def measure_picmus(beamformer, moniker, center_angle=False, verbose=True):
# Load PICMUS dataset
database_path = os.path.join("datasets", "picmus")
acq = "simulation"
target = "contrast_speckle"
dtype = "rf"
P = PICMUSData(database_path, acq, target, dtype)
# Define pixel grid limits (assume y == 0)
xlims = [P.ele_pos[0, 0], P.ele_pos[-1, 0]]
zlims = [5e-3, 55e-3]
wvln = P.c / P.fc
dx = wvln / 3
dz = dx # Use square pixels
grid = make_pixel_grid(xlims, zlims, dx, dz)
xext = (np.array([-0.5, grid.shape[1] - 0.5]) * dx + xlims[0]) * 1e3
zext = (np.array([-0.5, grid.shape[0] - 0.5]) * dz + zlims[0]) * 1e3
extent = [xext[0], xext[1], zext[1], zext[0]]
if center_angle:
# Grab only the center angle's worth of data
aidx = len(P.angles) // 2
P.idata = P.idata[[aidx]]
P.qdata = P.qdata[[aidx]]
P.angles = P.angles[[aidx]]
P.time_zero = P.time_zero[[aidx]]
# Beamform the ROI
bimg = beamformer(P, grid)
plt.figure(figsize=[5, 6])
plt.imshow(20 * np.log10(bimg),
vmin=-60,
cmap="gray",
extent=extent,
origin="upper")
plt.suptitle("%s: PICMUS" % moniker)
plt.pause(0.01)
outdir = os.path.join("results", moniker)
if not os.path.exists(outdir):
os.makedirs(outdir)
plt.savefig(os.path.join(outdir, "picmus.jpg"))
def rois_additional(idx, outdir=os.path.join("scoring","rois","additional")):
images = [
("JHU", 24),
("JHU", 25),
("JHU", 26),
("JHU", 27),
("JHU", 28),
("JHU", 29),
("JHU", 30),
("JHU", 31),
("JHU", 32),
("JHU", 33),
("JHU", 34),
]
data_source, acq = images[idx]
P, xl, zl = load_data(data_source, acq)
# Define pixel grid limits (assume y == 0)
wvln = P.c / P.fc
dx = wvln / 3
dz = dx # Use square pixels
desired_grid = [400, 300]
xlims = np.array([-0.5, 0.5]) * (desired_grid[1] - 1) * dx + (xl[0] + xl[1]) / 2
zlims = np.array([-0.5, 0.5]) * (desired_grid[0] - 1) * dz + (zl[0] + zl[1]) / 2
xlims = [np.maximum(xlims[0], xl[0]), np.minimum(xlims[1], xl[1])]
zlims = [np.maximum(zlims[0], zl[0]), np.minimum(zlims[1], zl[1])]
grid = make_pixel_grid(xlims, zlims, dx, dz)
fnum = 1
# Normalize input to [-1, 1] range
maxval = np.maximum(np.abs(P.idata).max(), np.abs(P.qdata).max())
P.idata /= maxval
P.qdata /= maxval
# Make data torch tensors
x = (P.idata, P.qdata)
# Make 75-angle image
dasN = DAS_PW(P, grid, rxfnum=fnum)
idasN, qdasN = dasN(x)
idasN, qdasN = idasN.detach().cpu().numpy(), qdasN.detach().cpu().numpy()
iqN = idasN + 1j * qdasN
ground_truth = np.abs(iqN)
ground_truth /= np.amax(ground_truth)
# Normalize to have RMS = 1
# ground_truth /= np.sqrt(np.mean(ground_truth ** 2))
# Display images via matplotlib
xext = (np.array([-0.5, grid.shape[1] - 0.5]) * dx + xlims[0]) * 1e3
zext = (np.array([-0.5, grid.shape[0] - 0.5]) * dz + zlims[0]) * 1e3
extent = [xext[0], xext[1], zext[1], zext[0]]
plt.clf()
bimgN = 20 * np.log10(ground_truth) # Log-compress
bimgN -= np.amax(bimgN) # Normalize by max value
plt.imshow(bimgN, vmin=-60, cmap="gray", extent=extent, origin="upper")
plt.suptitle("%s%03d (c = %d m/s)" % (data_source, acq, np.round(P.c)))
# Check to make sure save directory exists, if not, create it
if not os.path.exists(outdir):
os.makedirs(outdir)
plt.savefig(os.path.join(outdir,"roi%02d.png" % (idx)))
# Save
mdict = {
"grid": grid,
"data_source": data_source,
"acq": acq,
"extent": extent,
"ground_truth": ground_truth,
}
hdf5storage.savemat(os.path.join(outdir,"roi%02d" % (idx)), mdict)
from cubdl.PixelGrid import make_pixel_grid
# Load PICMUS dataset
database_path = os.path.join("datasets", "data", "picmus")
acq = "simulation"
target = "contrast_speckle"
dtype = "iq"
P = PICMUSData(database_path, acq, target, dtype)
# Define pixel grid limits (assume y == 0)
xlims = [P.ele_pos[0, 0], P.ele_pos[-1, 0]]
zlims = [5e-3, 55e-3]
wvln = P.c / P.fc
dx = wvln / 3
dz = dx # Use square pixels
grid = make_pixel_grid(xlims, zlims, dx, dz)
fnum = 1
# Create a DAS_PW neural network for all angles, for 1 angle
dasN = DAS_PW(P, grid)
idx = len(P.angles) // 2 # Choose center angle for 1-angle DAS
das1 = DAS_PW(P, grid, idx)
# Store I and Q components as a tuple
iqdata = (P.idata, P.qdata)
# Make 75-angle image
idasN, qdasN = dasN(iqdata)
idasN, qdasN = idasN.detach().cpu().numpy(), qdasN.detach().cpu().numpy()
iqN = idasN + 1j * qdasN # Tranpose for display purposes
bimgN = 20 * np.log10(np.abs(iqN)) # Log-compress
def rois_lesion(idx, outdir=os.path.join("scoring", "rois", "lesion")):
if idx == 0:
data_source, acq = "UFL", 1
xctr, zctr, r0, r1 = -0.8e-3, 30.3e-3, 2.8e-3, 4.5e-3
P, _, _ = load_data(data_source, acq)
elif idx == 1:
data_source, acq = "UFL", 5
xctr, zctr, r0, r1 = 0.2e-3, 16.4e-3, 2.2e-3, 3.5e-3
P, _, _ = load_data(data_source, acq)
elif idx == 2:
data_source, acq = "UFL", 5
xctr, zctr, r0, r1 = -0.5e-3, 45e-3, 2.2e-3, 3.5e-3
P, _, _ = load_data(data_source, acq)
elif idx == 3:
data_source, acq = "OSL", 7
xctr, zctr, r0, r1 = -8.2e-3, 39.1e-3, 2.5e-3, 4.5e-3
P, _, _ = load_data(data_source, acq)
elif idx == 4:
data_source, acq = "MYO", 1
xctr, zctr, r0, r1 = 15.5e-3, 16.5e-3, 1.3e-3, 3e-3
P, _, _ = load_data(data_source, acq)
elif idx == 5:
data_source, acq = "MYO", 4
xctr, zctr, r0, r1 = 12.2e-3, 25.8e-3, 1.3e-3, 3e-3
P, _, _ = load_data(data_source, acq)
elif idx == 6:
data_source, acq = "INS", 8
xctr, zctr, r0, r1 = 11.4e-3, 42.3e-3, 2.8e-3, 4.5e-3
P, _, _ = load_data(data_source, acq)
elif idx == 7:
data_source, acq = "INS", 21
xctr, zctr, r0, r1 = -7e-3, 41.2e-3, 2.8e-3, 4.5e-3
P, _, _ = load_data(data_source, acq)
else:
raise NotImplementedError
xlims = [-6e-3 + xctr, 6e-3 + xctr]
zlims = [-6e-3 + zctr, 6e-3 + zctr]
r2 = np.sqrt(r0**2 + r1**2)
# Define pixel grid limits (assume y == 0)
wvln = P.c / P.fc
dx = wvln / 3
dz = dx # Use square pixels
grid = make_pixel_grid(xlims, zlims, dx, dz)
fnum = 1
# Normalize input to [-1, 1] range
maxval = np.maximum(np.abs(P.idata).max(), np.abs(P.qdata).max())
P.idata /= maxval
P.qdata /= maxval
# Make ROI
dist = np.sqrt((grid[:, :, 0] - xctr)**2 + (grid[:, :, 2] - zctr)**2)
roi_i = dist <= r0
roi_o = (r1 <= dist) * (dist <= r2)
# Make data torch tensors
x = (P.idata, P.qdata)
# Make 75-angle image
dasN = DAS_PW(P, grid, rxfnum=fnum)
idasN, qdasN = dasN(x)
idasN, qdasN = idasN.detach().cpu().numpy(), qdasN.detach().cpu().numpy()
iqN = idasN + 1j * qdasN
bimgN = 20 * np.log10(np.abs(iqN)) # Log-compress
bimgN -= np.amax(bimgN) # Normalize by max value
# Display images via matplotlib
xext = (np.array([-0.5, grid.shape[1] - 0.5]) * dx + xlims[0]) * 1e3
zext = (np.array([-0.5, grid.shape[0] - 0.5]) * dz + zlims[0]) * 1e3
extent = [xext[0], xext[1], zext[1], zext[0]]
plt.clf()
opts = {"extent": extent, "origin": "upper"}
plt.imshow(bimgN, vmin=-40, cmap="gray", **opts)
plt.contour(roi_i, [0.5], colors="c", **opts)
plt.contour(roi_o, [0.5], colors="m", **opts)
plt.suptitle("%s%03d (c = %d m/s)" % (data_source, acq, np.round(P.c)))
# Check to make sure save directory exists, if not, create it
if not os.path.exists(outdir):
os.makedirs(outdir)
plt.savefig(os.path.join(outdir, "roi%02d.png" % (idx)))
# Save ROI locations
xgrid = grid[:, :, 0]
ygrid = grid[:, :, 1]
zgrid = grid[:, :, 2]
grid_i = np.stack([xgrid[roi_i], ygrid[roi_i], zgrid[roi_i]], axis=1)
grid_o = np.stack([xgrid[roi_o], ygrid[roi_o], zgrid[roi_o]], axis=1)
grid_i = np.expand_dims(grid_i, 1)
grid_o = np.expand_dims(grid_o, 1)
mdict = {
"grid": grid,
"grid_i": grid_i,
"grid_o": grid_o,
"data_source": data_source,
"acq": acq,
"xctr": xctr,
"zctr": zctr,
"r0": r0,
"r1": r1,
"r2": r2,
"extent": extent,
"roi_i": roi_i,
"roi_o": roi_o,
}
hdf5storage.savemat(os.path.join(outdir, "roi%02d" % (idx)), mdict)
dtype=torch.float,
device=torch.device("cuda:0"))
x = (idata, qdata)
# Make focused transmit
das = DAS_FT(F, grid, rxfnum=fnum)
idas, qdas = das(x)
idas, qdas = idas.detach().cpu().numpy(), qdas.detach().cpu().numpy()
iq = idas + 1j * qdas
bimg = np.abs(iq).T
# Scan convert if necessary
if scan_convert:
xlims = rlims[1] * np.array([-0.7, 0.7])
zlims = rlims[1] * np.array([0, 1])
img_grid = make_pixel_grid(xlims, zlims, wvln / 2, wvln / 2)
grid = np.transpose(grid, (1, 0, 2))
g1 = np.stack((grid[:, :, 2], grid[:, :, 0]), -1).reshape(-1, 2)
g2 = np.stack((img_grid[:, :, 2], img_grid[:, :, 0]),
-1).reshape(-1, 2)
bsc = griddata(g1, bimg.reshape(-1), g2, "linear", 1e-10)
bimg = np.reshape(bsc, img_grid.shape[:2])
grid = img_grid.transpose(1, 0, 2)
bimg = 20 * np.log10(bimg) # Log-compress
bimg -= np.amax(bimg) # Normalize by max value
# Display images via matplotlib
extent = [grid[0, 0, 0], grid[-1, 0, 0], grid[0, -1, 2], grid[0, 0, 2]]
extent = np.array(extent) * 1e3 # Convert to mm
plt.clf()
def rois_point(idx, outdir=os.path.join("scoring", "rois", "point")):
if idx == 0:
data_source, acq = "MYO", 3
P, _, _ = load_data(data_source, acq)
xlims = [0e-3, 20e-3]
zlims = [22e-3, 32e-3]
pts = [[13.4e-3, 0, 27.75e-3], [14.55e-3, 0, 26.8e-3],
[15.15e-3, 0, 25.85e-3]]
elif idx == 1:
data_source, acq = "UFL", 4
P, _, _ = load_data(data_source, acq)
xlims = [-6e-3, 10e-3]
zlims = [22e-3, 32e-3]
pts = [[-2.8e-3, 0, 27.65e-3], [-3.95e-3, 0, 26.7e-3],
[-4.56e-3, 0, 25.7e-3]]
elif idx == 2:
data_source, acq = "UFL", 2
P, _, _ = load_data(data_source, acq)
xlims = [-3e-3, 3e-3]
zlims = [12e-3, 38e-3]
pts = [
[-0.29e-3, 0, 15.5e-3],
[-0.31e-3, 0, 20.6e-3],
[-0.43e-3, 0, 25.4e-3],
[-0.20e-3, 0, 30.8e-3],
[-0.12e-3, 0, 35.7e-3],
]
elif idx == 3:
data_source, acq = "MYO", 2
P, _, _ = load_data(data_source, acq)
xlims = [-15e-3, 11e-3]
zlims = [38e-3, 42e-3]
pts = [[-11.95e-3, 0, 39.4e-3], [-2e-3, 0, 39.55e-3],
[8e-3, 0, 39.6e-3]]
else:
raise NotImplementedError
# Define pixel grid limits (assume y == 0)
wvln = P.c / P.fc
dx = wvln / 3
dz = dx # Use square pixels
grid = make_pixel_grid(xlims, zlims, dx, dz)
fnum = 1
# Normalize input to [-1, 1] range
maxval = np.maximum(np.abs(P.idata).max(), np.abs(P.qdata).max())
P.idata /= maxval
P.qdata /= maxval
# Make data torch tensors
x = (P.idata, P.qdata)
# Make 75-angle image
dasN = DAS_PW(P, grid, rxfnum=fnum)
idasN, qdasN = dasN(x)
idasN, qdasN = idasN.detach().cpu().numpy(), qdasN.detach().cpu().numpy()
iqN = idasN + 1j * qdasN
bimgN = 20 * np.log10(np.abs(iqN)) # Log-compress
bimgN -= np.amax(bimgN) # Normalize by max value
# Display images via matplotlib
xext = (np.array([-0.5, grid.shape[1] - 0.5]) * dx + xlims[0]) * 1e3
zext = (np.array([-0.5, grid.shape[0] - 0.5]) * dz + zlims[0]) * 1e3
extent = [xext[0], xext[1], zext[1], zext[0]]
plt.clf()
plt.imshow(bimgN, vmin=-40, cmap="gray", extent=extent, origin="upper")
plt.suptitle("%s%03d (c = %d m/s)" % (data_source, acq, np.round(P.c)))
bar = np.array([-1e-3, 1e-3])
tmp = np.array([0, 0])
for pt in pts:
plt.plot((pt[0] + bar) * 1e3, (pt[2] + tmp) * 1e3, "c-")
plt.plot((pt[0] + tmp) * 1e3, (pt[2] + bar) * 1e3, "c-")
# Check to make sure save directory exists, if not, create it
if not os.path.exists(outdir):
os.makedirs(outdir)
plt.savefig(os.path.join(outdir, "roi%02d.png" % (idx)))
# Save
mdict = {
"grid": grid,
"data_source": data_source,
"acq": acq,
"extent": extent,
"pts": pts,
}
hdf5storage.savemat(os.path.join(outdir, "roi%02d" % (idx)), mdict)
def rois_speckle(idx, outdir=os.path.join("scoring", "rois", "speckle")):
if idx == 0:
data_source, acq = "UFL", 4
P, _, _ = load_data(data_source, acq)
xlims = [0e-3, 12e-3]
zlims = [14e-3, 26e-3]
elif idx == 1:
data_source, acq = "OSL", 7
P, _, _ = load_data(data_source, acq)
xlims = [-6e-3, 6e-3]
zlims = [14e-3, 26e-3]
elif idx == 2:
data_source, acq = "MYO", 2
P, _, _ = load_data(data_source, acq)
xlims = [-6e-3, 6e-3]
zlims = [14e-3, 26e-3]
elif idx == 3:
data_source, acq = "EUT", 3
P, _, _ = load_data(data_source, acq)
xlims = [-6e-3, 6e-3]
zlims = [25e-3, 37e-3]
elif idx == 4:
data_source, acq = "INS", 4
P, _, _ = load_data(data_source, acq)
xlims = [-6e-3, 6e-3]
zlims = [25e-3, 37e-3]
elif idx == 5:
data_source, acq = "INS", 16
P, _, _ = load_data(data_source, acq)
xlims = [-6e-3, 6e-3]
zlims = [25e-3, 37e-3]
else:
raise NotImplementedError
# Define pixel grid limits (assume y == 0)
wvln = P.c / P.fc
dx = wvln / 3
dz = dx # Use square pixels
grid = make_pixel_grid(xlims, zlims, dx, dz)
fnum = 1
# Normalize input to [-1, 1] range
maxval = np.maximum(np.abs(P.idata).max(), np.abs(P.qdata).max())
P.idata /= maxval
P.qdata /= maxval
# Make data torch tensors
x = (P.idata, P.qdata)
# Make 75-angle image
dasN = DAS_PW(P, grid, rxfnum=fnum)
idasN, qdasN = dasN(x)
idasN, qdasN = idasN.detach().cpu().numpy(), qdasN.detach().cpu().numpy()
iqN = idasN + 1j * qdasN
bimgN = 20 * np.log10(np.abs(iqN)) # Log-compress
bimgN -= np.amax(bimgN) # Normalize by max value
# Display images via matplotlib
xext = (np.array([-0.5, grid.shape[1] - 0.5]) * dx + xlims[0]) * 1e3
zext = (np.array([-0.5, grid.shape[0] - 0.5]) * dz + zlims[0]) * 1e3
extent = [xext[0], xext[1], zext[1], zext[0]]
plt.clf()
plt.imshow(bimgN, vmin=-40, cmap="gray", extent=extent, origin="upper")
plt.suptitle("%s%03d (c = %d m/s)" % (data_source, acq, np.round(P.c)))
# Check to make sure save directory exists, if not, create it
if not os.path.exists(outdir):
os.makedirs(outdir)
plt.savefig(os.path.join(outdir, "roi%02d.png" % (idx)))
# Save
mdict = {
"grid": grid,
"data_source": data_source,
"acq": acq,
"extent": extent
}
hdf5storage.savemat(os.path.join(outdir, "roi%02d" % (idx)), mdict)