ifft` in keras.ops anyway."""
+        half = n_detectors // 2
+        n = ops.concatenate([ops.arange(1.0, half + 1, 2.0), ops.arange(half - 1.0, 0.0, -2.0)])
+        odd = -1.0 / (np.pi * n) ** 2
+        even = ops.concatenate([ops.convert_to_tensor([0.25]), ops.zeros((half - 1,))])
+        f = ops.reshape(ops.stack([even, odd], axis=1), (n_detectors,))
+        real, _ = ops.rfft(f)
+        return 2 * real
+
+    def call(self, **kwargs):
+        sinogram = kwargs[self.key]  # (n_angles, n_detectors)
+        n_angles, n_detectors = sinogram.shape
+
+        ramp_half = self._ramp_filter_half(n_detectors)
+        real, imag = ops.rfft(sinogram)
+        filtered = ops.irfft((real * ramp_half, imag * ramp_half), fft_length=n_detectors)
+
+        # Backprojection geometry (which detector index each pixel/angle maps to)
+        # depends only on (n_angles, n_detectors).
+        half = n_detectors // 2
+        angles = ops.arange(n_angles, dtype="float32") * (2 * np.pi / n_angles)
+        x = ops.arange(n_detectors) - half
+        X, Y = ops.meshgrid(x, x)  # (n_detectors, n_detectors)
+        t = X[None] * ops.cos(angles)[:, None, None] + Y[None] * ops.sin(angles)[:, None, None]
+        idx_raw = ops.cast(ops.round(t + half), "int32")  # (n_angles, D, D)
+        # Skip out-of-range detector indices per angle rather than clamping them
+        # to the edge, which would smear a repeated edge value into the image.
+        valid = ops.logical_and(idx_raw >= 0, idx_raw < n_detectors)
+        idx_safe = ops.clip(idx_raw, 0, n_detectors - 1)
+
+        gathered = ops.take_along_axis(
+            ops.repeat(filtered[:, None, :], n_detectors, axis=1), idx_safe, axis=2
+        )  # (n_angles, D, D)
+        image = ops.sum(ops.where(valid, gathered, 0.0), axis=0)
+        return {self.output_key: image}
+
+
+def build_pipeline(
+    reference: float = 1.0,
+    aperture_offset: int = APERTURE_OFFSET,
+    probe_radius: float = PROBE_RADIUS,
+    smooth_sigma: float = 1.0,
+) -> Pipeline:
+    """Every acquisition-specific constant is passed explicitly, so it round-trips
+    into `pipeline.yaml` and the reconstruction is fully described by that file
+    rather than by constants in this script."""
+    return Pipeline(
+        operations=[
+            Cast(dtype="float32"),
+            TransmissionAttenuation(reference=reference),
+            ApertureRearrange(aperture_offset=aperture_offset),
+            ResortSinogram(),
+            LinearizeDetectors(probe_radius=probe_radius),
+            FilteredBackprojection(),
+            GaussianBlur(sigma=smooth_sigma, axes=(-2, -1)),
+        ],
+        with_batch_dim=False,
+    )
+
+
+def write_config(pipeline: Pipeline, path: Path) -> None:
+    config = pipeline.to_config()
+    config.to_yaml(str(path))
+
+
+def parse_dead_channels(description: str) -> np.ndarray:
+    """Dead-element indices, parsed from the file's root `description` text."""
+    match = re.search(r"turned off[^:]*:\s*([\d,\s]+?)\s*\(numbering", description)
+    if not match:
+        return np.array([], dtype=np.int64)
+    return np.array([int(x) for x in match.group(1).split(",") if x.strip()], dtype=np.int64)
+
+
+def ring_center_and_radius(probe_geometry: np.ndarray) -> tuple[np.ndarray, float]:
+    """Ring center [x, z] and radius [m], measured (this ring is centered at
+    z ~ 0.13 m, not the origin)."""
+    xz = probe_geometry[:, [0, 2]]
+    center = xz.mean(axis=0)
+    radius = float(np.linalg.norm(xz - center, axis=-1).mean())
+    return center, radius
+
+
+def rx_element_indices(aperture_offset: int, n_tx: int, n_rx: int) -> np.ndarray:
+    """Physical ring-element index for every (transmit, raw receive channel)
+    pair: `(n_tx, n_rx)`. Sizes come from the file, not from constants."""
+    k = np.arange(n_rx)
+    return (np.arange(n_tx)[:, None] + aperture_offset + k[None, :]) % n_tx
+
+
+def validate_geometry(rf, tx_position, rx_positions, sound_speed, initial_time, fs):
+    """Predicted vs. measured direct-arrival sample, per receive channel, for one
+    transmit. Agreement confirms the ring geometry, `initial_times`, and the
+    aperture offset at once. `rf` is `(n_ax, n_rx)` real RF for one transmit."""
+    distance = np.linalg.norm(rx_positions - tx_position, axis=-1)
+    predicted = (distance / sound_speed - initial_time) * fs
+    envelope = np.abs(rf)
+    measured = envelope.argmax(axis=0)
+    strong = envelope.max(axis=0) > 0.2 * envelope.max()
+    residual = float(np.median(np.abs(measured[strong] - predicted[strong])))
+    return predicted, measured, residual
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--input", type=Path, required=True, help="a *.hdf5 file in this folder")
+    parser.add_argument("--frame", type=int, default=0)
+    parser.add_argument("--aperture_offset", type=int, default=APERTURE_OFFSET)
+    parser.add_argument(
+        "--probe_radius", type=float, default=PROBE_RADIUS, help="ring radius in metres"
+    )
+    parser.add_argument(
+        "--smooth_sigma", type=float, default=1.0, help="post-FBP Gaussian smoothing"
+    )
+    args = parser.parse_args()
+
+    input_path = args.input if args.input.exists() else HERE / args.input
+    if not input_path.exists():
+        raise FileNotFoundError(f"{args.input} not found (looked in cwd and {HERE}).")
+    output_path = input_path.with_name(f"{input_path.stem}_reconstruct.png")
+
+    # Build with every parameter explicit, save to pipeline.yaml, then reload and
+    # run from that file - so the YAML alone fully describes the reconstruction.
+    pipeline = build_pipeline(
+        aperture_offset=args.aperture_offset,
+        probe_radius=args.probe_radius,
+        smooth_sigma=args.smooth_sigma,
+    )
+    write_config(pipeline, CONFIG)
+    config = Config.from_path(str(CONFIG))
+    pipeline = Pipeline.from_config(config)
+
+    with File(str(input_path)) as f:
+        probe_geometry = f.probe.probe_geometry[:]
+        center, radius = ring_center_and_radius(probe_geometry)
+        sound_speed = float(f.scan.sound_speed)
+        fs = float(f.scan.sampling_frequency)
+        initial_times = f.scan.initial_times[:]
+        dead_elements = parse_dead_channels(str(f.attrs.get("description", "")))
+        zea.log.info(f"Dead elements parsed from description: {dead_elements.tolist()}")
+        # Cross-check the --probe_radius used by LinearizeDetectors against the
+        # radius actually implied by probe_geometry.
+        if abs(radius - args.probe_radius) > 1e-3:
+            zea.log.warning(
+                f"probe_radius={args.probe_radius:.4f} m differs from the measured ring "
+                f"radius {radius:.4f} m (centre {center[0]:.4f}, {center[1]:.4f} m)"
+            )
+        else:
+            zea.log.info(f"Ring radius: {radius:.4f} m (matches --probe_radius)")
+
+        raw = np.asarray(f.data.raw_data[args.frame])  # (n_tx, n_ax, n_rx, n_ch)
+
+        # As of the Sep-2026 resubmission these live in `data` as spatial maps
+        # (with coordinates); older files put them in the free-form `custom`
+        # group. Read `data` first, fall back to `custom` for the old layout.
+        # us4us ship their own FBP output and sinogram alongside the raw data, as
+        # spatial maps under `data` with `coordinates`.
+        stored_image = np.asarray(f.data.image.values[args.frame])
+        image_coords = np.asarray(f.data.image.coordinates)
+        stored_sinogram = np.asarray(f.data.sinogram.values[args.frame])
+        sinogram_coords = np.asarray(f.data.sinogram.coordinates)
+
+    rx_idx = rx_element_indices(
+        args.aperture_offset, n_tx=probe_geometry.shape[0], n_rx=raw.shape[2]
+    )  # (n_tx, n_rx)
+    ring_xz = probe_geometry[:, [0, 2]].astype(np.float32)
+
+    predicted, measured, residual = validate_geometry(
+        raw[0, :, :, 0].astype(np.float32),
+        ring_xz[0],
+        ring_xz[rx_idx[0]],
+        sound_speed,
+        float(initial_times[0]),
+        fs,
+    )
+    zea.log.info(f"Direct-arrival check: median |measured - predicted| = {residual:.2f} samples")
+
+    outputs = pipeline(data=raw, return_numpy=True)
+    recon = outputs["data"]  # (n_detectors, n_detectors)
+
+    zea.visualize.set_mpl_style()
+    fig, axes = plt.subplots(1, 4, figsize=(24, 5.5))
+
+    n_rx = raw.shape[2]
+    rf_db = 20 * np.log10(np.abs(raw[0, :, :, 0]) / (np.abs(raw[0, :, :, 0]).max() + 1e-12) + 1e-10)
+    axes[0].imshow(rf_db, aspect="auto", cmap="gray", vmin=-60, vmax=0)
+    axes[0].plot(np.arange(n_rx), predicted, "r--", lw=1.0, label="predicted direct arrival")
+    axes[0].plot(np.arange(n_rx), measured, "c:", lw=0.7, alpha=0.6, label="measured (argmax)")
+    axes[0].set_ylim(raw.shape[1], 0)
+    axes[0].legend(loc="lower left", fontsize=7)
+    axes[0].set_title(f"RF, transmit 0 [dB]\ngeometry check: residual {residual:.2f} samples")
+    axes[0].set_xlabel("Receive channel")
+    axes[0].set_ylabel("Axial sample")
+
+    # After LinearizeDetectors the backprojection grid spans the uniform chord
+    # axis, i.e. +-R*sqrt(2)/2 (~92 mm) about the ring centre - not +-R. Plotted
+    # in mm relative to the ring centre, matching the frame of the stored maps'
+    # own `coordinates` so the panels line up.
+    half_width = args.probe_radius * np.sqrt(2) / 2 * 1e3
+    extent = [-half_width, half_width, half_width, -half_width]
+    vmin, vmax = np.percentile(recon, [1, 99])
+    handle = axes[1].imshow(recon, cmap="magma", extent=extent, vmin=vmin, vmax=vmax)
+    axes[1].set_title(f"Attenuation FBP (zea.Pipeline)\n({input_path.name}, frame {args.frame})")
+    axes[1].set_xlabel("X (mm)")
+    axes[1].set_ylabel("Z (mm)")
+    cax = make_axes_locatable(axes[1]).append_axes("right", size="5%", pad=0.05)
+    fig.colorbar(handle, cax=cax)
+
+    # Stored sinogram: x is detector position in metres; the "z" slot carries the
+    # projection angle in radians (0..2*pi) rather than a Cartesian coordinate.
+    sino_extent = [
+        sinogram_coords[..., 0].min() * 1e3,
+        sinogram_coords[..., 0].max() * 1e3,
+        sinogram_coords[..., 2].max(),
+        sinogram_coords[..., 2].min(),
+    ]
+    axes[2].imshow(stored_sinogram, cmap="viridis", aspect="auto", extent=sino_extent)
+    axes[2].set_title("Stored sinogram (us4us)")
+    axes[2].set_xlabel("Detector position (mm)")
+    axes[2].set_ylabel("Projection angle (rad)")
+
+    img_extent = [
+        image_coords[..., 0].min() * 1e3,
+        image_coords[..., 0].max() * 1e3,
+        image_coords[..., 2].max() * 1e3,
+        image_coords[..., 2].min() * 1e3,
+    ]
+    svmin, svmax = np.percentile(stored_image, [1, 99])
+    h = axes[3].imshow(
+        stored_image, cmap="magma", extent=img_extent, vmin=svmin, vmax=svmax
+    )
+    axes[3].set_title("Stored FBP (us4us)")
+    axes[3].set_xlabel("X (mm)")
+    axes[3].set_ylabel("Z (mm)")
+    cax = make_axes_locatable(axes[3]).append_axes("right", size="5%", pad=0.05)
+    fig.colorbar(h, cax=cax)
+
+    fig.suptitle(f"{input_path.name} — frame {args.frame}")
+    fig.tight_layout()
+    plt.savefig(str(output_path), bbox_inches="tight", dpi=100)
+
+    print(f"Reconstructed  : {recon.shape}")
+    print(f"Saved          : {output_path}")
+
+
+if __name__ == "__main__":
+    main()