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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)
from plotly.subplots import make_subplots
import os 

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

sessions = ['vendor750rev','vendorPRIrev','vendorSKYrev',
           'rth750rev','rthPRIrev','rthSKYrev']
sides = ["negative","negative","negative","positive","positive","positive"]
pointpos = [0,0,-0.14,0.14,0,0]
convert_tag = {'rth750':'G1<sub>VENUS</sub>','rthPRI':'S1<sub>VENUS</sub>','rthSKY':'S2<sub>VENUS</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(0,75,255,1)','vendorPRI':'rgba(24,231,234,1)','vendorSKY':'rgba(24,234,141,1)'}
colors_line = {'rth750':'rgba(255,8,30,0.35)','rthPRI':'rgba(255,117,0,0.35)','rthSKY':'rgba(255,220,0,0.35)',
         'vendor750':'rgba(0,75,255,0.35)','vendorPRI':'rgba(24,231,234,0.35)','vendorSKY':'rgba(24,234,141,0.35)'}

def plot_ridgelines(subject,sessions,metric,region,colors,sides,sampN,leg,lowlim,uplim):
    df = pd.DataFrame()
    traces = []
    for jj in range(0,len(sessions),1):
        avg_array  = []
        std_array  =  []
        cur_region = derivativesDir + '/sub-' + subject  + '/ses-' + sessions[jj] + '/stat/sub-' + subject + '_ses-' + sessions[jj] + '_desc-' + region + '_metrics.mat'
        cur_contents = sio.loadmat(cur_region)
        cur_contents = cur_contents[metric]
        cur_contents = np.delete(cur_contents, np.where(cur_contents < lowlim))
        cur_contents = np.delete(cur_contents, np.where(cur_contents > uplim))
        cur_tag = sessions[jj][0:-3]
        cur_contents = random.sample(list(cur_contents),sampN)
        traces.append(go.Violin(
            x=cur_contents,
            name = convert_tag[cur_tag],
            marker=dict(
            opacity = 0.7,    
            outliercolor='rgba(255, 255, 255, 0.6)',
            line=dict(
                outliercolor='rgba(219, 64, 82, 0.6)',
                outlierwidth=2)),
            line_color= colors[cur_tag],
            legendgroup= convert_tag[cur_tag],
            showlegend=leg,
            side=sides[jj],
            pointpos = pointpos[jj]))
    return traces

def plot_mosaic(metric,axis_title,lowlim,uplim):
    nSamp = 10000
    traces1 = plot_ridgelines('invivo1',sessions,metric,'wm',colors,sides,nSamp,True,lowlim,uplim)
    traces2 = plot_ridgelines('invivo1',sessions,metric,'gm',colors,sides,nSamp,False,lowlim,uplim)
    traces3 = plot_ridgelines('invivo2',sessions,metric,'wm',colors,sides,nSamp,False,lowlim,uplim)
    traces4 = plot_ridgelines('invivo2',sessions,metric,'gm',colors,sides,nSamp,False,lowlim,uplim)
    traces5 = plot_ridgelines('invivo3',sessions,metric,'wm',colors,sides,nSamp,False,lowlim,uplim)
    traces6 = plot_ridgelines('invivo3',sessions,metric,'gm',colors,sides,nSamp,False,lowlim,uplim)
    fig = make_subplots(rows=3, cols=2,
                   subplot_titles=("P1 White Matter", "P1 Gray Matter", "P2 White Matter", "P2 Gray Matter", "P3 White Matter", "P3 Gray Matter"),
                   vertical_spacing = 0.08,
                   shared_xaxes=True)
    for trace in traces1:
        fig.add_trace(trace,row=1, col=1)
    for trace in traces2:
        fig.add_trace(trace,row=1, col=2)
    for trace in traces3:
        fig.add_trace(trace,row=2, col=1)
    for trace in traces4:
        fig.add_trace(trace,row=2, col=2)
    for trace in traces5:
        fig.add_trace(trace,row=3, col=1)
    for trace in traces6:
        fig.add_trace(trace,row=3, col=2)
    axis_template = dict(linecolor = 'white', 
                     showticklabels = True,
                     tickfont=dict(color="white",size=12), 
                     gridcolor = 'rgba(255,255,255,0.2)')
    fig.update_yaxes(axis_template,showgrid=False)
    fig.update_traces(orientation='h')
    fig.update_layout(paper_bgcolor = '#1c2a3d',plot_bgcolor = '#1c2a3d')
    fig.update_traces(meanline=dict(visible=True,color="white",width=3),
                      points= 'outliers', # show all points
                      jitter=0.1,
                      width=3)  # add some jitter on points for better visibility
                      #scalemode='count')
    fig.update_xaxes(axis_template)
    fig.update_layout(width=800,height=950,title=f"VENUS vs NATIVE in-vivo {metric} distributions for each participant (P)", title_font_color="white")
    fig.update_annotations(font=dict(color="white", family = "Courier"))
    for i in range(1,7): 
        fig['layout']['xaxis{}'.format(i)]['title']= axis_title
    fig.for_each_xaxis(lambda axis: axis.title.update(font=dict(color = 'white', size=12,family="Courier"),standoff=0))
    fig.update_layout(
        legend=dict(
            y = 0.5,
            font=dict(
                family="Courier",
                size=16,
                color="white")))
    return fig

import warnings
warnings.filterwarnings('ignore')
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fig =  plot_mosaic('T1','T1 (s)',0.5,3)
fig.show()
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fig =  plot_mosaic('MTR','MTR (%)',35,70)
fig.show()
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fig =  plot_mosaic('MTsat','MTsat (a.u.)',0.5,5)
fig.show()
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from scipy.stats import norm
# generate random numbers from N(0,1)

fig = go.Figure()


data_normal = norm.rvs(size=10000,loc=0.5,scale=1)
data_normal2 = norm.rvs(size=10000,loc=0,scale=2)

# Figure for ideal case 
#data_normal = norm.rvs(size=10000,loc=0,scale=1)
#data_normal2 = norm.rvs(size=10000,loc=0,scale=1)

fig.add_trace(go.Violin(x=data_normal,
    opacity = 1,    
    line_color= "crimson",
    side="positive",
    name = "Y",
    meanline=dict(visible=True,color="crimson",width=4),
    width=2
))
fig.add_trace(go.Violin(x=data_normal2,
    marker=dict(
    opacity = 0.3,    
    outliercolor='rgba(255, 255, 255, 0.6)',
    line=dict(
        outliercolor='rgba(219, 64, 82, 0.6)',
        outlierwidth=2)),
    line_color= "limegreen",
    side="positive",
    name = "X",
    meanline=dict(visible=True,color="limegreen",width=4),
    width=2                    
))


fig.update_traces(orientation='h')
fig.update_layout(paper_bgcolor = 'white',plot_bgcolor = 'white',height=600, width=800)
fig.update_traces(points=False)
qs = pd.qcut(data_normal2, 10, labels=False)
aa = []
for ii in range(10):
    aa.append(np.median(data_normal2[qs==ii]))
fig.add_vrect(
    x0=aa[9] + 0.8, x1=aa[9]+0.9,
    fillcolor="limegreen", opacity=0.7,
    layer="above", line_width=0,
)

for ii in range(0,9,1):
    fig.add_vrect(
    x0=aa[ii], x1=aa[ii] + 0.02,
    fillcolor="limegreen", opacity=0.3,
    layer="above", line_width=0)
fig.add_vrect(
    x0=aa[0], x1=aa[0] + 0.1,
    fillcolor="limegreen", opacity=0.7,
    layer="above", line_width=0,
)
qs = pd.qcut(data_normal, 10, labels=False)
aa = []
for ii in range(10):
    aa.append(np.median(data_normal[qs==ii]))  
fig.add_vrect(
    x0=aa[9], x1=aa[9]+0.1,
    fillcolor="crimson", opacity=0.7,
    layer="above", line_width=0,
)
fig.add_vrect(
    x0=aa[0], x1=aa[0]+0.1,
    fillcolor="crimson", opacity=0.7,
    layer="above", line_width=0,
)
for ii in range(0,9,1):
    fig.add_vrect(
    x0=aa[ii], x1=aa[ii] + 0.02,
    fillcolor="crimson", opacity=0.3,
    layer="above", line_width=0)
    
fig.add_annotation(x=aa[0], y=0.8,
            text="<b><-- D1</b>",
            showarrow=False,
            yshift=10)
fig.add_annotation(x=aa[-1], y=0.8,
            text="<b>D9--></b>",
            showarrow=False,
            yshift=10)



fig.show()
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Vendor-neutral sequences and their implications for the reproducibility of quantitative MRI
Vendor-neutral sequences and their implications for the reproducibility of quantitative MRI
Vendor-neutral sequences and their implications for the reproducibility of quantitative MRI
Phantom Python