{"cells":[{"cell_type":"markdown","id":"cell-001","metadata":{},"source":"# RSNA Knee MRI | Correct DICOM Ordering & Sampling\n\nKnee MRI studies contain multiple imaging series, and each series contains many\nDICOM slices. The filenames are identifiers, not anatomical coordinates, so\nlexicographic filename order can scramble a volume. This notebook builds a\ncompact, reusable loader that prefers DICOM geometry, samples slices\ndeterministically, and applies a lightweight display-oriented normalization.\n\nThe notebook reads headers for only three representative series (axial,\nsagittal, and coronal) and decodes only the displayed slices. It does **not**\nrebuild the full metadata index, train a model, require a GPU, use the internet,\nor write persistent artifacts.\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-002","metadata":{},"outputs":[],"source":"from collections.abc import Sequence\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom time import perf_counter\nfrom typing import Any, Literal, cast\n\nimport matplotlib.pyplot as plt  # type: ignore[import-not-found]\nimport numpy as np\nimport pandas as pd  # type: ignore[import-untyped]\nfrom IPython.display import display  # type: ignore[import-not-found]\nfrom pydicom import dcmread\nfrom pydicom.dataset import Dataset\nfrom pydicom.pixels.processing import apply_modality_lut\n\nNOTEBOOK_STARTED_AT = perf_counter()\nPLANES = (\"Axial\", \"Sagittal\", \"Coronal\")\nDICOM_SUFFIXES = {\".dcm\", \".dicom\"}\n\n"},{"cell_type":"markdown","id":"cell-003","metadata":{},"source":"## 1. Competition dataset at a glance\n\nKaggle can mount competition inputs under different parent directories. Instead\nof assuming one absolute path, the discovery function checks the shallow input\nlayout for the expected CSV files and image directories, then requires exactly\none matching root.\n\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-004","metadata":{},"outputs":[],"source":"def discover_competition_root(input_root: Path = Path(\"/kaggle/input\")) -> Path:\n    required_files = {\n        \"train.csv\",\n        \"train_series.csv\",\n        \"test.csv\",\n        \"test_series.csv\",\n        \"sample_submission.csv\",\n    }\n    required_directories = {\"train_series\", \"test_series\"}\n    csv_candidates = {\n        *input_root.glob(\"train_series.csv\"),\n        *input_root.glob(\"*/train_series.csv\"),\n        *input_root.glob(\"*/*/train_series.csv\"),\n    }\n    roots = sorted(\n        {\n            path.parent\n            for path in csv_candidates\n            if all((path.parent / name).is_file() for name in required_files)\n            and all((path.parent / name).is_dir() for name in required_directories)\n        },\n        key=lambda path: path.as_posix(),\n    )\n    if len(roots) != 1:\n        raise FileNotFoundError(\n            f\"Expected one competition root below {input_root}, found {len(roots)}\"\n        )\n    return roots[0]\n\n\ncompetition_root = discover_competition_root()\nprint(f\"Competition root: {competition_root}\")\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-005","metadata":{},"outputs":[],"source":"metadata_files = sorted(competition_root.glob(\"*.csv\"))\nmetadata_inventory = pd.DataFrame(\n    [\n        {\n            \"file\": path.name,\n            \"columns\": \", \".join(pd.read_csv(path, nrows=0).columns),\n        }\n        for path in metadata_files\n    ]\n)\ndisplay(metadata_inventory)\n\nsample_submission_columns = tuple(\n    pd.read_csv(competition_root / \"sample_submission.csv\", nrows=0).columns\n)\nif not sample_submission_columns or sample_submission_columns[0] != \"StudyInstanceUID\":\n    raise ValueError(\"Unexpected sample submission identifier column\")\ntarget_names = sample_submission_columns[1:]\nif not target_names:\n    raise ValueError(\"Sample submission contains no target columns\")\n\ntrain_studies = pd.read_csv(\n    competition_root / \"train.csv\", usecols=[\"StudyInstanceUID\"]\n)\ntest_studies = pd.read_csv(competition_root / \"test.csv\", usecols=[\"StudyInstanceUID\"])\ntrain_series = pd.read_csv(competition_root / \"train_series.csv\").assign(\n    split=\"train\", image_root=\"train_series\"\n)\ntest_series = pd.read_csv(competition_root / \"test_series.csv\").assign(\n    split=\"test\", image_root=\"test_series\"\n)\nseries_table = pd.concat([train_series, test_series], ignore_index=True)\n\ncurrent_mount = pd.DataFrame(\n    [\n        {\n            \"split\": \"train\",\n            \"studies in CSV\": len(train_studies),\n            \"series in CSV\": len(train_series),\n        },\n        {\n            \"split\": \"example test\",\n            \"studies in CSV\": len(test_studies),\n            \"series in CSV\": len(test_series),\n        },\n    ]\n)\ndisplay(current_mount)\n\n"},{"cell_type":"markdown","id":"cell-006","metadata":{},"source":"The target names and order come directly from `sample_submission.csv`:\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-007","metadata":{},"outputs":[],"source":"display(\n    pd.DataFrame(\n        {\n            \"submission position\": range(1, len(target_names) + 1),\n            \"target\": target_names,\n        }\n    )\n)\n\n"},{"cell_type":"markdown","id":"cell-008","metadata":{},"source":"### Verified snapshot scale\n\nThe following figures come from a completed header-only index of the mounted\ncompetition snapshot (verified 11 August 2026). They are not recomputed here:\ndoing so required about 2 hours 48 minutes on Kaggle CPU and would defeat this\nnotebook's lightweight purpose.\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-009","metadata":{},"outputs":[],"source":"verified_snapshot = pd.DataFrame(\n    [\n        {\n            \"scope\": \"train\",\n            \"studies\": 4_407,\n            \"series\": 24_371,\n            \"DICOM headers\": 819_078,\n            \"unique sampled positions\": 194_968,\n        },\n        {\n            \"scope\": \"example test\",\n            \"studies\": 3,\n            \"series\": 15,\n            \"DICOM headers\": 557,\n            \"unique sampled positions\": 120,\n        },\n        {\n            \"scope\": \"total\",\n            \"studies\": 4_410,\n            \"series\": 24_386,\n            \"DICOM headers\": 819_635,\n            \"unique sampled positions\": 195_088,\n        },\n    ]\n)\ndisplay(verified_snapshot)\n\n"},{"cell_type":"markdown","id":"cell-010","metadata":{},"source":"All 819,635 headers in that snapshot were indexed without a structured error,\nand geometry-based ordering was available and selected for all 24,386 series.\nThe eight-position policy selected 195,088 unique positions without duplicating\nslices from short series.\nThis is a metadata-wide result. It does **not** mean every pixel array was\ndecoded or every ordered volume was visually inspected. Pixel decoding and\nvisual checks were performed only on representative series.\n\nThe identifier hierarchy is:\n\n```text\nStudyInstanceUID               one MRI study\n└── SeriesInstanceUID          one acquisition within that study\n    └── SOPInstanceUID.dcm     one image/slice in that series\n```\n\n"},{"cell_type":"markdown","id":"cell-011","metadata":{},"source":"## 2. Choose representative axial, sagittal, and coronal series\n\nSelection is deterministic: example-test rows are considered before training\nrows, then identifiers are sorted. Only the first existing series for each\nplane is used.\n\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-012","metadata":{},"outputs":[],"source":"def dicom_paths(series_directory: Path) -> tuple[Path, ...]:\n    return tuple(\n        sorted(\n            (\n                path\n                for path in series_directory.iterdir()\n                if path.is_file() and path.suffix.casefold() in DICOM_SUFFIXES\n            ),\n            key=lambda path: path.name,\n        )\n    )\n\n\ndef select_representative_series(\n    table: pd.DataFrame, root: Path\n) -> list[dict[str, Any]]:\n    ordered = table.assign(\n        split_priority=table[\"split\"].map({\"test\": 0, \"train\": 1})\n    ).sort_values(\n        [\"split_priority\", \"StudyInstanceUID\", \"SeriesInstanceUID\"],\n        kind=\"stable\",\n    )\n    selected: list[dict[str, Any]] = []\n    for plane in PLANES:\n        candidates = ordered[\n            ordered[\"Anatomical_Plane\"].astype(str).str.casefold() == plane.casefold()\n        ]\n        for row in candidates.to_dict(orient=\"records\"):\n            directory = (\n                root\n                / str(row[\"image_root\"])\n                / str(row[\"StudyInstanceUID\"])\n                / str(row[\"SeriesInstanceUID\"])\n            )\n            paths = dicom_paths(directory)\n            if paths:\n                selected.append({**row, \"directory\": directory, \"paths\": paths})\n                break\n        else:\n            raise FileNotFoundError(f\"No readable {plane} series was found\")\n    return selected\n\n\nrepresentatives = select_representative_series(series_table, competition_root)\nrepresentative_summary = pd.DataFrame(\n    [\n        {\n            \"plane\": item[\"Anatomical_Plane\"],\n            \"split\": item[\"split\"],\n            \"StudyInstanceUID\": item[\"StudyInstanceUID\"],\n            \"SeriesInstanceUID\": item[\"SeriesInstanceUID\"],\n            \"slices\": len(item[\"paths\"]),\n        }\n        for item in representatives\n    ]\n)\ndisplay(representative_summary)\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-013","metadata":{},"outputs":[],"source":"example_study_uid = str(representatives[0][\"StudyInstanceUID\"])\nstudy_hierarchy = series_table[\n    series_table[\"StudyInstanceUID\"].astype(str) == example_study_uid\n][\n    [\n        \"StudyInstanceUID\",\n        \"SeriesInstanceUID\",\n        \"Anatomical_Plane\",\n        \"Fluid_Sensitive\",\n        \"Fat_Suppression\",\n    ]\n].sort_values(\"SeriesInstanceUID\")\ndisplay(study_hierarchy)\n\n"},{"cell_type":"markdown","id":"cell-014","metadata":{},"source":"## 3. Inspect DICOM headers without decoding pixels\n\nFor spatial ordering we need `ImageOrientationPatient` and\n`ImagePositionPatient`. `InstanceNumber` is useful as a comparison or fallback,\nwhile the filename is only a deterministic last resort.\n\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-015","metadata":{},"outputs":[],"source":"@dataclass(frozen=True, slots=True)\nclass SliceRecord:\n    path: Path\n    instance_number: int | None\n    orientation: tuple[float, ...] | None\n    position: tuple[float, ...] | None\n\n\n@dataclass(frozen=True, slots=True)\nclass OrderedSeries:\n    records: tuple[SliceRecord, ...]\n    method: Literal[\"geometry\", \"instance_number\", \"filename\"]\n    geometry_coordinates: dict[Path, float]\n\n\ndef float_sequence(\n    dataset: Dataset, attribute: str, expected_length: int\n) -> tuple[float, ...] | None:\n    value: object = getattr(dataset, attribute, None)\n    if isinstance(value, (str, bytes)) or not isinstance(value, Sequence):\n        return None\n    try:\n        result = tuple(float(item) for item in value)\n    except (TypeError, ValueError):\n        return None\n    return result if len(result) == expected_length else None\n\n\ndef integer_attribute(dataset: Dataset, attribute: str) -> int | None:\n    value: object = getattr(dataset, attribute, None)\n    try:\n        return int(cast(Any, value))\n    except (TypeError, ValueError):\n        return None\n\n\ndef read_series_headers(paths: Sequence[Path]) -> tuple[SliceRecord, ...]:\n    records: list[SliceRecord] = []\n    for path in paths:\n        dataset = dcmread(\n            path,\n            stop_before_pixels=True,\n            specific_tags=[\n                \"InstanceNumber\",\n                \"ImageOrientationPatient\",\n                \"ImagePositionPatient\",\n            ],\n        )\n        records.append(\n            SliceRecord(\n                path=path,\n                instance_number=integer_attribute(dataset, \"InstanceNumber\"),\n                orientation=float_sequence(dataset, \"ImageOrientationPatient\", 6),\n                position=float_sequence(dataset, \"ImagePositionPatient\", 3),\n            )\n        )\n    return tuple(records)\n\n\nrepresentative_records = [\n    read_series_headers(item[\"paths\"]) for item in representatives\n]\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-016","metadata":{},"outputs":[],"source":"header_rows: list[dict[str, Any]] = []\nfor item, records in zip(representatives, representative_records, strict=True):\n    dataset = dcmread(records[0].path, stop_before_pixels=True)\n    header_rows.append(\n        {\n            \"plane\": item[\"Anatomical_Plane\"],\n            \"Modality\": getattr(dataset, \"Modality\", None),\n            \"Rows × Columns\": f\"{getattr(dataset, 'Rows', '?')} × {getattr(dataset, 'Columns', '?')}\",\n            \"InstanceNumber\": records[0].instance_number,\n            \"ImageOrientationPatient\": records[0].orientation,\n            \"ImagePositionPatient\": records[0].position,\n            \"PhotometricInterpretation\": getattr(\n                dataset, \"PhotometricInterpretation\", None\n            ),\n            \"RescaleSlope\": float(getattr(dataset, \"RescaleSlope\", 1.0)),\n            \"RescaleIntercept\": float(getattr(dataset, \"RescaleIntercept\", 0.0)),\n            \"TransferSyntaxUID\": str(\n                getattr(dataset.file_meta, \"TransferSyntaxUID\", \"\")\n            ),\n        }\n    )\ndisplay(pd.DataFrame(header_rows))\n\n"},{"cell_type":"markdown","id":"cell-017","metadata":{},"source":"## 4. Order slices by geometry\n\nA slice plane is defined by two direction vectors in\n`ImageOrientationPatient`. Their cross product is the plane normal. Projecting\n`ImagePositionPatient` onto a consistent unit normal gives one scalar coordinate\nper slice; sorting that coordinate produces a spatially consistent sequence.\nThe sign of the normal depends on the stored direction cosines, so ascending\nprojected coordinate can run either way through the anatomy.\nIt does not define a universal clinical anatomical direction.\n\nIf complete, consistent, unique geometry is unavailable, the reusable function\nbelow tries unique `InstanceNumber` values, then filename order. It never treats\na partially populated or duplicated `InstanceNumber` sequence as authoritative.\n\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-018","metadata":{},"outputs":[],"source":"def geometry_coordinates(\n    records: Sequence[SliceRecord],\n) -> dict[Path, float] | None:\n    normals: list[np.ndarray] = []\n    for record in records:\n        if record.orientation is None or record.position is None:\n            return None\n        row = np.asarray(record.orientation[:3], dtype=np.float64)\n        column = np.asarray(record.orientation[3:], dtype=np.float64)\n        normal = np.cross(row, column)\n        norm = float(np.linalg.norm(normal))\n        if not np.isfinite(normal).all() or norm < 1e-6:\n            return None\n        normals.append(normal / norm)\n        if not np.isfinite(np.asarray(record.position, dtype=np.float64)).all():\n            return None\n\n    reference = normals[0]\n    if any(float(np.dot(reference, normal)) < 0.999 for normal in normals[1:]):\n        return None\n    coordinates = {\n        record.path: float(np.dot(reference, np.asarray(record.position)))\n        for record in records\n    }\n    rounded = [round(value, 6) for value in coordinates.values()]\n    return coordinates if len(set(rounded)) == len(rounded) else None\n\n\ndef order_series(records: Sequence[SliceRecord]) -> OrderedSeries:\n    if not records:\n        raise ValueError(\"Cannot order an empty series\")\n\n    coordinates = geometry_coordinates(records)\n    if coordinates is not None:\n        return OrderedSeries(\n            records=tuple(sorted(records, key=lambda item: coordinates[item.path])),\n            method=\"geometry\",\n            geometry_coordinates=coordinates,\n        )\n\n    instance_numbers = [record.instance_number for record in records]\n    if all(value is not None for value in instance_numbers) and len(\n        set(instance_numbers)\n    ) == len(records):\n        return OrderedSeries(\n            records=tuple(\n                sorted(records, key=lambda item: cast(int, item.instance_number))\n            ),\n            method=\"instance_number\",\n            geometry_coordinates={},\n        )\n\n    return OrderedSeries(\n        records=tuple(sorted(records, key=lambda item: item.path.name)),\n        method=\"filename\",\n        geometry_coordinates={},\n    )\n\n\ndef instance_order(\n    records: Sequence[SliceRecord],\n) -> tuple[SliceRecord, ...] | None:\n    values = [record.instance_number for record in records]\n    if any(value is None for value in values) or len(set(values)) != len(records):\n        return None\n    return tuple(sorted(records, key=lambda item: cast(int, item.instance_number)))\n\n\nordered_representatives = [order_series(records) for records in representative_records]\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-019","metadata":{},"outputs":[],"source":"comparison_rows = []\nfor item, records, ordered in zip(\n    representatives,\n    representative_records,\n    ordered_representatives,\n    strict=True,\n):\n    filename_records = tuple(sorted(records, key=lambda record: record.path.name))\n    by_instance = instance_order(records)\n    comparison_rows.append(\n        {\n            \"plane\": item[\"Anatomical_Plane\"],\n            \"slices\": len(records),\n            \"selected method\": ordered.method,\n            \"filename = geometry\": filename_records == ordered.records,\n            \"InstanceNumber = geometry\": (\n                by_instance == ordered.records if by_instance is not None else None\n            ),\n        }\n    )\nordering_comparison = pd.DataFrame(comparison_rows)\ndisplay(ordering_comparison)\n\n"},{"cell_type":"markdown","id":"cell-020","metadata":{},"source":"In the three representative series shown here, filename ordering disagrees\nwith geometry in all three, while `InstanceNumber` disagrees in two of three.\nSeparately, the 11 August 2026 header-only index found valid geometry for all\n24,386 series in the mounted snapshot, so no fallback ordering was required\nduring indexing.\n\n"},{"cell_type":"markdown","id":"cell-021","metadata":{},"source":"## 5. Visual comparison: filename order versus geometry\n\nEach row samples the same relative positions from a different ordering. The\ntitle `g=` gives that image's rank in the geometry-ordered series. A smoothly\nincreasing geometry rank is what filename order cannot guarantee. The rank\ndescribes sequence consistency, not a universal clinical anatomical direction.\n\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-022","metadata":{},"outputs":[],"source":"def uniform_indices(length: int, count: int) -> tuple[int, ...]:\n    if length < 0 or count <= 0:\n        raise ValueError(\"length must be non-negative and count must be positive\")\n    if length <= count:\n        return tuple(range(length))\n    return tuple(np.linspace(0, length - 1, count, dtype=int).tolist())\n\n\ndef label_figure_rows(figure: Any, axes: np.ndarray, labels: Sequence[str]) -> None:\n    if len(axes) != len(labels):\n        raise ValueError(\"Each plot row needs exactly one label\")\n    for row_axes, label in zip(axes, labels, strict=True):\n        position = row_axes[0].get_position()\n        figure.text(\n            0.015,\n            (position.y0 + position.y1) / 2,\n            label,\n            rotation=90,\n            ha=\"center\",\n            va=\"center\",\n            fontsize=12,\n        )\n\n\ndef decode_pixels(path: Path) -> np.ndarray:\n    dataset = dcmread(path)\n    stored_pixels = dataset.pixel_array\n    if \"ModalityLUTSequence\" in dataset:\n        transformed = np.asarray(\n            apply_modality_lut(stored_pixels, dataset), dtype=np.float64\n        )\n    else:\n        slope = float(getattr(dataset, \"RescaleSlope\", 1.0))\n        intercept = float(getattr(dataset, \"RescaleIntercept\", 0.0))\n        if not np.isfinite([slope, intercept]).all():\n            raise ValueError(f\"Non-finite rescale metadata in {path.name}\")\n        transformed = np.asarray(stored_pixels, dtype=np.float64) * slope + intercept\n\n    if getattr(dataset, \"PhotometricInterpretation\", \"\") == \"MONOCHROME1\":\n        transformed = transformed.max() + transformed.min() - transformed\n    output = np.asarray(transformed, dtype=np.float32)\n    if not np.isfinite(output).all():\n        raise ValueError(f\"Decoded pixels contain non-finite values: {path.name}\")\n    return output\n\n\ndef normalize_images(\n    images: Sequence[np.ndarray],\n    lower_percentile: float = 1,\n    upper_percentile: float = 99,\n) -> tuple[np.ndarray, ...]:\n    if not images:\n        raise ValueError(\"Cannot normalize an empty image sequence\")\n    values = np.concatenate([image.reshape(-1) for image in images])\n    lower, upper = np.percentile(values, (lower_percentile, upper_percentile))\n    if not np.isfinite([lower, upper]).all():\n        raise ValueError(\"Normalization percentiles must be finite\")\n    if lower == upper:\n        return tuple(np.zeros_like(image, dtype=np.float32) for image in images)\n    normalized = tuple(\n        np.clip((image - lower) / (upper - lower), 0, 1).astype(np.float32)\n        for image in images\n    )\n    if not all(np.isfinite(image).all() for image in normalized):\n        raise ValueError(\"Normalized pixels contain non-finite values\")\n    return normalized\n\n\ncontrast_index = next(\n    (\n        index\n        for index, (records, ordered) in enumerate(\n            zip(representative_records, ordered_representatives, strict=True)\n        )\n        if tuple(sorted(records, key=lambda item: item.path.name)) != ordered.records\n    ),\n    0,\n)\ncontrast_records = representative_records[contrast_index]\ncontrast_geometry = ordered_representatives[contrast_index].records\ncontrast_filename = tuple(sorted(contrast_records, key=lambda item: item.path.name))\ncomparison_count = 6\nfilename_positions = uniform_indices(len(contrast_filename), comparison_count)\ngeometry_positions = uniform_indices(len(contrast_geometry), comparison_count)\nfilename_sample = tuple(contrast_filename[index] for index in filename_positions)\ngeometry_sample = tuple(contrast_geometry[index] for index in geometry_positions)\nunique_paths = tuple(\n    dict.fromkeys(record.path for record in (*filename_sample, *geometry_sample))\n)\nnormalized_by_path = dict(\n    zip(\n        unique_paths,\n        normalize_images([decode_pixels(path) for path in unique_paths]),\n        strict=True,\n    )\n)\ngeometry_rank = {record.path: index for index, record in enumerate(contrast_geometry)}\n\nfigure, axes = plt.subplots(2, comparison_count, figsize=(18, 7))\nfor row_index, sample in enumerate((filename_sample, geometry_sample)):\n    for column_index, record in enumerate(sample):\n        axes[row_index, column_index].imshow(\n            normalized_by_path[record.path], cmap=\"gray\", vmin=0, vmax=1\n        )\n        title = f\"g={geometry_rank[record.path]}\"\n        if row_index == 0:\n            title = f\"sample {column_index + 1}\\n{title}\"\n        axes[row_index, column_index].set_title(title, fontsize=11, pad=3)\n        axes[row_index, column_index].axis(\"off\")\nfigure.suptitle(\n    f\"{representatives[contrast_index]['Anatomical_Plane']} series: filename vs geometry order\",\n    fontsize=16,\n)\nfigure.tight_layout(rect=(0.045, 0, 1, 0.94), h_pad=2.5)\nlabel_figure_rows(figure, axes, (\"Filename order\", \"Geometry order\"))\nplt.show()\n\n"},{"cell_type":"markdown","id":"cell-023","metadata":{},"source":"## 6. Deterministic slice sampling\n\n`uniform_indices` spans the first through last slice for a long series. When a\nseries is shorter than the requested count, it returns every position once and\nnever duplicates a slice.\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-024","metadata":{},"outputs":[],"source":"long_length = len(ordered_representatives[0].records)\nsampling_examples = pd.DataFrame(\n    [\n        {\n            \"case\": \"representative long series\",\n            \"series length\": long_length,\n            \"requested\": 8,\n            \"selected positions\": uniform_indices(long_length, 8),\n        },\n        {\n            \"case\": \"short series example\",\n            \"series length\": 5,\n            \"requested\": 8,\n            \"selected positions\": uniform_indices(5, 8),\n        },\n    ]\n)\ndisplay(sampling_examples)\n\n"},{"cell_type":"markdown","id":"cell-025","metadata":{},"source":"## 7. Lightweight preprocessing and three-plane visualization\n\nThe baseline below applies DICOM modality rescale information, inverts\n`MONOCHROME1` images when needed, and clips to the 1st and 99th percentile\nvalues computed jointly across the deterministically selected slices in each\nseries. The loader preserves each slice's native row and column resolution; it\ndoes not resize or pad the decoded images.\n\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-026","metadata":{},"outputs":[],"source":"def load_series(\n    paths: Sequence[Path], count: int = 8\n) -> tuple[tuple[np.ndarray, ...], tuple[Path, ...], tuple[int, ...], str]:\n    ordered = order_series(read_series_headers(paths))\n    selected_positions = uniform_indices(len(ordered.records), count)\n    selected_paths = tuple(ordered.records[index].path for index in selected_positions)\n    normalized = normalize_images([decode_pixels(path) for path in selected_paths])\n    if not all(\n        float(image.min()) >= 0 and float(image.max()) <= 1 for image in normalized\n    ):\n        raise ValueError(\"Expected normalized finite images in [0, 1]\")\n    return normalized, selected_paths, selected_positions, ordered.method\n\n\nloaded_representatives = [load_series(item[\"paths\"]) for item in representatives]\npreprocessing_checks = pd.DataFrame(\n    [\n        {\n            \"plane\": item[\"Anatomical_Plane\"],\n            \"ordering\": method,\n            \"selected positions\": positions,\n            \"decoded slices\": len(images),\n            \"native shapes\": tuple(sorted({image.shape for image in images})),\n            \"all finite\": all(np.isfinite(image).all() for image in images),\n            \"minimum\": min(float(image.min()) for image in images),\n            \"maximum\": max(float(image.max()) for image in images),\n        }\n        for item, (images, _paths, positions, method) in zip(\n            representatives, loaded_representatives, strict=True\n        )\n    ]\n)\ndisplay(preprocessing_checks)\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-027","metadata":{},"outputs":[],"source":"figure, axes = plt.subplots(len(PLANES), 8, figsize=(20, 8))\nplane_row_labels = []\nfor row_index, (item, loaded) in enumerate(\n    zip(representatives, loaded_representatives, strict=True)\n):\n    images, _paths, positions, method = loaded\n    plane_row_labels.append(f\"{item['Anatomical_Plane']}\\n({method})\")\n    for column_index, (image, position) in enumerate(\n        zip(images, positions, strict=True)\n    ):\n        axes[row_index, column_index].imshow(image, cmap=\"gray\", vmin=0, vmax=1)\n        axes[row_index, column_index].set_title(f\"position {position}\")\n        axes[row_index, column_index].axis(\"off\")\nfigure.suptitle(\n    \"Geometry-ordered, uniformly sampled representative knee MRI series\", fontsize=16\n)\nfigure.tight_layout(rect=(0.045, 0, 1, 0.94), h_pad=1.5)\nlabel_figure_rows(figure, axes, plane_row_labels)\nplt.show()\n\n"},{"cell_type":"markdown","id":"cell-028","metadata":{},"source":"In this notebook run, the table and figure above directly demonstrate eight\ndeterministically selected slices from each representative axial, sagittal, and\ncoronal series. Every displayed slice is finite and normalized into `[0, 1]`.\nThe `native shapes` column reports the actual rows and columns returned for each\nseries; no resizing or padding is performed. This representative demonstration\ndoes not show that all 819,635 pixel arrays can be decoded.\n\n"},{"cell_type":"markdown","id":"cell-029","metadata":{},"source":"## 8. Reusable loader\n\nThe small public interface is `load_series(paths, count=8)`. It returns the\nnormalized images, their source paths, their positions in the geometry-ordered\nseries, and the ordering method that was actually used. Each returned array\nretains its native DICOM dimensions. DICOM read or decode failures are\ndeliberately allowed to raise with their path context rather than being silently\nswallowed.\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-030","metadata":{},"outputs":[],"source":"example_images, example_paths, example_positions, example_method = load_series(\n    representatives[0][\"paths\"], count=8\n)\nprint(\n    {\n        \"ordering_method\": example_method,\n        \"selected_positions\": example_positions,\n        \"selected_file_count\": len(example_paths),\n        \"decoded_slices\": len(example_images),\n        \"native_shapes\": tuple(sorted({image.shape for image in example_images})),\n    }\n)\n\n"},{"cell_type":"markdown","id":"cell-031","metadata":{},"source":"## 9. Practical takeaways\n\n- Prefer geometry from `ImageOrientationPatient` and `ImagePositionPatient` for\n  a spatially consistent sequence, without assigning a universal anatomical\n  direction to ascending coordinates.\n- Do not assume DICOM filenames encode anatomical order.\n- Use a complete, unique `InstanceNumber` sequence only as a fallback or\n  comparison signal.\n- Keep filename sorting as a deterministic final fallback.\n- Inspect headers first; avoid decoding an entire corpus for basic structure.\n- Sample deterministically, and never duplicate slices from a short series.\n- Compute display percentiles jointly across the selected slices, while keeping\n  their native image dimensions.\n\nThis notebook uses NumPy, pandas, Matplotlib, and pydicom. DICOM geometry and\nmodality transforms follow the corresponding DICOM metadata fields exposed by\npydicom.\n\n"},{"cell_type":"code","execution_count":null,"id":"cell-032","metadata":{},"outputs":[],"source":"runtime_seconds = perf_counter() - NOTEBOOK_STARTED_AT\nprint(f\"Notebook runtime: {runtime_seconds:.1f} seconds on this session\")\n"}],"metadata":{"jupytext":{"formats":"py:percent","text_representation":{"extension":".py","format_name":"percent","format_version":"1.3","jupytext_version":"1.17"}},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.12"}},"nbformat":4,"nbformat_minor":5}