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| 1 | +#!/usr/bin/env bash |
| 2 | +# Shared infrastructure for AMICO CWL test scripts. |
| 3 | +# Source this file at the top of every test_*.sh script. |
| 4 | + |
| 5 | +# Chain to the structural MRI common infrastructure |
| 6 | +source "$(cd "$(dirname "${BASH_SOURCE[1]:-${BASH_SOURCE[0]}"}")/../structural_mri_tests" && pwd)/_common.sh" |
| 7 | +
|
| 8 | +# Docker image |
| 9 | +AMICO_IMAGE="${AMICO_DOCKER_IMAGE:-cookpa/amico-noddi:latest}" |
| 10 | +
|
| 11 | +docker_amico() { |
| 12 | + _docker_run "$AMICO_IMAGE" "$@" |
| 13 | +} |
| 14 | +
|
| 15 | +# ── Synthetic multi-shell DWI data generation ────────────────── |
| 16 | +
|
| 17 | +prepare_amico_data() { |
| 18 | + local amico_data="${DERIVED_DIR}" |
| 19 | + local dwi="${amico_data}/dwi.nii.gz" |
| 20 | + local bvals="${amico_data}/dwi.bval" |
| 21 | + local bvecs="${amico_data}/dwi.bvec" |
| 22 | + local mask="${amico_data}/mask.nii.gz" |
| 23 | +
|
| 24 | + if [[ -f "$dwi" && -f "$bvals" && -f "$bvecs" && -f "$mask" ]]; then |
| 25 | + AMICO_DWI="$dwi" |
| 26 | + AMICO_BVALS="$bvals" |
| 27 | + AMICO_BVECS="$bvecs" |
| 28 | + AMICO_MASK="$mask" |
| 29 | + return 0 |
| 30 | + fi |
| 31 | +
|
| 32 | + echo "Generating synthetic multi-shell DWI data for AMICO..." |
| 33 | +
|
| 34 | + python3 - "$amico_data" <<'PY' |
| 35 | +import sys |
| 36 | +import os |
| 37 | +import numpy as np |
| 38 | +
|
| 39 | +outdir = sys.argv[1] |
| 40 | +os.makedirs(outdir, exist_ok=True) |
| 41 | +
|
| 42 | +# Volume dimensions: 16x16x8, 35 directions |
| 43 | +# b-values: 5x b=0, 15x b=1000, 15x b=2000 |
| 44 | +nx, ny, nz = 16, 16, 8 |
| 45 | +nb0, nb1, nb2 = 5, 15, 15 |
| 46 | +nvols = nb0 + nb1 + nb2 |
| 47 | +
|
| 48 | +# Generate b-values |
| 49 | +bvals = [0]*nb0 + [1000]*nb1 + [2000]*nb2 |
| 50 | +
|
| 51 | +# Generate b-vectors (zero for b=0, random unit vectors for others) |
| 52 | +bvecs = np.zeros((3, nvols)) |
| 53 | +for i in range(nb0, nvols): |
| 54 | + v = np.random.randn(3) |
| 55 | + v /= np.linalg.norm(v) |
| 56 | + bvecs[:, i] = v |
| 57 | +
|
| 58 | +# Generate DWI signal: S = S0 * exp(-b * D) |
| 59 | +D = 0.001 # diffusion coefficient |
| 60 | +S0 = 1000.0 |
| 61 | +data = np.zeros((nx, ny, nz, nvols), dtype=np.float32) |
| 62 | +for v in range(nvols): |
| 63 | + signal = S0 * np.exp(-bvals[v] * D) |
| 64 | + # Add some spatial variation and noise |
| 65 | + base = np.random.normal(signal, signal * 0.05, (nx, ny, nz)).astype(np.float32) |
| 66 | + base = np.clip(base, 0, None) |
| 67 | + data[:, :, :, v] = base |
| 68 | +
|
| 69 | +# Create mask (all ones) |
| 70 | +mask = np.ones((nx, ny, nz), dtype=np.uint8) |
| 71 | +
|
| 72 | +# Save using nibabel |
| 73 | +try: |
| 74 | + import nibabel as nib |
| 75 | + affine = np.eye(4) * 2.0 |
| 76 | + affine[3, 3] = 1.0 |
| 77 | +
|
| 78 | + dwi_img = nib.Nifti1Image(data, affine) |
| 79 | + nib.save(dwi_img, os.path.join(outdir, "dwi.nii.gz")) |
| 80 | +
|
| 81 | + mask_img = nib.Nifti1Image(mask, affine) |
| 82 | + nib.save(mask_img, os.path.join(outdir, "mask.nii.gz")) |
| 83 | +
|
| 84 | + # Save bvals (space-separated, single line) |
| 85 | + with open(os.path.join(outdir, "dwi.bval"), "w") as f: |
| 86 | + f.write(" ".join(str(b) for b in bvals) + "\n") |
| 87 | +
|
| 88 | + # Save bvecs (3 rows) |
| 89 | + with open(os.path.join(outdir, "dwi.bvec"), "w") as f: |
| 90 | + for row in range(3): |
| 91 | + f.write(" ".join(f"{bvecs[row, i]:.6f}" for i in range(nvols)) + "\n") |
| 92 | +
|
| 93 | + print(f" Created DWI: {nx}x{ny}x{nz}x{nvols}") |
| 94 | + print(f" b-values: {nb0}x b=0, {nb1}x b=1000, {nb2}x b=2000") |
| 95 | +
|
| 96 | +except ImportError: |
| 97 | + print("ERROR: nibabel is required for AMICO test data generation") |
| 98 | + sys.exit(1) |
| 99 | +PY |
| 100 | +
|
| 101 | + AMICO_DWI="$dwi" |
| 102 | + AMICO_BVALS="$bvals" |
| 103 | + AMICO_BVECS="$bvecs" |
| 104 | + AMICO_MASK="$mask" |
| 105 | +} |
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