516 lines
21 KiB
Python
516 lines
21 KiB
Python
import numpy as np
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from scipy.interpolate import interp1d
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from scipy.constants import golden_ratio
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from ._warps import warp
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from ._radon_transform import sart_projection_update
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from .._shared.fft import fftmodule
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from .._shared.utils import deprecate_kwarg, convert_to_float
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from warnings import warn
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from functools import partial
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if fftmodule is np.fft:
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# fallback from scipy.fft to scipy.fftpack instead of numpy.fft
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# (fftpack preserves single precision while numpy.fft does not)
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from scipy.fftpack import fft, ifft
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else:
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fft = fftmodule.fft
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ifft = fftmodule.ifft
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__all__ = ['radon', 'order_angles_golden_ratio', 'iradon', 'iradon_sart']
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def radon(image, theta=None, circle=True, *, preserve_range=False):
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"""
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Calculates the radon transform of an image given specified
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projection angles.
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Parameters
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----------
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image : array_like
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Input image. The rotation axis will be located in the pixel with
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indices ``(image.shape[0] // 2, image.shape[1] // 2)``.
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theta : array_like, optional
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Projection angles (in degrees). If `None`, the value is set to
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np.arange(180).
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circle : boolean, optional
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Assume image is zero outside the inscribed circle, making the
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width of each projection (the first dimension of the sinogram)
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equal to ``min(image.shape)``.
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preserve_range : bool, optional
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Whether to keep the original range of values. Otherwise, the input
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image is converted according to the conventions of `img_as_float`.
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Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
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Returns
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-------
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radon_image : ndarray
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Radon transform (sinogram). The tomography rotation axis will lie
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at the pixel index ``radon_image.shape[0] // 2`` along the 0th
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dimension of ``radon_image``.
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References
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----------
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.. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic
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Imaging", IEEE Press 1988.
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.. [2] B.R. Ramesh, N. Srinivasa, K. Rajgopal, "An Algorithm for Computing
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the Discrete Radon Transform With Some Applications", Proceedings of
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the Fourth IEEE Region 10 International Conference, TENCON '89, 1989
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Notes
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-----
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Based on code of Justin K. Romberg
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(https://www.clear.rice.edu/elec431/projects96/DSP/bpanalysis.html)
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"""
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if image.ndim != 2:
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raise ValueError('The input image must be 2-D')
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if theta is None:
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theta = np.arange(180)
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image = convert_to_float(image, preserve_range)
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if circle:
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shape_min = min(image.shape)
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radius = shape_min // 2
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img_shape = np.array(image.shape)
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coords = np.array(np.ogrid[:image.shape[0], :image.shape[1]],
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dtype=object)
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dist = ((coords - img_shape // 2) ** 2).sum(0)
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outside_reconstruction_circle = dist > radius ** 2
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if np.any(image[outside_reconstruction_circle]):
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warn('Radon transform: image must be zero outside the '
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'reconstruction circle')
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# Crop image to make it square
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slices = tuple(slice(int(np.ceil(excess / 2)),
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int(np.ceil(excess / 2) + shape_min))
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if excess > 0 else slice(None)
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for excess in (img_shape - shape_min))
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padded_image = image[slices]
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else:
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diagonal = np.sqrt(2) * max(image.shape)
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pad = [int(np.ceil(diagonal - s)) for s in image.shape]
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new_center = [(s + p) // 2 for s, p in zip(image.shape, pad)]
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old_center = [s // 2 for s in image.shape]
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pad_before = [nc - oc for oc, nc in zip(old_center, new_center)]
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pad_width = [(pb, p - pb) for pb, p in zip(pad_before, pad)]
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padded_image = np.pad(image, pad_width, mode='constant',
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constant_values=0)
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# padded_image is always square
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if padded_image.shape[0] != padded_image.shape[1]:
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raise ValueError('padded_image must be a square')
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center = padded_image.shape[0] // 2
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radon_image = np.zeros((padded_image.shape[0], len(theta)),
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dtype=image.dtype)
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for i, angle in enumerate(np.deg2rad(theta)):
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cos_a, sin_a = np.cos(angle), np.sin(angle)
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R = np.array([[cos_a, sin_a, -center * (cos_a + sin_a - 1)],
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[-sin_a, cos_a, -center * (cos_a - sin_a - 1)],
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[0, 0, 1]])
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rotated = warp(padded_image, R, clip=False)
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radon_image[:, i] = rotated.sum(0)
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return radon_image
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def _sinogram_circle_to_square(sinogram):
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diagonal = int(np.ceil(np.sqrt(2) * sinogram.shape[0]))
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pad = diagonal - sinogram.shape[0]
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old_center = sinogram.shape[0] // 2
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new_center = diagonal // 2
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pad_before = new_center - old_center
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pad_width = ((pad_before, pad - pad_before), (0, 0))
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return np.pad(sinogram, pad_width, mode='constant', constant_values=0)
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def _get_fourier_filter(size, filter_name):
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"""Construct the Fourier filter.
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This computation lessens artifacts and removes a small bias as
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explained in [1], Chap 3. Equation 61.
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Parameters
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----------
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size: int
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filter size. Must be even.
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filter_name: str
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Filter used in frequency domain filtering. Filters available:
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ramp, shepp-logan, cosine, hamming, hann. Assign None to use
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no filter.
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Returns
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-------
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fourier_filter: ndarray
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The computed Fourier filter.
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References
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----------
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.. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic
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Imaging", IEEE Press 1988.
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"""
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n = np.concatenate((np.arange(1, size / 2 + 1, 2, dtype=int),
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np.arange(size / 2 - 1, 0, -2, dtype=int)))
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f = np.zeros(size)
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f[0] = 0.25
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f[1::2] = -1 / (np.pi * n) ** 2
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# Computing the ramp filter from the fourier transform of its
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# frequency domain representation lessens artifacts and removes a
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# small bias as explained in [1], Chap 3. Equation 61
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fourier_filter = 2 * np.real(fft(f)) # ramp filter
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if filter_name == "ramp":
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pass
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elif filter_name == "shepp-logan":
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# Start from first element to avoid divide by zero
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omega = np.pi * fftmodule.fftfreq(size)[1:]
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fourier_filter[1:] *= np.sin(omega) / omega
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elif filter_name == "cosine":
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freq = np.linspace(0, np.pi, size, endpoint=False)
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cosine_filter = fftmodule.fftshift(np.sin(freq))
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fourier_filter *= cosine_filter
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elif filter_name == "hamming":
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fourier_filter *= fftmodule.fftshift(np.hamming(size))
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elif filter_name == "hann":
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fourier_filter *= fftmodule.fftshift(np.hanning(size))
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elif filter_name is None:
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fourier_filter[:] = 1
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return fourier_filter[:, np.newaxis]
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@deprecate_kwarg(kwarg_mapping={'filter': 'filter_name'},
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removed_version="0.19")
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def iradon(radon_image, theta=None, output_size=None,
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filter_name="ramp", interpolation="linear", circle=True,
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preserve_range=True):
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"""Inverse radon transform.
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Reconstruct an image from the radon transform, using the filtered
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back projection algorithm.
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Parameters
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----------
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radon_image : array
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Image containing radon transform (sinogram). Each column of
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the image corresponds to a projection along a different
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angle. The tomography rotation axis should lie at the pixel
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index ``radon_image.shape[0] // 2`` along the 0th dimension of
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``radon_image``.
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theta : array_like, optional
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Reconstruction angles (in degrees). Default: m angles evenly spaced
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between 0 and 180 (if the shape of `radon_image` is (N, M)).
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output_size : int, optional
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Number of rows and columns in the reconstruction.
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filter_name : str, optional
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Filter used in frequency domain filtering. Ramp filter used by default.
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Filters available: ramp, shepp-logan, cosine, hamming, hann.
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Assign None to use no filter.
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interpolation : str, optional
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Interpolation method used in reconstruction. Methods available:
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'linear', 'nearest', and 'cubic' ('cubic' is slow).
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circle : boolean, optional
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Assume the reconstructed image is zero outside the inscribed circle.
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Also changes the default output_size to match the behaviour of
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``radon`` called with ``circle=True``.
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preserve_range : bool, optional
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Whether to keep the original range of values. Otherwise, the input
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image is converted according to the conventions of `img_as_float`.
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Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
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Returns
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-------
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reconstructed : ndarray
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Reconstructed image. The rotation axis will be located in the pixel
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with indices
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``(reconstructed.shape[0] // 2, reconstructed.shape[1] // 2)``.
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.. versionchanged :: 0.19
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In ``iradon``, ``filter`` argument is deprecated in favor of
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``filter_name``.
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References
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----------
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.. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic
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Imaging", IEEE Press 1988.
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.. [2] B.R. Ramesh, N. Srinivasa, K. Rajgopal, "An Algorithm for Computing
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the Discrete Radon Transform With Some Applications", Proceedings of
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the Fourth IEEE Region 10 International Conference, TENCON '89, 1989
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Notes
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-----
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It applies the Fourier slice theorem to reconstruct an image by
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multiplying the frequency domain of the filter with the FFT of the
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projection data. This algorithm is called filtered back projection.
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"""
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if radon_image.ndim != 2:
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raise ValueError('The input image must be 2-D')
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if theta is None:
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theta = np.linspace(0, 180, radon_image.shape[1], endpoint=False)
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angles_count = len(theta)
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if angles_count != radon_image.shape[1]:
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raise ValueError("The given ``theta`` does not match the number of "
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"projections in ``radon_image``.")
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interpolation_types = ('linear', 'nearest', 'cubic')
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if interpolation not in interpolation_types:
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raise ValueError("Unknown interpolation: %s" % interpolation)
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filter_types = ('ramp', 'shepp-logan', 'cosine', 'hamming', 'hann', None)
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if filter_name not in filter_types:
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raise ValueError("Unknown filter: %s" % filter_name)
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radon_image = convert_to_float(radon_image, preserve_range)
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dtype = radon_image.dtype
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img_shape = radon_image.shape[0]
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if output_size is None:
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# If output size not specified, estimate from input radon image
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if circle:
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output_size = img_shape
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else:
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output_size = int(np.floor(np.sqrt((img_shape) ** 2 / 2.0)))
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if circle:
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radon_image = _sinogram_circle_to_square(radon_image)
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img_shape = radon_image.shape[0]
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# Resize image to next power of two (but no less than 64) for
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# Fourier analysis; speeds up Fourier and lessens artifacts
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projection_size_padded = max(64, int(2 ** np.ceil(np.log2(2 * img_shape))))
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pad_width = ((0, projection_size_padded - img_shape), (0, 0))
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img = np.pad(radon_image, pad_width, mode='constant', constant_values=0)
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# Apply filter in Fourier domain
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fourier_filter = _get_fourier_filter(projection_size_padded, filter_name)
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projection = fft(img, axis=0) * fourier_filter
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radon_filtered = np.real(ifft(projection, axis=0)[:img_shape, :])
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# Reconstruct image by interpolation
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reconstructed = np.zeros((output_size, output_size),
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dtype=dtype)
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radius = output_size // 2
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xpr, ypr = np.mgrid[:output_size, :output_size] - radius
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x = np.arange(img_shape) - img_shape // 2
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for col, angle in zip(radon_filtered.T, np.deg2rad(theta)):
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t = ypr * np.cos(angle) - xpr * np.sin(angle)
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if interpolation == 'linear':
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interpolant = partial(np.interp, xp=x, fp=col, left=0, right=0)
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else:
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interpolant = interp1d(x, col, kind=interpolation,
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bounds_error=False, fill_value=0)
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reconstructed += interpolant(t)
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if circle:
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out_reconstruction_circle = (xpr ** 2 + ypr ** 2) > radius ** 2
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reconstructed[out_reconstruction_circle] = 0.
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return reconstructed * np.pi / (2 * angles_count)
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def order_angles_golden_ratio(theta):
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"""Order angles to reduce the amount of correlated information in
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subsequent projections.
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Parameters
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----------
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theta : 1D array of floats
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Projection angles in degrees. Duplicate angles are not allowed.
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Returns
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-------
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indices_generator : generator yielding unsigned integers
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The returned generator yields indices into ``theta`` such that
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``theta[indices]`` gives the approximate golden ratio ordering
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of the projections. In total, ``len(theta)`` indices are yielded.
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All non-negative integers < ``len(theta)`` are yielded exactly once.
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Notes
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-----
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The method used here is that of the golden ratio introduced
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by T. Kohler.
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References
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----------
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.. [1] Kohler, T. "A projection access scheme for iterative
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reconstruction based on the golden section." Nuclear Science
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Symposium Conference Record, 2004 IEEE. Vol. 6. IEEE, 2004.
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.. [2] Winkelmann, Stefanie, et al. "An optimal radial profile order
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based on the Golden Ratio for time-resolved MRI."
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Medical Imaging, IEEE Transactions on 26.1 (2007): 68-76.
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"""
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interval = 180
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remaining_indices = list(np.argsort(theta)) # indices into theta
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# yield an arbitrary angle to start things off
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angle = theta[remaining_indices[0]]
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yield remaining_indices.pop(0)
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# determine subsequent angles using the golden ratio method
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angle_increment = interval / golden_ratio ** 2
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while remaining_indices:
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remaining_angles = theta[remaining_indices]
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angle = (angle + angle_increment) % interval
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index_above = np.searchsorted(remaining_angles, angle)
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index_below = index_above - 1
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index_above %= len(remaining_indices)
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diff_below = abs(angle - remaining_angles[index_below])
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distance_below = min(diff_below % interval, diff_below % -interval)
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diff_above = abs(angle - remaining_angles[index_above])
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distance_above = min(diff_above % interval, diff_above % -interval)
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if distance_below < distance_above:
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yield remaining_indices.pop(index_below)
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else:
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yield remaining_indices.pop(index_above)
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def iradon_sart(radon_image, theta=None, image=None, projection_shifts=None,
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clip=None, relaxation=0.15, dtype=None):
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"""Inverse radon transform.
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Reconstruct an image from the radon transform, using a single iteration of
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the Simultaneous Algebraic Reconstruction Technique (SART) algorithm.
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Parameters
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----------
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radon_image : 2D array
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Image containing radon transform (sinogram). Each column of
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the image corresponds to a projection along a different angle. The
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tomography rotation axis should lie at the pixel index
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``radon_image.shape[0] // 2`` along the 0th dimension of
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``radon_image``.
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theta : 1D array, optional
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Reconstruction angles (in degrees). Default: m angles evenly spaced
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between 0 and 180 (if the shape of `radon_image` is (N, M)).
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image : 2D array, optional
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Image containing an initial reconstruction estimate. Shape of this
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array should be ``(radon_image.shape[0], radon_image.shape[0])``. The
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default is an array of zeros.
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projection_shifts : 1D array, optional
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Shift the projections contained in ``radon_image`` (the sinogram) by
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this many pixels before reconstructing the image. The i'th value
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defines the shift of the i'th column of ``radon_image``.
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clip : length-2 sequence of floats, optional
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Force all values in the reconstructed tomogram to lie in the range
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``[clip[0], clip[1]]``
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relaxation : float, optional
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Relaxation parameter for the update step. A higher value can
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improve the convergence rate, but one runs the risk of instabilities.
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Values close to or higher than 1 are not recommended.
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dtype : dtype, optional
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Output data type, must be floating point. By default, if input
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data type is not float, input is cast to double, otherwise
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dtype is set to input data type.
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Returns
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-------
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reconstructed : ndarray
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Reconstructed image. The rotation axis will be located in the pixel
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with indices
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``(reconstructed.shape[0] // 2, reconstructed.shape[1] // 2)``.
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Notes
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-----
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Algebraic Reconstruction Techniques are based on formulating the tomography
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reconstruction problem as a set of linear equations. Along each ray,
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the projected value is the sum of all the values of the cross section along
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the ray. A typical feature of SART (and a few other variants of algebraic
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techniques) is that it samples the cross section at equidistant points
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along the ray, using linear interpolation between the pixel values of the
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cross section. The resulting set of linear equations are then solved using
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a slightly modified Kaczmarz method.
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When using SART, a single iteration is usually sufficient to obtain a good
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reconstruction. Further iterations will tend to enhance high-frequency
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information, but will also often increase the noise.
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References
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----------
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.. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic
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Imaging", IEEE Press 1988.
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.. [2] AH Andersen, AC Kak, "Simultaneous algebraic reconstruction
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technique (SART): a superior implementation of the ART algorithm",
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Ultrasonic Imaging 6 pp 81--94 (1984)
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.. [3] S Kaczmarz, "Angenäherte auflösung von systemen linearer
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gleichungen", Bulletin International de l’Academie Polonaise des
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Sciences et des Lettres 35 pp 355--357 (1937)
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.. [4] Kohler, T. "A projection access scheme for iterative
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reconstruction based on the golden section." Nuclear Science
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Symposium Conference Record, 2004 IEEE. Vol. 6. IEEE, 2004.
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.. [5] Kaczmarz' method, Wikipedia,
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https://en.wikipedia.org/wiki/Kaczmarz_method
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"""
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if radon_image.ndim != 2:
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raise ValueError('radon_image must be two dimensional')
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if dtype is None:
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if radon_image.dtype.char in 'fd':
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dtype = radon_image.dtype
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else:
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warn("Only floating point data type are valid for SART inverse "
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"radon transform. Input data is cast to float. To disable "
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"this warning, please cast image_radon to float.")
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dtype = np.dtype(float)
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elif np.dtype(dtype).char not in 'fd':
|
||
raise ValueError("Only floating point data type are valid for inverse "
|
||
"radon transform.")
|
||
|
||
dtype = np.dtype(dtype)
|
||
radon_image = radon_image.astype(dtype, copy=False)
|
||
|
||
reconstructed_shape = (radon_image.shape[0], radon_image.shape[0])
|
||
|
||
if theta is None:
|
||
theta = np.linspace(0, 180, radon_image.shape[1],
|
||
endpoint=False, dtype=dtype)
|
||
elif len(theta) != radon_image.shape[1]:
|
||
raise ValueError('Shape of theta (%s) does not match the '
|
||
'number of projections (%d)'
|
||
% (len(theta), radon_image.shape[1]))
|
||
else:
|
||
theta = np.asarray(theta, dtype=dtype)
|
||
|
||
if image is None:
|
||
image = np.zeros(reconstructed_shape, dtype=dtype)
|
||
elif image.shape != reconstructed_shape:
|
||
raise ValueError('Shape of image (%s) does not match first dimension '
|
||
'of radon_image (%s)'
|
||
% (image.shape, reconstructed_shape))
|
||
elif image.dtype != dtype:
|
||
warn("image dtype does not match output dtype: "
|
||
"image is cast to {}".format(dtype))
|
||
|
||
image = np.asarray(image, dtype=dtype)
|
||
|
||
if projection_shifts is None:
|
||
projection_shifts = np.zeros((radon_image.shape[1],), dtype=dtype)
|
||
elif len(projection_shifts) != radon_image.shape[1]:
|
||
raise ValueError('Shape of projection_shifts (%s) does not match the '
|
||
'number of projections (%d)'
|
||
% (len(projection_shifts), radon_image.shape[1]))
|
||
else:
|
||
projection_shifts = np.asarray(projection_shifts, dtype=dtype)
|
||
if clip is not None:
|
||
if len(clip) != 2:
|
||
raise ValueError('clip must be a length-2 sequence')
|
||
clip = np.asarray(clip, dtype=dtype)
|
||
|
||
for angle_index in order_angles_golden_ratio(theta):
|
||
image_update = sart_projection_update(image, theta[angle_index],
|
||
radon_image[:, angle_index],
|
||
projection_shifts[angle_index])
|
||
image += relaxation * image_update
|
||
if clip is not None:
|
||
image = np.clip(image, clip[0], clip[1])
|
||
return image
|