| |
| """Generate the deterministic M4Raw Brain homepage montage.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| from m4raw_io import read_target |
|
|
|
|
| DEFAULT_SCAN_ID = "2022061003" |
| SEQUENCES = ( |
| ("T1-weighted", "multicoil_train", "T1", 3), |
| ("T2-weighted", "multicoil_train", "T2", 3), |
| ("FLAIR", "multicoil_train", "FLAIR", 2), |
| ("GRE", "multicoil_gre", "GRE", 2), |
| ) |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--release-root", type=Path, default=Path(".")) |
| parser.add_argument("--scan-id", default=DEFAULT_SCAN_ID) |
| parser.add_argument("--slice", type=int, default=9) |
| parser.add_argument("--output", type=Path) |
| return parser.parse_args() |
|
|
|
|
| def shared_intensity_limits(images: list[np.ndarray]) -> tuple[float, float]: |
| values = np.concatenate( |
| [image[np.isfinite(image) & (image > 0)].ravel() for image in images] |
| ) |
| if values.size == 0: |
| return 0.0, 1.0 |
| low, high = np.percentile(values, (0.5, 99.5)) |
| return float(low), float(max(high, low + 1e-6)) |
|
|
|
|
| def load_repetitions( |
| root: Path, folder: str, scan_id: str, suffix: str, count: int |
| ) -> list[np.ndarray]: |
| return [ |
| read_target(root / folder / f"{scan_id}_{suffix}{rep:02d}.h5") |
| for rep in range(1, count + 1) |
| ] |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| root = args.release_root.resolve() |
| output = args.output or root / "assets" / "representative_images.png" |
|
|
| figure, axes = plt.subplots( |
| 2, |
| 4, |
| figsize=(15.0, 7.4), |
| facecolor="white", |
| constrained_layout=True, |
| ) |
| for column, (title, folder, suffix, repetition_count) in enumerate(SEQUENCES): |
| repetitions = load_repetitions( |
| root, folder, args.scan_id, suffix, repetition_count |
| ) |
| if args.slice < 0 or args.slice >= repetitions[0].shape[0]: |
| raise IndexError( |
| f"Slice {args.slice} is outside {suffix} volume with " |
| f"{repetitions[0].shape[0]} slices" |
| ) |
| |
| single = np.asarray(repetitions[0][args.slice]) |
| average = np.mean( |
| np.stack([volume[args.slice] for volume in repetitions]), |
| axis=0, |
| dtype=np.float64, |
| ).astype(np.float32) |
| limits = shared_intensity_limits([single, average]) |
| axes[0, column].set_title( |
| title, fontsize=18, fontweight="semibold", pad=10 |
| ) |
| for row, image in enumerate((single, average)): |
| axes[row, column].imshow( |
| image, |
| cmap="gray", |
| vmin=limits[0], |
| vmax=limits[1], |
| interpolation="nearest", |
| origin="upper", |
| ) |
| axes[row, column].set_xticks([]) |
| axes[row, column].set_yticks([]) |
| for spine in axes[row, column].spines.values(): |
| spine.set_visible(False) |
| axes[1, column].text( |
| 0.03, |
| 0.04, |
| f"{repetition_count} acquisitions", |
| transform=axes[1, column].transAxes, |
| color="white", |
| fontsize=12, |
| ha="left", |
| va="bottom", |
| bbox={"facecolor": "black", "alpha": 0.55, "edgecolor": "none", "pad": 3}, |
| ) |
|
|
| axes[0, 0].set_ylabel( |
| "Single acquisition", fontsize=16, fontweight="semibold", labelpad=14 |
| ) |
| axes[1, 0].set_ylabel( |
| "Repeat average", fontsize=16, fontweight="semibold", labelpad=14 |
| ) |
| output.parent.mkdir(parents=True, exist_ok=True) |
| figure.savefig(output, dpi=200, facecolor="white", bbox_inches="tight") |
| plt.close(figure) |
| print(output) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
