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Vendor-neutral sequences and their implications for the reproducibility of quantitative MRI

https://doi.org/10.55458/neurolibre.00027
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import plotly.graph_objs as go
import plotly.express as px
from plotly import tools, subplots
import numpy as np
import pandas as pd
import ipywidgets as widgets
import math
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from ipywidgets import interact, interactive, fixed, interact_manual
init_notebook_mode(connected=True)
import scipy.io as sio
import random
random.seed(123)

import plotly.io as pio
import os
pio.renderers.default = "plotly_mimetype"
data_path = os.path.join("..","data","venus-data")
derivativesDir = os.path.join(data_path,"qMRFlow")

spheres = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
refMean = [1.989,1.454,0.9841,0.706,0.4967,0.3515,0.24713,0.1753,0.1259,0.089]
sessions = ['rth750rev','rthPRIrev','rthSKYrev',
           'vendor750rev','vendorPRIrev','vendorSKYrev']
convert_tag = {'rth750':'G1<sub>neutral</sub>','rthPRI':'S1<sub>neutral</sub>','rthSKY':'S2<sub>neutral</sub>',
               'vendor750':'G1<sub>native</sub>','vendorPRI':'S1<sub>native</sub>','vendorSKY':'S2<sub>native</sub>'
              }
colors = {'rth750':'rgba(255,8,30,1)','rthPRI':'rgba(255,117,0,1)','rthSKY':'rgba(255,220,0,1)',
         'vendor750':'rgba(13,186,233,1)','vendorPRI':'rgba(2.5,187,180,1)','vendorSKY':'rgba(0,248,205,1)'}
colors_line = {'rth750':'rgba(255,8,30,0.2)','rthPRI':'rgba(255,117,0,0.2)','rthSKY':'rgba(255,220,0,0.2)',
         'vendor750':'rgba(13,186,233,0.2)','vendorPRI':'rgba(2.5,187,180,0.2)','vendorSKY':'rgba(0,248,205,0.2)'}
line_dash = {'rth750':'dash','rthPRI':'dash','rthSKY':'dash',
         'vendor750':'solid','vendorPRI':'solid','vendorSKY':'solid'}
line_sym = {'rth750':'circle','rthPRI':'circle','rthSKY':'circle',
         'vendor750':'square','vendorPRI':'square','vendorSKY':'square'}
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colors = {'rth750':'rgba(255,8,30,1)','rthPRI':'rgba(255,117,0,1)','rthSKY':'rgba(255,220,0,1)',
         'vendor750':'rgba(0,75,255,1)','vendorPRI':'rgba(24,231,234,1)','vendorSKY':'rgba(24,234,141,1)'}
df = pd.DataFrame()
df['signal'] = [1649,240,264,793,2330,2379,767,110,117,362,1023,1016]
df['noise'] = [1.68,0.34,0.33,0.66,1.64,1.65,1.66,0.32,0.34,0.6,1.72,1.71]
df['session'] = ["rth750","rthPRI","rthSKY","vendor750","vendorPRI","vendorSKY"]*2
df['labels'] = ["G1<sub>VENUS</sub>","S1<sub>VENUS</sub>","S2<sub>VENUS</sub>","G1<sub>NATIVE</sub>","S1<sub>NATIVE</sub>","S2<sub>NATIVE</sub>"]*2
df['acq'] = ["T1w"]*6 + ["PDw"]*6 
df['SNR'] = 10*np.log10(df['signal']/df['noise'])
fig = go.Figure()
base = np.char.add(['<br>'] * 6, df['acq'][0:6])
text = np.char.add(np.char.mod('%.2f', df['SNR'][0:6]), base)
fig.add_trace(go.Bar(
        y=df['SNR'][0:6],
        x=df["labels"][0:6],
        showlegend=False,
        name="T1w",
        text=text,
        textposition='auto',
        marker_color= [colors[x] for x in df['session']]))
#aa = list(map(str,np.round(df['SNR'][6:12],2)))
base = np.char.add(['<br>'] * 6, df['acq'][6:12])
text = np.char.add(np.char.mod('%.2f', df['SNR'][6:12]), base)
fig.add_trace(go.Bar(
        y=df['SNR'][6:12],
        x=df["labels"][6:12],
        textposition='auto',
        showlegend=False,
        text=text,
        name="PDw",
        marker_color=[colors[x] for x in df['session']]))
fig.update_layout(barmode='group')
fig.update_layout(height=300, width=900)
fig.update_yaxes(title="PSNR")

fig.show()
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fig = go.Figure()
for jj in range(0,len(sessions),1):
    avg_array  = []
    std_array  =  []
    cur_tag = sessions[jj][0:-3]
    cur_csv = pd.read_csv(derivativesDir + '/sub-phantom/' + 'ses-' + sessions[jj] + '/stat/sub-phantom_ses-' + sessions[jj] + '_stat_summary.csv')
    avg_array = np.array(cur_csv['T1 (mean)'])
    std_array = np.array(cur_csv['T1 (std)'])
    upper = list(np.squeeze(np.array(avg_array)) + np.squeeze(np.array(std_array)))
    lower = list(np.squeeze(np.array(avg_array)) - np.squeeze(np.array(std_array)))
    # Add std lines
    fig.add_trace(go.Scatter(
        x= refMean + refMean[::-1],
        y=upper+lower[::-1],
        fill='toself',
        fillcolor= colors_line[cur_tag],
        line_color='rgba(255,0,0,0)',
        name= convert_tag[cur_tag]
    ))
    # Add markers
    fig.add_trace(go.Scatter(
        x=refMean, y=avg_array,
        line_color=colors[cur_tag],
        name= convert_tag[cur_tag],
        marker = dict(size=20,symbol=line_sym[cur_tag],line=dict(color='black',width=1.5),opacity=0.85),
        line=dict(width=2, dash=line_dash[cur_tag])
    ))

fig.update_layout(height=1000, width=800)
fig.update_layout(legend=dict(x=1, y=1,tracegroupgap=300,font=dict(color="black",size=14)))
axis_template = dict(linecolor = 'white', 
                     showticklabels = True,
                     tickfont=dict(color="black"), 
                     gridcolor = 'lightsteelblue',
                     tickvals = refMean[::-1])
fig.update_xaxes(axis_template,range=[0,2.1], title = "Reference T1 (s)")

fig.update_yaxes(axis_template,tickmode="array",tickvals=refMean,ticktext=["R10","R9","R8","R7","R6","R5","R4","R3","R2","R1"])
fig.update_layout(title='',title_font_color="white",margin=dict(l=0,r=0,t=0,b=0))
fig.update_layout(hovermode = "x")
fig.add_trace(go.Scatter(
    x=refMean, y=refMean,
    line_color='black',
    mode = 'markers',
    name = 'Reference',
    marker = dict(size=30,symbol='cross-open',line = dict(width=2))

))


fig.show()
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df = pd.DataFrame()
fig = go.Figure()
for jj in range(0,len(sessions),1):
    cur_csv = pd.read_csv(derivativesDir + '/sub-phantom/' + 'ses-' + sessions[jj] + '/stat/sub-phantom_ses-' + sessions[jj] + '_stat_summary.csv')
    avg_array = np.array(cur_csv['T1 (mean)'])
    std_array = np.array(cur_csv['T1 (std)'])
    cur_tag = sessions[jj][0:-3]
    # Measurement error
    pcterr = ((np.array(avg_array) - np.array(refMean))/np.array(refMean))*100
    fig.add_trace(go.Scatter(
        x=pcterr, y=spheres[::-1],
        line_color=colors[cur_tag],
        name= convert_tag[cur_tag],
        marker = dict(size=20,symbol=line_sym[cur_tag],line=dict(color='black',width=1.5),opacity=0.75),
        line=dict(width=2, dash=line_dash[cur_tag]),
        line_shape='vhv'
    ))
fig.update_layout(height=1000, width=800)
fig.update_layout(legend=dict(x=1, y=1,tracegroupgap=300,font=dict(color="black")),margin=dict(l=0,r=0,t=0,b=0))
axis_template = dict(linecolor = 'black', 
                     showticklabels = True,
                     gridcolor = 'lightsteelblue'
                    )
fig.update_xaxes(axis_template,zerolinecolor="black",zerolinewidth=3)
fig.update_yaxes(axis_template,zerolinecolor="black")
fig.update_layout(hovermode = "y")
fig.update_xaxes(range=[-18,70],title="∆T1 (%)")
fig.update_yaxes(tickmode="array",tickvals=spheres[::-1],ticktext=["R10","R9","R8","R7","R6","R5","R4","R3","R2","R1"],title = "Reference Spheres (R1 - R10)")
fig.update_layout(title_font_color="black")

fig.show()
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sessions = ['vendor750rev','vendorPRIrev','vendorSKYrev',
           'rth750rev','rthPRIrev','rthSKYrev']

# Set to R7-R10
lastN = 4

df = pd.DataFrame()
fig = go.Figure()

rth750 = derivativesDir + '/sub-phantom/' + 'ses-' + sessions[3] + '/stat/sub-phantom_ses-' + sessions[3] + '_stat_summary.csv'
rthPRI = derivativesDir + '/sub-phantom/' + 'ses-' + sessions[4] + '/stat/sub-phantom_ses-' + sessions[4] + '_stat_summary.csv'
rthSKY = derivativesDir + '/sub-phantom/' + 'ses-' + sessions[5] + '/stat/sub-phantom_ses-' + sessions[5] + '_stat_summary.csv'
rth750 = pd.read_csv(rth750)
rthPRI = pd.read_csv(rthPRI)
rthSKY = pd.read_csv(rthSKY)
    
avg_array_ge = rth750['T1 (mean)']
std_array_ge = rth750['T1 (std)']
cur_tag_ge = 'neutral G1'

avg_array_sie = np.mean([rthPRI['T1 (mean)'],rthSKY['T1 (mean)']],axis=0)
std_array_sie = np.mean([rthPRI['T1 (std)'],rthSKY['T1 (std)']],axis=0)
cur_tag = 'neutral S1-2'

avg_array_all = np.mean([rth750['T1 (mean)'],rthPRI['T1 (mean)'],rthSKY['T1 (mean)']],axis=0)
std_array_all = np.mean([rthPRI['T1 (std)'],rthSKY['T1 (std)']],axis=0)

df[cur_tag + '_mean'] = avg_array
df[cur_tag + '_std'] = std_array
upper_sie = list(np.squeeze(np.array(avg_array_sie)) + np.squeeze(np.array(std_array_sie)))
lower_sie = list(np.squeeze(np.array(avg_array_sie)) - np.squeeze(np.array(std_array_sie)))
upper_ge = list(np.squeeze(np.array(avg_array_ge)) + np.squeeze(np.array(std_array_ge)))
lower_ge = list(np.squeeze(np.array(avg_array_ge)) - np.squeeze(np.array(std_array_ge)))


fig.add_trace(go.Scatter(
    x=refMean[:lastN], y=refMean[:lastN],
    line_color='black',
    mode = 'markers',
    name = 'Reference',
    marker = dict(size=30,symbol='cross-open',line = dict(width=2))

))
fig.add_trace(go.Scatter(
    x=refMean[:lastN], y=avg_array_sie[:lastN],
    line_color='orange',
    name= 'neutral S1-2',
    marker = dict(size=20,symbol='circle',line=dict(color='black',width=1.5),opacity=0.9),
    line=dict(width=2, dash='dash'),
    line_shape='spline',
    customdata= np.round(((np.array(avg_array_sie[:lastN]) - np.array(refMean[:lastN]))/np.array(refMean[:lastN]))*100,2),
    hovertemplate='<br>%{y},<b>∆T1:</b>%{customdata}',
    hoverlabel = dict(namelength = -1)
))
fig.add_trace(go.Scatter(
    x=refMean[:lastN], y=avg_array_ge[:lastN],
    line_color='rgba(202,73,68,1)',
    name= 'neutral G1',
    marker = dict(size=20,symbol='circle',line=dict(color='black',width=1.5),opacity=0.9),
    line=dict(width=2, dash='dash'),
    line_shape='spline',
    customdata= np.round(((np.array(avg_array_ge[:lastN]) - np.array(refMean[:lastN]))/np.array(refMean[:lastN]))*100,2),
    hovertemplate='<br>%{y},<b>∆T1:</b>%{customdata}',
    hoverlabel = dict(namelength = -1)
))
fig.add_trace(go.Scatter(
    x=refMean[:lastN], y=avg_array_all[:lastN],
    line_color='rgba(0,0,0,1)',
    name= 'neutral',
    marker = dict(size=5,symbol='circle',line=dict(color='black',width=1.5),opacity=0.9),
    line=dict(width=2, dash='dash'),
    line_shape='spline',
    customdata= np.round(((np.array(avg_array_all[:lastN]) - np.array(refMean[:lastN]))/np.array(refMean[:lastN]))*100,2),
    hovertemplate='<br>%{y},<b>∆T1:</b>%{customdata}',
    hoverlabel = dict(namelength = -1)
))
vendor750 = derivativesDir + '/sub-phantom/' + 'ses-' + sessions[0] + '/stat/sub-phantom_ses-' + sessions[0] + '_stat_summary.csv'
vendorPRI = derivativesDir + '/sub-phantom/' + 'ses-' + sessions[1] + '/stat/sub-phantom_ses-' + sessions[1] + '_stat_summary.csv'
vendorSKY = derivativesDir + '/sub-phantom/' + 'ses-' + sessions[2] + '/stat/sub-phantom_ses-' + sessions[2] + '_stat_summary.csv'
vendor750 = pd.read_csv(vendor750)
vendorPRI = pd.read_csv(vendorPRI)
vendorSKY = pd.read_csv(vendorSKY)
    
avg_array_ge = vendor750['T1 (mean)']
std_array_ge = vendor750['T1 (std)']

avg_array_sie = np.mean([vendorPRI['T1 (mean)'],vendorSKY['T1 (mean)']],axis=0)
std_array_sie = np.mean([vendorPRI['T1 (std)'],vendorSKY['T1 (std)']],axis=0)
cur_tag = 'neutral'

avg_array_all = np.mean([vendor750['T1 (mean)'],vendorPRI['T1 (mean)'],vendorSKY['T1 (mean)']],axis=0)

df[cur_tag + '_mean'] = avg_array
df[cur_tag + '_std'] = std_array
upper_sie = list(np.squeeze(np.array(avg_array_sie)) + np.squeeze(np.array(std_array_sie)))
lower_sie = list(np.squeeze(np.array(avg_array_sie)) - np.squeeze(np.array(std_array_sie)))
upper_ge = list(np.squeeze(np.array(avg_array_ge)) + np.squeeze(np.array(std_array_ge)))
lower_ge = list(np.squeeze(np.array(avg_array_ge)) - np.squeeze(np.array(std_array_ge)))

fig.add_trace(go.Scatter(
    x=refMean[:lastN], y=avg_array_sie[:lastN],
    line_color='green',
    name= 'native S1-2',
    marker = dict(size=20,symbol='square',line=dict(color='black',width=1.5),opacity=0.9),
    line=dict(width=2, dash='solid'),
    line_shape='spline',
    customdata= np.round(((np.array(avg_array_sie[:lastN]) - np.array(refMean[:lastN]))/np.array(refMean[:lastN]))*100,2),
    hovertemplate='<br>%{y}, <b>∆T1:</b>%{customdata}',
    hoverlabel = dict(namelength = -1)

))
fig.add_trace(go.Scatter(
    x=refMean[:lastN], y=avg_array_ge[:lastN],
    line_color='rgba(31,119,180,0.8)',
    name= 'native G1',
    marker = dict(size=20,symbol='square',line=dict(color='black',width=1.5),opacity=0.9),
    line=dict(width=2, dash='solid'),
    line_shape='spline',
    customdata= np.round(((np.array(avg_array_ge[:lastN]) - np.array(refMean[:lastN]))/np.array(refMean[:lastN]))*100,2),
    hovertemplate='<br>%{y}, <b>∆T1:</b>%{customdata}',
    hoverlabel = dict(namelength = -1)
))
fig.add_trace(go.Scatter(
    x=refMean[:lastN], y=avg_array_all[:lastN],
    line_color='rgba(0,0,0,1)',
    name= 'native',
    marker = dict(size=5,symbol='square',line=dict(color='black',width=1.5),opacity=0.9),
    line=dict(width=2, dash='solid'),
    line_shape='spline',
    customdata= np.round(((np.array(avg_array_all[:lastN]) - np.array(refMean[:lastN]))/np.array(refMean[:lastN]))*100,2),
    hovertemplate='<br>%{y},<b>∆T1:</b>%{customdata}',
    hoverlabel = dict(namelength = -1)
))
fig.update_layout(height=1100, width=800)
axis_template = dict(linecolor = 'black', 
                     showticklabels = True,
                     tickfont=dict(color="black"), 
                     gridcolor = 'lightsteelblue',
                     tickvals = refMean[::-1],)
tickvals=refMean
ticktext=["R10","R9","R8","R7","R6","R5","R4","R3","R2","R1"]

fig.update_xaxes(axis_template,title="Reference T1 (s)")
fig.update_yaxes(axis_template,tickmode="array",tickvals=tickvals[:lastN],ticktext=ticktext[:lastN],title="Reference Spheres")

fig.update_layout(hovermode = "x")


fig.show()
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